Publications

@article{awada_predicting_2023,

title = {Predicting Office Workers’ Productivity: A Machine Learning Approach Integrating Physiological, Behavioral, and Psychological Indicators},

author = {Mohamad Awada and Burcin Becerik-Gerber and Gale Lucas and Shawn C. Roll},

url = {https://www.mdpi.com/1424-8220/23/21/8694},

doi = {10.3390/s23218694},

issn = {1424-8220},

year  = {2023},

date = {2023-10-01},

urldate = {2023-12-07},

journal = {Sensors},

volume = {23},

number = {21},

pages = {8694},

abstract = {This research pioneers the application of a machine learning framework to predict the perceived productivity of office workers using physiological, behavioral, and psychological features. Two approaches were compared: the baseline model, predicting productivity based on physiological and behavioral characteristics, and the extended model, incorporating predictions of psychological states such as stress, eustress, distress, and mood. Various machine learning models were utilized and compared to assess their predictive accuracy for psychological states and productivity, with XGBoost emerging as the top performer. The extended model outperformed the baseline model, achieving an R2 of 0.60 and a lower MAE of 10.52, compared to the baseline model’s R2 of 0.48 and MAE of 16.62. The extended model’s feature importance analysis revealed valuable insights into the key predictors of productivity, shedding light on the role of psychological states in the prediction process. Notably, mood and eustress emerged as significant predictors of productivity. Physiological and behavioral features, including skin temperature, electrodermal activity, facial movements, and wrist acceleration, were also identified. Lastly, a comparative analysis revealed that wearable devices (Empatica E4 and H10 Polar) outperformed workstation addons (Kinect camera and computer-usage monitoring application) in predicting productivity, emphasizing the potential utility of wearable devices as an independent tool for assessment of productivity. Implementing the model within smart workstations allows for adaptable environments that boost productivity and overall well-being among office workers.},

keywords = {DTIC, Machine Learning, UARC, Virtual Humans},

pubstate = {published},

tppubtype = {article}

}

@article{seyedrezaei_interaction_2023,

title = {Interaction effects of indoor environmental quality factors on cognitive performance and perceived comfort of young adults in open plan offices in North American Mediterranean climate},

author = {Mirmahdi Seyedrezaei and Mohamad Awada and Burcin Becerik-Gerber and Gale Lucas and Shawn Roll},

url = {https://linkinghub.elsevier.com/retrieve/pii/S0360132323007709},

doi = {10.1016/j.buildenv.2023.110743},

issn = {03601323},

year  = {2023},

date = {2023-10-01},

urldate = {2023-09-20},

journal = {Building and Environment},

volume = {244},

pages = {110743},

keywords = {DTIC, UARC, Virtual Humans},

pubstate = {published},

tppubtype = {article}

}

@article{mozgai_machine_2023,

title = {Machine learning for semi-automated scoping reviews},

author = {Sharon Mozgai and Cari Kaurloto and Jade Winn and Andrew Leeds and Dirk Heylen and Arno Hartholt and Stefan Scherer},

url = {https://linkinghub.elsevier.com/retrieve/pii/S2667305323000741},

doi = {10.1016/j.iswa.2023.200249},

issn = {26673053},

year  = {2023},

date = {2023-09-01},

urldate = {2023-08-23},

journal = {Intelligent Systems with Applications},

volume = {19},

pages = {200249},

keywords = {DTIC, UARC, VHTL, Virtual Humans},

pubstate = {published},

tppubtype = {article}

}

@article{kappas_these_2023,

title = {These Aren’t The Droids You Are Looking for: Promises and Challenges for the Intersection of Affective Science and Robotics/AI},

author = {Arvid Kappas and Jonathan Gratch},

url = {https://link.springer.com/10.1007/s42761-023-00211-3},

doi = {10.1007/s42761-023-00211-3},

issn = {2662-2041, 2662-205X},

year  = {2023},

date = {2023-08-01},

urldate = {2023-09-20},

journal = {Affective Science},

abstract = {Abstract 

 AI research focused on interactions with humans, particularly in the form of robots or virtual agents, has expanded in the last two decades to include concepts related to affective processes. Affective computing is an emerging field that deals with issues such as how the diagnosis of affective states of users can be used to improve such interactions, also with a view to demonstrate affective behavior towards the user. This type of research often is based on two beliefs: (1) artificial emotional intelligence will improve human computer interaction (or more specifically human robot interaction), and (2) we understand the role of affective behavior in human interaction sufficiently to tell artificial systems what to do. However, within affective science the focus of research is often to test a particular assumption, such as “smiles affect liking.” Such focus does not provide the information necessary to synthesize affective behavior in long dynamic and real-time interactions. In consequence, theories do not play a large role in the development of artificial affective systems by engineers, but self-learning systems develop their behavior out of large corpora of recorded interactions. The status quo is characterized by measurement issues, theoretical lacunae regarding prevalence and functions of affective behavior in interaction, and underpowered studies that cannot provide the solid empirical foundation for further theoretical developments. This contribution will highlight some of these challenges and point towards next steps to create a rapprochement between engineers and affective scientists with a view to improving theory and solid applications.},

