Evidence map›Paper›PMID 35344258›Full record

ArticleHuman brain mapping2022

Detecting spontaneous deception in the brain.

Yen-Ju Feng, Shao-Min Hung, Po-Jang Hsieh

Abstract read
In one paragraph

Article in Human brain mapping, 2022. 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. Distinguishing deception from its confounds by improving the validity of fMRI-based neural prediction.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  2. Article
  3. 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

3 authors.

Yen-Ju FengDepartment of Psychology, National Taiwan University, Taipei, Taiwan.ORCID 0000-0002-7265-6709
Shao-Min HungBiology and Biological Engineering, California Institute of Technology, Pasadena, California, USA.
Po-Jang HsiehDepartment of Psychology, National Taiwan University, Taipei, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Deception detection can be of great value during the juristic investigation. Although the neural signatures of deception have been widely documented, most prior studies were biased by difficulty levels. That is, deceptive behavior typically required more effort, making deception detection possibly effort detection. Furthermore, no study has examined the generalizability across instructed and spontaneous responses and across participants. To explore these issues, we used a dual-task paradigm, where the difficulty level was balanced between truth-telling and lying, and the instructed and spontaneous truth-telling and lying were collected independently. Using Multivoxel pattern analysis, we were able to decode truth-telling versus lying with a balanced difficulty level. Results showed that the angular gyrus (AG), inferior frontal gyrus (IFG), and postcentral gyrus could differentiate lying from truth-telling. Critically, linear classifiers trained to distinguish instructed truthful and deceptive responses could correctly differentiate spontaneous truthful and deceptive responses in AG and IFG with above-chance accuracy. In addition, with a leave-one-participant-out analysis, multivoxel neural patterns from AG could classify if the left-out participant was lying or not in a trial. These results indicate the commonality of neural responses subserved instructed and spontaneous deceptive behavior as well as the feasibility of cross-participant deception validation.

Indexed as

BrainDeceptionBrain MappingHumansParietal LobePrefrontal Cortexangular gyrusdeception detectioninferior frontal gyruslyingmultivoxel pattern analysis

Identifiers

PMID35344258
PMCPMC9189038

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.