Evidence map›Paper›PMID 38627400›Full record

ArticleScientific data2024

An EEG Dataset of Neural Signatures in a Competitive Two-Player Game Encouraging Deceptive Behavior.

Yiyu Chen, Siamac Fazli, Christian Wallraven

Abstract readDataset
In one paragraph

Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Review
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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.

Yiyu ChenDepartment of Artificial Intelligence, Korea University, Seoul, 02841, South Korea.ORCID 0000-0002-0617-4656
Siamac FazliDepartment of Computer Science, Nazarbayev University, Astana, 010000, Kazakhstan.
Christian WallravenDepartment of Artificial Intelligence, Korea University, Seoul, 02841, South Korea. wallraven@korea.ac.kr.ORCID 0000-0002-2604-9115

Funding

National Research Foundation of Korea (NRF) BK21 FOUR, NRF-2022R1A2C2092118, NRF-2022R1H1A2092007, NRF-2019R1A2C2007612,
6 · The paper itself

Abstract

Studying deception is vital for understanding decision-making and social dynamics. Recent EEG research has deepened insights into the brain mechanisms behind deception. Standard methods in this field often rely on memory, are vulnerable to countermeasures, yield false positives, and lack real-world relevance. Here, we present a comprehensive dataset from an EEG-monitored competitive, two-player card game designed to elicit authentic deception behavior. Our extensive dataset contains EEG data from 12 pairs (N = 24 participants with role switching), controlled for age, gender, and risk-taking, with detailed labels and annotations. The dataset combines standard event-related potential and microstate analyses with state-of-the-art decoding approaches of four scenarios: spontaneous/instructed truth-telling and lying. This demonstrates game-based methods' efficacy in studying deception and sets a benchmark for future research. Overall, our dataset represents a unique resource with applications in cognitive neuroscience and related fields for studying deception, competitive behavior, decision-making, inter-brain synchrony, and benchmarking of decoding frameworks in a difficult, high-level cognitive task.

Indexed as

Competitive BehaviorDeceptionElectroencephalographyBrainEvoked PotentialsHumans

Identifiers

PMID38627400
PMCPMC11021485

What OpenQuestion holds

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LicenceCC BY
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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.