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  1. 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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  • 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 Then I argue that early formulations of these reasons are outdated.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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  • Axiomatic and Ecological Rationality: Choosing Costs and Benefits.Patricia Rich - 2016 - Erasmus Journal for Philosophy and Economics 9 (2):90.
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  • Rational Analysis, Intractability, and the Prospects of ‘as If’-Explanations.Iris van Rooij, Cory Wright, Johan Kwisthout & Todd Wareham - 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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  • 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.
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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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  • 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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  • 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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  • 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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