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21.
Chadalapaka, Viswanath; Ustun, Volkan; Liu, Lixing
Leveraging Graph Networks to Model Environments in Reinforcement Learning Journal Article
In: FLAIRS, vol. 36, 2023, ISSN: 2334-0762.
@article{chadalapaka_leveraging_2023,
title = {Leveraging Graph Networks to Model Environments in Reinforcement Learning},
author = {Viswanath Chadalapaka and Volkan Ustun and Lixing Liu},
url = {https://journals.flvc.org/FLAIRS/article/view/133118},
doi = {10.32473/flairs.36.133118},
issn = {2334-0762},
year = {2023},
date = {2023-05-01},
urldate = {2023-08-04},
journal = {FLAIRS},
volume = {36},
abstract = {This paper proposes leveraging graph neural networks (GNNs) to model an agent’s environment to construct superior policy networks in reinforcement learning (RL). To this end, we explore the effects of different combinations of GNNs and graph network pooling functions on policy performance. We also run experiments at different levels of problem complexity, which affect how easily we expect an agent to learn an optimal policy and therefore show whether or not graph networks are effective at various problem complexity levels. The efficacy of our approach is shown via experimentation in a partially-observable, non-stationary environment that parallels the highly-practical scenario of a military training exercise with human trainees, where the learning goal is to become the best sparring partner possible for human trainees. Our results present that our models can generate better-performing sparring partners by employing GNNs, as demonstrated by these experiments in the proof-of-concept environment. We also explore our model’s applicability in Multi-Agent RL scenarios. Our code is available online at https://github.com/Derposoft/GNNsAsEnvs.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This paper proposes leveraging graph neural networks (GNNs) to model an agent’s environment to construct superior policy networks in reinforcement learning (RL). To this end, we explore the effects of different combinations of GNNs and graph network pooling functions on policy performance. We also run experiments at different levels of problem complexity, which affect how easily we expect an agent to learn an optimal policy and therefore show whether or not graph networks are effective at various problem complexity levels. The efficacy of our approach is shown via experimentation in a partially-observable, non-stationary environment that parallels the highly-practical scenario of a military training exercise with human trainees, where the learning goal is to become the best sparring partner possible for human trainees. Our results present that our models can generate better-performing sparring partners by employing GNNs, as demonstrated by these experiments in the proof-of-concept environment. We also explore our model’s applicability in Multi-Agent RL scenarios. Our code is available online at https://github.com/Derposoft/GNNsAsEnvs.
22.
Pal, Debaditya; Leuski, Anton; Traum, David
Comparing Statistical Models for Retrieval based Question-answering Dialogue: BERT vs Relevance Models Journal Article
In: FLAIRS, vol. 36, 2023, ISSN: 2334-0762.
@article{pal_comparing_2023,
title = {Comparing Statistical Models for Retrieval based Question-answering Dialogue: BERT vs Relevance Models},
author = {Debaditya Pal and Anton Leuski and David Traum},
url = {https://journals.flvc.org/FLAIRS/article/view/133386},
doi = {10.32473/flairs.36.133386},
issn = {2334-0762},
year = {2023},
date = {2023-05-01},
urldate = {2023-08-23},
journal = {FLAIRS},
volume = {36},
abstract = {In this paper, we compare the performance of four models in a retrieval based question answering dialogue task on two moderately sized corpora (textasciitilde 10,000 utterances). One model is a statistical model and uses cross language relevance while the others are deep neural networks utilizing the BERT architecture along with different retrieval methods. The statistical model has previously outperformed LSTM based neural networks in a similar task whereas BERT has been proven to perform well on a variety of NLP tasks, achieving state-of-the-art results in many of them. Results show that the statistical cross language relevance model outperforms the BERT based architectures in learning question-answer mappings. BERT achieves better results by mapping new questions to existing questions.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In this paper, we compare the performance of four models in a retrieval based question answering dialogue task on two moderately sized corpora (textasciitilde 10,000 utterances). One model is a statistical model and uses cross language relevance while the others are deep neural networks utilizing the BERT architecture along with different retrieval methods. The statistical model has previously outperformed LSTM based neural networks in a similar task whereas BERT has been proven to perform well on a variety of NLP tasks, achieving state-of-the-art results in many of them. Results show that the statistical cross language relevance model outperforms the BERT based architectures in learning question-answer mappings. BERT achieves better results by mapping new questions to existing questions.
23.
Rothbaum, Barbara; Difede, JoAnn; Rizzo, Albert; Wyka, Katarzyna; Spielman, Lisa; Reist, Christopher; Roy, Michael; Jovanovic, Tanja; Norrholm, Seth; Cukor, Judith; Olden, Megan; Glatt, Charles; Lee, Francis
Virtual Reality Exposure Therapy Compared to Prolonged Exposure Therapy With and Without D-Cycloserine Journal Article
In: Biological Psychiatry, vol. 93, no. 9, pp. S28–S29, 2023, ISSN: 00063223.
@article{rothbaum_virtual_2023,
title = {Virtual Reality Exposure Therapy Compared to Prolonged Exposure Therapy With and Without D-Cycloserine},
author = {Barbara Rothbaum and JoAnn Difede and Albert Rizzo and Katarzyna Wyka and Lisa Spielman and Christopher Reist and Michael Roy and Tanja Jovanovic and Seth Norrholm and Judith Cukor and Megan Olden and Charles Glatt and Francis Lee},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0006322323001622},
doi = {10.1016/j.biopsych.2023.02.088},
issn = {00063223},
year = {2023},
date = {2023-05-01},
urldate = {2023-08-24},
journal = {Biological Psychiatry},
volume = {93},
number = {9},
pages = {S28–S29},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
24.
Gibson, C. Michael; Steinhubl, Steven; Lakkireddy, Dhanunjaya; Turakhia, Mintu P.; Passman, Rod; Jones, W. Schuyler; Bunch, T. Jared; Curtis, Anne B.; Peterson, Eric D.; Ruskin, Jeremy; Saxon, Leslie; Tarino, Michael; Tarakji, Khaldoun G.; Marrouche, Nassir; Patel, Mithun; Harxhi, Ante; Kaul, Simrati; Nikolovski, Janeta; Juan, Stephanie; Wildenhaus, Kevin; Damaraju, C. V.; Spertus, John A.
Does early detection of atrial fibrillation reduce the risk of thromboembolic events? Rationale and design of the Heartline study Journal Article
In: American Heart Journal, vol. 259, pp. 30–41, 2023, ISSN: 0002-8703.
@article{gibson_does_2023,
title = {Does early detection of atrial fibrillation reduce the risk of thromboembolic events? Rationale and design of the Heartline study},
author = {C. Michael Gibson and Steven Steinhubl and Dhanunjaya Lakkireddy and Mintu P. Turakhia and Rod Passman and W. Schuyler Jones and T. Jared Bunch and Anne B. Curtis and Eric D. Peterson and Jeremy Ruskin and Leslie Saxon and Michael Tarino and Khaldoun G. Tarakji and Nassir Marrouche and Mithun Patel and Ante Harxhi and Simrati Kaul and Janeta Nikolovski and Stephanie Juan and Kevin Wildenhaus and C. V. Damaraju and John A. Spertus},
url = {https://www.sciencedirect.com/science/article/pii/S0002870323000145},
doi = {10.1016/j.ahj.2023.01.004},
issn = {0002-8703},
year = {2023},
date = {2023-05-01},
urldate = {2023-03-31},
journal = {American Heart Journal},
volume = {259},
pages = {30–41},
abstract = {Background
The impact of using direct-to-consumer wearable devices as a means to timely detect atrial fibrillation (AF) and to improve clinical outcomes is unknown.
Methods
Heartline is a pragmatic, randomized, and decentralized application-based trial of US participants aged ≥65 years. Two randomized cohorts include adults with possession of an iPhone and without a history of AF and those with a diagnosis of AF taking a direct oral anticoagulant (DOAC) for ≥30 days. Participants within each cohort are randomized (3:1) to either a core digital engagement program (CDEP) via iPhone application (Heartline application) and an Apple Watch (Apple Watch Group) or CDEP alone (iPhone-only Group). The Apple Watch Group has the watch irregular rhythm notification (IRN) feature enabled and access to the ECG application on the Apple Watch. If an IRN notification is issued for suspected AF then the study application instructs participants in the Apple Watch Group to seek medical care. All participants were “watch-naïve” at time of enrollment and have an option to either buy or loan an Apple Watch as part of this study. The primary end point is time from randomization to clinical diagnosis of AF, with confirmation by health care claims. Key secondary endpoint are claims-based incidence of a 6-component composite cardiovascular/systemic embolism/mortality event, DOAC medication use and adherence, costs/health resource utilization, and frequency of hospitalizations for bleeding. All study assessments, including patient-reported outcomes, are conducted through the study application. The target study enrollment is approximately 28,000 participants in total; at time of manuscript submission, a total of 26,485 participants have been enrolled into the study.
Conclusion
The Heartline Study will assess if an Apple Watch with the IRN and ECG application, along with application-facilitated digital health engagement modules, improves time to AF diagnosis and cardiovascular outcomes in a real-world environment.
Trial registration
ClinicalTrials.gov Identifier: NCT04276441.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background
The impact of using direct-to-consumer wearable devices as a means to timely detect atrial fibrillation (AF) and to improve clinical outcomes is unknown.
Methods
Heartline is a pragmatic, randomized, and decentralized application-based trial of US participants aged ≥65 years. Two randomized cohorts include adults with possession of an iPhone and without a history of AF and those with a diagnosis of AF taking a direct oral anticoagulant (DOAC) for ≥30 days. Participants within each cohort are randomized (3:1) to either a core digital engagement program (CDEP) via iPhone application (Heartline application) and an Apple Watch (Apple Watch Group) or CDEP alone (iPhone-only Group). The Apple Watch Group has the watch irregular rhythm notification (IRN) feature enabled and access to the ECG application on the Apple Watch. If an IRN notification is issued for suspected AF then the study application instructs participants in the Apple Watch Group to seek medical care. All participants were “watch-naïve” at time of enrollment and have an option to either buy or loan an Apple Watch as part of this study. The primary end point is time from randomization to clinical diagnosis of AF, with confirmation by health care claims. Key secondary endpoint are claims-based incidence of a 6-component composite cardiovascular/systemic embolism/mortality event, DOAC medication use and adherence, costs/health resource utilization, and frequency of hospitalizations for bleeding. All study assessments, including patient-reported outcomes, are conducted through the study application. The target study enrollment is approximately 28,000 participants in total; at time of manuscript submission, a total of 26,485 participants have been enrolled into the study.
Conclusion
The Heartline Study will assess if an Apple Watch with the IRN and ECG application, along with application-facilitated digital health engagement modules, improves time to AF diagnosis and cardiovascular outcomes in a real-world environment.
Trial registration
ClinicalTrials.gov Identifier: NCT04276441.
The impact of using direct-to-consumer wearable devices as a means to timely detect atrial fibrillation (AF) and to improve clinical outcomes is unknown.
Methods
Heartline is a pragmatic, randomized, and decentralized application-based trial of US participants aged ≥65 years. Two randomized cohorts include adults with possession of an iPhone and without a history of AF and those with a diagnosis of AF taking a direct oral anticoagulant (DOAC) for ≥30 days. Participants within each cohort are randomized (3:1) to either a core digital engagement program (CDEP) via iPhone application (Heartline application) and an Apple Watch (Apple Watch Group) or CDEP alone (iPhone-only Group). The Apple Watch Group has the watch irregular rhythm notification (IRN) feature enabled and access to the ECG application on the Apple Watch. If an IRN notification is issued for suspected AF then the study application instructs participants in the Apple Watch Group to seek medical care. All participants were “watch-naïve” at time of enrollment and have an option to either buy or loan an Apple Watch as part of this study. The primary end point is time from randomization to clinical diagnosis of AF, with confirmation by health care claims. Key secondary endpoint are claims-based incidence of a 6-component composite cardiovascular/systemic embolism/mortality event, DOAC medication use and adherence, costs/health resource utilization, and frequency of hospitalizations for bleeding. All study assessments, including patient-reported outcomes, are conducted through the study application. The target study enrollment is approximately 28,000 participants in total; at time of manuscript submission, a total of 26,485 participants have been enrolled into the study.
Conclusion
The Heartline Study will assess if an Apple Watch with the IRN and ECG application, along with application-facilitated digital health engagement modules, improves time to AF diagnosis and cardiovascular outcomes in a real-world environment.
Trial registration
ClinicalTrials.gov Identifier: NCT04276441.
25.
Liu, Ruying; Zhu, Runhe; Becerik‐Gerber, Burcin; Lucas, Gale M.; Southers, Erroll G.
Be prepared: How training and emergency type affect evacuation behaviour Journal Article
In: Computer Assisted Learning, pp. jcal.12812, 2023, ISSN: 0266-4909, 1365-2729.
@article{liu_be_2023,
title = {Be prepared: How training and emergency type affect evacuation behaviour},
author = {Ruying Liu and Runhe Zhu and Burcin Becerik‐Gerber and Gale M. Lucas and Erroll G. Southers},
url = {https://onlinelibrary.wiley.com/doi/10.1111/jcal.12812},
doi = {10.1111/jcal.12812},
issn = {0266-4909, 1365-2729},
year = {2023},
date = {2023-04-01},
urldate = {2023-08-22},
journal = {Computer Assisted Learning},
pages = {jcal.12812},
abstract = {Abstract
Background
Video‐based training has been widely adopted by private organizations and public authorities to educate occupants on various types of building emergencies. However, the effectiveness of video‐based training for preparing occupants for building emergencies has not been rigorously studied nor has the impact of emergency type been investigated on training effectiveness.
Objectives
This study examines whether video‐based training is an effective method to prepare occupants for building emergencies and how the effectiveness differs in the context of different building emergencies.
Methods
We simulated fire and active shooter emergencies in a virtual office building and conducted evacuation experiments to examine participants' emergency responses using both objective and subjective metrics. A total of 108 participants were recruited and responded to the fire or active shooter incident with or without video‐based training.
Results and Conclusions
The results revealed that participants with video‐based training more often chose to follow other recommendations when responding to building emergencies instead of simply following others. Results from ANOVA showed that training increased participants' self‐efficacy significantly, especially for those in the active shooter group. Moreover, participants in the active shooter simulation had a higher level of response efficacy than those in the fire emergency simulation. Our results also demonstrated the influence of emergency type on participants' final decisions and considerations of the recommendations.
Implications
Our results suggested that video‐based training is effective in improving participants' emergency preparedness and changing their behaviour patterns to a certain extent such as reducing following behaviour and encouraging safe evacuations. Additionally, statistically significant interactions between video‐based training and emergency types suggested that training effectiveness should be considered in accordance with the emergency type.
,
Lay Description
What is already known about this topic
People can behave differently in different types of building emergencies. Understanding human behaviours in building emergencies is essential for developing emergency preparedness strategies.
Emergency training is important for building occupants and video is a widely used media for emergency training. However, its training effectiveness needs to be evaluated.
What this paper adds
We used virtual environments to investigate evacuation behaviour.
The effectiveness of video‐based training and human responses in building emergencies were studied on both subjective responses and objective measurements.
Video‐based training significantly reduced the occurrence of following behaviours.
The different natures of the fire emergency and active shooter incidents shape the effectiveness of video‐based training.
Implications of study findings for practitioners
Video‐based training can improve building occupants' emergency preparedness to a certain extent.
Emergency training media should be designed considering the influence of emergency type.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Abstract
Background
Video‐based training has been widely adopted by private organizations and public authorities to educate occupants on various types of building emergencies. However, the effectiveness of video‐based training for preparing occupants for building emergencies has not been rigorously studied nor has the impact of emergency type been investigated on training effectiveness.
Objectives
This study examines whether video‐based training is an effective method to prepare occupants for building emergencies and how the effectiveness differs in the context of different building emergencies.
Methods
We simulated fire and active shooter emergencies in a virtual office building and conducted evacuation experiments to examine participants' emergency responses using both objective and subjective metrics. A total of 108 participants were recruited and responded to the fire or active shooter incident with or without video‐based training.
Results and Conclusions
The results revealed that participants with video‐based training more often chose to follow other recommendations when responding to building emergencies instead of simply following others. Results from ANOVA showed that training increased participants' self‐efficacy significantly, especially for those in the active shooter group. Moreover, participants in the active shooter simulation had a higher level of response efficacy than those in the fire emergency simulation. Our results also demonstrated the influence of emergency type on participants' final decisions and considerations of the recommendations.
Implications
Our results suggested that video‐based training is effective in improving participants' emergency preparedness and changing their behaviour patterns to a certain extent such as reducing following behaviour and encouraging safe evacuations. Additionally, statistically significant interactions between video‐based training and emergency types suggested that training effectiveness should be considered in accordance with the emergency type.
,
Lay Description
What is already known about this topic
People can behave differently in different types of building emergencies. Understanding human behaviours in building emergencies is essential for developing emergency preparedness strategies.
Emergency training is important for building occupants and video is a widely used media for emergency training. However, its training effectiveness needs to be evaluated.
What this paper adds
We used virtual environments to investigate evacuation behaviour.
The effectiveness of video‐based training and human responses in building emergencies were studied on both subjective responses and objective measurements.
Video‐based training significantly reduced the occurrence of following behaviours.
The different natures of the fire emergency and active shooter incidents shape the effectiveness of video‐based training.
Implications of study findings for practitioners
Video‐based training can improve building occupants' emergency preparedness to a certain extent.
Emergency training media should be designed considering the influence of emergency type.
Background
Video‐based training has been widely adopted by private organizations and public authorities to educate occupants on various types of building emergencies. However, the effectiveness of video‐based training for preparing occupants for building emergencies has not been rigorously studied nor has the impact of emergency type been investigated on training effectiveness.
Objectives
This study examines whether video‐based training is an effective method to prepare occupants for building emergencies and how the effectiveness differs in the context of different building emergencies.
Methods
We simulated fire and active shooter emergencies in a virtual office building and conducted evacuation experiments to examine participants' emergency responses using both objective and subjective metrics. A total of 108 participants were recruited and responded to the fire or active shooter incident with or without video‐based training.
Results and Conclusions
The results revealed that participants with video‐based training more often chose to follow other recommendations when responding to building emergencies instead of simply following others. Results from ANOVA showed that training increased participants' self‐efficacy significantly, especially for those in the active shooter group. Moreover, participants in the active shooter simulation had a higher level of response efficacy than those in the fire emergency simulation. Our results also demonstrated the influence of emergency type on participants' final decisions and considerations of the recommendations.
Implications
Our results suggested that video‐based training is effective in improving participants' emergency preparedness and changing their behaviour patterns to a certain extent such as reducing following behaviour and encouraging safe evacuations. Additionally, statistically significant interactions between video‐based training and emergency types suggested that training effectiveness should be considered in accordance with the emergency type.
,
Lay Description
What is already known about this topic
People can behave differently in different types of building emergencies. Understanding human behaviours in building emergencies is essential for developing emergency preparedness strategies.
Emergency training is important for building occupants and video is a widely used media for emergency training. However, its training effectiveness needs to be evaluated.
What this paper adds
We used virtual environments to investigate evacuation behaviour.
The effectiveness of video‐based training and human responses in building emergencies were studied on both subjective responses and objective measurements.
Video‐based training significantly reduced the occurrence of following behaviours.
The different natures of the fire emergency and active shooter incidents shape the effectiveness of video‐based training.
Implications of study findings for practitioners
Video‐based training can improve building occupants' emergency preparedness to a certain extent.
Emergency training media should be designed considering the influence of emergency type.
26.
Murawski, Alaine; Ramirez-Zohfeld, Vanessa; Schierer, Allison; Olvera, Charles; Mell, Johnathan; Gratch, Jonathan; Brett, Jeanne; Lindquist, Lee A.
Transforming a Negotiation Framework to Resolve Conflicts among Older Adults and Family Caregivers Journal Article
In: Geriatrics, vol. 8, no. 2, pp. 36, 2023, ISSN: 2308-3417, (Number: 2 Publisher: Multidisciplinary Digital Publishing Institute).
