Mental models, computational explanation and Bayesian cognitive science

Commentary on Knauff and Gazzo Castañeda (2023)

Bibliographic Data

ID21689057
AuthorsMike Oaksford (0000-0003-3383-3642, Birkbeck College, University of London, London, UK, corresponding author)
Year2023
Volume29
Issue3
Pages371-382
Publication date2023-07-03
Open AccessNo
TypeARTICLE
VenueThinking & Reasoning (JOURNAL)
Journal identifiersISSN: 1354-6783 • E-ISSN: 1464-0708
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/13546783.2021.2022531
OpenAlexW4206543683
LanguageEN
Citations received1
References cited46

Knauff and Gazzo Castañeda (2022) object to using the term “new paradigm” to describe recent developments in the psychology of reasoning. This paper concedes that the Kuhnian term “paradigm” may be queried. What cannot is that the work subsumed under this heading is part of a new, progressive movement that spans the brain and cognitive sciences: Bayesian cognitive science. Sampling algorithms and Bayes nets used to explain biases in JDM can implement the Bayesian new paradigm approach belying any advantages of mental models theory (MMT) at the algorithmic level. Moreover, this paper argues that new versions of MMT lack a computational level theory and questions the grounds for MMTs much-vaunted generality. The paper then examines common ground on the importance of small-scale models/simulations of the world and the importance of argumentation in the social domain rather than individual reasoning. Finally, the paper concludes that although there may be prospects for moving reasoning research forward in a more collective, collaborative manner, many disagreements remain to be resolved

Argumentation theory · Bayesian probability · Cognition · Cognitive computing · Cognitive science · Data science · Epistemology · Generality · Child and Animal Learning Development · Cognitive Science and Mapping · Computer Science · Philosophy and History of Science · Psychology · Artificial Intelligence

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