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  1. Rational analysis, intractability, and the prospects of ‘as if’-explanations.Iris van Rooij, Johan Kwisthout, Todd Wareham & Cory Wright - 2018 - Synthese 195 (2):491-510.
    Despite their success in describing and predicting cognitive behavior, the plausibility of so-called ‘rational explanations’ is often contested on the grounds of computational intractability. Several cognitive scientists have argued that such intractability is an orthogonal pseudoproblem, however, since rational explanations account for the ‘why’ of cognition but are agnostic about the ‘how’. Their central premise is that humans do not actually perform the rational calculations posited by their models, but only act as if they do. Whether or not the problem (...)
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  • Self-Organization Takes Time Too.Iris van Rooij - 2012 - Topics in Cognitive Science 4 (1):63-71.
    Four articles in this issue of topiCS (volume 4, issue 1) argue against a computational approach in cognitive science in favor of a dynamical approach. I concur that the computational approach faces some considerable explanatory challenges. Yet the dynamicists’ proposal that cognition is self-organized seems to only go so far in addressing these challenges. Take, for instance, the hypothesis that cognitive behavior emerges when brain and body (re-)configure to satisfy task and environmental constraints. It is known that for certain systems (...)
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  • Complexity Level Analysis Revisited: What Can 30 Years of Hindsight Tell Us about How the Brain Might Represent Visual Information?John K. Tsotsos - 2017 - Frontiers in Psychology 8.
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  • How Intractability Spans the Cognitive and Evolutionary Levels of Explanation.Patricia Rich, Mark Blokpoel, Ronald de Haan & Iris van Rooij - 2020 - Topics in Cognitive Science 12 (4):1382-1402.
    This paper focuses on the cognitive/computational and evolutionary levels. It describes three proposals to make cognition computationally tractable, namely: Resource Rationality, the Adaptive Toolbox and Massive Modularity. While each of these proposals appeals to evolutionary considerations to dissolve the intractability of cognition, Rich, Blokpoel, de Haan, and van Rooij argue that, in each case, the intractability challenge is not resolved, but just relocated to the level of evolution.
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  • How Intractability Spans the Cognitive and Evolutionary Levels of Explanation.Patricia Rich, Mark Blokpoel, Ronald Haan & Iris Rooij - 2020 - Topics in Cognitive Science 12 (4):1382-1402.
    This paper focuses on the cognitive/computational and evolutionary levels. It describes three proposals to make cognition computationally tractable, namely: Resource Rationality, the Adaptive Toolbox and Massive Modularity. While each of these proposals appeals to evolutionary considerations to dissolve the intractability of cognition, Rich, Blokpoel, de Haan, and van Rooij argue that, in each case, the intractability challenge is not resolved, but just relocated to the level of evolution.
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  • Axiomatic and ecological rationality: choosing costs and benefits.Patricia Rich - 2016 - Erasmus Journal for Philosophy and Economics 9 (2):90.
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  • The Computational Challenges of Means Selection Problems: Network Structure of Goal Systems Predicts Human Performance.Daniel Reichman, Falk Lieder, David D. Bourgin, Nimrod Talmon & Thomas L. Griffiths - 2023 - Cognitive Science 47 (8):e13330.
    We study human performance in two classical NP‐hard optimization problems: Set Cover and Maximum Coverage. We suggest that Set Cover and Max Coverage are related to means selection problems that arise in human problem‐solving and in pursuing multiple goals: The relationship between goals and means is expressed as a bipartite graph where edges between means and goals indicate which means can be used to achieve which goals. While these problems are believed to be computationally intractable in general, they become more (...)
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  • Demons of Ecological Rationality.Maria Otworowska, Mark Blokpoel, Marieke Sweers, Todd Wareham & Iris van Rooij - 2018 - Cognitive Science 42 (3):1057-1066.
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  • Demons of Ecological Rationality.Maria Otworowska, Mark Blokpoel, Marieke Sweers, Todd Wareham & Iris Rooij - 2018 - Cognitive Science 42 (3):1057-1066.
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  • From Computer Metaphor to Computational Modeling: The Evolution of Computationalism.Marcin Miłkowski - 2018 - Minds and Machines 28 (3):515-541.
    In this paper, I argue that computationalism is a progressive research tradition. Its metaphysical assumptions are that nervous systems are computational, and that information processing is necessary for cognition to occur. First, the primary reasons why information processing should explain cognition are reviewed. Then I argue that early formulations of these reasons are outdated. However, by relying on the mechanistic account of physical computation, they can be recast in a compelling way. Next, I contrast two computational models of working memory (...)
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  • Rational Foundations of Fast and Frugal Heuristics: The Ecological Rationality of Strategy Selection via Improper Linear Models.Jason Dana & Clintin P. Davis-Stober - 2016 - Minds and Machines 26 (1-2):61-86.
    Research on “improper” linear models has shown that predetermined weighting schemes for the linear model, such as equally weighting all predictors, can be surprisingly accurate on cross-validation. We review recent advances that can characterize the optimal choice of an improper linear model. We extend this research to the understanding of fast and frugal heuristics, particularly to the ecologically rational goal of understanding in which task environments given heuristics are optimal. We demonstrate how to test this model using the Recognition Heuristic (...)
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  • Sculpting Computational‐Level Models.Mark Blokpoel - 2018 - Topics in Cognitive Science 10 (3):641-648.
    In this commentary, I advocate for strict relations between Marr's levels of analysis. Under a strict relationship, each level is exactly implemented by the subordinate level. This yields two benefits. First, it brings consistency for multilevel explanations. Second, similar to how a sculptor chisels away superfluous marble, a modeler can chisel a computational-level model by applying constraints. By sculpting the model, one restricts the set of possible algorithmic- and implementational-level theories.
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