Evidence map›Paper›PMID 42521946›Full record

ReviewCognitive, affective & behavioral neuroscience2026

The social life of Pavlovian learning.

Carmelo M Vicario, Michael Petrides, Francesco Tomaiuolo, Simona Massimino

Abstract readReview
PubMed Publisher
In one paragraph

Review in Cognitive, affective & behavioral neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Carmelo M VicarioDepartment of Cognitive Sciences, Psychology, Education and Cultural Studies, University of Messina, Messina, Italy. cvicario@unime.it.
Michael PetridesMontreal Neurological Institute, McGill University, Montreal, Quebec, Canada.
Francesco TomaiuoloDepartment of Clinical and Experimental Medicine, University of Messina, Messina, Italy.
Simona MassiminoDepartment of Cognitive Sciences, Psychology, Education and Cultural Studies, University of Messina, Messina, Italy.

Funding

Ministero Istruzione Università e Ricerca CUP J53D23008320006Ministero Istruzione Università e Ricerca Prin 2022Ministero Istruzione Università e Ricerca Prot. 2022WPY4L3_0
6 · The paper itself

Abstract

This article proposes that Pavlovian learning contributes to the formation and stabilization of social beliefs and collective commitments. We argue that associative processes linking reward and threat to social cues shape how beliefs acquire affective salience, motivational persistence, and resistance to revision. Within this framework, social cognition is conceptualized not as a purely inferential process, but as one constrained by affective learning mechanisms operating across distributed neural systems involved in reward, threat processing, and contextual regulation. Appetitive learning may promote affiliation and shared expectations, whereas threat-related learning may stabilize existing commitments by rendering alternative interpretations affectively aversive or psychologically unsafe. We further suggest that the integration of Pavlovian signals within expanded prefrontal networks allows associative learning processes to influence higher-order social representations, including norms, identities, and group affiliations. Reconsidering social cognition through the lens of associative learning may help bridge affective neuroscience, social neuroscience, and contemporary models of belief persistence and social behaviour.

Indexed as

Belief formationPavlovian learningPrefrontal cortexReward learningSocial cognitionThreat learning

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.