@article{murawski_transforming_2023,
title = {Transforming a Negotiation Framework to Resolve Conflicts among Older Adults and Family Caregivers},
author = {Alaine Murawski and Vanessa Ramirez-Zohfeld and Allison Schierer and Charles Olvera and Johnathan Mell and Jonathan Gratch and Jeanne Brett and Lee A. Lindquist},
url = {https://www.mdpi.com/2308-3417/8/2/36},
doi = {10.3390/geriatrics8020036},
issn = {2308-3417},
year = {2023},
date = {2023-04-01},
urldate = {2023-03-31},
journal = {Geriatrics},
volume = {8},
number = {2},
pages = {36},
abstract = {Background: Family caregivers of older people with Alzheimer’s dementia (PWD) often need to advocate and resolve health-related conflicts (e.g., determining treatment necessity, billing errors, and home health extensions). As they deal with these health system conflicts, family caregivers experience unnecessary frustration, anxiety, and stress. The goal of this research was to apply a negotiation framework to resolve real-world family caregiver–older adult conflicts. Methods: We convened an interdisciplinary team of national community-based family caregivers, social workers, geriatricians, and negotiation experts (n = 9; Illinois, Florida, New York, and California) to examine the applicability of negotiation and conflict management frameworks to three older adult–caregiver conflicts (i.e., caregiver–older adult, caregiver–provider, and caregiver–caregiver). The panel of caregivers provided scenarios and dialogue describing conflicts they experienced in these three settings. A qualitative analysis was then performed grouping the responses into a framework matrix. Results: Upon presenting the three conflicts to the caregivers, 96 responses (caregiver–senior), 75 responses (caregiver–caregiver), and 80 responses (caregiver–provider) were generated. A thematic analysis showed that the statements and responses fit the interest–rights–power (IRP) negotiation framework. Discussion: The interests–rights–power (IRP) framework, used in business negotiations, provided insight into how caregivers experienced conflict with older adults, providers, and other caregivers. Future research is needed to examine applying the IRP framework in the training of caregivers of older people with Alzheimer’s dementia.},
note = {Number: 2
Publisher: Multidisciplinary Digital Publishing Institute},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background: Family caregivers of older people with Alzheimer’s dementia (PWD) often need to advocate and resolve health-related conflicts (e.g., determining treatment necessity, billing errors, and home health extensions). As they deal with these health system conflicts, family caregivers experience unnecessary frustration, anxiety, and stress. The goal of this research was to apply a negotiation framework to resolve real-world family caregiver–older adult conflicts. Methods: We convened an interdisciplinary team of national community-based family caregivers, social workers, geriatricians, and negotiation experts (n = 9; Illinois, Florida, New York, and California) to examine the applicability of negotiation and conflict management frameworks to three older adult–caregiver conflicts (i.e., caregiver–older adult, caregiver–provider, and caregiver–caregiver). The panel of caregivers provided scenarios and dialogue describing conflicts they experienced in these three settings. A qualitative analysis was then performed grouping the responses into a framework matrix. Results: Upon presenting the three conflicts to the caregivers, 96 responses (caregiver–senior), 75 responses (caregiver–caregiver), and 80 responses (caregiver–provider) were generated. A thematic analysis showed that the statements and responses fit the interest–rights–power (IRP) negotiation framework. Discussion: The interests–rights–power (IRP) framework, used in business negotiations, provided insight into how caregivers experienced conflict with older adults, providers, and other caregivers. Future research is needed to examine applying the IRP framework in the training of caregivers of older people with Alzheimer’s dementia.
27.
Hsu, Wan-Yu; Anguera, Joaquin A.; Rizzo, Albert; Campusano, Richard; Chiaravalloti, Nancy D.; DeLuca, John; Gazzaley, Adam; Bove, Riley M.
A virtual reality program to assess cognitive function in multiple sclerosis: A pilot study Journal Article
In: Frontiers in Human Neuroscience, 2023, (Place: Lausanne, Switzerland Publisher: Frontiers Research Foundation Section: ORIGINAL RESEARCH article).
@article{hsu_virtual_2023,
title = {A virtual reality program to assess cognitive function in multiple sclerosis: A pilot study},
author = {Wan-Yu Hsu and Joaquin A. Anguera and Albert Rizzo and Richard Campusano and Nancy D. Chiaravalloti and John DeLuca and Adam Gazzaley and Riley M. Bove},
url = {https://www.proquest.com/docview/2787027204/abstract/BEA88F7BB72B4623PQ/1},
doi = {10.3389/fnhum.2023.1139316},
year = {2023},
date = {2023-03-01},
urldate = {2023-03-31},
journal = {Frontiers in Human Neuroscience},
abstract = {Introduction: Cognitive impairment is a debilitating symptom in people with multiple sclerosis (MS). Most of the neuropsychological tasks have little resemblance to everyday life. There is a need for ecologically valid tools for assessing cognition in real-life functional contexts in MS. One potential solution would involve the use of virtual reality (VR) to exert finer control over the task presentation environment; however, VR studies in the MS population are scarce. Objectives: To explore the utility and feasibility of a VR program for cognitive assessment in MS. Methods: A VR classroom embedded with a continuous performance task (CPT) was assessed in 10 non-MS adults and 10 people with MS with low cognitive functioning. Participants performed the CPT with distractors (ie. WD) and without distractors (ie. ND). The Symbol Digit Modalities Test (SDMT), California Verbal Learning Test – II (CVLT-II), and a feedback survey on the VR program were administered. Results: People with MS exhibited greater reaction time variability (RTV) compared to non-MS participants, and greater RTV in both WD and ND conditions was associated with lower SDMT. Conclusions: VR tools warrant further research to determine their value as an ecologically valid platform for assessing cognition and everyday functioning in people with MS.},
note = {Place: Lausanne, Switzerland
Publisher: Frontiers Research Foundation
Section: ORIGINAL RESEARCH article},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Introduction: Cognitive impairment is a debilitating symptom in people with multiple sclerosis (MS). Most of the neuropsychological tasks have little resemblance to everyday life. There is a need for ecologically valid tools for assessing cognition in real-life functional contexts in MS. One potential solution would involve the use of virtual reality (VR) to exert finer control over the task presentation environment; however, VR studies in the MS population are scarce. Objectives: To explore the utility and feasibility of a VR program for cognitive assessment in MS. Methods: A VR classroom embedded with a continuous performance task (CPT) was assessed in 10 non-MS adults and 10 people with MS with low cognitive functioning. Participants performed the CPT with distractors (ie. WD) and without distractors (ie. ND). The Symbol Digit Modalities Test (SDMT), California Verbal Learning Test – II (CVLT-II), and a feedback survey on the VR program were administered. Results: People with MS exhibited greater reaction time variability (RTV) compared to non-MS participants, and greater RTV in both WD and ND conditions was associated with lower SDMT. Conclusions: VR tools warrant further research to determine their value as an ecologically valid platform for assessing cognition and everyday functioning in people with MS.
28.
Pynadath, David V.; Dilkina, Bistra; Jeong, David C.; John, Richard S.; Marsella, Stacy C.; Merchant, Chirag; Miller, Lynn C.; Read, Stephen J.
Disaster world Journal Article
In: Comput Math Organ Theory, vol. 29, no. 1, pp. 84–117, 2023, ISSN: 1572-9346.
@article{pynadath_disaster_2023,
title = {Disaster world},
author = {David V. Pynadath and Bistra Dilkina and David C. Jeong and Richard S. John and Stacy C. Marsella and Chirag Merchant and Lynn C. Miller and Stephen J. Read},
url = {https://doi.org/10.1007/s10588-022-09359-y},
doi = {10.1007/s10588-022-09359-y},
issn = {1572-9346},
year = {2023},
date = {2023-03-01},
urldate = {2023-03-31},
journal = {Comput Math Organ Theory},
volume = {29},
number = {1},
pages = {84–117},
abstract = {Artificial intelligence (AI) research provides a rich source of modeling languages capable of generating socially plausible simulations of human behavior, while also providing a transparent ground truth that can support validation of social-science methods applied to that simulation. In this work, we leverage two established AI representations: decision-theoretic planning and recursive modeling. Decision-theoretic planning (specifically Partially Observable Markov Decision Processes) provides agents with quantitative models of their corresponding real-world entities’ subjective (and possibly incorrect) perspectives of ground truth in the form of probabilistic beliefs and utility functions. Recursive modeling gives an agent a theory of mind, which is necessary when a person’s (again, possibly incorrect) subjective perspectives are of another person, rather than of just his/her environment. We used PsychSim, a multiagent social-simulation framework combining these two AI frameworks, to build a general parameterized model of human behavior during disaster response, grounding the model in social-psychological theories to ensure social plausibility. We then instantiated that model into alternate ground truths for simulating population response to a series of natural disasters, namely, hurricanes. The simulations generate data in response to socially plausible instruments (e.g., surveys) that serve as input to the Ground Truth program’s designated research teams for them to conduct simulated social science. The simulation also provides a graphical ground truth and a set of outcomes to be used as the gold standard in evaluating the research teams’ inferences.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Artificial intelligence (AI) research provides a rich source of modeling languages capable of generating socially plausible simulations of human behavior, while also providing a transparent ground truth that can support validation of social-science methods applied to that simulation. In this work, we leverage two established AI representations: decision-theoretic planning and recursive modeling. Decision-theoretic planning (specifically Partially Observable Markov Decision Processes) provides agents with quantitative models of their corresponding real-world entities’ subjective (and possibly incorrect) perspectives of ground truth in the form of probabilistic beliefs and utility functions. Recursive modeling gives an agent a theory of mind, which is necessary when a person’s (again, possibly incorrect) subjective perspectives are of another person, rather than of just his/her environment. We used PsychSim, a multiagent social-simulation framework combining these two AI frameworks, to build a general parameterized model of human behavior during disaster response, grounding the model in social-psychological theories to ensure social plausibility. We then instantiated that model into alternate ground truths for simulating population response to a series of natural disasters, namely, hurricanes. The simulations generate data in response to socially plausible instruments (e.g., surveys) that serve as input to the Ground Truth program’s designated research teams for them to conduct simulated social science. The simulation also provides a graphical ground truth and a set of outcomes to be used as the gold standard in evaluating the research teams’ inferences.
29.
Gratch, Jonathan
The promise and peril of interactive embodied agents for studying non-verbal communication: a machine learning perspective Journal Article
In: Philosophical Transactions of the Royal Society B: Biological Sciences, vol. 378, no. 1875, pp. 20210475, 2023, (Publisher: Royal Society).
@article{gratch_promise_2023,
title = {The promise and peril of interactive embodied agents for studying non-verbal communication: a machine learning perspective},
author = {Jonathan Gratch},
url = {https://royalsocietypublishing.org/doi/abs/10.1098/rstb.2021.0475},
doi = {10.1098/rstb.2021.0475},
year = {2023},
date = {2023-03-01},
urldate = {2023-03-31},
journal = {Philosophical Transactions of the Royal Society B: Biological Sciences},
volume = {378},
number = {1875},
pages = {20210475},
abstract = {In face-to-face interactions, parties rapidly react and adapt to each other's words, movements and expressions. Any science of face-to-face interaction must develop approaches to hypothesize and rigorously test mechanisms that explain such interdependent behaviour. Yet conventional experimental designs often sacrifice interactivity to establish experimental control. Interactive virtual and robotic agents have been offered as a way to study true interactivity while enforcing a measure of experimental control by allowing participants to interact with realistic but carefully controlled partners. But as researchers increasingly turn to machine learning to add realism to such agents, they may unintentionally distort the very interactivity they seek to illuminate, particularly when investigating the role of non-verbal signals such as emotion or active-listening behaviours. Here I discuss some of the methodological challenges that may arise when machine learning is used to model the behaviour of interaction partners. By articulating and explicitly considering these commitments, researchers can transform ‘unintentional distortions’ into valuable methodological tools that yield new insights and better contextualize existing experimental findings that rely on learning technology.
This article is part of a discussion meeting issue ‘Face2face: advancing the science of social interaction’.},
note = {Publisher: Royal Society},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In face-to-face interactions, parties rapidly react and adapt to each other's words, movements and expressions. Any science of face-to-face interaction must develop approaches to hypothesize and rigorously test mechanisms that explain such interdependent behaviour. Yet conventional experimental designs often sacrifice interactivity to establish experimental control. Interactive virtual and robotic agents have been offered as a way to study true interactivity while enforcing a measure of experimental control by allowing participants to interact with realistic but carefully controlled partners. But as researchers increasingly turn to machine learning to add realism to such agents, they may unintentionally distort the very interactivity they seek to illuminate, particularly when investigating the role of non-verbal signals such as emotion or active-listening behaviours. Here I discuss some of the methodological challenges that may arise when machine learning is used to model the behaviour of interaction partners. By articulating and explicitly considering these commitments, researchers can transform ‘unintentional distortions’ into valuable methodological tools that yield new insights and better contextualize existing experimental findings that rely on learning technology.
This article is part of a discussion meeting issue ‘Face2face: advancing the science of social interaction’.
This article is part of a discussion meeting issue ‘Face2face: advancing the science of social interaction’.
30.
Awada, Mohamad; Becerik-Gerber, Burcin; Liu, Ruying; Seyedrezaei, Mirmahdi; Lu, Zheng; Xenakis, Matheos; Lucas, Gale; Roll, Shawn C.; Narayanan, Shrikanth
Ten questions concerning the impact of environmental stress on office workers Journal Article
In: Building and Environment, vol. 229, pp. 109964, 2023, ISSN: 0360-1323.
@article{awada_ten_2023,
title = {Ten questions concerning the impact of environmental stress on office workers},
author = {Mohamad Awada and Burcin Becerik-Gerber and Ruying Liu and Mirmahdi Seyedrezaei and Zheng Lu and Matheos Xenakis and Gale Lucas and Shawn C. Roll and Shrikanth Narayanan},
url = {https://www.sciencedirect.com/science/article/pii/S0360132322011945},
doi = {10.1016/j.buildenv.2022.109964},
issn = {0360-1323},
year = {2023},
date = {2023-02-01},
urldate = {2023-03-31},
journal = {Building and Environment},
volume = {229},
pages = {109964},
abstract = {We regularly face stress during our everyday activities, to the extent that stress is recognized by the World Health Organization as the epidemic of the 21st century. Stress is how humans respond physically and psychologically to adjustments, experiences, conditions, and circumstances in their lives. While there are many reasons for stress, work and job pressure remain the main cause. Thus, companies are increasingly interested in creating healthier, more comfortable, and stress-free offices for their workers. The indoor environment can induce environmental stress when it cannot satisfy the individual needs for health and comfort. In fact, office environmental conditions (e.g., thermal, and indoor air conditions, lighting, and noise) and interior design parameters (e.g., office layout, colors, furniture, access to views, distance to window, personal control and biophilic design) have been found to affect office workers' stress levels. A line of research based on the stress recovery theory offers new insights for establishing offices that limit environmental stress and help with work stress recovery. To that end, this paper answers ten questions that explore the relation between the indoor office-built environment and stress levels among workers. The answers to the ten questions are based on an extensive literature review to draw conclusions from what has been achieved to date. Thus, this study presents a foundation for future environmental stress related research in offices.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
We regularly face stress during our everyday activities, to the extent that stress is recognized by the World Health Organization as the epidemic of the 21st century. Stress is how humans respond physically and psychologically to adjustments, experiences, conditions, and circumstances in their lives. While there are many reasons for stress, work and job pressure remain the main cause. Thus, companies are increasingly interested in creating healthier, more comfortable, and stress-free offices for their workers. The indoor environment can induce environmental stress when it cannot satisfy the individual needs for health and comfort. In fact, office environmental conditions (e.g., thermal, and indoor air conditions, lighting, and noise) and interior design parameters (e.g., office layout, colors, furniture, access to views, distance to window, personal control and biophilic design) have been found to affect office workers' stress levels. A line of research based on the stress recovery theory offers new insights for establishing offices that limit environmental stress and help with work stress recovery. To that end, this paper answers ten questions that explore the relation between the indoor office-built environment and stress levels among workers. The answers to the ten questions are based on an extensive literature review to draw conclusions from what has been achieved to date. Thus, this study presents a foundation for future environmental stress related research in offices.
31.
Goel, Rahul; Tse, Teresa; Smith, Lia J.; Floren, Andrew; Naylor, Bruce; Williams, M. Wright; Salas, Ramiro; Rizzo, Albert S.; Ress, David
Framework for Accurate Classification of Self-Reported Stress From Multisession Functional MRI Data of Veterans With Posttraumatic Stress Journal Article
In: Chronic Stress, vol. 7, pp. 24705470231203655, 2023, ISSN: 2470-5470, 2470-5470.
@article{goel_framework_2023,
title = {Framework for Accurate Classification of Self-Reported Stress From Multisession Functional MRI Data of Veterans With Posttraumatic Stress},
author = {Rahul Goel and Teresa Tse and Lia J. Smith and Andrew Floren and Bruce Naylor and M. Wright Williams and Ramiro Salas and Albert S. Rizzo and David Ress},
url = {http://journals.sagepub.com/doi/10.1177/24705470231203655},
doi = {10.1177/24705470231203655},
issn = {2470-5470, 2470-5470},
year = {2023},
date = {2023-01-01},
urldate = {2023-12-07},
journal = {Chronic Stress},
volume = {7},
pages = {24705470231203655},
abstract = {Background: Posttraumatic stress disorder (PTSD) is a significant burden among combat Veterans returning from the wars in Iraq and Afghanistan. While empirically supported treatments have demonstrated reductions in PTSD symptomatology, there remains a need to improve treatment effectiveness. Functional magnetic resonance imaging (fMRI) neurofeedback has emerged as a possible treatment to ameliorate PTSD symptom severity. Virtual reality (VR) approaches have also shown promise in increasing treatment compliance and outcomes. To facilitate fMRI neurofeedback-associated therapies, it would be advantageous to accurately classify internal brain stress levels while Veterans are exposed to trauma-associated VR imagery. Methods: Across 2 sessions, we used fMRI to collect neural responses to trauma-associated VR-like stimuli among male combat Veterans with PTSD symptoms (N = 8). Veterans reported their self-perceived stress level on a scale from 1 to 8 every 15 s throughout the fMRI sessions. In our proposed framework, we precisely sample the fMRI data on cortical gray matter, blurring the data along the gray-matter manifold to reduce noise and dimensionality while preserving maximum neural information. Then, we independently applied 3 machine learning (ML) algorithms to this fMRI data collected across 2 sessions, separately for each Veteran, to build individualized ML models that predicted their internal brain states (self-reported stress responses). Results: We accurately classified the 8-class self-reported stress responses with a mean (± standard error) root mean square error of 0.6 (± 0.1) across all Veterans using the best ML approach. Conclusions: The findings demonstrate the predictive ability of ML algorithms applied to whole-brain cortical fMRI data collected during individual Veteran sessions. The framework we have developed to preprocess whole-brain cortical fMRI data and train ML models across sessions would provide a valuable tool to enable individualized real-time fMRI neurofeedback during VR-like exposure therapy for PTSD.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background: Posttraumatic stress disorder (PTSD) is a significant burden among combat Veterans returning from the wars in Iraq and Afghanistan. While empirically supported treatments have demonstrated reductions in PTSD symptomatology, there remains a need to improve treatment effectiveness. Functional magnetic resonance imaging (fMRI) neurofeedback has emerged as a possible treatment to ameliorate PTSD symptom severity. Virtual reality (VR) approaches have also shown promise in increasing treatment compliance and outcomes. To facilitate fMRI neurofeedback-associated therapies, it would be advantageous to accurately classify internal brain stress levels while Veterans are exposed to trauma-associated VR imagery. Methods: Across 2 sessions, we used fMRI to collect neural responses to trauma-associated VR-like stimuli among male combat Veterans with PTSD symptoms (N = 8). Veterans reported their self-perceived stress level on a scale from 1 to 8 every 15 s throughout the fMRI sessions. In our proposed framework, we precisely sample the fMRI data on cortical gray matter, blurring the data along the gray-matter manifold to reduce noise and dimensionality while preserving maximum neural information. Then, we independently applied 3 machine learning (ML) algorithms to this fMRI data collected across 2 sessions, separately for each Veteran, to build individualized ML models that predicted their internal brain states (self-reported stress responses). Results: We accurately classified the 8-class self-reported stress responses with a mean (± standard error) root mean square error of 0.6 (± 0.1) across all Veterans using the best ML approach. Conclusions: The findings demonstrate the predictive ability of ML algorithms applied to whole-brain cortical fMRI data collected during individual Veteran sessions. The framework we have developed to preprocess whole-brain cortical fMRI data and train ML models across sessions would provide a valuable tool to enable individualized real-time fMRI neurofeedback during VR-like exposure therapy for PTSD.
32.
Awada, Mohamad; Becerik-Gerber, Burcin; Lucas, Gale; Roll, Shawn; Liu, Ruying
A New Perspective on Stress Detection: An Automated Approach for Detecting Eustress and Distress Journal Article
In: IEEE Trans. Affective Comput., pp. 1–15, 2023, ISSN: 1949-3045, 2371-9850.
@article{awada_new_2023,
title = {A New Perspective on Stress Detection: An Automated Approach for Detecting Eustress and Distress},
author = {Mohamad Awada and Burcin Becerik-Gerber and Gale Lucas and Shawn Roll and Ruying Liu},
url = {https://ieeexplore.ieee.org/document/10286408/},
doi = {10.1109/TAFFC.2023.3324910},
issn = {1949-3045, 2371-9850},
year = {2023},
date = {2023-01-01},
urldate = {2023-12-07},
journal = {IEEE Trans. Affective Comput.},
pages = {1–15},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
33.
Tak, Ala N.; Gratch, Jonathan
Is GPT a Computational Model of Emotion? Detailed Analysis Journal Article
In: 2023, (Publisher: arXiv Version Number: 1).
@article{tak_is_2023,
title = {Is GPT a Computational Model of Emotion? Detailed Analysis},
author = {Ala N. Tak and Jonathan Gratch},
url = {https://arxiv.org/abs/2307.13779},
doi = {10.48550/ARXIV.2307.13779},
year = {2023},
date = {2023-01-01},
urldate = {2023-09-20},
abstract = {This paper investigates the emotional reasoning abilities of the GPT family of large language models via a component perspective. The paper first examines how the model reasons about autobiographical memories. Second, it systematically varies aspects of situations to impact emotion intensity and coping tendencies. Even without the use of prompt engineering, it is shown that GPT's predictions align significantly with human-provided appraisals and emotional labels. However, GPT faces difficulties predicting emotion intensity and coping responses. GPT-4 showed the highest performance in the initial study but fell short in the second, despite providing superior results after minor prompt engineering. This assessment brings up questions on how to effectively employ the strong points and address the weak areas of these models, particularly concerning response variability. These studies underscore the merits of evaluating models from a componential perspective.},
note = {Publisher: arXiv
Version Number: 1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This paper investigates the emotional reasoning abilities of the GPT family of large language models via a component perspective. The paper first examines how the model reasons about autobiographical memories. Second, it systematically varies aspects of situations to impact emotion intensity and coping tendencies. Even without the use of prompt engineering, it is shown that GPT's predictions align significantly with human-provided appraisals and emotional labels. However, GPT faces difficulties predicting emotion intensity and coping responses. GPT-4 showed the highest performance in the initial study but fell short in the second, despite providing superior results after minor prompt engineering. This assessment brings up questions on how to effectively employ the strong points and address the weak areas of these models, particularly concerning response variability. These studies underscore the merits of evaluating models from a componential perspective.
