Evidence map›Paper›PMID 41942483›Full record

ArticleNPJ science of learning2026

Neurophysiological prediction of competitive orientation via EEG during outcome evaluation in gamified learning.

Hiroki Watanabe, Ayato Tabata, Chikara Ishii, Yasushi Naruse

Abstract read
In one paragraph

Article in NPJ science of learning, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Hiroki WatanabeCenter for Information and Neural Networks, Advanced ICT Research Institute, National Institute of Information and Communications Technology, and Osaka University, 651-2492, Kobe, Japan. h-watanabe@nict.go.jp.
Ayato TabataCenter for Information and Neural Networks, Advanced ICT Research Institute, National Institute of Information and Communications Technology, and Osaka University, 651-2492, Kobe, Japan.
Chikara IshiiCenter for Information and Neural Networks, Advanced ICT Research Institute, National Institute of Information and Communications Technology, and Osaka University, 651-2492, Kobe, Japan.
Yasushi NaruseCenter for Information and Neural Networks, Advanced ICT Research Institute, National Institute of Information and Communications Technology, and Osaka University, 651-2492, Kobe, Japan. y_naruse@nict.go.jp.

Funding

Japan Society for the Promotion of Science JP23K12841
6 · The paper itself

Abstract

Gamification often employs competition-based mechanics to enhance motivation; however, their effectiveness varies depending on an individual's competitive orientation. This study proposes a method to assess competitive orientation using EEG data in a gamified learning environment. Twenty-eight participants completed a gamified mental arithmetic task under two conditions: competing against an opponent (competitive condition) and attempting to surpass their best score (self-referential condition). Event-related potentials (ERPs) in response to outcome feedback-P300, feedback-related negativity, and reward positivity (RewP)-were analyzed. Results showed that RewP amplitude in the competitive condition was significantly correlated with competitive orientation scores. P300 amplitude was associated with the scores regardless of condition. Furthermore, a support vector regression model trained on ERP and behavioral features from the competitive condition predicted competitive orientation scores (r = 0.468). These findings suggest that ERP responses provide objective information for estimating competitive orientation, highlighting a neurophysiological approach to personalized gamification design.

Identifiers

PMID41942483
PMCPMC13230860

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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.