Wired to believe: source-authority cues shape perceived AI capability and neural persuasion

AI advisors support judgement, yet users rely on credibility cues when evaluating AI-generated advice. Using fNIRS and behavioural measures, we examined associations of source-authority cues with perceived AI capability, judgement updating, and prefrontal processing. Seventy adults evaluated food-safety rumours before and after the same Pepper robot delivered corrections attributed to social media users, local news outlets, or national regulators. Corrections were followed by judgement change; descriptively, more scenario-level changes occurred in the corrective direction than in reverse. However, source condition did not significantly predict post-correction accuracy or successful correction. Prefrontal activation changed within conditions, but between-condition differences did not survive statistical correction. Exploratory functional-connectivity analyses revealed differentiated within-condition patterns. Orbitofrontal cortex activation correlated with perceived AI capability and trust, but not accuracy, suggesting an association with subjective advisor evaluation. These findings suggest that source cues influence perceived AI capability and are associated with aspects of prefrontal processing during AI-mediated correction.

Authors

Institutions

Publication Details

Journal
Ergonomics
Published
2026-09-21
DOI
https://doi.org/10.1080/00140139.2026.2734930
Primary Topic
Psychology of Moral and Emotional Judgment
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

Wired to believe: source-authority cues shape perceived AI capability and neural persuasion

Wengang Zhang, Yi Ding, Brendan M. Duffy, Dali Zhang et al.
Ergonomics
Psychology of Moral and Emotional Judgment
article

Wired to believe: source-authority cues shape perceived AI capability and neural persuasion

Wengang Zhang, Yi Ding, Brendan M. Duffy, Dali Zhang, Ziyan Deng, Wei Lyu
article en

Abstract

AI advisors support judgement, yet users rely on credibility cues when evaluating AI-generated advice. Using fNIRS and behavioural measures, we examined associations of source-authority cues with perceived AI capability, judgement updating, and prefrontal processing. Seventy adults evaluated food-safety rumours before and after the same Pepper robot delivered corrections attributed to social media users, local news outlets, or national regulators. Corrections were followed by judgement change; descriptively, more scenario-level changes occurred in the corrective direction than in reverse. However, source condition did not significantly predict post-correction accuracy or successful correction. Prefrontal activation changed within conditions, but between-condition differences did not survive statistical correction. Exploratory functional-connectivity analyses revealed differentiated within-condition patterns. Orbitofrontal cortex activation correlated with perceived AI capability and trust, but not accuracy, suggesting an association with subjective advisor evaluation. These findings suggest that source cues influence perceived AI capability and are associated with aspects of prefrontal processing during AI-mediated correction.

Ergonomics
Shanghai Jiao Tong University (CN), Anhui Polytechnic University (CN)
Zero hunger
Openalex Percentile: Top 10%
Psychology of Moral and Emotional Judgment
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.

Wired to believe: source-authority cues shape perceived AI capability and neural persuasion — Wengang Zhang, Yi Ding, et al. · Ergonomics (2026) | TGRS Research Map | TGRS