34.
Sato, Motoaki; Terada, Kazunori; Gratch, Jonathan
Teaching Reverse Appraisal to Improve Negotiation Skills Journal Article
In: IEEE Trans. Affective Comput., pp. 1–14, 2023, ISSN: 1949-3045, 2371-9850.
@article{sato_teaching_2023,
title = {Teaching Reverse Appraisal to Improve Negotiation Skills},
author = {Motoaki Sato and Kazunori Terada and Jonathan Gratch},
url = {https://ieeexplore.ieee.org/document/10189838/},
doi = {10.1109/TAFFC.2023.3285931},
issn = {1949-3045, 2371-9850},
year = {2023},
date = {2023-01-01},
urldate = {2023-09-20},
journal = {IEEE Trans. Affective Comput.},
pages = {1–14},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
35.
Melo, Celso M. De; Gratch, Jonathan; Marsella, Stacy; Pelachaud, Catherine
Social Functions of Machine Emotional Expressions Journal Article
In: Proc. IEEE, pp. 1–16, 2023, ISSN: 0018-9219, 1558-2256.
@article{de_melo_social_2023,
title = {Social Functions of Machine Emotional Expressions},
author = {Celso M. De Melo and Jonathan Gratch and Stacy Marsella and Catherine Pelachaud},
url = {https://ieeexplore.ieee.org/document/10093227/},
doi = {10.1109/JPROC.2023.3261137},
issn = {0018-9219, 1558-2256},
year = {2023},
date = {2023-01-01},
urldate = {2023-08-04},
journal = {Proc. IEEE},
pages = {1–16},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
36.
Lu, Shuhong; Yoon, Youngwoo; Feng, Andrew
Co-Speech Gesture Synthesis using Discrete Gesture Token Learning Journal Article
In: 2023, (Publisher: arXiv Version Number: 1).
@article{lu_co-speech_2023,
title = {Co-Speech Gesture Synthesis using Discrete Gesture Token Learning},
author = {Shuhong Lu and Youngwoo Yoon and Andrew Feng},
url = {https://arxiv.org/abs/2303.12822},
doi = {10.48550/ARXIV.2303.12822},
year = {2023},
date = {2023-01-01},
urldate = {2023-08-04},
abstract = {Synthesizing realistic co-speech gestures is an important and yet unsolved problem for creating believable motions that can drive a humanoid robot to interact and communicate with human users. Such capability will improve the impressions of the robots by human users and will find applications in education, training, and medical services. One challenge in learning the co-speech gesture model is that there may be multiple viable gesture motions for the same speech utterance. The deterministic regression methods can not resolve the conflicting samples and may produce over-smoothed or damped motions. We proposed a two-stage model to address this uncertainty issue in gesture synthesis by modeling the gesture segments as discrete latent codes. Our method utilizes RQ-VAE in the first stage to learn a discrete codebook consisting of gesture tokens from training data. In the second stage, a two-level autoregressive transformer model is used to learn the prior distribution of residual codes conditioned on input speech context. Since the inference is formulated as token sampling, multiple gesture sequences could be generated given the same speech input using top-k sampling. The quantitative results and the user study showed the proposed method outperforms the previous methods and is able to generate realistic and diverse gesture motions.},
note = {Publisher: arXiv
Version Number: 1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Synthesizing realistic co-speech gestures is an important and yet unsolved problem for creating believable motions that can drive a humanoid robot to interact and communicate with human users. Such capability will improve the impressions of the robots by human users and will find applications in education, training, and medical services. One challenge in learning the co-speech gesture model is that there may be multiple viable gesture motions for the same speech utterance. The deterministic regression methods can not resolve the conflicting samples and may produce over-smoothed or damped motions. We proposed a two-stage model to address this uncertainty issue in gesture synthesis by modeling the gesture segments as discrete latent codes. Our method utilizes RQ-VAE in the first stage to learn a discrete codebook consisting of gesture tokens from training data. In the second stage, a two-level autoregressive transformer model is used to learn the prior distribution of residual codes conditioned on input speech context. Since the inference is formulated as token sampling, multiple gesture sequences could be generated given the same speech input using top-k sampling. The quantitative results and the user study showed the proposed method outperforms the previous methods and is able to generate realistic and diverse gesture motions.
37.
Hale, James; Kim, Peter; Gratch, Jonathan
Risk Aversion and Demographic Factors Affect Preference Elicitation and Outcomes of a Salary Negotiation Journal Article
In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol. Volume 45, 2023.
@article{hale_risk_2023,
title = {Risk Aversion and Demographic Factors Affect Preference Elicitation and Outcomes of a Salary Negotiation},
author = {James Hale and Peter Kim and Jonathan Gratch},
url = {https://escholarship.org/uc/item/7n01v4f9#main},
year = {2023},
date = {2023-01-01},
journal = {Proceedings of the Annual Meeting of the Cognitive Science Society},
volume = {Volume 45},
abstract = {Women and minorities obtain lower salaries when negotiating their employment compensation. Some have suggested that automated negotiation and dispute-resolution technology might address such material inequities. These algorithms elicit the multi-criteria preferences of each side of a dispute and arrive at solutions that are efficient and "provably" fair. In a study that explores the potential benefit of these methods, we highlight cognitive factors that may allow inequities to persist despite these methods. Specifically, risk-averse individuals express lower preferences for salary and as risk-aversion is more common in women and minorities, this translates into a ``provably'' fair lower salary. While this may reflect actual underlying differences in preferences across groups, individuals may be confounding their preferences for salary with their risk preference (i.e., their fear of not reaching an agreement), such that these groups achieve worse outcomes than they should. We further highlight that methodological choices in how negotiation processes are often studied can obscure the magnitude of this effect.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Women and minorities obtain lower salaries when negotiating their employment compensation. Some have suggested that automated negotiation and dispute-resolution technology might address such material inequities. These algorithms elicit the multi-criteria preferences of each side of a dispute and arrive at solutions that are efficient and "provably" fair. In a study that explores the potential benefit of these methods, we highlight cognitive factors that may allow inequities to persist despite these methods. Specifically, risk-averse individuals express lower preferences for salary and as risk-aversion is more common in women and minorities, this translates into a ``provably'' fair lower salary. While this may reflect actual underlying differences in preferences across groups, individuals may be confounding their preferences for salary with their risk preference (i.e., their fear of not reaching an agreement), such that these groups achieve worse outcomes than they should. We further highlight that methodological choices in how negotiation processes are often studied can obscure the magnitude of this effect.
38.
Yang, Jing; Xiao, Hanyuan; Teng, Wenbin; Cai, Yunxuan; Zhao, Yajie
Light Sampling Field and BRDF Representation for Physically-based Neural Rendering Journal Article
In: 2023, (Publisher: arXiv Version Number: 1).
@article{yang_light_2023,
title = {Light Sampling Field and BRDF Representation for Physically-based Neural Rendering},
author = {Jing Yang and Hanyuan Xiao and Wenbin Teng and Yunxuan Cai and Yajie Zhao},
url = {https://arxiv.org/abs/2304.05472},
doi = {10.48550/ARXIV.2304.05472},
year = {2023},
date = {2023-01-01},
urldate = {2023-08-22},
abstract = {Physically-based rendering (PBR) is key for immersive rendering effects used widely in the industry to showcase detailed realistic scenes from computer graphics assets. A well-known caveat is that producing the same is computationally heavy and relies on complex capture devices. Inspired by the success in quality and efficiency of recent volumetric neural rendering, we want to develop a physically-based neural shader to eliminate device dependency and significantly boost performance. However, no existing lighting and material models in the current neural rendering approaches can accurately represent the comprehensive lighting models and BRDFs properties required by the PBR process. Thus, this paper proposes a novel lighting representation that models direct and indirect light locally through a light sampling strategy in a learned light sampling field. We also propose BRDF models to separately represent surface/subsurface scattering details to enable complex objects such as translucent material (i.e., skin, jade). We then implement our proposed representations with an end-to-end physically-based neural face skin shader, which takes a standard face asset (i.e., geometry, albedo map, and normal map) and an HDRI for illumination as inputs and generates a photo-realistic rendering as output. Extensive experiments showcase the quality and efficiency of our PBR face skin shader, indicating the effectiveness of our proposed lighting and material representations.},
note = {Publisher: arXiv
Version Number: 1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Physically-based rendering (PBR) is key for immersive rendering effects used widely in the industry to showcase detailed realistic scenes from computer graphics assets. A well-known caveat is that producing the same is computationally heavy and relies on complex capture devices. Inspired by the success in quality and efficiency of recent volumetric neural rendering, we want to develop a physically-based neural shader to eliminate device dependency and significantly boost performance. However, no existing lighting and material models in the current neural rendering approaches can accurately represent the comprehensive lighting models and BRDFs properties required by the PBR process. Thus, this paper proposes a novel lighting representation that models direct and indirect light locally through a light sampling strategy in a learned light sampling field. We also propose BRDF models to separately represent surface/subsurface scattering details to enable complex objects such as translucent material (i.e., skin, jade). We then implement our proposed representations with an end-to-end physically-based neural face skin shader, which takes a standard face asset (i.e., geometry, albedo map, and normal map) and an HDRI for illumination as inputs and generates a photo-realistic rendering as output. Extensive experiments showcase the quality and efficiency of our PBR face skin shader, indicating the effectiveness of our proposed lighting and material representations.
39.
Wu, Haochen; Sequeira, Pedro; Pynadath, David V.
Multiagent Inverse Reinforcement Learning via Theory of Mind Reasoning Journal Article
In: 2023, (Publisher: arXiv Version Number: 2).
@article{wu_multiagent_2023,
title = {Multiagent Inverse Reinforcement Learning via Theory of Mind Reasoning},
author = {Haochen Wu and Pedro Sequeira and David V. Pynadath},
url = {https://arxiv.org/abs/2302.10238},
doi = {10.48550/ARXIV.2302.10238},
year = {2023},
date = {2023-01-01},
urldate = {2023-08-24},
abstract = {We approach the problem of understanding how people interact with each other in collaborative settings, especially when individuals know little about their teammates, via Multiagent Inverse Reinforcement Learning (MIRL), where the goal is to infer the reward functions guiding the behavior of each individual given trajectories of a team's behavior during some task. Unlike current MIRL approaches, we do not assume that team members know each other's goals a priori; rather, that they collaborate by adapting to the goals of others perceived by observing their behavior, all while jointly performing a task. To address this problem, we propose a novel approach to MIRL via Theory of Mind (MIRL-ToM). For each agent, we first use ToM reasoning to estimate a posterior distribution over baseline reward profiles given their demonstrated behavior. We then perform MIRL via decentralized equilibrium by employing single-agent Maximum Entropy IRL to infer a reward function for each agent, where we simulate the behavior of other teammates according to the time-varying distribution over profiles. We evaluate our approach in a simulated 2-player search-and-rescue operation where the goal of the agents, playing different roles, is to search for and evacuate victims in the environment. Our results show that the choice of baseline profiles is paramount to the recovery of the ground-truth rewards, and that MIRL-ToM is able to recover the rewards used by agents interacting both with known and unknown teammates.},
note = {Publisher: arXiv
Version Number: 2},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
We approach the problem of understanding how people interact with each other in collaborative settings, especially when individuals know little about their teammates, via Multiagent Inverse Reinforcement Learning (MIRL), where the goal is to infer the reward functions guiding the behavior of each individual given trajectories of a team's behavior during some task. Unlike current MIRL approaches, we do not assume that team members know each other's goals a priori; rather, that they collaborate by adapting to the goals of others perceived by observing their behavior, all while jointly performing a task. To address this problem, we propose a novel approach to MIRL via Theory of Mind (MIRL-ToM). For each agent, we first use ToM reasoning to estimate a posterior distribution over baseline reward profiles given their demonstrated behavior. We then perform MIRL via decentralized equilibrium by employing single-agent Maximum Entropy IRL to infer a reward function for each agent, where we simulate the behavior of other teammates according to the time-varying distribution over profiles. We evaluate our approach in a simulated 2-player search-and-rescue operation where the goal of the agents, playing different roles, is to search for and evacuate victims in the environment. Our results show that the choice of baseline profiles is paramount to the recovery of the ground-truth rewards, and that MIRL-ToM is able to recover the rewards used by agents interacting both with known and unknown teammates.
40.
Yu, Zifan; Chen, Meida; Zhang, Zhikang; You, Suya; Ren, Fengbo
TransUPR: A Transformer-based Uncertain Point Refiner for LiDAR Point Cloud Semantic Segmentation Journal Article
In: 2023, (Publisher: arXiv Version Number: 2).
@article{yu_transupr_2023,
title = {TransUPR: A Transformer-based Uncertain Point Refiner for LiDAR Point Cloud Semantic Segmentation},
author = {Zifan Yu and Meida Chen and Zhikang Zhang and Suya You and Fengbo Ren},
url = {https://arxiv.org/abs/2302.08594},
doi = {10.48550/ARXIV.2302.08594},
year = {2023},
date = {2023-01-01},
urldate = {2023-08-24},
abstract = {In this work, we target the problem of uncertain points refinement for image-based LiDAR point cloud semantic segmentation (LiDAR PCSS). This problem mainly results from the boundary-blurring problem of convolution neural networks (CNNs) and quantitation loss of spherical projection, which are often hard to avoid for common image-based LiDAR PCSS approaches. We propose a plug-and-play transformer-based uncertain point refiner (TransUPR) to address the problem. Through local feature aggregation, uncertain point localization, and self-attention-based transformer design, TransUPR, integrated into an existing range image-based LiDAR PCSS approach (e.g., CENet), achieves the state-of-the-art performance (68.2% mIoU) on Semantic-KITTI benchmark, which provides a performance improvement of 0.6% on the mIoU.},
note = {Publisher: arXiv
Version Number: 2},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In this work, we target the problem of uncertain points refinement for image-based LiDAR point cloud semantic segmentation (LiDAR PCSS). This problem mainly results from the boundary-blurring problem of convolution neural networks (CNNs) and quantitation loss of spherical projection, which are often hard to avoid for common image-based LiDAR PCSS approaches. We propose a plug-and-play transformer-based uncertain point refiner (TransUPR) to address the problem. Through local feature aggregation, uncertain point localization, and self-attention-based transformer design, TransUPR, integrated into an existing range image-based LiDAR PCSS approach (e.g., CENet), achieves the state-of-the-art performance (68.2% mIoU) on Semantic-KITTI benchmark, which provides a performance improvement of 0.6% on the mIoU.
Filter
2022
Zhu, Runhe; Lucas, Gale M.; Becerik-Gerber, Burcin; Southers, Erroll G.; Landicho, Earl
The impact of security countermeasures on human behavior during active shooter incidents Journal Article
In: Sci Rep, vol. 12, no. 1, pp. 929, 2022, ISSN: 2045-2322.
Abstract | Links | BibTeX | Tags: DTIC, UARC
@article{zhu_impact_2022,
title = {The impact of security countermeasures on human behavior during active shooter incidents},
author = {Runhe Zhu and Gale M. Lucas and Burcin Becerik-Gerber and Erroll G. Southers and Earl Landicho},
url = {https://www.nature.com/articles/s41598-022-04922-8},
doi = {10.1038/s41598-022-04922-8},
issn = {2045-2322},
year = {2022},
date = {2022-12-01},
urldate = {2022-09-26},
journal = {Sci Rep},
volume = {12},
number = {1},
pages = {929},
abstract = {Abstract Active shooter incidents represent an increasing threat to American society, especially in commercial and educational buildings. In recent years, a wide variety of security countermeasures have been recommended by public and governmental agencies. Many of these countermeasures are aimed to increase building security, yet their impact on human behavior when an active shooter incident occurs remains underexplored. To fill this research gap, we conducted virtual experiments to evaluate the impact of countermeasures on human behavior during active shooter incidents. A total of 162 office workers and middle/high school teachers were recruited to respond to an active shooter incident in virtual office and school buildings with or without the implementation of multiple countermeasures. The experiment results showed countermeasures significantly influenced participants’ response time and decisions (e.g., run, hide, fight). Participants’ responses and perceptions of the active shooter incident were also contingent on their daily roles, as well as building and social contexts. Teachers had more concerns for occupants’ safety than office workers. Moreover, teachers had more positive perceptions of occupants in the school, whereas office workers had more positive perceptions of occupants in the office.},
keywords = {DTIC, UARC},
pubstate = {published},
tppubtype = {article}
}
Abstract Active shooter incidents represent an increasing threat to American society, especially in commercial and educational buildings. In recent years, a wide variety of security countermeasures have been recommended by public and governmental agencies. Many of these countermeasures are aimed to increase building security, yet their impact on human behavior when an active shooter incident occurs remains underexplored. To fill this research gap, we conducted virtual experiments to evaluate the impact of countermeasures on human behavior during active shooter incidents. A total of 162 office workers and middle/high school teachers were recruited to respond to an active shooter incident in virtual office and school buildings with or without the implementation of multiple countermeasures. The experiment results showed countermeasures significantly influenced participants’ response time and decisions (e.g., run, hide, fight). Participants’ responses and perceptions of the active shooter incident were also contingent on their daily roles, as well as building and social contexts. Teachers had more concerns for occupants’ safety than office workers. Moreover, teachers had more positive perceptions of occupants in the school, whereas office workers had more positive perceptions of occupants in the office.
Pauw, Lisanne S.; Sauter, Disa A.; Kleef, Gerben A.; Lucas, Gale M.; Gratch, Jonathan; Fischer, Agneta H.
The avatar will see you now: Support from a virtual human provides socio-emotional benefits Journal Article
In: Computers in Human Behavior, vol. 136, pp. 107368, 2022, ISSN: 07475632.
Links | BibTeX | Tags: DTIC, Virtual Humans
@article{pauw_avatar_2022,
title = {The avatar will see you now: Support from a virtual human provides socio-emotional benefits},
author = {Lisanne S. Pauw and Disa A. Sauter and Gerben A. Kleef and Gale M. Lucas and Jonathan Gratch and Agneta H. Fischer},
url = {https://linkinghub.elsevier.com/retrieve/pii/S074756322200190X},
doi = {10.1016/j.chb.2022.107368},
issn = {07475632},
year = {2022},
date = {2022-11-01},
urldate = {2022-09-28},
journal = {Computers in Human Behavior},
volume = {136},
pages = {107368},
keywords = {DTIC, Virtual Humans},
pubstate = {published},
tppubtype = {article}
}
Liu, Shichen; Cai, Yunxuan; Chen, Haiwei; Zhou, Yichao; Zhao, Yajie
Rapid Face Asset Acquisition with Recurrent Feature Alignment Journal Article
In: ACM Trans. Graph., vol. 41, no. 6, pp. 214:1–214:17, 2022, ISSN: 0730-0301.
Abstract | Links | BibTeX | Tags: VGL
@article{liu_rapid_2022,
title = {Rapid Face Asset Acquisition with Recurrent Feature Alignment},
author = {Shichen Liu and Yunxuan Cai and Haiwei Chen and Yichao Zhou and Yajie Zhao},
url = {https://dl.acm.org/doi/10.1145/3550454.3555509},
doi = {10.1145/3550454.3555509},
issn = {0730-0301},
year = {2022},
date = {2022-11-01},
urldate = {2023-03-31},
journal = {ACM Trans. Graph.},
volume = {41},
number = {6},
pages = {214:1–214:17},
abstract = {We present Recurrent Feature Alignment (ReFA), an end-to-end neural network for the very rapid creation of production-grade face assets from multi-view images. ReFA is on par with the industrial pipelines in quality for producing accurate, complete, registered, and textured assets directly applicable to physically-based rendering, but produces the asset end-to-end, fully automatically at a significantly faster speed at 4.5 FPS, which is unprecedented among neural-based techniques. Our method represents face geometry as a position map in the UV space. The network first extracts per-pixel features in both the multi-view image space and the UV space. A recurrent module then iteratively optimizes the geometry by projecting the image-space features to the UV space and comparing them with a reference UV-space feature. The optimized geometry then provides pixel-aligned signals for the inference of high-resolution textures. Experiments have validated that ReFA achieves a median error of 0.603mm in geometry reconstruction, is robust to extreme pose and expression, and excels in sparse-view settings. We believe that the progress achieved by our network enables lightweight, fast face assets acquisition that significantly boosts the downstream applications, such as avatar creation and facial performance capture. It will also enable massive database capturing for deep learning purposes.},
keywords = {VGL},
pubstate = {published},
tppubtype = {article}
}
We present Recurrent Feature Alignment (ReFA), an end-to-end neural network for the very rapid creation of production-grade face assets from multi-view images. ReFA is on par with the industrial pipelines in quality for producing accurate, complete, registered, and textured assets directly applicable to physically-based rendering, but produces the asset end-to-end, fully automatically at a significantly faster speed at 4.5 FPS, which is unprecedented among neural-based techniques. Our method represents face geometry as a position map in the UV space. The network first extracts per-pixel features in both the multi-view image space and the UV space. A recurrent module then iteratively optimizes the geometry by projecting the image-space features to the UV space and comparing them with a reference UV-space feature. The optimized geometry then provides pixel-aligned signals for the inference of high-resolution textures. Experiments have validated that ReFA achieves a median error of 0.603mm in geometry reconstruction, is robust to extreme pose and expression, and excels in sparse-view settings. We believe that the progress achieved by our network enables lightweight, fast face assets acquisition that significantly boosts the downstream applications, such as avatar creation and facial performance capture. It will also enable massive database capturing for deep learning purposes.