keywords = {DTIC, UARC, Virtual Humans},

pubstate = {published},

tppubtype = {article}

}

@article{liu_effectiveness_2023,

title = {Effectiveness of VR-based training on improving occupants’ response and preparedness for active shooter incidents},

author = {Ruying Liu and Burcin Becerik-Gerber and Gale M. Lucas},

url = {https://linkinghub.elsevier.com/retrieve/pii/S0925753523001170},

doi = {10.1016/j.ssci.2023.106175},

issn = {09257535},

year  = {2023},

date = {2023-08-01},

urldate = {2023-08-22},

journal = {Safety Science},

volume = {164},

pages = {106175},

keywords = {DTIC, Simulation, UARC, virtual reality},

pubstate = {published},

tppubtype = {article}

}

@article{gurney_role_2023,

title = {The Role of Heuristics and Biases during Complex Choices with an AI Teammate},

author = {Nikolos Gurney and John H. Miller and David V. Pynadath},

url = {https://ojs.aaai.org/index.php/AAAI/article/view/25741},

doi = {10.1609/aaai.v37i5.25741},

issn = {2374-3468, 2159-5399},

year  = {2023},

date = {2023-06-01},

urldate = {2023-12-08},

journal = {Proceedings of the AAAI Conference on Artificial Intelligence},

volume = {37},

number = {5},

pages = {5993–6001},

abstract = {Behavioral scientists have classically documented aversion to algorithmic decision aids, from simple linear models to AI. Sentiment, however, is changing and possibly accelerating AI helper usage. AI assistance is, arguably, most valuable when humans must make complex choices. We argue that classic experimental methods used to study heuristics and biases are insufficient for studying complex choices made with AI helpers. We adapted an experimental paradigm designed for studying complex choices in such contexts. We show that framing and anchoring effects impact how people work with an AI helper and are predictive of choice outcomes. The evidence suggests that some participants, particularly those in a loss frame, put too much faith in the AI helper and experienced worse choice outcomes by doing so. The paradigm also generates computational modeling-friendly data allowing future studies of human-AI decision making.},

keywords = {AI, DTIC, Social Simulation, UARC},

pubstate = {published},

tppubtype = {article}

}

@article{leitner_designing_2023,

title = {Designing Game-Based Learning for High School Artificial Intelligence Education},

author = {Maxyn Leitner and Eric Greenwald and Ning Wang and Ryan Montgomery and Chirag Merchant},

url = {https://link.springer.com/10.1007/s40593-022-00327-w},

doi = {10.1007/s40593-022-00327-w},

issn = {1560-4292, 1560-4306},

year  = {2023},

date = {2023-06-01},

urldate = {2023-09-20},

journal = {International Journal of Artificial Intelligence in Education},

volume = {33},

number = {2},

pages = {384–398},

abstract = {Abstract 

 Artificial Intelligence (AI) permeates every aspect of our daily lives and is no longer a subject reserved for a select few in higher education but is essential knowledge that our youth need for the future. Much is unknown about the level of AI knowledge that is age and developmentally appropriate for high school, let alone about how to teach AI to even younger learners. In this theoretical paper, we discuss the design of a game-based learning environment for high school AI education, drawing upon insights gained from a prior cognitive interview study at a STEM focused private high school. We argue that game-based learning is an excellent fit for AI education due to the commonality of problem solving in both game playing and AI.},

keywords = {AI, Virtual Humans},

pubstate = {published},

tppubtype = {article}

}

@article{rodrigues_multidimensional_2023,

title = {A multidimensional taxonomy for human-robot interaction in construction},

author = {Patrick B. Rodrigues and Rashmi Singh and Mert Oytun and Pooya Adami and Peter J. Woods and Burcin Becerik-Gerber and Lucio Soibelman and Yasemin Copur-Gencturk and Gale M. Lucas},

