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Active Task Engagement Enhances Auditory Brain–Behaviour Prediction From Single‐Trial EEG Compared With Passive Listening

Year: 2026

Type: article

Abstract: Auditory neural processing during active task engagement and passive listening reflects distinct task contexts with potentially different behavioural relevance. While both contexts elicit deviance-related responses, it remains unclear, which yields neural measures that more reliably predict individual differences in behavioural performance. To address this question, we employed a multi-feature auditory paradigm in which frequency, duration, and intensity deviants were presented under passive (no response required) and active (explicit detection required) conditions. EEG was recorded from 47 participants; passive listening was characterized by a prominent mismatch negativity (MMN), whereas active discrimination was characterized by an additional P3b component. Beyond conventional ERP measures, we quantified individual-level neural discriminability using EEGNet, a neural-network-based classifier, by classifying deviant versus standard single-trial epochs and deriving cross-validated decoding accuracy. Behavioural performance was quantified using an efficiency score (ES) that integrates hit rate and reaction time. Participants were stratified into high- and low-performance groups based on a median split of ES. Results showed that the expected MMN during passive listening and the P3b during active discrimination were elicited, as confirmed by spatiotemporal cluster-based permutation analysis. Furthermore, decoding accuracy derived from the active discrimination condition robustly separated high- and low-performance groups (Group × Task: F = 29.62, p < 0.001) and predicted behavioural efficiency across individuals (r = 0.53, p < 0.01). In contrast, passive-listening decoding showed reduced overall discriminability and minimal group separation. Together, these findings indicate that task engagement amplifies the behavioural relevance of single-trial neural discriminability, enabling stronger auditory brain-behaviour prediction than passive listening.

Source: European Journal of Neuroscience

DOI: 10.1111/ejn.70438

Authors: Zhaonan Ma, Xiaoyu Wang, Xiao Yang, Chao Guo, Tommi Kärkkäinen, Fengyu Cong

Institutions: Dalian University of Technology, University of Jyväskylä, Western University, Dalian University

Language: English

Topic: Neuroscience and Music Perception

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: hybrid

Estimated APC paid: $3,560

Funder: China Scholarship Council