Literature DB >> 29023832

Pupil dilation indicates the coding of past prediction errors: Evidence for attentional learning theory.

Stephan Koenig1, Metin Uengoer1, Harald Lachnit1.   

Abstract

The attentional learning theory of Pearce and Hall () predicts more attention to uncertain cues that have caused a high prediction error in the past. We examined how the cue-elicited pupil dilation during associative learning was linked to such error-driven attentional processes. In three experiments, participants were trained to acquire associations between different cues and their appetitive (Experiment 1), motor (Experiment 2), or aversive (Experiment 3) outcomes. All experiments were designed to examine differences in the processing of continuously reinforced cues (consistently followed by the outcome) versus partially reinforced, uncertain cues (randomly followed by the outcome). We measured the pupil dilation elicited by the cues in anticipation of the outcome and analyzed how this conditioned pupil response changed over the course of learning. In all experiments, changes in pupil size complied with the same basic pattern: During early learning, consistently reinforced cues elicited greater pupil dilation than uncertain, randomly reinforced cues, but this effect gradually reversed to yield a greater pupil dilation for uncertain cues toward the end of learning. The pattern of data accords with the changes in prediction error and error-driven attention formalized by the Pearce-Hall theory.
© 2017 The Authors. Psychophysiology published by Wiley Periodicals, Inc. on behalf of Society for Psychophysiological Research.

Entities:  

Keywords:  associative learning; attention; human fear conditioning; pupil dilation; reward

Mesh:

Year:  2017        PMID: 29023832     DOI: 10.1111/psyp.13020

Source DB:  PubMed          Journal:  Psychophysiology        ISSN: 0048-5772            Impact factor:   4.016


  8 in total

Review 1.  Best Practices and Advice for Using Pupillometry to Measure Listening Effort: An Introduction for Those Who Want to Get Started.

Authors:  Matthew B Winn; Dorothea Wendt; Thomas Koelewijn; Stefanie E Kuchinsky
Journal:  Trends Hear       Date:  2018 Jan-Dec       Impact factor: 3.293

2.  Opposing Timing Constraints Severely Limit the Use of Pupillometry to Investigate Visual Statistical Learning.

Authors:  Felicia Zhang; Lauren L Emberson
Journal:  Front Psychol       Date:  2019-08-06

3.  Cortical modulation of pupillary function: systematic review.

Authors:  Costanza Peinkhofer; Daniel Kondziella; Gitte M Knudsen; Rita Moretti
Journal:  PeerJ       Date:  2019-05-07       Impact factor: 2.984

4.  Pupillometric investigation into the speed-accuracy trade-off in a visuo-motor aiming task.

Authors:  Marnix Naber; Peter Murphy
Journal:  Psychophysiology       Date:  2019-11-17       Impact factor: 4.016

5.  Pupil dilation and response slowing distinguish deliberate explorative choices in the probabilistic learning task.

Authors:  Galina L Kozunova; Ksenia E Sayfulina; Andrey O Prokofyev; Vladimir A Medvedev; Anna M Rytikova; Tatiana A Stroganova; Boris V Chernyshev
Journal:  Cogn Affect Behav Neurosci       Date:  2022-04-01       Impact factor: 3.526

6.  Human Pavlovian fear conditioning conforms to probabilistic learning.

Authors:  Athina Tzovara; Christoph W Korn; Dominik R Bach
Journal:  PLoS Comput Biol       Date:  2018-08-31       Impact factor: 4.475

7.  Pupillary reactivity to alcohol cues as a predictive biomarker of alcohol relapse following treatment in a pilot study.

Authors:  Timo L Kvamme; Mads Uffe Pedersen; Morten Overgaard; Kristine Rømer Thomsen; Valerie Voon
Journal:  Psychopharmacology (Berl)       Date:  2019-01-03       Impact factor: 4.530

8.  Pupil size changes signal hippocampus-related memory functions.

Authors:  Péter Pajkossy; Ágnes Szőllősi; Mihály Racsmány
Journal:  Sci Rep       Date:  2020-10-02       Impact factor: 4.379

  8 in total

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