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  1. Models and Mechanisms in Psychological Explanation.Daniel A. Weiskopf - 2011 - Synthese 183 (3):313-338.
    Mechanistic explanation has an impressive track record of advancing our understanding of complex, hierarchically organized physical systems, particularly biological and neural systems. But not every complex system can be understood mechanistically. Psychological capacities are often understood by providing cognitive models of the systems that underlie them. I argue that these models, while superficially similar to mechanistic models, in fact have a substantially more complex relation to the real underlying system. They are typically constructed using a range of techniques for abstracting (...)
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  • Category Coherence and Category-Based Property Induction.Bob Rehder & Reid Hastie - 2004 - Cognition 91 (2):113-153.
  • Predicting Human Cooperation in the Prisoner’s Dilemma Using Case-Based Decision Theory.Todd Guilfoos & Andreas Duus Pape - 2016 - Theory and Decision 80 (1):1-32.
  • Categorization as Causal Reasoning⋆.Bob Rehder - 2003 - Cognitive Science 27 (5):709-748.
    A theory of categorization is presented in which knowledge of causal relationships between category features is represented in terms of asymmetric and probabilistic causal mechanisms. According to causal‐model theory, objects are classified as category members to the extent they are likely to have been generated or produced by those mechanisms. The empirical results confirmed that participants rated exemplars good category members to the extent their features manifested the expectations that causal knowledge induces, such as correlations between feature pairs that are (...)
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  • An Evolutionary Analysis of Learned Attention.Richard A. Hullinger, John K. Kruschke & Peter M. Todd - 2015 - Cognitive Science 39 (6):1172-1215.
    Humans and many other species selectively attend to stimuli or stimulus dimensions—but why should an animal constrain information input in this way? To investigate the adaptive functions of attention, we used a genetic algorithm to evolve simple connectionist networks that had to make categorization decisions in a variety of environmental structures. The results of these simulations show that while learned attention is not universally adaptive, its benefit is not restricted to the reduction of input complexity in order to keep it (...)
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