PSYCHOLOGICALLY  VALID AGENTS FOR PANDEMIC BEHAVIOR MODELING

(See also the ASPIRE website)

Pirolli, P., Mitsopoulos, K., Teng, C.M., Lebiere, C., Orr, M. (2023). Towards population-scale models of pandemic attitutdes and behaviors. Presentation at the Thirtieth Annual ACT-R Workshop.

Orr, M., Mortveit, H. S., Lebiere, C., & Pirolli, P. (2023). A 10-year prospectus for mathematical epidemiology [Perspective]. Frontiers in psychology, 14. 0000795881f43b1d7f48af2c825dc48527630000000085ab0000https://doi.org/10.3389/fpsyg.2023.986289

Pirolli, P., Lebiere, C., & Orr, M. (2022). A computational cognitive model of behaviors and decisions that modulate pandemic transmission: Expectancy-value, attitudes, self-efficacy, and motivational intensity. Front Psychol, 13, 981983. 0000795881f43b1d7f48af2c825dc48527630000000085ab0000https://doi.org/10.3389/fpsyg.2022.981983

Pirolli, P., Bhatia, A., Mitsopoulos, K., Lebiere, C. and Orr, M. Cognitive modeling for computational epidemiology. in 2020 International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation (SPB-BRIMS 2020), Springer, Washington, DC, 2020.

Pirolli, P., Carley, K. M., Dalton, A., Dorr, B. J., Lebiere, C., Martin, M. K., Mather, B., Mitsopoulos, K., Orr, M., & Strzalkowski, T. (2021). Mining Online Social Media to Drive Psychologically Valid Agent Models of Regional Covid-19 Mask Wearing. In R. Thomson, M. N. Hussain, C. Dancy, & A. Pyke (Eds.), Social, Cultural, and Behavioral Modeling (pp. 46-56). Springer International Publishing.

HUMAN CENTERED ARTIFICIAL INTELLIGENCE

Stefik, M, Youngblood, M, Pirolli, P, et al. Explaining autonomous drones: An XAI journey. Applied AI Letters. 2021; 2( 4):e54. doi:10.1002/ail2.54

Mitsopoulos, K., Somers, S., Schooler, J., Lebiere, C., Pirolli, P. and Thomson, R. (2021), Toward a Psychology of Deep Reinforcement Learning Agents Using a Cognitive Architecture. Top. Cogn. Sci.. https://doi.org/10.1111/tops.12573

INTELLIGENT MOBILE  HEALTH

A. Mahyari, P. Pirolli and J. A. LeBlanc, "Real-Time Learning from an Expert in Deep Recommendation Systems with Application to mHealth for Physical Exercises," in IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 8, pp. 4281-4290, Aug. 2022, doi: 10.1109/JBHI.2022.3167314.

Pirolli, P., Youngblood, G. M., Du, H., Konrad, A., Nelson, L., & Springer, A. (2018). Scaffolding the Mastery of Healthy Behaviors with Fittle+ Systems: Evidence-Based Interventions and Theory. Human–Computer Interaction, 1-34. doi:10.1080/07370024.2018.1512414

Springer A, Venkatakrishnan A, Mohan S, Nelson L, Silva M, Pirolli P

Leveraging Self-Affirmation to Improve Behavior Change: A Mobile Health App Experiment

JMIR Mhealth Uhealth 2018;6(7)

Pirolli P, Mohan S, Venkatakrishnan A, Nelson L, Silva M, Springer A

Implementation Intention and Reminder Effects on Behavior Change in a Mobile Health System: A Predictive Cognitive Model

J Med Internet Res 2017;19(11):e397

Hernandez, M., Sharit, J. Pirolli, P. & Czaja, S. (2017). Adapting Information Search Tools for use by Health Consumers: Challenges and Lessons for Software Designers. International Journal of Human Computer Interaction.

Mohan, S., Venkatakrishnan, A., Silva, M,. & Pirolli, P. (2017). On designing a social coach to promote regular aerobic exercise. IAAI 2017.

Pirolli, P. (2016). From good intentions to healthy habits: Towards integrated computational models of goal striving and habit formation. Annual International; Conference of the IEEE Engineering in Medicine and Biology Society.

