Computational Cognitive Modeling of Adaptive Choice Behavior in a Dynamic Decision Paradigm

2006 ◽  
Author(s):  
Wayne D. Gray
Keyword(s):  
Cognitive Modeling ◽  
Choice Behavior ◽  
Computational Cognitive Modeling ◽  
Adaptive Choice

scholarly journals Neural Correlates of Causal Confounding

2020 ◽  
Vol 32 (2) ◽  
pp. 301-314
Author(s):  
Mimi Liljeholm
Keyword(s):  
Cognitive Modeling ◽  
Causal Model ◽  
Neural Substrates ◽  
Neural Correlates ◽  
Learning Context ◽  
Causal Knowledge ◽  
Causal Induction ◽  
General Constraints ◽  
Computational Cognitive Modeling ◽  
Adults And Children

As scientists, we are keenly aware that if putative causes perfectly covary, the independent influence of neither can be discerned—a “no confounding” constraint on inference, fundamental to philosophical and statistical perspectives on causation. Intriguingly, a substantial behavioral literature suggests that naïve human reasoners, adults and children, are tacitly sensitive to causal confounding. Here, a combination of fMRI and computational cognitive modeling was used to investigate neural substrates mediating such sensitivity. While being scanned, participants observed and judged the influences of various putative causes with confounded or nonconfounded, deterministic or stochastic, influences. During judgments requiring generalization of causal knowledge from a feedback-based learning context to a transfer probe, activity in the dorsomedial pFC was better accounted for by a Bayesian causal model, sensitive to both confounding and stochasticity, than a purely error-driven algorithm, sensitive only to stochasticity. Implications for the detection and estimation of distinct forms of uncertainty, and for a neural mediation of domain-general constraints on causal induction, are discussed.

scholarly journals Towards Replication in Computational Cognitive Modeling: a Machine Learning Perspective

2019 ◽  
Vol 2 (3-4) ◽  
pp. 242-246 ◽  
Author(s):  
Chris Emmery ◽  
Ákos Kádár ◽  
Travis J. Wiltshire ◽  
Andrew T. Hendrickson
Keyword(s):  
Machine Learning ◽  
Cognitive Modeling ◽  
Computational Cognitive Modeling

From biophysics to cognition: reward-dependent adaptive choice behavior

2008 ◽  
Vol 18 (2) ◽  
pp. 209-216 ◽  
Author(s):  
Alireza Soltani ◽  
Xiao-Jing Wang
Keyword(s):  
Choice Behavior ◽  
Adaptive Choice

scholarly journals Empirical Aesthetics, Computational Cognitive Modeling, and Experimental Phenomenology: Methodological remarks on “Shaping and Co-Shaping Forms of Vitality in Music: Beyond Cognitivist and Emotivist Approaches to Musical Expressiveness” by Jin Hyun Kim

2013 ◽  
Vol 8 (3-4) ◽  
pp. 178
Author(s):  
Uwe Seifert
Keyword(s):  
Cognitive Modeling ◽  
Emotional Processing ◽  
Affective Computing ◽  
Human Robot Interaction ◽  
Empirical Adequacy ◽  
Robot Interaction ◽  
Experimental Phenomenology ◽  
Computational Cognitive Modeling ◽  
The Mind ◽  
Near Future

The core ideas of the proposed framework for empirical aesthetics are interpreted as focusing on processes, interaction, and phenomenological experience. This commentary first touches on some methodological impediments to developing theories of processing and interaction, and emphasizes the necessity of computational cognitive modeling using robots to test the empirical adequacy of such theories. Further, the importance of developing and integrating phenomenological methods into current experimental research is stressed, using experimental phenomenology as reference. Situated cognition, affective computing, human-robot interaction research, computational cognitive modeling and social and cultural neuroscience are noted as providing relevant insight into the empirical adequacy of current theories of cognitive and emotional processing. In the near future these fields will have a stimulating impact on empirical aesthetics and research on music and the mind.

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