Associative learning shapes visual discrimination in a web-based classical conditioning task

Yannik Stegmann*, Marta Andreatta, Paul Pauli, Matthias J. Wieser

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

6 Citations (Scopus)
16 Downloads (Pure)


Threat detection plays a vital role in adapting behavior to changing environments. A fundamental function to improve threat detection is learning to differentiate between stimuli predicting danger and safety. Accordingly, aversive learning should lead to enhanced sensory discrimination of danger and safety cues. However, studies investigating the psychophysics of visual and auditory perception after aversive learning show divergent findings, and both enhanced and impaired discrimination after aversive learning have been reported. Therefore, the aim of this web-based study is to examine the impact of aversive learning on a continuous measure of visual discrimination. To this end, 205 participants underwent a differential fear conditioning paradigm before and after completing a visual discrimination task using differently oriented grating stimuli. Participants saw either unpleasant or neutral pictures as unconditioned stimuli (US). Results demonstrated sharpened visual discrimination for the US-associated stimulus (CS+), but not for the unpaired conditioned stimuli (CS−). Importantly, this finding was irrespective of the US’s valence. These findings suggest that associative learning results in increased stimulus salience, which facilitates perceptual discrimination in order to prioritize attentional deployment.

Original languageEnglish
Article number15762
JournalScientific Reports
Issue number1
Publication statusPublished - 3 Aug 2021

Bibliographical note

Funding Information:
This publication was supported by the Open Access Publication Fund of the University of Wuerzburg.

Publisher Copyright:
© 2021, The Author(s).


Dive into the research topics of 'Associative learning shapes visual discrimination in a web-based classical conditioning task'. Together they form a unique fingerprint.

Cite this