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ArticleNeuroscience insights2020

Toward a Brain-Based Bio-Marker of Guilt.

Hongbo Yu, Leonie Koban, Molly J Crockett, Xiaolin Zhou, Tor D Wager

Abstract readComment
In one paragraph

Article in Neuroscience insights, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Modulating subjective pain perception with decoded Montreal Neurological Institute-space neurofeedback: a proof-of-concept study.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024
    Article
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Hongbo YuDepartment of Psychological and Brain Sciences, University of California Santa Barbara, Santa Barbara, California, United States of America.ORCID https://orcid.org/0000-0002-3384-7772
Leonie KobanInstitute of Cognitive Science, University of Colorado, Boulder, CO, USA.
Molly J CrockettDepartment of Psychology, Yale University, New Haven, Connecticut, United States of America.
Xiaolin ZhouSchool of Psychological and Cognitive Sciences, Peking University, Beijing, China.
Tor D WagerInstitute of Cognitive Science, University of Colorado, Boulder, CO, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Guilt is a quintessential emotion in interpersonal interactions and moral cognition. Detecting the presence and measuring the intensity of guilt-related neurocognitive processes is crucial to understanding the mechanisms of social and moral phenomena. Existing neuroscience research on guilt has been focused on the neural correlates of guilt states induced by various types of stimuli. While valuable in their own right, these studies have not provided a sensitive and specific bio-marker of guilt suitable for use as an indicator of guilt-related neurocognitive processes in novel experimental settings. In a recent study, we identified a distributed Guilt-Related Brain Signature (GRBS) based on 2 independent functional MRI datasets. We demonstrated the sensitivity of GRBS in detecting a critical cognitive antecedent of guilt, namely one's responsibility in causing harm to another person, across participant populations from 2 distinct cultures (ie, Chinese and Swiss). We also showed that the sensitivity of GRBS did not generalize to other types of negative affective states (eg, physical and vicarious pain). In this commentary, we discuss the relevance of guilt in the broader scope of social and moral phenomena, and discuss how guilt-related biomarkers can be useful in understanding their psychological and neurocognitive mechanisms underlying these phenomena.

Indexed as

biomarkerfunction MRIGuiltmoralitysocial cognition

Identifiers

PMID32995750
PMCPMC7503000

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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.