Fehr-Schmidt inequality aversion constrains vmPFC value coding in social context: A computational fMRI study

The ventromedial prefrontal cortex (vmPFC) encodes subjective value across monetary and social outcomes, but whether it tracks individual differences in fairness preferences remains unclear. We applied computational modeling to an openly available fMRI dataset in which participants completed an Ultimatum Game with a computer, an ingroup peer, and an outgroup peer. Individual disadvantageous inequality aversion parameters (α) were estimated from behavioral data using a one-parameter Fehr-Schmidt model, and vmPFC activation was extracted trial-by-trial using a coordinate-based region of interest. Offer size predicted vmPFC activation across proposer types, consistent with a value-coding role. α moderated vmPFC responses for ingroup offers: participants with stronger inequality aversion showed a weaker vmPFC signal when receiving offers from an ingroup peer. A direct contrast of the ingroup and outgroup slopes did not reach significance, and the corresponding effect in a ventral striatum control region was null. At the subject level, α did not predict overall vmPFC engagement. These results indicate that vmPFC value signals are associated with individual fairness preferences within the ingroup context, with only weak evidence that this association differs across proposer types. The findings extend prior work on group-modulated fairness processing.

Authors

Institutions

Publication Details

Journal
Brain and Cognition
Published
2026-07-16
DOI
https://doi.org/10.1016/j.bandc.2026.106454
Primary Topic
Neural and Behavioral Psychology Studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Fehr-Schmidt inequality aversion constrains vmPFC value coding in social context: A computational fMRI study

Waldir M. Sampaio
Brain and Cognition
Neural and Behavioral Psychology Studies
article

Fehr-Schmidt inequality aversion constrains vmPFC value coding in social context: A computational fMRI study

Waldir M. Sampaio
article en

Abstract

The ventromedial prefrontal cortex (vmPFC) encodes subjective value across monetary and social outcomes, but whether it tracks individual differences in fairness preferences remains unclear. We applied computational modeling to an openly available fMRI dataset in which participants completed an Ultimatum Game with a computer, an ingroup peer, and an outgroup peer. Individual disadvantageous inequality aversion parameters (α) were estimated from behavioral data using a one-parameter Fehr-Schmidt model, and vmPFC activation was extracted trial-by-trial using a coordinate-based region of interest. Offer size predicted vmPFC activation across proposer types, consistent with a value-coding role. α moderated vmPFC responses for ingroup offers: participants with stronger inequality aversion showed a weaker vmPFC signal when receiving offers from an ingroup peer. A direct contrast of the ingroup and outgroup slopes did not reach significance, and the corresponding effect in a ventral striatum control region was null. At the subject level, α did not predict overall vmPFC engagement. These results indicate that vmPFC value signals are associated with individual fairness preferences within the ingroup context, with only weak evidence that this association differs across proposer types. The findings extend prior work on group-modulated fairness processing.

Brain and CognitionVol. 197
Pontifícia Universidade Católica do Rio de Janeiro (BR)
Reduced inequalities
Openalex Percentile: Top 7%
Neural and Behavioral Psychology Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.