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Unlimited Associative Learning as a Null Hypothesis

Bibliographic Data

ID10704103
AuthorsMarta Halina (0000-0002-3482-4281, University of Cambridge, corresponding author)
Year2022
Volume89
Issue5
Pages1186-1195
Publication date2022-12-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenuePhilosophy of Science (JOURNAL)
Journal identifiersISSN: 0031-8248 • E-ISSN: 1539-767X
PublisherCambridge University Press (CUP) (PUBLISHER)
DOI10.1017/psa.2022.66
OpenAlexW4281564995
LanguageEN
Citations received1

A common strategy in comparative cognition is to require that one reject associative learning as an explanation for behavior before concluding that an organism is capable of causal reasoning. In this paper, I argue that standard causal-reasoning tasks can be explained by a powerful form of associative learning: unlimited associative learning (UAL). The lesson, however, is not that researchers should conduct more studies to reject UAL, but that they should instead focus on 1) enriching the cognitive hypothesis space and 2) testing a broader range of information processing patterns—errors, biases and limits, rather than successful problem solving alone

Artificial neural network · Associative learning · Associative property · Cognition · Cognitive psychology · Cognitive science · Content-addressable memory · Focus (optics) · Range (aeronautics) · Space (punctuation) · Artificial Intelligence · Bayesian Modeling and Causal Inference · Child and Animal Learning Development · Cognitive Science and Mapping · Computer Science · Mathematics · Neuroscience · Psychology

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Unique citing works1
Citations per year1
Citation span2025 - 2025 (1)
Citation velocityrecent
Highly citedNo
Citation typesNeutral: 1

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