Gratch, Jonathan; Fast, Nathanael J.
The power to harm: AI assistants pave the way to unethical behavior Journal Article
In: Current Opinion in Psychology, vol. 47, pp. 101382, 2022, ISSN: 2352250X.
Links | BibTeX | Tags: AI, DTIC, Virtual Humans
@article{gratch_power_2022,
title = {The power to harm: AI assistants pave the way to unethical behavior},
author = {Jonathan Gratch and Nathanael J. Fast},
url = {https://linkinghub.elsevier.com/retrieve/pii/S2352250X22001014},
doi = {10.1016/j.copsyc.2022.101382},
issn = {2352250X},
year = {2022},
date = {2022-10-01},
urldate = {2022-09-28},
journal = {Current Opinion in Psychology},
volume = {47},
pages = {101382},
keywords = {AI, DTIC, Virtual Humans},
pubstate = {published},
tppubtype = {article}
}
Awada, Mohamad; Becerik-Gerber, Burcin; Lucas, Gale; Roll, Shawn
Cognitive performance, creativity and stress levels of neurotypical young adults under different white noise levels Journal Article
In: Sci Rep, vol. 12, no. 1, pp. 14566, 2022, ISSN: 2045-2322, (Number: 1 Publisher: Nature Publishing Group).
Abstract | Links | BibTeX | Tags: UARC, Virtual Humans
@article{awada_cognitive_2022,
title = {Cognitive performance, creativity and stress levels of neurotypical young adults under different white noise levels},
author = {Mohamad Awada and Burcin Becerik-Gerber and Gale Lucas and Shawn Roll},
url = {https://www.nature.com/articles/s41598-022-18862-w},
doi = {10.1038/s41598-022-18862-w},
issn = {2045-2322},
year = {2022},
date = {2022-08-01},
urldate = {2023-03-31},
journal = {Sci Rep},
volume = {12},
number = {1},
pages = {14566},
abstract = {Noise is often considered a distractor; however recent studies suggest that sub-attentive individuals or individuals diagnosed with attention deficit hyperactivity disorder can benefit from white noise to enhance their cognitive performance. Research regarding the effect of white noise on neurotypical adults presents mixed results, thus the implications of white noise on the neurotypical population remain unclear. Thus, this study investigates the effect of 2 white noise conditions, white noise level at 45 dB and white noise level at 65 dB, on the cognitive performance, creativity, and stress levels of neurotypical young adults in a private office space. These conditions are compared to a baseline condition where participants are exposed to the office ambient noise. Our findings showed that the white noise level at 45 dB resulted in better cognitive performance in terms of sustained attention, accuracy, and speed of performance as well as enhanced creativity and lower stress levels. On the other hand, the 65 dB white noise condition led to improved working memory but higher stress levels, which leads to the conclusion that different tasks might require different noise levels for optimal performance. These results lay the foundation for the integration of white noise into office workspaces as a tool to enhance office workers’ performance.},
note = {Number: 1
Publisher: Nature Publishing Group},
keywords = {UARC, Virtual Humans},
pubstate = {published},
tppubtype = {article}
}
Noise is often considered a distractor; however recent studies suggest that sub-attentive individuals or individuals diagnosed with attention deficit hyperactivity disorder can benefit from white noise to enhance their cognitive performance. Research regarding the effect of white noise on neurotypical adults presents mixed results, thus the implications of white noise on the neurotypical population remain unclear. Thus, this study investigates the effect of 2 white noise conditions, white noise level at 45 dB and white noise level at 65 dB, on the cognitive performance, creativity, and stress levels of neurotypical young adults in a private office space. These conditions are compared to a baseline condition where participants are exposed to the office ambient noise. Our findings showed that the white noise level at 45 dB resulted in better cognitive performance in terms of sustained attention, accuracy, and speed of performance as well as enhanced creativity and lower stress levels. On the other hand, the 65 dB white noise condition led to improved working memory but higher stress levels, which leads to the conclusion that different tasks might require different noise levels for optimal performance. These results lay the foundation for the integration of white noise into office workspaces as a tool to enhance office workers’ performance.
Melo, Celso; Gratch, Jonathan; Krueger, Frank
Heuristic thinking and altruism toward machines in people impacted by COVID-19 Journal Article
In: Yearb Med Inform, vol. 31, no. 1, pp. 226–227, 2022, ISSN: 0943-4747, 2364-0502, (Publisher: Georg Thieme Verlag KG).
Abstract | Links | BibTeX | Tags: UARC, Virtual Humans
@article{de_melo_heuristic_2022,
title = {Heuristic thinking and altruism toward machines in people impacted by COVID-19},
author = {Celso Melo and Jonathan Gratch and Frank Krueger},
url = {http://www.thieme-connect.de/DOI/DOI?10.1055/s-0042-1742544},
doi = {10.1055/s-0042-1742544},
issn = {0943-4747, 2364-0502},
year = {2022},
date = {2022-08-01},
urldate = {2023-03-31},
journal = {Yearb Med Inform},
volume = {31},
number = {1},
pages = {226–227},
abstract = {he authors conducted a study of how human interaction with machines needs to be studied, given the advent of intelligent systems in everyday life (such as autonomous vehicles) and how COVID-19 experiences shape human altruistic responses to machines. The authors correctly claim that more study of how humans can collaborate, and their attitudes and behavior toward machines differs from social norms with humans. They make use of the ‘Computers as Social Actors’ theory of Reeves and Nass (1996), which was influential in human computer and robot interaction research. It argues that people heuristically treat machines like people, and that encouraging intuitive thinking, in contrast to deliberation, led to increased cooperation in non-strategic settings. The authors are the first to apply and test this with concrete cognitive studies. The dictator game is used to measure altruism; the user has options to give tokens to another user (in this case the computer or a ‘human’ (both delivered by computer message to obscure the source). 186 participants were used as senders, across 40 US states, and provided a diverse sample. They were administered the abbreviated Post-Traumatic Stress Disorder (PTSD) checklist (to measure COVID-19 impact), and three subjective scales to gain insight on mechanisms. These were the Cognitive Reflection test to measure if those impacted engage in reduced reflection, i.e., more intuitive thinking, the Faith in Technology scale, and the Moral Foundations Questionnaire. Results showed a reduction in the usual bias against fairness toward machines the more the user had been impacted by COVID-19. There were also sharp increases in intuitive (and incorrect) thinking and faith in technology among the most highly affected group. The authors through multiple mediation analysis showed that faith in technology and heuristic thinking mediate the offer bias. They also caution that in times of stress the disproportional impact of COVID-19 on vulnerable groups leads to the need for ethical guidelines and regulations to ensure altruism/cooperation shown to machines is well deserved. They also point out the factors such as individual stress propensity, education level, and socioeconomic status could make individuals susceptible to heuristic thinking, and other social norms such as reciprocity, trust and fairness may also shape collaboration with machines.},
note = {Publisher: Georg Thieme Verlag KG},
keywords = {UARC, Virtual Humans},
pubstate = {published},
tppubtype = {article}
}
he authors conducted a study of how human interaction with machines needs to be studied, given the advent of intelligent systems in everyday life (such as autonomous vehicles) and how COVID-19 experiences shape human altruistic responses to machines. The authors correctly claim that more study of how humans can collaborate, and their attitudes and behavior toward machines differs from social norms with humans. They make use of the ‘Computers as Social Actors’ theory of Reeves and Nass (1996), which was influential in human computer and robot interaction research. It argues that people heuristically treat machines like people, and that encouraging intuitive thinking, in contrast to deliberation, led to increased cooperation in non-strategic settings. The authors are the first to apply and test this with concrete cognitive studies. The dictator game is used to measure altruism; the user has options to give tokens to another user (in this case the computer or a ‘human’ (both delivered by computer message to obscure the source). 186 participants were used as senders, across 40 US states, and provided a diverse sample. They were administered the abbreviated Post-Traumatic Stress Disorder (PTSD) checklist (to measure COVID-19 impact), and three subjective scales to gain insight on mechanisms. These were the Cognitive Reflection test to measure if those impacted engage in reduced reflection, i.e., more intuitive thinking, the Faith in Technology scale, and the Moral Foundations Questionnaire. Results showed a reduction in the usual bias against fairness toward machines the more the user had been impacted by COVID-19. There were also sharp increases in intuitive (and incorrect) thinking and faith in technology among the most highly affected group. The authors through multiple mediation analysis showed that faith in technology and heuristic thinking mediate the offer bias. They also caution that in times of stress the disproportional impact of COVID-19 on vulnerable groups leads to the need for ethical guidelines and regulations to ensure altruism/cooperation shown to machines is well deserved. They also point out the factors such as individual stress propensity, education level, and socioeconomic status could make individuals susceptible to heuristic thinking, and other social norms such as reciprocity, trust and fairness may also shape collaboration with machines.
Korand, Sridevi; Fung, Cha Chi; Cohen, Sammy; Talbot, Thomas B.; Fischer, Susan; Luu, Cindy; Sargsyan, Mariam; Ben-Isaac, Eyal; Espinoza, Juan; Chang, Todd P.
In: Simulation & Gaming, vol. 53, no. 4, pp. 335–352, 2022, ISSN: 1046-8781, 1552-826X.
Abstract | Links | BibTeX | Tags: MedVR
@article{korand_association_2022,
title = {The Association Between Multitasking and Multi-Patient Care Skills in a Simulated Patient Care Video Game Among Second Year Medical Students Based on Specialty Choice},
author = {Sridevi Korand and Cha Chi Fung and Sammy Cohen and Thomas B. Talbot and Susan Fischer and Cindy Luu and Mariam Sargsyan and Eyal Ben-Isaac and Juan Espinoza and Todd P. Chang},
url = {http://journals.sagepub.com/doi/10.1177/10468781221103460},
doi = {10.1177/10468781221103460},
issn = {1046-8781, 1552-826X},
year = {2022},
date = {2022-08-01},
urldate = {2022-09-21},
journal = {Simulation & Gaming},
volume = {53},
number = {4},
pages = {335–352},
abstract = {Background and Objective Healthcare providers require multitasking and multi-patient care skills, and training programs do not formally incorporate curricula specifically for multitasking skills to trainees. The medical education community is in equipoise on whether multitasking ability is a fixed trait. Furthermore, it is unclear whether multitasking ability affects those who gravitate toward careers that demand it, particularly among medical students deciding on a specialty. We sought to define the association between specialty choice, multitasking abilities and multi-patient care delivery among pre-clinical medical students. For this study, we examined both efficiency and accuracy metrics within multitasking and whether they were different between students choosing specialties. Methods This was a planned cross-sectional sub-study focused on 2nd year medical students (MS-IIs) within a parent study evaluating multi-patient care skills using a serious game (VitalSigns:ED TM ) depicting a pediatric emergency department. Subjects completed a Multitasking Ability Test (MTAT) and five VitalSigns:ED gameplays. The predictor variable was specialty choice, categorized into multitasking and non-multitasking groups. Outcome variables measuring efficiency and diagnostic accuracy were obtained from the MTAT and the game. The primary analysis was a Mann–Whitney U test, and secondary analyses employed Spearman Rank correlations. Results Twelve students applied to multitasking specialties and 18 applied to others. Those in the multitasking specialties had faster MTAT completions than the other cohort (29.8 vs. 59.7 sec, 95%CI difference -0.9 to -39.8 sec). Differential diagnoses were higher in multitasking specialties in VitalSigns:ED (2.03 vs. 1.06, 95%CI difference +0.05 to +1.54) but efficiency metrics in the game did not differ. Conclusion Multitasking and multi-patient care performance show some association with preferred specialty choices for MS-IIs prior to clinical exposure.},
keywords = {MedVR},
pubstate = {published},
tppubtype = {article}
}
Background and Objective Healthcare providers require multitasking and multi-patient care skills, and training programs do not formally incorporate curricula specifically for multitasking skills to trainees. The medical education community is in equipoise on whether multitasking ability is a fixed trait. Furthermore, it is unclear whether multitasking ability affects those who gravitate toward careers that demand it, particularly among medical students deciding on a specialty. We sought to define the association between specialty choice, multitasking abilities and multi-patient care delivery among pre-clinical medical students. For this study, we examined both efficiency and accuracy metrics within multitasking and whether they were different between students choosing specialties. Methods This was a planned cross-sectional sub-study focused on 2nd year medical students (MS-IIs) within a parent study evaluating multi-patient care skills using a serious game (VitalSigns:ED TM ) depicting a pediatric emergency department. Subjects completed a Multitasking Ability Test (MTAT) and five VitalSigns:ED gameplays. The predictor variable was specialty choice, categorized into multitasking and non-multitasking groups. Outcome variables measuring efficiency and diagnostic accuracy were obtained from the MTAT and the game. The primary analysis was a Mann–Whitney U test, and secondary analyses employed Spearman Rank correlations. Results Twelve students applied to multitasking specialties and 18 applied to others. Those in the multitasking specialties had faster MTAT completions than the other cohort (29.8 vs. 59.7 sec, 95%CI difference -0.9 to -39.8 sec). Differential diagnoses were higher in multitasking specialties in VitalSigns:ED (2.03 vs. 1.06, 95%CI difference +0.05 to +1.54) but efficiency metrics in the game did not differ. Conclusion Multitasking and multi-patient care performance show some association with preferred specialty choices for MS-IIs prior to clinical exposure.
Barrett, Trevor J.; Sobhani, Mona; Fox, Glenn R.; Files, Benjamin; Patitsas, Nicholas; Duhaime, Josiah; Ebert, Rebecca; Faulk, Rob; Saxon, Leslie
Diverse predictors of early attrition in an elite Marine training school Journal Article
In: Military Psychology, vol. 34, no. 4, pp. 388–397, 2022, ISSN: 0899-5605, 1532-7876.
Links | BibTeX | Tags: CBC, DTIC
@article{barrett_diverse_2022,
title = {Diverse predictors of early attrition in an elite Marine training school},
author = {Trevor J. Barrett and Mona Sobhani and Glenn R. Fox and Benjamin Files and Nicholas Patitsas and Josiah Duhaime and Rebecca Ebert and Rob Faulk and Leslie Saxon},
url = {https://www.tandfonline.com/doi/full/10.1080/08995605.2021.1993721},
doi = {10.1080/08995605.2021.1993721},
issn = {0899-5605, 1532-7876},
year = {2022},
date = {2022-07-01},
urldate = {2022-09-27},
journal = {Military Psychology},
volume = {34},
number = {4},
pages = {388–397},
keywords = {CBC, DTIC},
pubstate = {published},
tppubtype = {article}
}
Cleland, John G. F.; Bristow, Michael R.; Freemantle, Nicholas; Olshansky, Brian; Gras, Daniel; Saxon, Leslie; Tavazzi, Luigi; Boehmer, John; Ghio, Stefano; Feldman, Arthur M.; Daubert, Jean‐Claude; Mets, David
In: European J of Heart Fail, vol. 24, no. 6, pp. 1080–1090, 2022, ISSN: 1388-9842, 1879-0844.
@article{cleland_effect_2022,
title = {The effect of cardiac resynchronization without a defibrillator on morbidity and mortality: an individual patient data meta‐analysis of companion and care-hf},
author = {John G. F. Cleland and Michael R. Bristow and Nicholas Freemantle and Brian Olshansky and Daniel Gras and Leslie Saxon and Luigi Tavazzi and John Boehmer and Stefano Ghio and Arthur M. Feldman and Jean‐Claude Daubert and David Mets},
url = {https://onlinelibrary.wiley.com/doi/10.1002/ejhf.2524},
doi = {10.1002/ejhf.2524},
issn = {1388-9842, 1879-0844},
year = {2022},
date = {2022-06-01},
urldate = {2022-09-27},
journal = {European J of Heart Fail},
volume = {24},
number = {6},
pages = {1080–1090},
keywords = {CBC},
pubstate = {published},
tppubtype = {article}
}
Aris, Timothy; Ustun, Volkan; Kumar, Rajay
Learning to Take Cover on Geo-Specific Terrains via Reinforcement Learning Journal Article
In: FLAIRS, vol. 35, 2022, ISSN: 2334-0762.
Abstract | Links | BibTeX | Tags: DTIC, Integration Technology
@article{aris_learning_2022,
title = {Learning to Take Cover on Geo-Specific Terrains via Reinforcement Learning},
author = {Timothy Aris and Volkan Ustun and Rajay Kumar},
url = {https://journals.flvc.org/FLAIRS/article/view/130871},
doi = {10.32473/flairs.v35i.130871},
issn = {2334-0762},
year = {2022},
date = {2022-05-01},
urldate = {2022-09-15},
journal = {FLAIRS},
volume = {35},
abstract = {This paper presents a reinforcement learning model designed to learn how to take cover on geo-specific terrains, an essential behavior component for military training simulations. Training of the models is performed on the Rapid Integration and Development Environment (RIDE) leveraging the Unity ML-Agents framework. We show that increasing the number of novel situations the agent is exposed to increases the performance on the test set. In addition, the trained models possess some ability to generalize across terrains, and it can also take less time to retrain an agent to a new terrain, if that terrain has a level of complexity less than or equal to the terrain it was previously trained on.},
keywords = {DTIC, Integration Technology},
pubstate = {published},
tppubtype = {article}
}
This paper presents a reinforcement learning model designed to learn how to take cover on geo-specific terrains, an essential behavior component for military training simulations. Training of the models is performed on the Rapid Integration and Development Environment (RIDE) leveraging the Unity ML-Agents framework. We show that increasing the number of novel situations the agent is exposed to increases the performance on the test set. In addition, the trained models possess some ability to generalize across terrains, and it can also take less time to retrain an agent to a new terrain, if that terrain has a level of complexity less than or equal to the terrain it was previously trained on.
Schweitzer, Julie B.; Rizzo, Albert “Skip”
Virtual Reality and ADHD: Clinical Assessment and Treatment in the Metaverse Journal Article
In: The ADHD Report, vol. 30, no. 3, pp. 1–9, 2022, ISSN: 1065-8025.
Links | BibTeX | Tags: MedVR, VR
@article{schweitzer_virtual_2022,
title = {Virtual Reality and ADHD: Clinical Assessment and Treatment in the Metaverse},
author = {Julie B. Schweitzer and Albert “Skip” Rizzo},
url = {https://guilfordjournals.com/doi/abs/10.1521/adhd.2022.30.3.1},
doi = {10.1521/adhd.2022.30.3.1},
issn = {1065-8025},
year = {2022},
date = {2022-05-01},
urldate = {2022-09-13},
journal = {The ADHD Report},
volume = {30},
number = {3},
pages = {1–9},
keywords = {MedVR, VR},
pubstate = {published},
tppubtype = {article}
}
Pynadath, David V.; Dilkina, Bistra; Jeong, David C.; John, Richard S.; Marsella, Stacy C.; Merchant, Chirag; Miller, Lynn C.; Read, Stephen J.
Disaster world Journal Article
In: Comput Math Organ Theory, 2022, ISSN: 1572-9346.
Abstract | Links | BibTeX | Tags: DTIC, Social Simulation
@article{pynadath_disaster_2022,
title = {Disaster world},
author = {David V. Pynadath and Bistra Dilkina and David C. Jeong and Richard S. John and Stacy C. Marsella and Chirag Merchant and Lynn C. Miller and Stephen J. Read},
url = {https://doi.org/10.1007/s10588-022-09359-y},
doi = {10.1007/s10588-022-09359-y},
issn = {1572-9346},
year = {2022},
date = {2022-05-01},
urldate = {2022-09-28},
journal = {Comput Math Organ Theory},
abstract = {Artificial intelligence (AI) research provides a rich source of modeling languages capable of generating socially plausible simulations of human behavior, while also providing a transparent ground truth that can support validation of social-science methods applied to that simulation. In this work, we leverage two established AI representations: decision-theoretic planning and recursive modeling. Decision-theoretic planning (specifically Partially Observable Markov Decision Processes) provides agents with quantitative models of their corresponding real-world entities’ subjective (and possibly incorrect) perspectives of ground truth in the form of probabilistic beliefs and utility functions. Recursive modeling gives an agent a theory of mind, which is necessary when a person’s (again, possibly incorrect) subjective perspectives are of another person, rather than of just his/her environment. We used PsychSim, a multiagent social-simulation framework combining these two AI frameworks, to build a general parameterized model of human behavior during disaster response, grounding the model in social-psychological theories to ensure social plausibility. We then instantiated that model into alternate ground truths for simulating population response to a series of natural disasters, namely, hurricanes. The simulations generate data in response to socially plausible instruments (e.g., surveys) that serve as input to the Ground Truth program’s designated research teams for them to conduct simulated social science. The simulation also provides a graphical ground truth and a set of outcomes to be used as the gold standard in evaluating the research teams’ inferences.},
keywords = {DTIC, Social Simulation},
pubstate = {published},
tppubtype = {article}
}
Artificial intelligence (AI) research provides a rich source of modeling languages capable of generating socially plausible simulations of human behavior, while also providing a transparent ground truth that can support validation of social-science methods applied to that simulation. In this work, we leverage two established AI representations: decision-theoretic planning and recursive modeling. Decision-theoretic planning (specifically Partially Observable Markov Decision Processes) provides agents with quantitative models of their corresponding real-world entities’ subjective (and possibly incorrect) perspectives of ground truth in the form of probabilistic beliefs and utility functions. Recursive modeling gives an agent a theory of mind, which is necessary when a person’s (again, possibly incorrect) subjective perspectives are of another person, rather than of just his/her environment. We used PsychSim, a multiagent social-simulation framework combining these two AI frameworks, to build a general parameterized model of human behavior during disaster response, grounding the model in social-psychological theories to ensure social plausibility. We then instantiated that model into alternate ground truths for simulating population response to a series of natural disasters, namely, hurricanes. The simulations generate data in response to socially plausible instruments (e.g., surveys) that serve as input to the Ground Truth program’s designated research teams for them to conduct simulated social science. The simulation also provides a graphical ground truth and a set of outcomes to be used as the gold standard in evaluating the research teams’ inferences.
