Refining the Bayesian approach to unifying generalisation
- Tenenbaum and Griffiths (\(\textit {Behavioral and Brain Sciences}\) 24(4):629–640, 2001) have proposed that their Bayesian model of generalisation unifies Shepard's (\(\it Science\) 237(4820): 1317–1323, 1987) and Tversky's (\(\textit {Psychological Review}\) 84(4): 327–352, 1977) similarity-based explanations of two distinct patterns of generalisation behaviours by reconciling them under a single coherent task analysis. I argue that this proposal needs refinement: instead of unifying the heterogeneous notion of psychological similarity, the Bayesian approach unifies generalisation by rendering the distinct patterns of behaviours informationally relevant. I suggest that generalisation as a Bayesian inference should be seen as a complement to, instead of a replacement of, similarity-based explanations. Furthermore, I show that the unificatory powers of the Bayesian model of generalisation can contribute to the selection of one of these models of psychological similarity.
Author: | Nina PothORCiDGND |
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URN: | urn:nbn:de:hbz:294-88702 |
DOI: | https://doi.org/10.1007/s13164-022-00613-5 |
Parent Title (English): | Review of philosophy and psychology |
Publisher: | Springer Netherlands |
Place of publication: | Dordrecht |
Document Type: | Article |
Language: | English |
Date of Publication (online): | 2022/04/29 |
Date of first Publication: | 2022/02/18 |
Publishing Institution: | Ruhr-Universität Bochum, Universitätsbibliothek |
Tag: | Bayesian inference; Generalisation; Mutual informational relevance; Similarity; Unification |
Volume: | 2022 |
First Page: | 1 |
Last Page: | 31 |
Institutes/Facilities: | Institut für Philosophie II |
Dewey Decimal Classification: | Philosophie und Psychologie / Philosophie |
open_access (DINI-Set): | open_access |
faculties: | Fakultät für Philosophie und Erziehungswissenschaft |
Licence (English): | Creative Commons - CC BY 4.0 - Attribution 4.0 International |