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Sub-sampling of cues in associative learning

Pérez, Omar D. and Vogel, Edgar H. and Narasiwodeyar, Sanjay and Soto, Fabián A. (2019) Sub-sampling of cues in associative learning. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20190724-154336969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 employing 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 test. 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 other’s instances of lack of summation to the customary idea of configural processing, we offer a formal sub-sampling rule that might be applied to situations in which the stimuli are hard to parse from each other.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
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
Additional Information:The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license. Version 1 - July 24, 2019; Version 2 - August 13, 2019; Version 3 - February 29, 2020; Version 4 - September 24, 2021; Version 5 - December 25, 2021; Version 6 - April 29, 2022. We thank Jaron Colas, Tomislav Zbozinek and Tony Dickinson for their valuable comments on a previous version of this manuscript. S.D.G. Open Practices Statement: The data and materials for all experiments are available at https://osf.io/xqnpk/. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Subject Keywords:generalization, Rescorla-Wagner, configural, summation, elemental
DOI:10.1101/713420
Record Number:CaltechAUTHORS:20190724-154336969
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190724-154336969
Official Citation:Sub-sampling of cues in associative learning. Omar D. Perez, Edgar H. Vogel, Sanjay Narasiwodeyar, Fabian A. Soto. bioRxiv 713420; doi: https://doi.org/10.1101/713420
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:10 May 2022 16:10

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