Adami, Pooya; Rodrigues, Patrick B.; Woods, Peter J.; Becerik-Gerber, Burcin; Soibelman, Lucio; Copur-Gencturk, Yasemin; Lucas, Gale
Impact of VR-Based Training on Human–Robot Interaction for Remote Operating Construction Robots Journal Article
In: J. Comput. Civ. Eng., vol. 36, no. 3, pp. 04022006, 2022, ISSN: 0887-3801, 1943-5487.
Links | BibTeX | Tags: DTIC, UARC, Virtual Humans, VR
@article{adami_impact_2022,
title = {Impact of VR-Based Training on Human–Robot Interaction for Remote Operating Construction Robots},
author = {Pooya Adami and Patrick B. Rodrigues and Peter J. Woods and Burcin Becerik-Gerber and Lucio Soibelman and Yasemin Copur-Gencturk and Gale Lucas},
url = {https://ascelibrary.org/doi/10.1061/%28ASCE%29CP.1943-5487.0001016},
doi = {10.1061/(ASCE)CP.1943-5487.0001016},
issn = {0887-3801, 1943-5487},
year = {2022},
date = {2022-05-01},
urldate = {2022-09-23},
journal = {J. Comput. Civ. Eng.},
volume = {36},
number = {3},
pages = {04022006},
keywords = {DTIC, UARC, Virtual Humans, VR},
pubstate = {published},
tppubtype = {article}
}
Rodrigues, Patrick B.; Xiao, Yijing; Fukumura, Yoko E.; Awada, Mohamad; Aryal, Ashrant; Becerik-Gerber, Burcin; Lucas, Gale; Roll, Shawn C.
Ergonomic assessment of office worker postures using 3D automated joint angle assessment Journal Article
In: Advanced Engineering Informatics, vol. 52, pp. 101596, 2022, ISSN: 14740346.
Links | BibTeX | Tags: DTIC, Machine Learning, UARC
@article{rodrigues_ergonomic_2022,
title = {Ergonomic assessment of office worker postures using 3D automated joint angle assessment},
author = {Patrick B. Rodrigues and Yijing Xiao and Yoko E. Fukumura and Mohamad Awada and Ashrant Aryal and Burcin Becerik-Gerber and Gale Lucas and Shawn C. Roll},
url = {https://linkinghub.elsevier.com/retrieve/pii/S1474034622000672},
doi = {10.1016/j.aei.2022.101596},
issn = {14740346},
year = {2022},
date = {2022-04-01},
urldate = {2022-09-26},
journal = {Advanced Engineering Informatics},
volume = {52},
pages = {101596},
keywords = {DTIC, Machine Learning, UARC},
pubstate = {published},
tppubtype = {article}
}
Fujiwara, Ken; Hoegen, Rens; Gratch, Jonathan; Dunbar, Norah E.
Synchrony facilitates altruistic decision making for non-human avatars Journal Article
In: Computers in Human Behavior, vol. 128, pp. 107079, 2022, ISSN: 07475632.
Links | BibTeX | Tags: DTIC, Virtual Humans
@article{fujiwara_synchrony_2022,
title = {Synchrony facilitates altruistic decision making for non-human avatars},
author = {Ken Fujiwara and Rens Hoegen and Jonathan Gratch and Norah E. Dunbar},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0747563221004027},
doi = {10.1016/j.chb.2021.107079},
issn = {07475632},
year = {2022},
date = {2022-03-01},
urldate = {2022-09-28},
journal = {Computers in Human Behavior},
volume = {128},
pages = {107079},
keywords = {DTIC, Virtual Humans},
pubstate = {published},
tppubtype = {article}
}
Visser, Ewart J.; Topoglu, Yigit; Joshi, Shawn; Krueger, Frank; Phillips, Elizabeth; Gratch, Jonathan; Tossell, Chad C.; Ayaz, Hasan
Designing Man’s New Best Friend: Enhancing Human-Robot Dog Interaction through Dog-Like Framing and Appearance Journal Article
In: Sensors, vol. 22, no. 3, pp. 1287, 2022, ISSN: 1424-8220.
Abstract | Links | BibTeX | Tags: DTIC, Virtual Humans
@article{de_visser_designing_2022,
title = {Designing Man’s New Best Friend: Enhancing Human-Robot Dog Interaction through Dog-Like Framing and Appearance},
author = {Ewart J. Visser and Yigit Topoglu and Shawn Joshi and Frank Krueger and Elizabeth Phillips and Jonathan Gratch and Chad C. Tossell and Hasan Ayaz},
url = {https://www.mdpi.com/1424-8220/22/3/1287},
doi = {10.3390/s22031287},
issn = {1424-8220},
year = {2022},
date = {2022-02-01},
urldate = {2022-09-28},
journal = {Sensors},
volume = {22},
number = {3},
pages = {1287},
abstract = {To understand how to improve interactions with dog-like robots, we evaluated the importance of “dog-like” framing and physical appearance on interaction, hypothesizing multiple interactive benefits of each. We assessed whether framing Aibo as a puppy (i.e., in need of development) versus simply a robot would result in more positive responses and interactions. We also predicted that adding fur to Aibo would make it appear more dog-like, likable, and interactive. Twenty-nine participants engaged with Aibo in a 2 × 2 (framing × appearance) design by issuing commands to the robot. Aibo and participant behaviors were monitored per second, and evaluated via an analysis of commands issued, an analysis of command blocks (i.e., chains of commands), and using a T-pattern analysis of participant behavior. Participants were more likely to issue the “Come Here” command than other types of commands. When framed as a puppy, participants used Aibo’s dog name more often, praised it more, and exhibited more unique, interactive, and complex behavior with Aibo. Participants exhibited the most smiling and laughing behaviors with Aibo framed as a puppy without fur. Across conditions, after interacting with Aibo, participants felt Aibo was more trustworthy, intelligent, warm, and connected than at their initial meeting. This study shows the benefits of introducing a socially robotic agent with a particular frame and importance on realism (i.e., introducing the robot dog as a puppy) for more interactive engagement.},
keywords = {DTIC, Virtual Humans},
pubstate = {published},
tppubtype = {article}
}
To understand how to improve interactions with dog-like robots, we evaluated the importance of “dog-like” framing and physical appearance on interaction, hypothesizing multiple interactive benefits of each. We assessed whether framing Aibo as a puppy (i.e., in need of development) versus simply a robot would result in more positive responses and interactions. We also predicted that adding fur to Aibo would make it appear more dog-like, likable, and interactive. Twenty-nine participants engaged with Aibo in a 2 × 2 (framing × appearance) design by issuing commands to the robot. Aibo and participant behaviors were monitored per second, and evaluated via an analysis of commands issued, an analysis of command blocks (i.e., chains of commands), and using a T-pattern analysis of participant behavior. Participants were more likely to issue the “Come Here” command than other types of commands. When framed as a puppy, participants used Aibo’s dog name more often, praised it more, and exhibited more unique, interactive, and complex behavior with Aibo. Participants exhibited the most smiling and laughing behaviors with Aibo framed as a puppy without fur. Across conditions, after interacting with Aibo, participants felt Aibo was more trustworthy, intelligent, warm, and connected than at their initial meeting. This study shows the benefits of introducing a socially robotic agent with a particular frame and importance on realism (i.e., introducing the robot dog as a puppy) for more interactive engagement.
Hoegen, Jessie; DeVault, David; Gratch, Jonathan
Exploring the Function of Expressions in Negotiation: the DyNego-WOZ Corpus Journal Article
In: IEEE Transactions on Affective Computing, pp. 1–12, 2022, ISSN: 1949-3045, (Conference Name: IEEE Transactions on Affective Computing).
Abstract | Links | BibTeX | Tags: UARC, Virtual Humans
@article{hoegen_exploring_2022,
title = {Exploring the Function of Expressions in Negotiation: the DyNego-WOZ Corpus},
author = {Jessie Hoegen and David DeVault and Jonathan Gratch},
doi = {10.1109/TAFFC.2022.3223030},
issn = {1949-3045},
year = {2022},
date = {2022-01-01},
journal = {IEEE Transactions on Affective Computing},
pages = {1–12},
abstract = {For affective computing to have an impact outside the laboratory, facial expressions must be studied in rich naturalistic situations. We argue negotiations are one such situation as they are ubiquitous in daily life, often evoke strong emotions, and perceived emotion shapes decisions and outcomes. Negotiations are a growing focus in AI research and applications, including agents that negotiate directly with people and attempt to use affective information. We introduce the DyNego-WOZ Corpus, which includes dyadic negotiation between participants and wizard-controlled virtual humans. We demonstrate the value of this corpus to the affective computing community by examining participants' facial expressions in response to a virtual human negotiation partner. We show that people's facial expressions typically co-occur with the end of their partner's speech (suggesting they reflect a reaction to the content of this speech), that these reactions do not correspond to prototypical emotional expressions, and that these reactions can help predict the expresser's subsequent action. We highlight challenges in working with such naturalistic data, including difficulties of expression recognition during speech, and the extreme variability of expressions, both across participants and within a negotiation. Our findings reinforce arguments that facial expressions convey more than emotional state but serve important communicative functions.},
note = {Conference Name: IEEE Transactions on Affective Computing},
keywords = {UARC, Virtual Humans},
pubstate = {published},
tppubtype = {article}
}
For affective computing to have an impact outside the laboratory, facial expressions must be studied in rich naturalistic situations. We argue negotiations are one such situation as they are ubiquitous in daily life, often evoke strong emotions, and perceived emotion shapes decisions and outcomes. Negotiations are a growing focus in AI research and applications, including agents that negotiate directly with people and attempt to use affective information. We introduce the DyNego-WOZ Corpus, which includes dyadic negotiation between participants and wizard-controlled virtual humans. We demonstrate the value of this corpus to the affective computing community by examining participants' facial expressions in response to a virtual human negotiation partner. We show that people's facial expressions typically co-occur with the end of their partner's speech (suggesting they reflect a reaction to the content of this speech), that these reactions do not correspond to prototypical emotional expressions, and that these reactions can help predict the expresser's subsequent action. We highlight challenges in working with such naturalistic data, including difficulties of expression recognition during speech, and the extreme variability of expressions, both across participants and within a negotiation. Our findings reinforce arguments that facial expressions convey more than emotional state but serve important communicative functions.
Saxon, Leslie; Faulk, Robert T; Barrett, Travor; McLelland, Steve; Boberg, Jill
A Novel Digital Research Methodology for Continuous Health Assessment of the Special Operations Warfighter: The Digital cORA Study Journal Article
In: J. Spec. Oper. Med., vol. 22, no. 4, pp. 78, 2022, ISSN: 1553-9768.
Links | BibTeX | Tags: CBC, UARC
@article{saxon_novel_2022,
title = {A Novel Digital Research Methodology for Continuous Health Assessment of the Special Operations Warfighter: The Digital cORA Study},
author = {Leslie Saxon and Robert T Faulk and Travor Barrett and Steve McLelland and Jill Boberg},
url = {https://www.jsomonline.org/Citations/4SSJ-AHIB.php},
doi = {10.55460/4SSJ-AHIB},
issn = {1553-9768},
year = {2022},
date = {2022-01-01},
urldate = {2023-03-31},
journal = {J. Spec. Oper. Med.},
volume = {22},
number = {4},
pages = {78},
keywords = {CBC, UARC},
pubstate = {published},
tppubtype = {article}
}
Chawla, Kushal; Shi, Weiyan; Zhang, Jingwen; Lucas, Gale; Yu, Zhou; Gratch, Jonathan
Social Influence Dialogue Systems: A Survey of Datasets and Models For Social Influence Tasks Journal Article
In: 2022, (Publisher: arXiv Version Number: 2).
Abstract | Links | BibTeX | Tags: UARC, Virtual Humans
@article{chawla_social_2022,
title = {Social Influence Dialogue Systems: A Survey of Datasets and Models For Social Influence Tasks},
author = {Kushal Chawla and Weiyan Shi and Jingwen Zhang and Gale Lucas and Zhou Yu and Jonathan Gratch},
url = {https://arxiv.org/abs/2210.05664},
doi = {10.48550/ARXIV.2210.05664},
year = {2022},
date = {2022-01-01},
urldate = {2023-08-22},
abstract = {Dialogue systems capable of social influence such as persuasion, negotiation, and therapy, are essential for extending the use of technology to numerous realistic scenarios. However, existing research primarily focuses on either task-oriented or open-domain scenarios, a categorization that has been inadequate for capturing influence skills systematically. There exists no formal definition or category for dialogue systems with these skills and data-driven efforts in this direction are highly limited. In this work, we formally define and introduce the category of social influence dialogue systems that influence users' cognitive and emotional responses, leading to changes in thoughts, opinions, and behaviors through natural conversations. We present a survey of various tasks, datasets, and methods, compiling the progress across seven diverse domains. We discuss the commonalities and differences between the examined systems, identify limitations, and recommend future directions. This study serves as a comprehensive reference for social influence dialogue systems to inspire more dedicated research and discussion in this emerging area.},
note = {Publisher: arXiv
Version Number: 2},
keywords = {UARC, Virtual Humans},
pubstate = {published},
tppubtype = {article}
}
Dialogue systems capable of social influence such as persuasion, negotiation, and therapy, are essential for extending the use of technology to numerous realistic scenarios. However, existing research primarily focuses on either task-oriented or open-domain scenarios, a categorization that has been inadequate for capturing influence skills systematically. There exists no formal definition or category for dialogue systems with these skills and data-driven efforts in this direction are highly limited. In this work, we formally define and introduce the category of social influence dialogue systems that influence users' cognitive and emotional responses, leading to changes in thoughts, opinions, and behaviors through natural conversations. We present a survey of various tasks, datasets, and methods, compiling the progress across seven diverse domains. We discuss the commonalities and differences between the examined systems, identify limitations, and recommend future directions. This study serves as a comprehensive reference for social influence dialogue systems to inspire more dedicated research and discussion in this emerging area.
Herrick, Imogen; Sinatra, Gale; Kennedy, Alana; Nye, Benjamin; Swartout, William; Lindsey, Emily
Using Augmented Reality (AR) to Bring the Past to Life in Informal Science Learning Journal Article
In: NSF-PAR, 2022.
Abstract | Links | BibTeX | Tags: Learning Sciences, UARC
@article{herrick_using_2022,
title = {Using Augmented Reality (AR) to Bring the Past to Life in Informal Science Learning},
author = {Imogen Herrick and Gale Sinatra and Alana Kennedy and Benjamin Nye and William Swartout and Emily Lindsey},
url = {https://par.nsf.gov/biblio/10344989},
year = {2022},
date = {2022-01-01},
journal = {NSF-PAR},
abstract = {A key mission for museums is to engage a large and diverse public audience in science learning (Macdonald, 1997). To that end, science museums attempt to use immersive technologies in entertaining, socially oriented, and innovative ways. An example is the use of augmented reality (AR) to overlay virtual objects onto the real-world (Azuma, Baillot, Behringer, Feiner, Julier, & MacIntyre, 2001).We used a Design Based Research (DBR) approach to develop and test four features of an AR experience to promote place-based science learning in an museum setting. While quantitative differences were not found among conditions in knowledge gained, significant learning gains were seen from pre to post, illustrating the potential for place-based informal science learning. Incorporating AR technology into museum exhibits can update them with 21st tools to support visitor engagement in the learning experience. This research contributes to understanding of usability and logistical issues for different AR designs for a public, outdoor informal settings.},
keywords = {Learning Sciences, UARC},
pubstate = {published},
tppubtype = {article}
}
A key mission for museums is to engage a large and diverse public audience in science learning (Macdonald, 1997). To that end, science museums attempt to use immersive technologies in entertaining, socially oriented, and innovative ways. An example is the use of augmented reality (AR) to overlay virtual objects onto the real-world (Azuma, Baillot, Behringer, Feiner, Julier, & MacIntyre, 2001).We used a Design Based Research (DBR) approach to develop and test four features of an AR experience to promote place-based science learning in an museum setting. While quantitative differences were not found among conditions in knowledge gained, significant learning gains were seen from pre to post, illustrating the potential for place-based informal science learning. Incorporating AR technology into museum exhibits can update them with 21st tools to support visitor engagement in the learning experience. This research contributes to understanding of usability and logistical issues for different AR designs for a public, outdoor informal settings.
Davis, Matt; Nye, Benjamin; Sinatra, Gale; Swartout, William; Sjӧberg, Molly; Porter, Molly; Nelson, David; Kennedy, Alana; Herrick, Imogen; Weeks, Danaan DeNeve; Lindsey, Emily
Designing scientifically-grounded paleoart for augmented reality at La Brea Tar Pits Journal Article
In: Palaeontol Electron, 2022, ISSN: 19353952, 10948074.
Links | BibTeX | Tags: AR, MxR, VR
@article{davis_designing_2022,
title = {Designing scientifically-grounded paleoart for augmented reality at La Brea Tar Pits},
author = {Matt Davis and Benjamin Nye and Gale Sinatra and William Swartout and Molly Sjӧberg and Molly Porter and David Nelson and Alana Kennedy and Imogen Herrick and Danaan DeNeve Weeks and Emily Lindsey},
url = {https://palaeo-electronica.org/content/2022/3524-la-brea-tar-pits-paleoart},
doi = {10.26879/1191},
issn = {19353952, 10948074},
year = {2022},
date = {2022-01-01},
urldate = {2022-09-15},
journal = {Palaeontol Electron},
keywords = {AR, MxR, VR},
pubstate = {published},
tppubtype = {article}
}
Stokes, Jared D.; Rizzo, Albert; Geng, Joy J.; Schweitzer, Julie B.
Measuring Attentional Distraction in Children With ADHD Using Virtual Reality Technology With Eye-Tracking Journal Article
In: Frontiers in Virtual Reality, vol. 3, 2022, ISSN: 2673-4192.
Abstract | Links | BibTeX | Tags: MedVR, VR
@article{stokes_measuring_2022,
title = {Measuring Attentional Distraction in Children With ADHD Using Virtual Reality Technology With Eye-Tracking},
author = {Jared D. Stokes and Albert Rizzo and Joy J. Geng and Julie B. Schweitzer},
url = {https://www.frontiersin.org/articles/10.3389/frvir.2022.855895},
issn = {2673-4192},
year = {2022},
date = {2022-01-01},
urldate = {2022-09-13},
journal = {Frontiers in Virtual Reality},
volume = {3},
abstract = {Objective: Distractions inordinately impair attention in children with Attention-Deficit Hyperactivity Disorder (ADHD) but examining this behavior under real-life conditions poses a challenge for researchers and clinicians. Virtual reality (VR) technologies may mitigate the limitations of traditional laboratory methods by providing a more ecologically relevant experience. The use of eye-tracking measures to assess attentional functioning in a VR context in ADHD is novel. In this proof of principle project, we evaluate the temporal dynamics of distraction via eye-tracking measures in a VR classroom setting with 20 children diagnosed with ADHD between 8 and 12 years of age.Method: We recorded continuous eye movements while participants performed math, Stroop, and continuous performance test (CPT) tasks with a series of “real-world” classroom distractors presented. We analyzed the impact of the distractors on rates of on-task performance and on-task, eye-gaze (i.e., looking at a classroom whiteboard) versus off-task eye-gaze (i.e., looking away from the whiteboard).Results: We found that while children did not always look at distractors themselves for long periods of time, the presence of a distractor disrupted on-task gaze at task-relevant whiteboard stimuli and lowered rates of task performance. This suggests that children with attention deficits may have a hard time returning to tasks once those tasks are interrupted, even if the distractor itself does not hold attention. Eye-tracking measures within the VR context can reveal rich information about attentional disruption.Conclusions: Leveraging virtual reality technology in combination with eye-tracking measures is well-suited to advance the understanding of mechanisms underlying attentional impairment in naturalistic settings. Assessment within these immersive and well-controlled simulated environments provides new options for increasing our understanding of distractibility and its potential impact on the development of interventions for children with ADHD.},
keywords = {MedVR, VR},
pubstate = {published},
tppubtype = {article}
}
Objective: Distractions inordinately impair attention in children with Attention-Deficit Hyperactivity Disorder (ADHD) but examining this behavior under real-life conditions poses a challenge for researchers and clinicians. Virtual reality (VR) technologies may mitigate the limitations of traditional laboratory methods by providing a more ecologically relevant experience. The use of eye-tracking measures to assess attentional functioning in a VR context in ADHD is novel. In this proof of principle project, we evaluate the temporal dynamics of distraction via eye-tracking measures in a VR classroom setting with 20 children diagnosed with ADHD between 8 and 12 years of age.Method: We recorded continuous eye movements while participants performed math, Stroop, and continuous performance test (CPT) tasks with a series of “real-world” classroom distractors presented. We analyzed the impact of the distractors on rates of on-task performance and on-task, eye-gaze (i.e., looking at a classroom whiteboard) versus off-task eye-gaze (i.e., looking away from the whiteboard).Results: We found that while children did not always look at distractors themselves for long periods of time, the presence of a distractor disrupted on-task gaze at task-relevant whiteboard stimuli and lowered rates of task performance. This suggests that children with attention deficits may have a hard time returning to tasks once those tasks are interrupted, even if the distractor itself does not hold attention. Eye-tracking measures within the VR context can reveal rich information about attentional disruption.Conclusions: Leveraging virtual reality technology in combination with eye-tracking measures is well-suited to advance the understanding of mechanisms underlying attentional impairment in naturalistic settings. Assessment within these immersive and well-controlled simulated environments provides new options for increasing our understanding of distractibility and its potential impact on the development of interventions for children with ADHD.