Pirolli, P. (2016). A computational cognitive model of self-efficacy and daily adherence in mHealth. Translational behavioral medicine, 1-13.

Du, H., Venkatakrishnan, A., Youngblood, G. M., Ram, A., & Pirolli, P. (2016). A Group-Based Mobile Application to Increase Adherence in Exercise and Nutrition Programs: A Factorial Design Feasibility Study. JMIR Mhealth Uhealth, 4(1),

Konrad, A., Bellotti, V., Crenshaw, N., Tucker, S., Nelson, L., Du, H., Pirolli,P., & Whittaker, S. (2015). Finding the Adaptive Sweet Spot: Balancing Compliance and Achievement in Automated Stress Reduction. Paper presented at the SIGCHI Conference on Human Factors in Computing Systems (CHI 2015), Seoul, Korea.

Du, Youngblood, & Pirolli. (2014). Efficacy of a smartphone system to support groups in behavior change programs. Wireless Health 2014.

COGNITIVE MODELS OF FORAGING AND SENSEMAKING

Paik, Zhang, & Pirolli (2014). Counterfactual reasoning as a key for explaining adaptive behavior in a changing environment. BICA 2014.

Lebiere et al. (2013). A functional model of sensemaking in a neuroscience architecture. Computational Intelligence and Neuroscience.

Paik, Pirolli, Dong, Lebiere, & Thomson (2013). An ACT-R model of sensemaking in geospatial intelligence tasks. BRIMS 2013

Fu & Pirolli (2007). SNIF-ACT: A cognitive model of user navigation on the World Wide Web

SOCIAL COMPUTATIONAL SYSTEMS

Vydiswaran, Zhai, Roth, & Pirolli (2014). Overcoming bias to learn about controversial topics. Journal of the American Society for Information Science and Technology

Mangel, Satterthwaite, Pirolli, Suh, & Zhang (2013). Invasion biology and the success of social collaboration networks, with application to Wikipedia. Israel Journal of Ecology and Evolution.     

Peng, Zhang, Pirolli, & Hogg. (2012) Thermodynamic principles in social collaborations Collective Intelligence.

Liao, Q. V., Pirolli, P., & Fu, W. (2012). An ACT-R model of credibility judgment of micro-blogging Web Pages. Proceedings of the International Conference on Cognitive Modeling (ICCM 2012) (pp. 103-108). Berlin, Germany: Universitätsverlag der TU Berlin.

Chen, J., & Pirolli, P. (2012). Why You Are More Engaged: Factors Influencing Twitter Engagement in Occupy Wall Street. International AAAI Conference on Weblogs and Social Media, ICWSM 2012

Vydiswaran, Zhai, Roth, & Pirolli (2012). Unbiased learning of controversial topics. ASIST 2012.

Shneiderman, Preece, & Pirolli. Realizing the value of social media requires innovative computing research. CACM

Suh, Hong, Pirolli, & Chi (2010). Want to be retweeted? Large scale analytics on factors impacting retweet in Twitter. SocialCom 201

Fu & Pirolli (2013). Establishing the micro-to-macro link in cognitive engineering: Multi-level models of socio-computer engineering. Handbook of Cognitive Engineering

Pirolli (2007). Cognitive models of human-information interaction. Handbook of Applied Cognition (2nd Ed)

Pirolli (2007). Ch 1 of  Information Foraging Theory (uncorrected proof)

Mauro, Charles and Pirolli, Peter and Morley, Chris, A Critical Analysis of FDA Guidance for User Percentile Device Design Criteria versus Currently Available Human Factors Engineering Data Sources and Industry Best Practices (June 25, 2019). Available at SSRN: https://ssrn.com/abstract=3408117

Cavalli, E, Gilsenan, M, Van Doren, J, Grahek‐Ogden, D, Richardson, J, Abbinante, F, Cascio, C, Devalier, P, Brun, N, Linkov, I, Marchal, K, Meek, B, Pagliari, C, Pasquetto, I, Pirolli, P, Sloman, S, Tossounidis, L, Waigmann, E, Schünemann, H and Verhagen, H. (2019).  Managing evidence in food safety and nutrition. EFSA Journal, 17(S1), e170704. doi:10.2903/j.efsa.2019.e17070