Trial-Aligned Joint EEG--Behaviour Modelling for Pedestrian Road-Crossing Decisions: Cross-Modal Prediction Beyond Two-Stage Pipelines

Abstract

Pedestrian road-crossing is a safety-critical, time-pressured decision that produces both choices and response times, and recent work has analysed EEG correlates such as the centro-parietal positivity (CPP) in a separate, post-hoc step. We present a single-trial joint neurocognitive model that treats crossing choice, response time, and trial-wise CPP features as co-generated by shared latent evidence-accumulation dynamics. The model is fitted hierarchically to an EEG+behaviour pedestrian crossing dataset with repeated time-to-arrival manipulations, using simulation-based Bayesian inference. The joint formulation enables cross-modal prediction (early EEG windows sharpening predictions of imminent choice/RT; observed behaviour predicting CPP) and provides an explicit trial-alignment diagnostic via EEG–behaviour shuffling. Results show that one parameterisation can reproduce behavioural and neural patterns simultaneously and yields improved mechanistic interpretability compared to two-stage pipelines, supporting more robust prediction and model checking in road-user behaviour research.