Aster, Hans-Christoph; Romanos, Marcel; Walitza, Susanne; Gerlach, Manfred; Mühlberger, Andreas; Rizzo, Albert; Andreatta, Marta; Hasenauer, Natalie; Hartrampf, Philipp E.; Nerlich, Kai; Reiners, Christoph; Lorenz, Reinhard; Buck, Andreas K.; Deserno, Lorenz
In: Frontiers in Psychiatry, vol. 13, 2022, ISSN: 1664-0640.
Abstract | Links | BibTeX | Tags: MedVR
@article{aster_responsivity_2022,
title = {Responsivity of the Striatal Dopamine System to Methylphenidate—A Within-Subject I-123-β-CIT-SPECT Study in Male Children and Adolescents With Attention-Deficit/Hyperactivity Disorder},
author = {Hans-Christoph Aster and Marcel Romanos and Susanne Walitza and Manfred Gerlach and Andreas Mühlberger and Albert Rizzo and Marta Andreatta and Natalie Hasenauer and Philipp E. Hartrampf and Kai Nerlich and Christoph Reiners and Reinhard Lorenz and Andreas K. Buck and Lorenz Deserno},
url = {https://www.frontiersin.org/articles/10.3389/fpsyt.2022.804730},
issn = {1664-0640},
year = {2022},
date = {2022-01-01},
urldate = {2022-09-13},
journal = {Frontiers in Psychiatry},
volume = {13},
abstract = {Background:Methylphenidate (MPH) is the first-line pharmacological treatment of attention-deficit/hyperactivity disorder (ADHD). MPH binds to the dopamine (DA) transporter (DAT), which has high density in the striatum. Assessments of the striatal dopamine transporter by single positron emission computed tomography (SPECT) in childhood and adolescent patients are rare but can provide insight on how the effects of MPH affect DAT availability. The aim of our within-subject study was to investigate the effect of MPH on DAT availability and how responsivity to MPH in DAT availability is linked to clinical symptoms and cognitive functioning.MethodsThirteen adolescent male patients (9–16 years) with a diagnosis of ADHD according to the DSM-IV and long-term stimulant medication (for at least 6 months) with MPH were assessed twice within 7 days using SPECT after application of I-123-β-CIT to examine DAT binding potential (DAT BP). SPECT measures took place in an on- and off-MPH status balanced for order across participants. A virtual reality continuous performance test was performed at each time point. Further clinical symptoms were assessed for baseline off-MPH.ResultsOn-MPH status was associated with a highly significant change (−29.9%) of striatal DAT BP as compared to off-MPH (t = −4.12},
keywords = {MedVR},
pubstate = {published},
tppubtype = {article}
}
Background:Methylphenidate (MPH) is the first-line pharmacological treatment of attention-deficit/hyperactivity disorder (ADHD). MPH binds to the dopamine (DA) transporter (DAT), which has high density in the striatum. Assessments of the striatal dopamine transporter by single positron emission computed tomography (SPECT) in childhood and adolescent patients are rare but can provide insight on how the effects of MPH affect DAT availability. The aim of our within-subject study was to investigate the effect of MPH on DAT availability and how responsivity to MPH in DAT availability is linked to clinical symptoms and cognitive functioning.MethodsThirteen adolescent male patients (9–16 years) with a diagnosis of ADHD according to the DSM-IV and long-term stimulant medication (for at least 6 months) with MPH were assessed twice within 7 days using SPECT after application of I-123-β-CIT to examine DAT binding potential (DAT BP). SPECT measures took place in an on- and off-MPH status balanced for order across participants. A virtual reality continuous performance test was performed at each time point. Further clinical symptoms were assessed for baseline off-MPH.ResultsOn-MPH status was associated with a highly significant change (−29.9%) of striatal DAT BP as compared to off-MPH (t = −4.12
Hou, Yu; Chen, Meida; Volk, Rebekka; Soibelman, Lucio
In: Journal of Building Engineering, vol. 45, pp. 103380, 2022, ISSN: 23527102.
Links | BibTeX | Tags: Graphics
@article{hou_investigation_2022,
title = {Investigation on performance of RGB point cloud and thermal information data fusion for 3D building thermal map modeling using aerial images under different experimental conditions},
author = {Yu Hou and Meida Chen and Rebekka Volk and Lucio Soibelman},
url = {https://linkinghub.elsevier.com/retrieve/pii/S2352710221012389},
doi = {10.1016/j.jobe.2021.103380},
issn = {23527102},
year = {2022},
date = {2022-01-01},
urldate = {2022-09-28},
journal = {Journal of Building Engineering},
volume = {45},
pages = {103380},
keywords = {Graphics},
pubstate = {published},
tppubtype = {article}
}
Marge, Matthew; Espy-Wilson, Carol; Ward, Nigel G.; Alwan, Abeer; Artzi, Yoav; Bansal, Mohit; Blankenship, Gil; Chai, Joyce; Daumé, Hal; Dey, Debadeepta; Harper, Mary; Howard, Thomas; Kennington, Casey; Kruijff-Korbayová, Ivana; Manocha, Dinesh; Matuszek, Cynthia; Mead, Ross; Mooney, Raymond; Moore, Roger K.; Ostendorf, Mari; Pon-Barry, Heather; Rudnicky, Alexander I.; Scheutz, Matthias; Amant, Robert St.; Sun, Tong; Tellex, Stefanie; Traum, David; Yu, Zhou
Spoken language interaction with robots: Recommendations for future research Journal Article
In: Computer Speech & Language, vol. 71, pp. 101255, 2022, ISSN: 08852308.
Links | BibTeX | Tags: ARL, Dialogue
@article{marge_spoken_2022,
title = {Spoken language interaction with robots: Recommendations for future research},
author = {Matthew Marge and Carol Espy-Wilson and Nigel G. Ward and Abeer Alwan and Yoav Artzi and Mohit Bansal and Gil Blankenship and Joyce Chai and Hal Daumé and Debadeepta Dey and Mary Harper and Thomas Howard and Casey Kennington and Ivana Kruijff-Korbayová and Dinesh Manocha and Cynthia Matuszek and Ross Mead and Raymond Mooney and Roger K. Moore and Mari Ostendorf and Heather Pon-Barry and Alexander I. Rudnicky and Matthias Scheutz and Robert St. Amant and Tong Sun and Stefanie Tellex and David Traum and Zhou Yu},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0885230821000620},
doi = {10.1016/j.csl.2021.101255},
issn = {08852308},
year = {2022},
date = {2022-01-01},
urldate = {2022-09-23},
journal = {Computer Speech & Language},
volume = {71},
pages = {101255},
keywords = {ARL, Dialogue},
pubstate = {published},
tppubtype = {article}
}
Liu, Shichen; Li, Tianye; Chen, Weikai; Li, Hao
A General Differentiable Mesh Renderer for Image-Based 3D Reasoning Journal Article
In: IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 1, pp. 50–62, 2022, ISSN: 1939-3539, (Conference Name: IEEE Transactions on Pattern Analysis and Machine Intelligence).
Abstract | Links | BibTeX | Tags: VGL
@article{liu_general_2022,
title = {A General Differentiable Mesh Renderer for Image-Based 3D Reasoning},
author = {Shichen Liu and Tianye Li and Weikai Chen and Hao Li},
doi = {10.1109/TPAMI.2020.3007759},
issn = {1939-3539},
year = {2022},
date = {2022-01-01},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {44},
number = {1},
pages = {50–62},
abstract = {Rendering bridges the gap between 2D vision and 3D scenes by simulating the physical process of image formation. By inverting such renderer, one can think of a learning approach to infer 3D information from 2D images. However, standard graphics renderers involve a fundamental step called rasterization, which prevents rendering to be differentiable. Unlike the state-of-the-art differentiable renderers (Kato et al. 2018 and Loper 2018), which only approximate the rendering gradient in the backpropagation, we propose a natually differentiable rendering framework that is able to (1) directly render colorized mesh using differentiable functions and (2) back-propagate efficient supervisions to mesh vertices and their attributes from various forms of image representations. The key to our framework is a novel formulation that views rendering as an aggregation function that fuses the probabilistic contributions of all mesh triangles with respect to the rendered pixels. Such formulation enables our framework to flow gradients to the occluded and distant vertices, which cannot be achieved by the previous state-of-the-arts. We show that by using the proposed renderer, one can achieve significant improvement in 3D unsupervised single-view reconstruction both qualitatively and quantitatively. Experiments also demonstrate that our approach can handle the challenging tasks in image-based shape fitting, which remain nontrivial to existing differentiable renders.},
note = {Conference Name: IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {VGL},
pubstate = {published},
tppubtype = {article}
}
Rendering bridges the gap between 2D vision and 3D scenes by simulating the physical process of image formation. By inverting such renderer, one can think of a learning approach to infer 3D information from 2D images. However, standard graphics renderers involve a fundamental step called rasterization, which prevents rendering to be differentiable. Unlike the state-of-the-art differentiable renderers (Kato et al. 2018 and Loper 2018), which only approximate the rendering gradient in the backpropagation, we propose a natually differentiable rendering framework that is able to (1) directly render colorized mesh using differentiable functions and (2) back-propagate efficient supervisions to mesh vertices and their attributes from various forms of image representations. The key to our framework is a novel formulation that views rendering as an aggregation function that fuses the probabilistic contributions of all mesh triangles with respect to the rendered pixels. Such formulation enables our framework to flow gradients to the occluded and distant vertices, which cannot be achieved by the previous state-of-the-arts. We show that by using the proposed renderer, one can achieve significant improvement in 3D unsupervised single-view reconstruction both qualitatively and quantitatively. Experiments also demonstrate that our approach can handle the challenging tasks in image-based shape fitting, which remain nontrivial to existing differentiable renders.
2021
Ionescu, Alina; Daele, Tom Van; Rizzo, Albert; Blair, Carolyn; Best, Paul
360° Videos for Immersive Mental Health Interventions: a Systematic Review Journal Article
In: J. technol. behav. sci., vol. 6, no. 4, pp. 631–651, 2021, ISSN: 2366-5963.
Abstract | Links | BibTeX | Tags: MedVR, VR
@article{ionescu_360_2021,
title = {360° Videos for Immersive Mental Health Interventions: a Systematic Review},
author = {Alina Ionescu and Tom Van Daele and Albert Rizzo and Carolyn Blair and Paul Best},
url = {https://doi.org/10.1007/s41347-021-00221-7},
doi = {10.1007/s41347-021-00221-7},
issn = {2366-5963},
year = {2021},
date = {2021-12-01},
urldate = {2022-09-13},
journal = {J. technol. behav. sci.},
volume = {6},
number = {4},
pages = {631–651},
abstract = {Virtual reality is increasingly recognized as a powerful method for clinical interventions in the mental health field, but has yet to achieve mainstream adoption in routine mental healthcare settings. A similar, yet slightly different technology, immersive 360° videos might have the potential to cover this gap, by requiring both lower costs and less technical skills to construct and operate such virtual environments. This systematic review therefore aims to identify, evaluate, and summarize mental health interventions using immersive 360° videos to support an understanding of their implementation in daily clinical practice. The quality of the 14 selected studies was evaluated using a critical appraisal tool, addressing populations with clinical levels of psychopathological symptoms, somatic conditions associated with psychological implications, and other at-risk groups. Immersive 360° videos successfully increased users’ feelings of presence, given their realistic features, and therefore yielded positive outcomes in clinical interventions where presence is considered as an essential precondition. Because the technical skills required to create immersive 360° video footage are fairly limited, most of the interventions using this approach have been created by mental health researchers or clinicians themselves. Immersive 360° videos are still in an early phase of implementation as a tool for clinical interventions for mental health, resulting in high heterogeneity in focus, procedures, and research designs. An important next step for making use of this technology may therefore involve the creation of standardized procedures, as a means to increase the quality of research and evidence-based interventions.},
keywords = {MedVR, VR},
pubstate = {published},
tppubtype = {article}
}
Virtual reality is increasingly recognized as a powerful method for clinical interventions in the mental health field, but has yet to achieve mainstream adoption in routine mental healthcare settings. A similar, yet slightly different technology, immersive 360° videos might have the potential to cover this gap, by requiring both lower costs and less technical skills to construct and operate such virtual environments. This systematic review therefore aims to identify, evaluate, and summarize mental health interventions using immersive 360° videos to support an understanding of their implementation in daily clinical practice. The quality of the 14 selected studies was evaluated using a critical appraisal tool, addressing populations with clinical levels of psychopathological symptoms, somatic conditions associated with psychological implications, and other at-risk groups. Immersive 360° videos successfully increased users’ feelings of presence, given their realistic features, and therefore yielded positive outcomes in clinical interventions where presence is considered as an essential precondition. Because the technical skills required to create immersive 360° video footage are fairly limited, most of the interventions using this approach have been created by mental health researchers or clinicians themselves. Immersive 360° videos are still in an early phase of implementation as a tool for clinical interventions for mental health, resulting in high heterogeneity in focus, procedures, and research designs. An important next step for making use of this technology may therefore involve the creation of standardized procedures, as a means to increase the quality of research and evidence-based interventions.
Talbot, Thomas B.; Rizzo, Albert S.; Soleymani, Mohammed
In: Technology, Mind, and Behavior, 2021.
Abstract | Links | BibTeX | Tags: MedVR, UARC
@article{talbot_advances_2021,
title = {Advances in Affective Computing for Psychological Applications: From the Fundamentals to the Future of Emotional Cognizant Artificial Intelligence Entities},
author = {Thomas B. Talbot and Albert S. Rizzo and Mohammed Soleymani},
url = {https://tmb.apaopen.org/pub/zm0la9di/release/1},
doi = {10.1037/tms0000011},
year = {2021},
date = {2021-11-01},
urldate = {2023-03-31},
journal = {Technology, Mind, and Behavior},
abstract = {Keywords: Affective Computing, Emotion Perception, Virtual Humans, Conversational Simulations},
keywords = {MedVR, UARC},
pubstate = {published},
tppubtype = {article}
}
Keywords: Affective Computing, Emotion Perception, Virtual Humans, Conversational Simulations
Hartholt, Arno; McCullough, Kyle; Mozgai, Sharon; Ustun, Volkan; Gordon, Andrew S
Introducing RIDE: Lowering the Barrier of Entry to Simulation and Training through the Rapid Integration & Development Environment Journal Article
In: pp. 11, 2021.
Abstract | Links | BibTeX | Tags: VHTL
@article{hartholt_introducing_2021,
title = {Introducing RIDE: Lowering the Barrier of Entry to Simulation and Training through the Rapid Integration & Development Environment},
author = {Arno Hartholt and Kyle McCullough and Sharon Mozgai and Volkan Ustun and Andrew S Gordon},
url = {https://dl.acm.org/doi/10.1145/3472306.3478363},
doi = {10.1145/3472306.3478363},
year = {2021},
date = {2021-11-01},
urldate = {2023-03-31},
booktitle = {Proceedings of the 21st ACM International Conference on Intelligent Virtual Agents},
pages = {11},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
series = {IVA '21},
abstract = {This paper describes the design, development, and philosophy of the Rapid Integration & Development Environment (RIDE). RIDE is a simulation platform that unites many Department of Defense (DoD) and Army simulation efforts to provide an accelerated development foundation and prototyping sandbox that provides direct benefit to the U.S. Army’s Synthetic Training Environment (STE) as well as the larger DoD and Army simulation communities. RIDE integrates a range of capabilities, including One World Terrain, Non-Player Character AI behaviors, xAPI logging, multiplayer networking, scenario creation, destructibility, machine learning approaches, and multi-platform support. The goal of RIDE is to create a simple, drag-and-drop development environment usable by people across all technical levels. RIDE leverages robust game engine technology while designed to be agnostic to any specific game or simulation engine. It provides decision makers with the tools needed to better define requirements and identify potential solutions in much less time and at much reduced costs. RIDE is available through Government Purpose Rights. We aim for RIDE to lower the barrier of entry to research and development efforts within the simulation community in order to reduce required time and effort for simulation and training prototyping. This paper provides an overview of our objective, overall approach, and next steps, in pursuit of these goals.},
keywords = {VHTL},
pubstate = {published},
tppubtype = {article}
}
This paper describes the design, development, and philosophy of the Rapid Integration & Development Environment (RIDE). RIDE is a simulation platform that unites many Department of Defense (DoD) and Army simulation efforts to provide an accelerated development foundation and prototyping sandbox that provides direct benefit to the U.S. Army’s Synthetic Training Environment (STE) as well as the larger DoD and Army simulation communities. RIDE integrates a range of capabilities, including One World Terrain, Non-Player Character AI behaviors, xAPI logging, multiplayer networking, scenario creation, destructibility, machine learning approaches, and multi-platform support. The goal of RIDE is to create a simple, drag-and-drop development environment usable by people across all technical levels. RIDE leverages robust game engine technology while designed to be agnostic to any specific game or simulation engine. It provides decision makers with the tools needed to better define requirements and identify potential solutions in much less time and at much reduced costs. RIDE is available through Government Purpose Rights. We aim for RIDE to lower the barrier of entry to research and development efforts within the simulation community in order to reduce required time and effort for simulation and training prototyping. This paper provides an overview of our objective, overall approach, and next steps, in pursuit of these goals.
Schuller, Bjorn W.; Picard, Rosalind; Andre, Elisabeth; Gratch, Jonathan; Tao, Jianhua
Intelligent Signal Processing for Affective Computing [From the Guest Editors] Journal Article
In: IEEE Signal Process. Mag., vol. 38, no. 6, pp. 9–11, 2021, ISSN: 1053-5888, 1558-0792.
Links | BibTeX | Tags: Emotions, Virtual Humans
@article{schuller_intelligent_2021,
title = {Intelligent Signal Processing for Affective Computing [From the Guest Editors]},
author = {Bjorn W. Schuller and Rosalind Picard and Elisabeth Andre and Jonathan Gratch and Jianhua Tao},
url = {https://ieeexplore.ieee.org/document/9591500/},
doi = {10.1109/MSP.2021.3096415},
issn = {1053-5888, 1558-0792},
year = {2021},
date = {2021-11-01},
urldate = {2022-09-29},
journal = {IEEE Signal Process. Mag.},
volume = {38},
number = {6},
pages = {9–11},
keywords = {Emotions, Virtual Humans},
pubstate = {published},
tppubtype = {article}
}
We've Entered a New Era of Streaming Health Care. Now What? Journal Article
In: IEEE Spectrum, 2021.
Abstract | Links | BibTeX | Tags: CBC
@article{noauthor_weve_2021,
title = {We've Entered a New Era of Streaming Health Care. Now What?},
url = {https://spectrum.ieee.org/digital-health},
year = {2021},
date = {2021-11-01},
urldate = {2022-09-29},
journal = {IEEE Spectrum},
abstract = {COVID-19 forced the transition to digital medicine, but there's much still to do.},
keywords = {CBC},
pubstate = {published},
tppubtype = {article}
}
COVID-19 forced the transition to digital medicine, but there's much still to do.
Awada, Mohamad; Becerik-Gerber, Burcin; Lucas, Gale; Roll, Shawn C.
Associations Among Home Indoor Environmental Quality Factors and Worker Health While Working From Home During COVID-19 Pandemic Journal Article
In: ASME Journal of Engineering for Sustainable Buildings and Cities, vol. 2, no. 4, pp. 041001, 2021, ISSN: 2642-6641, 2642-6625.
