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Subsampling of cues in associative learning

Pérez, Omar D. and Vogel, Edgar H. and Narasiwodeyar, Sanjay and Soto, Fabián A. (2022) Subsampling of cues in associative learning. Learning & Memory, 29 (7). pp. 160-170. ISSN 1072-0502. doi:10.1101/lm.053602.122. https://resolver.caltech.edu/CaltechAUTHORS:20190724-154336969

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Abstract

Theories of learning distinguish between elemental and configural stimulus processing depending on whether stimuli are processed independently or as whole configurations. Evidence for elemental processing comes from findings of summation in animals where a compound of two dissimilar stimuli is deemed to be more predictive than each stimulus alone, whereas configural processing is supported by experiments using similar stimuli in which summation is not found. However, in humans the summation effect is robust and impervious to similarity manipulations. In three experiments in human predictive learning, we show that summation can be obliterated when partially reinforced cues are added to the summands in training and tests. This lack of summation only holds when the partially reinforced cues are similar to the reinforced cues (experiment 1) and seems to depend on participants sampling only the most salient cue in each trial (experiments 2a and 2b) in a sequential visual search process. Instead of attributing our and others’ instances of lack of summation to the customary idea of configural processing, we offer a formal subsampling rule that might be applied to situations in which the stimuli are hard to parse from each other.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1101/lm.053602.122DOIArticle
https://doi.org/10.1101/713420DOIDiscussion Paper
https://osf.io/xqnpkRelated ItemData
ORCID:
AuthorORCID
Pérez, Omar D.0000-0002-4168-5435
Vogel, Edgar H.0000-0001-6999-6991
Alternate Title:Visual search mimics configural processing in associative learning, Visual search mimics configural processing in human causal learning, Sub-sampling of cues in associative learning
Additional Information:© 2022 Perez et al.; Published by Cold Spring Harbor Laboratory Press. This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the first 12 months after the full-issue publication date (see http://learnmem.cshlp.org/site/misc/terms.xhtml). After 12 months, it is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/. Received April 21, 2022. Accepted April 29, 2022. We thank Jaron Colas, Tomislav Zbozinek, and Tony Dickinson for their valuable comments on a previous version of this manuscript. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. This work was supported by grant from Fondecyt N 1210719 to E.H.V. Data deposition: The data and materials for all experiments are available at https://osf.io/xqnpk.
Funders:
Funding AgencyGrant Number
Fondo Nacional de Desarrollo Científico y Tecnológico (FONDECYT)1210719
Issue or Number:7
DOI:10.1101/lm.053602.122
Record Number:CaltechAUTHORS:20190724-154336969
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190724-154336969
Official Citation:Subsampling of cues in associative learning Omar D. Perez, Edgar H. Vogel, Sanjay Narasiwodeyar, and Fabian A. Soto Learn. Mem. July 2022 29: 160-170; Published Online June 16, 2022, doi:10.1101/lm.053602.122
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:97393
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:24 Jul 2019 22:50
Last Modified:30 Jun 2022 17:08

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