Literature DB >> 35710303

Subsampling of cues in associative learning.

Omar D Perez1,2,3, Edgar H Vogel4,5,6, Sanjay Narasiwodeyar7, Fabian A Soto7.   

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.
© 2022 Perez et al.; Published by Cold Spring Harbor Laboratory Press.

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Year:  2022        PMID: 35710303      PMCID: PMC9291202          DOI: 10.1101/lm.053602.122

Source DB:  PubMed          Journal:  Learn Mem        ISSN: 1072-0502            Impact factor:   2.699


  29 in total

1.  Spatial separation of target and competitor cues enhances blocking of human causality judgements.

Authors:  Steven Glautier
Journal:  Q J Exp Psychol B       Date:  2002-04

2.  Beyond the F test: Effect size confidence intervals and tests of close fit in the analysis of variance and contrast analysis.

Authors:  James H Steiger
Journal:  Psychol Methods       Date:  2004-06

3.  Elemental representations of stimuli in associative learning.

Authors:  Justin A Harris
Journal:  Psychol Rev       Date:  2006-07       Impact factor: 8.934

4.  View-invariance learning in object recognition by pigeons depends on error-driven associative learning processes.

Authors:  Fabian A Soto; Jeffrey Y M Siow; Edward A Wasserman
Journal:  Vision Res       Date:  2012-04-17       Impact factor: 1.886

5.  How the associative strengths of stimuli combine in compound: summation and overshadowing.

Authors:  Thida Thein; R Frederick Westbrook; Justin A Harris
Journal:  J Exp Psychol Anim Behav Process       Date:  2008-01

6.  Convergent validation of information processing constructs with Pavlovian methodology.

Authors:  Harald Lachnit
Journal:  J Exp Psychol Hum Percept Perform       Date:  1988-02       Impact factor: 3.332

7.  Explaining compound generalization in associative and causal learning through rational principles of dimensional generalization.

Authors:  Fabian A Soto; Samuel J Gershman; Yael Niv
Journal:  Psychol Rev       Date:  2014-07       Impact factor: 8.934

8.  Generality of the summation effect in human causal learning.

Authors:  Fabian A Soto; Edgar H Vogel; Ramón D Castillo; Allan R Wagner
Journal:  Q J Exp Psychol (Hove)       Date:  2008-12-01       Impact factor: 2.143

9.  Peck tracking: a method for localizing critical features within complex pictures for pigeons.

Authors:  Lars Dittrich; Jonas Rose; Jens-Uwe Frank Buschmann; Morgane Bourdonnais; Onur Güntürkün
Journal:  Anim Cogn       Date:  2009-06-26       Impact factor: 3.084

10.  PsychoPy--Psychophysics software in Python.

Authors:  Jonathan W Peirce
Journal:  J Neurosci Methods       Date:  2007-01-23       Impact factor: 2.390

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