Challenges for a Computational Cognitive Psychology for the New Digital Ecosystem

Advances in computational cognitive psychology have played an important role in understanding and engineering human–information interaction systems. These computational models include several addressing the cognition involved in the human sensemaking process, user models that capture the knowledge that humans acquire from interaction, and how people judge the credibility of online Twitter users who influence decision-making. The models presented in this chapter build on earlier information foraging models in which it is important to model individual-level knowledge and experience because these clearly influence human–information interaction processes. This chapter concludes with a discussion of challenges to computational cognitive models as digital information interaction becomes increasingly pervasive and complex.

[1]  Cleotilde Gonzalez,et al.  Instance-based learning in dynamic decision making , 2003 .

[2]  Kevin Robert Canini,et al.  Finding Credible Information Sources in Social Networks Based on Content and Social Structure , 2011, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing.

[3]  G. Michael Youngblood,et al.  Scaffolding the Mastery of Healthy Behaviors with Fittle+ Systems: Evidence-Based Interventions and Theory , 2018, Hum. Comput. Interact..

[4]  Dan Roth,et al.  BiasTrust: teaching biased users about controversial topics , 2012, CIKM '12.

[5]  Yvonne Kammerer,et al.  Signpost from the masses: learning effects in an exploratory social tag search browser , 2009, CHI.

[6]  Stuart K. Card,et al.  The cost structure of sensemaking , 1993, INTERCHI.

[7]  A. Newell Unified Theories of Cognition , 1990 .

[8]  Mohamad Noorman Masrek,et al.  Website credibility and user engagement: A theoretical integration , 2016, 2016 4th International Conference on User Science and Engineering (i-USEr).

[9]  Michael H. Birnbaum,et al.  Source Credibility in Social Judgment: Bias, Expertise, and the Judge's Point of View , 1979 .

[10]  Dan Roth,et al.  Unbiased learning of controversial topics , 2012, ASIST.

[11]  Ryen W. White,et al.  Supporting exploratory search , 2006 .

[12]  A. Tversky,et al.  Judgment under Uncertainty: Heuristics and Biases , 1974, Science.

[13]  John K. Tsotsos,et al.  40 years of cognitive architectures: core cognitive abilities and practical applications , 2018, Artificial Intelligence Review.

[14]  Mark Steyvers,et al.  Topics in semantic representation. , 2007, Psychological review.

[15]  R. Nickerson Confirmation Bias: A Ubiquitous Phenomenon in Many Guises , 1998 .

[16]  John R. Anderson,et al.  How Can the Human Mind Occur , 2007 .

[17]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[18]  Peter Pirolli,et al.  Cognitive Models of Human–Information Interaction , 2008 .

[19]  John E. Laird,et al.  A Standard Model of the Mind: Toward a Common Computational Framework across Artificial Intelligence, Cognitive Science, Neuroscience, and Robotics , 2017, AI Mag..

[20]  Ambuj Tewari,et al.  Microrandomized trials: An experimental design for developing just-in-time adaptive interventions. , 2015, Health psychology : official journal of the Division of Health Psychology, American Psychological Association.

[21]  Gary Klein,et al.  Making Sense of Sensemaking 2: A Macrocognitive Model , 2006, IEEE Intelligent Systems.

[22]  Peter Pirolli,et al.  A knowledge-tracing model of learning from a social tagging system , 2013, User Modeling and User-Adapted Interaction.

[23]  K. Vohs,et al.  Psychology as the Science of Self-reports and Finger Movements Whatever Happened to Actual Behavior? , 2022 .

[24]  Marti A. Hearst,et al.  Scatter/gather browsing communicates the topic structure of a very large text collection , 1996, CHI.

[25]  H. Kelley,et al.  Communication And Persuasion , 1953 .

[26]  Ed H. Chi,et al.  With a little help from my friends: examining the impact of social annotations in sensemaking tasks , 2009, CHI.

[27]  Paula J. Durlach,et al.  Open Social Student Modeling for Personalized Learning , 2016, IEEE Transactions on Emerging Topics in Computing.

[28]  Christopher Olston,et al.  ScentTrails: Integrating browsing and searching on the Web , 2003, TCHI.

[29]  John R. Anderson,et al.  A Functional Model of Sensemaking in a Neurocognitive Architecture , 2013, Comput. Intell. Neurosci..

[30]  Daniel M. Russell,et al.  Introduction to this Special Issue on Sensemaking , 2011, Hum. Comput. Interact..

[31]  S. Murphy,et al.  The multiphase optimization strategy (MOST) and the sequential multiple assignment randomized trial (SMART): new methods for more potent eHealth interventions. , 2007, American journal of preventive medicine.

[32]  Gary Marchionini,et al.  Exploratory search , 2006, Commun. ACM.

[33]  Allison Woodruff,et al.  Popout prism: adding perceptual principles to overview+detail document interfaces , 2002, CHI.

[34]  Gary Klein,et al.  Making Sense of Sensemaking 1: Alternative Perspectives , 2006, IEEE Intelligent Systems.

[35]  John R. Anderson How Can the Human Mind Occur in the Physical Universe , 2007 .

[36]  P. Pirolli Information Foraging Theory: Adaptive Interaction with Information , 2007 .

[37]  Christian Lebiere,et al.  Multi-scale resolution of neural, cognitive and social systems , 2019, Computational and Mathematical Organization Theory.

[38]  Michael E. Atwood,et al.  Project Ernestine: Validating a GOMS Analysis for Predicting and Explaining Real-World Task Performance , 1993, Hum. Comput. Interact..

[39]  Marti A. Hearst Search User Interfaces , 2009 .