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Modeling relational responding with artificial neural networks
Stockholm University, Faculty of Humanities, Department of Archaeology and Classical Studies, Centre for Cultural Evolution. CUNY Graduate Center, USA; Brooklyn College, USA.ORCID iD: 0000-0002-7270-9612
Number of Authors: 22023 (English)In: Behavioural Processes, ISSN 0376-6357, E-ISSN 1872-8308, Vol. 205, article id 104816Article in journal (Refereed) Published
Abstract [en]

Relational responding refers to behavior that conforms to a rule for com- paring stimuli. Lazareva et al. (2014) trained pigeons to choose either the smaller or the larger of two circles, using 1–3 pairs of circles for training and 17–19 new pairs for testing. The pigeons showed relational responding on some test pairs and systematic failures on others. We present a simple artificial neural network model that reproduces the animals’ behavior well, similarly to Lazareva et al.’s (2014) statistical model based on stimulus features and stimulus relationships. We analyze how the network model gener- alizes from training to test stimuli, and show that it can reconcile contrasting ideas about relational responding from the seminal works by Köhler (1929, 1918/1938, 1924), positing that animals can learn relational rules such as “choose the larger stimulus,” and Spence (1937), positing that relational re- sponding can be explained based on stimulus generalization.

Place, publisher, year, edition, pages
2023. Vol. 205, article id 104816
Keywords [en]
Relational cognition, Stimulus generalization, Computational modeling, Artificial neural networks
National Category
Neurosciences
Identifiers
URN: urn:nbn:se:su:diva-216907DOI: 10.1016/j.beproc.2022.104816ISI: 000960815800001PubMedID: 36584963Scopus ID: 2-s2.0-85145964470OAI: oai:DiVA.org:su-216907DiVA, id: diva2:1756759
Available from: 2023-05-15 Created: 2023-05-15 Last updated: 2023-10-23Bibliographically approved

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Ghirlanda, Stefano

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