Abstract | Links | BibTeX | Tags: DTIC, UARC
@article{awada_associations_2021,
title = {Associations Among Home Indoor Environmental Quality Factors and Worker Health While Working From Home During COVID-19 Pandemic},
author = {Mohamad Awada and Burcin Becerik-Gerber and Gale Lucas and Shawn C. Roll},
url = {https://asmedigitalcollection.asme.org/sustainablebuildings/article/2/4/041001/1122847/Associations-Among-Home-Indoor-Environmental},
doi = {10.1115/1.4052822},
issn = {2642-6641, 2642-6625},
year = {2021},
date = {2021-11-01},
urldate = {2022-09-26},
journal = {ASME Journal of Engineering for Sustainable Buildings and Cities},
volume = {2},
number = {4},
pages = {041001},
abstract = {Abstract The outbreak of SARS-CoV-2 virus forced office workers to conduct their daily work activities from home over an extended period. Given this unique situation, an opportunity emerged to study the satisfaction of office workers with indoor environmental quality (IEQ) factors of their houses where work activities took place and associate these factors with mental and physical health. We designed and administered a questionnaire that was open for 45 days during the COVID-19 pandemic and received valid data from 988 respondents. The results show that low satisfaction with natural lighting, glare, and humidity predicted eye-related symptoms, while low satisfaction with noise was a strong predictor of fatigue or tiredness, headaches or migraines, anxiety, and depression or sadness. Nose- and throat-related symptoms and skin-related symptoms were only uniquely predicted by low satisfaction with humidity. Low satisfaction with glare uniquely predicted an increase in musculoskeletal discomfort. Symptoms related to mental stress, rumination, or worry were predicted by low satisfaction with air quality and noise. Finally, low satisfaction with noise and indoor temperature predicted the prevalence of symptoms related to trouble concentrating, maintaining attention, or focus. Workers with higher income were more satisfied with humidity, air quality, and indoor temperature and had better overall mental health. Older individuals had increased satisfaction with natural lighting, humidity, air quality, noise, and indoor temperature. Findings from this study can inform future design practices that focus on hybrid home-work environments by highlighting the impact of IEQ factors on occupant well-being.},
keywords = {DTIC, UARC},
pubstate = {published},
tppubtype = {article}
}
Abstract The outbreak of SARS-CoV-2 virus forced office workers to conduct their daily work activities from home over an extended period. Given this unique situation, an opportunity emerged to study the satisfaction of office workers with indoor environmental quality (IEQ) factors of their houses where work activities took place and associate these factors with mental and physical health. We designed and administered a questionnaire that was open for 45 days during the COVID-19 pandemic and received valid data from 988 respondents. The results show that low satisfaction with natural lighting, glare, and humidity predicted eye-related symptoms, while low satisfaction with noise was a strong predictor of fatigue or tiredness, headaches or migraines, anxiety, and depression or sadness. Nose- and throat-related symptoms and skin-related symptoms were only uniquely predicted by low satisfaction with humidity. Low satisfaction with glare uniquely predicted an increase in musculoskeletal discomfort. Symptoms related to mental stress, rumination, or worry were predicted by low satisfaction with air quality and noise. Finally, low satisfaction with noise and indoor temperature predicted the prevalence of symptoms related to trouble concentrating, maintaining attention, or focus. Workers with higher income were more satisfied with humidity, air quality, and indoor temperature and had better overall mental health. Older individuals had increased satisfaction with natural lighting, humidity, air quality, noise, and indoor temperature. Findings from this study can inform future design practices that focus on hybrid home-work environments by highlighting the impact of IEQ factors on occupant well-being.
Hou, Yu; Chen, Meida; Volk, Rebekka; Soibelman, Lucio
An Approach to Semantically Segmenting Building Components and Outdoor Scenes Based on Multichannel Aerial Imagery Datasets Journal Article
In: Remote Sensing, vol. 13, no. 21, pp. 4357, 2021, ISSN: 2072-4292.
Abstract | Links | BibTeX | Tags:
@article{hou_approach_2021,
title = {An Approach to Semantically Segmenting Building Components and Outdoor Scenes Based on Multichannel Aerial Imagery Datasets},
author = {Yu Hou and Meida Chen and Rebekka Volk and Lucio Soibelman},
url = {https://www.mdpi.com/2072-4292/13/21/4357},
doi = {10.3390/rs13214357},
issn = {2072-4292},
year = {2021},
date = {2021-10-01},
urldate = {2022-09-28},
journal = {Remote Sensing},
volume = {13},
number = {21},
pages = {4357},
abstract = {As-is building modeling plays an important role in energy audits and retrofits. However, in order to understand the source(s) of energy loss, researchers must know the semantic information of the buildings and outdoor scenes. Thermal information can potentially be used to distinguish objects that have similar surface colors but are composed of different materials. To utilize both the red–green–blue (RGB) color model and thermal information for the semantic segmentation of buildings and outdoor scenes, we deployed and adapted various pioneering deep convolutional neural network (DCNN) tools that combine RGB information with thermal information to improve the semantic and instance segmentation processes. When both types of information are available, the resulting DCNN models allow us to achieve better segmentation performance. By deploying three case studies, we experimented with our proposed DCNN framework, deploying datasets of building components and outdoor scenes, and testing the models to determine whether the segmentation performance had improved or not. In our observation, the fusion of RGB and thermal information can help the segmentation task in specific cases, but it might also make the neural networks hard to train or deteriorate their prediction performance in some cases. Additionally, different algorithms perform differently in semantic and instance segmentation.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
As-is building modeling plays an important role in energy audits and retrofits. However, in order to understand the source(s) of energy loss, researchers must know the semantic information of the buildings and outdoor scenes. Thermal information can potentially be used to distinguish objects that have similar surface colors but are composed of different materials. To utilize both the red–green–blue (RGB) color model and thermal information for the semantic segmentation of buildings and outdoor scenes, we deployed and adapted various pioneering deep convolutional neural network (DCNN) tools that combine RGB information with thermal information to improve the semantic and instance segmentation processes. When both types of information are available, the resulting DCNN models allow us to achieve better segmentation performance. By deploying three case studies, we experimented with our proposed DCNN framework, deploying datasets of building components and outdoor scenes, and testing the models to determine whether the segmentation performance had improved or not. In our observation, the fusion of RGB and thermal information can help the segmentation task in specific cases, but it might also make the neural networks hard to train or deteriorate their prediction performance in some cases. Additionally, different algorithms perform differently in semantic and instance segmentation.
Adami, Pooya; Rodrigues, Patrick B.; Woods, Peter J.; Becerik-Gerber, Burcin; Soibelman, Lucio; Copur-Gencturk, Yasemin; Lucas, Gale
Effectiveness of VR-based training on improving construction workers’ knowledge, skills, and safety behavior in robotic teleoperation Journal Article
In: Advanced Engineering Informatics, vol. 50, pp. 101431, 2021, ISSN: 14740346.
Links | BibTeX | Tags: DTIC, Learning Sciences, UARC, VR
@article{adami_effectiveness_2021,
title = {Effectiveness of VR-based training on improving construction workers’ knowledge, skills, and safety behavior in robotic teleoperation},
author = {Pooya Adami and Patrick B. Rodrigues and Peter J. Woods and Burcin Becerik-Gerber and Lucio Soibelman and Yasemin Copur-Gencturk and Gale Lucas},
url = {https://linkinghub.elsevier.com/retrieve/pii/S147403462100183X},
doi = {10.1016/j.aei.2021.101431},
issn = {14740346},
year = {2021},
date = {2021-10-01},
urldate = {2022-09-26},
journal = {Advanced Engineering Informatics},
volume = {50},
pages = {101431},
keywords = {DTIC, Learning Sciences, UARC, VR},
pubstate = {published},
tppubtype = {article}
}
Hernandez, Stephanie; Artstein, Ron
Annotating low-confidence questions improves classifier performance Journal Article
In: Proceedings of the 25th Workshop on the Semantics and Pragmatics of Dialogue - Poster Abstracts, 2021.
Abstract | Links | BibTeX | Tags: Natural Language, UARC
@article{hernandez_annotating_2021,
title = {Annotating low-confidence questions improves classifier performance},
author = {Stephanie Hernandez and Ron Artstein},
url = {https://par.nsf.gov/biblio/10313591-annotating-low-confidence-questions-improves-classifier-performance},
year = {2021},
date = {2021-09-01},
urldate = {2023-03-31},
journal = {Proceedings of the 25th Workshop on the Semantics and Pragmatics of Dialogue - Poster Abstracts},
abstract = {This paper compares methods to select data for annotation in order to improve a classifier used in a question-answering dialogue system. With a classifier trained on 1,500 questions, adding 300 training questions on which the classifier is least confident results in consistently improved performance, whereas adding 300 arbitrarily selected training questions does not yield consistent improvement, and sometimes even degrades performance. The paper uses a new method for comparative evaluation of classifiers for dialogue, which scores each classifier based on the number of appropriate responses retrieved.},
keywords = {Natural Language, UARC},
pubstate = {published},
tppubtype = {article}
}
This paper compares methods to select data for annotation in order to improve a classifier used in a question-answering dialogue system. With a classifier trained on 1,500 questions, adding 300 training questions on which the classifier is least confident results in consistently improved performance, whereas adding 300 arbitrarily selected training questions does not yield consistent improvement, and sometimes even degrades performance. The paper uses a new method for comparative evaluation of classifiers for dialogue, which scores each classifier based on the number of appropriate responses retrieved.
Rizzo, Albert “Skip”; Goodwin, Grace J.; Vito, Alyssa N. De; Bell, Joshua D.
Recent advances in virtual reality and psychology: Introduction to the special issue. Journal Article
In: Translational Issues in Psychological Science, vol. 7, no. 3, pp. 213–217, 2021, ISSN: 2332-2179, 2332-2136.
Links | BibTeX | Tags: DTIC, MedVR, VR
@article{rizzo_recent_2021,
title = {Recent advances in virtual reality and psychology: Introduction to the special issue.},
author = {Albert “Skip” Rizzo and Grace J. Goodwin and Alyssa N. De Vito and Joshua D. Bell},
url = {http://doi.apa.org/getdoi.cfm?doi=10.1037/tps0000316},
doi = {10.1037/tps0000316},
issn = {2332-2179, 2332-2136},
year = {2021},
date = {2021-09-01},
urldate = {2022-09-13},
journal = {Translational Issues in Psychological Science},
volume = {7},
number = {3},
pages = {213–217},
keywords = {DTIC, MedVR, VR},
pubstate = {published},
tppubtype = {article}
}
Schuster, Bianca A.; Fraser, Dagmar S.; Bosch, Jasper J. F.; Sowden, Sophie; Gordon, Andrew S.; Huh, Dongsung; Cook, Jennifer L.
Kinematics and observer-animator kinematic similarity predict mental state attribution from Heider–Simmel style animations Journal Article
In: Sci Rep, vol. 11, no. 1, pp. 18266, 2021, ISSN: 2045-2322.
Abstract | Links | BibTeX | Tags:
@article{schuster_kinematics_2021,
title = {Kinematics and observer-animator kinematic similarity predict mental state attribution from Heider–Simmel style animations},
author = {Bianca A. Schuster and Dagmar S. Fraser and Jasper J. F. Bosch and Sophie Sowden and Andrew S. Gordon and Dongsung Huh and Jennifer L. Cook},
url = {https://www.nature.com/articles/s41598-021-97660-2},
doi = {10.1038/s41598-021-97660-2},
issn = {2045-2322},
year = {2021},
date = {2021-09-01},
urldate = {2022-09-29},
journal = {Sci Rep},
volume = {11},
number = {1},
pages = {18266},
abstract = {The ability to ascribe mental states, such as beliefs or desires to oneself and other individuals forms an integral part of everyday social interaction. Animations tasks, in which observers watch videos of interacting triangles, have been extensively used to test mental state attribution in a variety of clinical populations. Compared to control participants, individuals with clinical conditions such as autism typically offer less appropriate mental state descriptions of such videos. Recent research suggests that stimulus kinematics and movement similarity (between the video and the observer) may contribute to mental state attribution difficulties. Here we present a novel adaptation of the animations task, suitable to track and compare animation generator and -observer kinematics. Using this task and a population-derived stimulus database, we confirmed the hypotheses that an animation’s jerk and jerk similarity between observer and animator significantly contribute to the correct identification of an animation. By employing random forest analysis to explore other stimulus characteristics, we reveal that other indices of movement similarity, including acceleration- and rotation-based similarity, also predict performance. Our results highlight the importance of movement similarity between observer and animator and raise new questions about reasons why some clinical populations exhibit difficulties with this task.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The ability to ascribe mental states, such as beliefs or desires to oneself and other individuals forms an integral part of everyday social interaction. Animations tasks, in which observers watch videos of interacting triangles, have been extensively used to test mental state attribution in a variety of clinical populations. Compared to control participants, individuals with clinical conditions such as autism typically offer less appropriate mental state descriptions of such videos. Recent research suggests that stimulus kinematics and movement similarity (between the video and the observer) may contribute to mental state attribution difficulties. Here we present a novel adaptation of the animations task, suitable to track and compare animation generator and -observer kinematics. Using this task and a population-derived stimulus database, we confirmed the hypotheses that an animation’s jerk and jerk similarity between observer and animator significantly contribute to the correct identification of an animation. By employing random forest analysis to explore other stimulus characteristics, we reveal that other indices of movement similarity, including acceleration- and rotation-based similarity, also predict performance. Our results highlight the importance of movement similarity between observer and animator and raise new questions about reasons why some clinical populations exhibit difficulties with this task.
Ghasem, Wesley; Valenzuela, Joshua; Saxon, Leslie A.
Player Tracking Technology and Data for Injury Prevention in the National Football League Journal Article
In: Curr Sports Med Rep, vol. 20, no. 9, pp. 436–439, 2021, ISSN: 1537-8918.
@article{ghasem_player_2021,
title = {Player Tracking Technology and Data for Injury Prevention in the National Football League},
author = {Wesley Ghasem and Joshua Valenzuela and Leslie A. Saxon},
url = {https://journals.lww.com/10.1249/JSR.0000000000000873},
doi = {10.1249/JSR.0000000000000873},
issn = {1537-8918},
year = {2021},
date = {2021-09-01},
urldate = {2022-09-28},
journal = {Curr Sports Med Rep},
volume = {20},
number = {9},
pages = {436–439},
keywords = {CBC},
pubstate = {published},
tppubtype = {article}
}
Awada, Mohamad; Lucas, Gale; Becerik-Gerber, Burcin; Roll, Shawn
Working from home during the COVID-19 pandemic: Impact on office worker productivity and work experience Journal Article
In: WOR, vol. 69, no. 4, pp. 1171–1189, 2021, ISSN: 10519815, 18759270.
Abstract | Links | BibTeX | Tags: DTIC, UARC
@article{awada_working_2021,
title = {Working from home during the COVID-19 pandemic: Impact on office worker productivity and work experience},
author = {Mohamad Awada and Gale Lucas and Burcin Becerik-Gerber and Shawn Roll},
url = {https://www.medra.org/servlet/aliasResolver?alias=iospress&doi=10.3233/WOR-210301},
doi = {10.3233/WOR-210301},
issn = {10519815, 18759270},
year = {2021},
date = {2021-08-01},
urldate = {2022-09-26},
journal = {WOR},
volume = {69},
number = {4},
pages = {1171–1189},
abstract = {BACKGROUND: With the COVID-19 pandemic, organizations embraced Work From Home (WFH). An important component of transitioning to WFH is the effect on workers, particularly related to their productivity and work experience. OBJECTIVES: The objective of this study is to examine how worker-, workspace-, and work-related factors affected productivity and time spent at a workstation on a typical WFH day during the pandemic. METHODS: An online questionnaire was designed and administered to collect the necessary information. Data from 988 respondents were included in the analyses. RESULTS: Overall perception of productivity level among workers did not change relative to their in-office productivity before the pandemic. Female, older, and high-income workers were likely to report increased productivity. Productivity was positively influenced by better mental and physical health statuses, having a teenager, increased communication with coworkers and having a dedicated room for work. Number of hours spent at a workstation increased by approximately 1.5 hours during a typical WFH day. Longer hours were reported by individuals who had school age children, owned an office desk or an adjustable chair, and had adjusted their work hours. CONCLUSION: The findings highlight key factors for employers and employees to consider for improving the WFH experience.},
keywords = {DTIC, UARC},
pubstate = {published},
tppubtype = {article}
}
BACKGROUND: With the COVID-19 pandemic, organizations embraced Work From Home (WFH). An important component of transitioning to WFH is the effect on workers, particularly related to their productivity and work experience. OBJECTIVES: The objective of this study is to examine how worker-, workspace-, and work-related factors affected productivity and time spent at a workstation on a typical WFH day during the pandemic. METHODS: An online questionnaire was designed and administered to collect the necessary information. Data from 988 respondents were included in the analyses. RESULTS: Overall perception of productivity level among workers did not change relative to their in-office productivity before the pandemic. Female, older, and high-income workers were likely to report increased productivity. Productivity was positively influenced by better mental and physical health statuses, having a teenager, increased communication with coworkers and having a dedicated room for work. Number of hours spent at a workstation increased by approximately 1.5 hours during a typical WFH day. Longer hours were reported by individuals who had school age children, owned an office desk or an adjustable chair, and had adjusted their work hours. CONCLUSION: The findings highlight key factors for employers and employees to consider for improving the WFH experience.
Kennedy, Alana A. U.; Thacker, Ian; Nye, Benjamin D.; Sinatra, Gale M.; Swartout, William; Lindsey, Emily
Promoting interest, positive emotions, and knowledge using augmented reality in a museum setting Journal Article
In: International Journal of Science Education, Part B, vol. 11, no. 3, pp. 242–258, 2021, ISSN: 2154-8455, (Publisher: Routledge _eprint: https://doi.org/10.1080/21548455.2021.1946619).
Abstract | Links | BibTeX | Tags: AR, Learning Sciences, UARC
@article{kennedy_promoting_2021,
title = {Promoting interest, positive emotions, and knowledge using augmented reality in a museum setting},
author = {Alana A. U. Kennedy and Ian Thacker and Benjamin D. Nye and Gale M. Sinatra and William Swartout and Emily Lindsey},
url = {https://doi.org/10.1080/21548455.2021.1946619},
doi = {10.1080/21548455.2021.1946619},
issn = {2154-8455},
year = {2021},
date = {2021-07-01},
urldate = {2023-03-31},
journal = {International Journal of Science Education, Part B},
volume = {11},
number = {3},
pages = {242–258},
abstract = {Informal learning environments, such as museums, provide unique opportunities for science learning. They are deliberately designed to impact public understanding of science and shape visitors’ attitudes and behaviors. As a developing technology, augmented reality (AR) offers the transformative potential to support museums’ educational missions by enhancing visitors’ experience, thereby creating effective conditions for learning and personalized interactions with science. We implemented an AR-enhanced exhibit at the La Brea Tar Pits (LBTP) to reduce scientific misconceptions and explore the role of interest and emotions around science and AR technology as it related to learning and knowledge revision. Using a pretest-posttest design, 62 adults completed an AR experience that addressed two scientific misconceptions related to the consistency of tar and frequency of large animal entrapment. We found that participants had significantly fewer misconceptions at posttest than at pretest. Participants also reported higher levels of interest in science content than AR technology and discriminated between emotions they experienced with regard to science content and AR technology. Feelings of curiosity predicted knowledge revision and interest in both science content and AR technology. These findings may be useful for museums and other science communicators seeking to create AR interventions that support learning and conceptual change.},
note = {Publisher: Routledge
_eprint: https://doi.org/10.1080/21548455.2021.1946619},
keywords = {AR, Learning Sciences, UARC},
pubstate = {published},
tppubtype = {article}
}
Informal learning environments, such as museums, provide unique opportunities for science learning. They are deliberately designed to impact public understanding of science and shape visitors’ attitudes and behaviors. As a developing technology, augmented reality (AR) offers the transformative potential to support museums’ educational missions by enhancing visitors’ experience, thereby creating effective conditions for learning and personalized interactions with science. We implemented an AR-enhanced exhibit at the La Brea Tar Pits (LBTP) to reduce scientific misconceptions and explore the role of interest and emotions around science and AR technology as it related to learning and knowledge revision. Using a pretest-posttest design, 62 adults completed an AR experience that addressed two scientific misconceptions related to the consistency of tar and frequency of large animal entrapment. We found that participants had significantly fewer misconceptions at posttest than at pretest. Participants also reported higher levels of interest in science content than AR technology and discriminated between emotions they experienced with regard to science content and AR technology. Feelings of curiosity predicted knowledge revision and interest in both science content and AR technology. These findings may be useful for museums and other science communicators seeking to create AR interventions that support learning and conceptual change.
Rizzo, Albert “Skip”; Hartholt, Arno; Mozgai, Sharon
From Combat to COVID-19 – Managing the Impact of Trauma Using Virtual Reality Journal Article
In: Journal of Technology in Human Services, vol. 39, no. 3, pp. 314–347, 2021, ISSN: 1522-8835, (Publisher: Routledge _eprint: https://doi.org/10.1080/15228835.2021.1915931).
Abstract | Links | BibTeX | Tags: MedVR, UARC, VHTL, Virtual Humans
@article{rizzo_combat_2021,
title = {From Combat to COVID-19 – Managing the Impact of Trauma Using Virtual Reality},
author = {Albert “Skip” Rizzo and Arno Hartholt and Sharon Mozgai},
url = {https://doi.org/10.1080/15228835.2021.1915931},
doi = {10.1080/15228835.2021.1915931},
issn = {1522-8835},
year = {2021},
date = {2021-07-01},
urldate = {2023-03-31},
journal = {Journal of Technology in Human Services},
volume = {39},
number = {3},
pages = {314–347},
abstract = {Research has documented the efficacy of clinical applications that leverage Virtual Reality (VR) for assessment and treatment purposes across a wide range of domains, including pain, phobias, and posttraumatic stress disorder (PTSD). As the field of Clinical VR matures, it is important to review its origins and examine how these initial explorations have progressed, what gaps remain, and what opportunities the community can pursue. We do this by reflecting on our personal scientific journey against the backdrop of the field in general. In particular, this paper discusses how a clinical research program that was initially designed to deliver trauma-focused VR exposure therapy (VRET) for combat-related PTSD has been evolved to expand its impact and address a wider range of trauma sources. Such trauma sources include sexual trauma and the needs of first responders and healthcare professionals serving on the frontlines of the COVID-19 pandemic. We provide an overview of the field and its general trends, discuss the genesis of our research agenda and its current status, and summarize upcoming opportunities, together with common challenges and lessons learned.},
note = {Publisher: Routledge
_eprint: https://doi.org/10.1080/15228835.2021.1915931},
keywords = {MedVR, UARC, VHTL, Virtual Humans},
pubstate = {published},
tppubtype = {article}
}
Research has documented the efficacy of clinical applications that leverage Virtual Reality (VR) for assessment and treatment purposes across a wide range of domains, including pain, phobias, and posttraumatic stress disorder (PTSD). As the field of Clinical VR matures, it is important to review its origins and examine how these initial explorations have progressed, what gaps remain, and what opportunities the community can pursue. We do this by reflecting on our personal scientific journey against the backdrop of the field in general. In particular, this paper discusses how a clinical research program that was initially designed to deliver trauma-focused VR exposure therapy (VRET) for combat-related PTSD has been evolved to expand its impact and address a wider range of trauma sources. Such trauma sources include sexual trauma and the needs of first responders and healthcare professionals serving on the frontlines of the COVID-19 pandemic. We provide an overview of the field and its general trends, discuss the genesis of our research agenda and its current status, and summarize upcoming opportunities, together with common challenges and lessons learned.
