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

## TL;DR

This study shows that neural responses during active listening better predict individual performance in auditory tasks than during passive listening.

## Contribution

The study demonstrates that active task engagement enhances brain-behavior prediction using single-trial EEG decoding.

## Key findings

- Active task engagement yields higher decoding accuracy for separating high- and low-performance groups.
- Decoding accuracy from active listening correlates with behavioral efficiency scores.
- Passive listening shows reduced discriminability and minimal group separation.

## 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.

This study investigates whether neural responses obtained under different task contexts provide reliable predictors of behavioural performance in an auditory discrimination task. Using EEGNet to classify single‐trial event‐related potentials, we analysed neural discriminability between standard and deviant stimuli during active task engagement and passive listening. The results demonstrate that classification accuracy derived from the active task significantly predicts individual efficiency scores (ES), whereas classification accuracy derived from passive listening shows no predictive value. These findings indicate that neural representations expressed during active task engagement provide a more robust and behaviourally informative predictor of performance than neural responses obtained during passive listening.