url = {https://www.sciencedirect.com/science/article/pii/S092658052300105X},

doi = {10.1016/j.autcon.2023.104845},

issn = {0926-5805},

year  = {2023},

date = {2023-06-01},

urldate = {2023-03-31},

journal = {Automation in Construction},

volume = {150},

pages = {104845},

abstract = {Despite the increased interest in construction robotics both in academia and the industry, insufficient attention has been given to aspects related to Human-Robot Interaction (HRI). Characterizing HRI for construction tasks can help researchers organize knowledge in a structured manner that allows for classifying construction robotics applications and comparing and benchmarking different studies. This paper builds upon existing taxonomies and empirical studies in HRI in various industries (e.g., construction, manufacturing, and military, among others) to propose a multidimensional taxonomy to characterize HRI applications in the construction industry. The taxonomy design followed a systematic literature review in which common themes were identified and grouped into 16 categories. The proposed taxonomy can be used as a foundation for systematic reviews and meta-analyses of HRI applications in construction and can benefit the construction industry by informing the design of collaborative tasks performed by human-robot teams.},

keywords = {DTIC, UARC, Virtual Humans},

pubstate = {published},

tppubtype = {article}

}

@article{aris_learning_2023,

title = {Learning to Take Cover with Navigation-Based Waypoints via Reinforcement Learning},

author = {Timothy Aris and Volkan Ustun and Rajay Kumar},

url = {https://journals.flvc.org/FLAIRS/article/view/133348},

doi = {10.32473/flairs.36.133348},

issn = {2334-0762},

year  = {2023},

date = {2023-05-01},

urldate = {2023-08-04},

journal = {The International FLAIRS Conference Proceedings},

volume = {36},

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. This work expands on previous work on raycast-based agents by increasing the number of enemies from one to three. We demonstrate an automated way of generating training and testing data within geo-specific terrains. We show that replacing the action space with a more abstracted, navmesh-based waypoint movement system can increase the generality and success rate of the models while providing similar results to our previous paper's results regarding retraining across terrains. We also comprehensively evaluate the differences between these and the previous models. Finally, we show that incorporating pixels into the model's input can increase performance at the cost of longer training times.},

keywords = {CogArch, Cognitive Architecture, DTIC, UARC, Virtual Humans},

pubstate = {published},

tppubtype = {article}

}

@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 = {The International FLAIRS Conference Proceedings},

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 = {CogArch, Cognitive Architecture, DTIC, UARC},

pubstate = {published},

tppubtype = {article}

}

@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 = {The International FLAIRS Conference Proceedings},

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 = {DTIC, Natural Language, UARC},

pubstate = {published},

tppubtype = {article}

}

@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 = {DTIC, MedVR},

pubstate = {published},

tppubtype = {article}

}

@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 = {MedVR, UARC},

pubstate = {published},

tppubtype = {article}

}

@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 = {Journal of 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 = {DTIC, Simulation, UARC},

pubstate = {published},

tppubtype = {article}

}

@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 = {DTIC, UARC, Virtual Humans},

pubstate = {published},

tppubtype = {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 = {DTIC, MedVR, UARC},

pubstate = {published},

tppubtype = {article}

}

@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 = {Computational and Mathematical Organization 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 = {DTIC, Social Simulation, UARC},

pubstate = {published},

tppubtype = {article}

}

@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 = {DTIC, UARC, Virtual Humans},

pubstate = {published},

tppubtype = {article}

}

@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 = {DTIC, UARC, Virtual Humans},

pubstate = {published},

tppubtype = {article}

}

@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 = {DTIC, MedVR, UARC},

pubstate = {published},

tppubtype = {article}

}

@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 Transactions on Affective Computing},

pages = {1–15},

keywords = {DTIC, Machine Learning, UARC},

pubstate = {published},

tppubtype = {article}

}

@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 = {DTIC, UARC, Virtual Humans},

pubstate = {published},

tppubtype = {article}

}

@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 Transactions on Affective Computing},

pages = {1–14},

keywords = {DTIC, UARC, Virtual Humans},

pubstate = {published},

tppubtype = {article}

}

@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 = {Proceedings of the IEEE},

pages = {1–16},

keywords = {DTIC, UARC, Virtual Humans},

pubstate = {published},

tppubtype = {article}

}

@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 = {DTIC, UARC, Virtual Humans},

pubstate = {published},

tppubtype = {article}

}

@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 = {DTIC, UARC, Virtual Humans},

pubstate = {published},

tppubtype = {article}

}

@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 = {DTIC, UARC, VGL},

pubstate = {published},

tppubtype = {article}

}