Stocco, Andrea; Sibert, Catherine; Steine-Hanson, Zoe; Koh, Natalie; Laird, John E.; Lebiere, Christian J.; Rosenbloom, Paul
Analysis of the human connectome data supports the notion of a “Common Model of Cognition” for human and human-like intelligence across domains Journal Article
In: NeuroImage, vol. 235, pp. 118035, 2021, ISSN: 10538119.
@article{stocco_analysis_2021-1,
title = {Analysis of the human connectome data supports the notion of a “Common Model of Cognition” for human and human-like intelligence across domains},
author = {Andrea Stocco and Catherine Sibert and Zoe Steine-Hanson and Natalie Koh and John E. Laird and Christian J. Lebiere and Paul Rosenbloom},
url = {https://linkinghub.elsevier.com/retrieve/pii/S1053811921003128},
doi = {10.1016/j.neuroimage.2021.118035},
issn = {10538119},
year = {2021},
date = {2021-07-01},
urldate = {2021-04-30},
journal = {NeuroImage},
volume = {235},
pages = {118035},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Dukes, Daniel; Abrams, Kathryn; Adolphs, Ralph; Ahmed, Mohammed E.; Beatty, Andrew; Berridge, Kent C.; Broomhall, Susan; Brosch, Tobias; Campos, Joseph J.; Clay, Zanna; Clément, Fabrice; Cunningham, William A.; Damasio, Antonio; Damasio, Hanna; D’Arms, Justin; Davidson, Jane W.; Gelder, Beatrice; Deonna, Julien; Sousa, Ronnie; Ekman, Paul; Ellsworth, Phoebe C.; Fehr, Ernst; Fischer, Agneta; Foolen, Ad; Frevert, Ute; Grandjean, Didier; Gratch, Jonathan; Greenberg, Leslie; Greenspan, Patricia; Gross, James J.; Halperin, Eran; Kappas, Arvid; Keltner, Dacher; Knutson, Brian; Konstan, David; Kret, Mariska E.; LeDoux, Joseph E.; Lerner, Jennifer S.; Levenson, Robert W.; Loewenstein, George; Manstead, Antony S. R.; Maroney, Terry A.; Moors, Agnes; Niedenthal, Paula; Parkinson, Brian; Pavlidis, Ioannis; Pelachaud, Catherine; Pollak, Seth D.; Pourtois, Gilles; Roettger-Roessler, Birgitt; Russell, James A.; Sauter, Disa; Scarantino, Andrea; Scherer, Klaus R.; Stearns, Peter; Stets, Jan E.; Tappolet, Christine; Teroni, Fabrice; Tsai, Jeanne; Turner, Jonathan; Reekum, Carien Van; Vuilleumier, Patrik; Wharton, Tim; Sander, David
The rise of affectivism Journal Article
In: Nat Hum Behav, vol. 5, no. 7, pp. 816–820, 2021, ISSN: 2397-3374.
Links | BibTeX | Tags: Emotions
@article{dukes_rise_2021,
title = {The rise of affectivism},
author = {Daniel Dukes and Kathryn Abrams and Ralph Adolphs and Mohammed E. Ahmed and Andrew Beatty and Kent C. Berridge and Susan Broomhall and Tobias Brosch and Joseph J. Campos and Zanna Clay and Fabrice Clément and William A. Cunningham and Antonio Damasio and Hanna Damasio and Justin D’Arms and Jane W. Davidson and Beatrice Gelder and Julien Deonna and Ronnie Sousa and Paul Ekman and Phoebe C. Ellsworth and Ernst Fehr and Agneta Fischer and Ad Foolen and Ute Frevert and Didier Grandjean and Jonathan Gratch and Leslie Greenberg and Patricia Greenspan and James J. Gross and Eran Halperin and Arvid Kappas and Dacher Keltner and Brian Knutson and David Konstan and Mariska E. Kret and Joseph E. LeDoux and Jennifer S. Lerner and Robert W. Levenson and George Loewenstein and Antony S. R. Manstead and Terry A. Maroney and Agnes Moors and Paula Niedenthal and Brian Parkinson and Ioannis Pavlidis and Catherine Pelachaud and Seth D. Pollak and Gilles Pourtois and Birgitt Roettger-Roessler and James A. Russell and Disa Sauter and Andrea Scarantino and Klaus R. Scherer and Peter Stearns and Jan E. Stets and Christine Tappolet and Fabrice Teroni and Jeanne Tsai and Jonathan Turner and Carien Van Reekum and Patrik Vuilleumier and Tim Wharton and David Sander},
url = {http://www.nature.com/articles/s41562-021-01130-8},
doi = {10.1038/s41562-021-01130-8},
issn = {2397-3374},
year = {2021},
date = {2021-07-01},
urldate = {2022-09-28},
journal = {Nat Hum Behav},
volume = {5},
number = {7},
pages = {816–820},
keywords = {Emotions},
pubstate = {published},
tppubtype = {article}
}
Stocco, Andrea; Sibert, Catherine; Steine-Hanson, Zoe; Koh, Natalie; Laird, John E.; Lebiere, Christian J.; Rosenbloom, Paul
Analysis of the human connectome data supports the notion of a “Common Model of Cognition” for human and human-like intelligence across domains Journal Article
In: NeuroImage, vol. 235, pp. 118035, 2021, ISSN: 10538119.
@article{stocco_analysis_2021,
title = {Analysis of the human connectome data supports the notion of a “Common Model of Cognition” for human and human-like intelligence across domains},
author = {Andrea Stocco and Catherine Sibert and Zoe Steine-Hanson and Natalie Koh and John E. Laird and Christian J. Lebiere and Paul Rosenbloom},
url = {https://linkinghub.elsevier.com/retrieve/pii/S1053811921003128},
doi = {10.1016/j.neuroimage.2021.118035},
issn = {10538119},
year = {2021},
date = {2021-07-01},
urldate = {2021-05-06},
journal = {NeuroImage},
volume = {235},
pages = {118035},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Xiang, Sitao
Eliminating topological errors in neural network rotation estimation using self-selecting ensembles Journal Article
In: ACM Trans. Graph., vol. 40, no. 4, pp. 167:1–167:21, 2021, ISSN: 0730-0301.
Abstract | Links | BibTeX | Tags: VGL
@article{xiang_eliminating_2021,
title = {Eliminating topological errors in neural network rotation estimation using self-selecting ensembles},
author = {Sitao Xiang},
url = {https://dl.acm.org/doi/10.1145/3450626.3459882},
doi = {10.1145/3450626.3459882},
issn = {0730-0301},
year = {2021},
date = {2021-07-01},
urldate = {2023-03-31},
journal = {ACM Trans. Graph.},
volume = {40},
number = {4},
pages = {167:1–167:21},
abstract = {Many problems in computer graphics and computer vision applications involves inferring a rotation from a variety of different forms of inputs. With the increasing use of deep learning, neural networks have been employed to solve such problems. However, the traditional representations for 3D rotations, the quaternions and Euler angles, are found to be problematic for neural networks in practice, producing seemingly unavoidable large estimation errors. Previous researches has identified the discontinuity of the mapping from SO(3) to the quaternions or Euler angles as the source of such errors, and to solve it, embeddings of SO(3) have been proposed as the output representation of rotation estimation networks instead. In this paper, we argue that the argument against quaternions and Euler angles from local discontinuities of the mappings from SO(3) is flawed, and instead provide a different argument from the global topological properties of SO(3) that also establishes the lower bound of maximum error when using quaternions and Euler angles for rotation estimation networks. Extending from this view, we discover that rotation symmetries in the input object causes additional topological problems that even using embeddings of SO(3) as the output representation would not correctly handle. We propose the self-selecting ensemble, a topologically motivated approach, where the network makes multiple predictions and assigns weights to them. We show theoretically and with experiments that our methods can be combined with a wide range of different rotation representations and can handle all kinds of finite symmetries in 3D rotation estimation problems.},
keywords = {VGL},
pubstate = {published},
tppubtype = {article}
}
Many problems in computer graphics and computer vision applications involves inferring a rotation from a variety of different forms of inputs. With the increasing use of deep learning, neural networks have been employed to solve such problems. However, the traditional representations for 3D rotations, the quaternions and Euler angles, are found to be problematic for neural networks in practice, producing seemingly unavoidable large estimation errors. Previous researches has identified the discontinuity of the mapping from SO(3) to the quaternions or Euler angles as the source of such errors, and to solve it, embeddings of SO(3) have been proposed as the output representation of rotation estimation networks instead. In this paper, we argue that the argument against quaternions and Euler angles from local discontinuities of the mappings from SO(3) is flawed, and instead provide a different argument from the global topological properties of SO(3) that also establishes the lower bound of maximum error when using quaternions and Euler angles for rotation estimation networks. Extending from this view, we discover that rotation symmetries in the input object causes additional topological problems that even using embeddings of SO(3) as the output representation would not correctly handle. We propose the self-selecting ensemble, a topologically motivated approach, where the network makes multiple predictions and assigns weights to them. We show theoretically and with experiments that our methods can be combined with a wide range of different rotation representations and can handle all kinds of finite symmetries in 3D rotation estimation problems.
Greenwald, Eric; Leitner, Maxyn; Wang, Ning
The Human-Interpreter Problem in Youth Encounters with AI Journal Article
In: Proceedings of the 15th International Conference of the Learning Sciences, pp. 1107–1108, 2021, (Publisher: International Society of the Learning Sciences).
Abstract | Links | BibTeX | Tags: AI, UARC
@article{greenwald_human-interpreter_2021,
title = {The Human-Interpreter Problem in Youth Encounters with AI},
author = {Eric Greenwald and Maxyn Leitner and Ning Wang},
url = {https://repository.isls.org//handle/1/7421},
year = {2021},
date = {2021-06-01},
urldate = {2023-03-31},
journal = {Proceedings of the 15th International Conference of the Learning Sciences},
pages = {1107–1108},
abstract = {Artificial Intelligence’s impact on society is increasingly pervasive. While innovative educational programs are being developed, there is yet little understanding of how pre-college aged students construct understanding of, and gain practice with, core AI concepts and strategies. In this paper, we discuss emerging findings from a cognitive interview study with middle school and high school students to better understand how students learn AI concepts. Drawing on these qualitative data, we present evidence for a conceptual challenge that may arise as youth develop understanding of AI: when considering how AI systems might use data to make decisions, students often began by drawing on prior experience to suggest underlying motivations within the decision space, rather than attending to features of the data themselves. We hypothesize that youth may begin with a working theory of AI that assumes general intelligence for the system, including the capacity to recognize and reason from human motivations.},
note = {Publisher: International Society of the Learning Sciences},
keywords = {AI, UARC},
pubstate = {published},
tppubtype = {article}
}
Artificial Intelligence’s impact on society is increasingly pervasive. While innovative educational programs are being developed, there is yet little understanding of how pre-college aged students construct understanding of, and gain practice with, core AI concepts and strategies. In this paper, we discuss emerging findings from a cognitive interview study with middle school and high school students to better understand how students learn AI concepts. Drawing on these qualitative data, we present evidence for a conceptual challenge that may arise as youth develop understanding of AI: when considering how AI systems might use data to make decisions, students often began by drawing on prior experience to suggest underlying motivations within the decision space, rather than attending to features of the data themselves. We hypothesize that youth may begin with a working theory of AI that assumes general intelligence for the system, including the capacity to recognize and reason from human motivations.
Holder, Eric; Wang, Ning
Explainable artificial intelligence (XAI) interactively working with humans as a junior cyber analyst Journal Article
In: Hum.-Intell. Syst. Integr., vol. 3, no. 2, pp. 139–153, 2021, ISSN: 2524-4884.
Abstract | Links | BibTeX | Tags: AI, UARC
@article{holder_explainable_2021,
title = {Explainable artificial intelligence (XAI) interactively working with humans as a junior cyber analyst},
author = {Eric Holder and Ning Wang},
url = {https://doi.org/10.1007/s42454-020-00021-z},
doi = {10.1007/s42454-020-00021-z},
issn = {2524-4884},
year = {2021},
date = {2021-06-01},
urldate = {2023-03-31},
journal = {Hum.-Intell. Syst. Integr.},
volume = {3},
number = {2},
pages = {139–153},
abstract = {There are many applications where artificial intelligence (AI) can add a benefit, but this benefit may not be fully realized, if the human cannot understand and interact with the output as required by their context. Allowing AI to explain its decisions can potentially mitigate this issue. To develop effective explainable AI methods to support this need, we need to understand both what the human needs for decision-making, as well as what information the AI has and can make available. This paper presents an example case of capturing those requirements. We explore how an operational planner (senior human analyst) for a cyber protection team could use a junior analyst virtual agent to scour, analyze, and present the data available on vulnerabilities and incidents on both the target systems as well as similar systems. We explore the interactions required to understand these outputs and to integrate additional knowledge held by the human. This is an exemplar case for integrating XAI into the real-world bi-directional workflow: the senior analyst needs to be able to understand the junior analysts results, particularly the assumptions and implications, in order to create a plan and brief it up the command chain. He or she may have further questions, or analysis needs to achieve this understanding. The application is the junior analyst agent and senior human analysts working together to create this understanding of threats, vulnerabilities, incidents, likely future attacks, and counteractions on the mission relevant cyber terrain that their unit has been assigned a mission on.},
keywords = {AI, UARC},
pubstate = {published},
tppubtype = {article}
}
There are many applications where artificial intelligence (AI) can add a benefit, but this benefit may not be fully realized, if the human cannot understand and interact with the output as required by their context. Allowing AI to explain its decisions can potentially mitigate this issue. To develop effective explainable AI methods to support this need, we need to understand both what the human needs for decision-making, as well as what information the AI has and can make available. This paper presents an example case of capturing those requirements. We explore how an operational planner (senior human analyst) for a cyber protection team could use a junior analyst virtual agent to scour, analyze, and present the data available on vulnerabilities and incidents on both the target systems as well as similar systems. We explore the interactions required to understand these outputs and to integrate additional knowledge held by the human. This is an exemplar case for integrating XAI into the real-world bi-directional workflow: the senior analyst needs to be able to understand the junior analysts results, particularly the assumptions and implications, in order to create a plan and brief it up the command chain. He or she may have further questions, or analysis needs to achieve this understanding. The application is the junior analyst agent and senior human analysts working together to create this understanding of threats, vulnerabilities, incidents, likely future attacks, and counteractions on the mission relevant cyber terrain that their unit has been assigned a mission on.
Horstmann, Aike C.; Gratch, Jonathan; Krämer, Nicole C.
I Just Wanna Blame Somebody, Not Something! Reactions to a Computer Agent Giving Negative Feedback Based on the Instructions of a Person Journal Article
In: International Journal of Human-Computer Studies, pp. 102683, 2021, ISSN: 10715819.
Abstract | Links | BibTeX | Tags: DTIC, UARC, Virtual Humans
@article{horstmann_i_2021,
title = {I Just Wanna Blame Somebody, Not Something! Reactions to a Computer Agent Giving Negative Feedback Based on the Instructions of a Person},
author = {Aike C. Horstmann and Jonathan Gratch and Nicole C. Krämer},
url = {https://linkinghub.elsevier.com/retrieve/pii/S1071581921001014},
doi = {10.1016/j.ijhcs.2021.102683},
issn = {10715819},
year = {2021},
date = {2021-06-01},
urldate = {2021-06-18},
journal = {International Journal of Human-Computer Studies},
pages = {102683},
abstract = {Previous research focused on differences between interacting with a person-controlled avatar and a computer-controlled virtual agent. This study however examines an aspiring form of technology called agent representative which constitutes a mix of the former two interaction partner types since it is a computer agent which was previously instructed by a person to take over a task on the person’s behalf. In an experimental lab study with a 2 x 3 between-subjects-design (N = 195), people believed to study together either with an agent representative, avatar, or virtual agent. The interaction partner was described to either possess high or low expertise, while always giving negative feedback regarding the participant’s performance. Results show small but interesting differences regarding the type of agency. People attributed the most agency and blame to the person(s) behind the software and reported the most negative affect when interacting with an avatar, which was less the case for a person’s agent representative and the least for a virtual agent. Level of expertise had no significant effect and other evaluation measures were not affected.},
keywords = {DTIC, UARC, Virtual Humans},
pubstate = {published},
tppubtype = {article}
}
Previous research focused on differences between interacting with a person-controlled avatar and a computer-controlled virtual agent. This study however examines an aspiring form of technology called agent representative which constitutes a mix of the former two interaction partner types since it is a computer agent which was previously instructed by a person to take over a task on the person’s behalf. In an experimental lab study with a 2 x 3 between-subjects-design (N = 195), people believed to study together either with an agent representative, avatar, or virtual agent. The interaction partner was described to either possess high or low expertise, while always giving negative feedback regarding the participant’s performance. Results show small but interesting differences regarding the type of agency. People attributed the most agency and blame to the person(s) behind the software and reported the most negative affect when interacting with an avatar, which was less the case for a person’s agent representative and the least for a virtual agent. Level of expertise had no significant effect and other evaluation measures were not affected.
Doran, Bethany; Mei, Chaoqun; Varosy, Paul D.; Kao, David P.; Saxon, Leslie A.; Feldman, Arthur M.; DeMets, David; Bristow, Michael R.
The Addition of a Defibrillator to Resynchronization Therapy Decreases Mortality in Patients With Nonischemic Cardiomyopathy Journal Article
In: JACC: Heart Failure, vol. 9, no. 6, pp. 439–449, 2021, (Publisher: American College of Cardiology Foundation).
Links | BibTeX | Tags: MedVR, UARC
@article{doran_addition_2021,
title = {The Addition of a Defibrillator to Resynchronization Therapy Decreases Mortality in Patients With Nonischemic Cardiomyopathy},
author = {Bethany Doran and Chaoqun Mei and Paul D. Varosy and David P. Kao and Leslie A. Saxon and Arthur M. Feldman and David DeMets and Michael R. Bristow},
url = {https://www.jacc.org/doi/abs/10.1016/j.jchf.2021.02.013},
doi = {10.1016/j.jchf.2021.02.013},
year = {2021},
date = {2021-06-01},
urldate = {2023-03-31},
journal = {JACC: Heart Failure},
volume = {9},
number = {6},
pages = {439–449},
note = {Publisher: American College of Cardiology Foundation},
keywords = {MedVR, UARC},
pubstate = {published},
tppubtype = {article}
}
Greenwald, Eric; Leitner, Maxyn; Wang, Ning
Learning Artificial Intelligence: Insights into How Youth Encounter and Build Understanding of AI Concepts Journal Article
In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 17, pp. 15526–15533, 2021, ISSN: 2374-3468, (Number: 17).
Abstract | Links | BibTeX | Tags: AI, UARC
@article{greenwald_learning_2021,
title = {Learning Artificial Intelligence: Insights into How Youth Encounter and Build Understanding of AI Concepts},
author = {Eric Greenwald and Maxyn Leitner and Ning Wang},
url = {https://ojs.aaai.org/index.php/AAAI/article/view/17828},
doi = {10.1609/aaai.v35i17.17828},
issn = {2374-3468},
year = {2021},
date = {2021-05-01},
urldate = {2023-03-31},
journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
volume = {35},
number = {17},
pages = {15526–15533},
abstract = {Artificial Intelligence’s impact on society is increasingly pervasive. While innovative educational programs are being developed, there has been little understanding of how students, especially pre-college aged students, construct understanding and gain practice with core ideas about AI or what concepts are most appropriate for what age-levels. In this paper, we discuss a cognitive interview study with high school students to better understand how students learn AI concepts. We aim to shed light on questions including: what is the range of background knowledge and experiences students are able to apply in encountering AI concepts; what concepts are most readily accessible and which are more challenging; what misconceptions do students bring to bear on AI problems; and how to help students approach AI concepts by leveraging related concepts, such as mathematical and computational thinking). Results from the exploratory study have the potential to provide important insights into AI learning for pre-college youth. These initial findings can inform further investigations to ground the design of learning and assessment in evidence-based learning progressions and grade-level performance expectations.},
note = {Number: 17},
keywords = {AI, UARC},
pubstate = {published},
tppubtype = {article}
}
Artificial Intelligence’s impact on society is increasingly pervasive. While innovative educational programs are being developed, there has been little understanding of how students, especially pre-college aged students, construct understanding and gain practice with core ideas about AI or what concepts are most appropriate for what age-levels. In this paper, we discuss a cognitive interview study with high school students to better understand how students learn AI concepts. We aim to shed light on questions including: what is the range of background knowledge and experiences students are able to apply in encountering AI concepts; what concepts are most readily accessible and which are more challenging; what misconceptions do students bring to bear on AI problems; and how to help students approach AI concepts by leveraging related concepts, such as mathematical and computational thinking). Results from the exploratory study have the potential to provide important insights into AI learning for pre-college youth. These initial findings can inform further investigations to ground the design of learning and assessment in evidence-based learning progressions and grade-level performance expectations.