Perceptual decision-making in the sound-induced flash illusion: a trial-level hierarchical Bayesian integration of EEG and behavior.

This study used the sound-induced flash illusion (SIFI) paradigm to investigate how sensory congruency, audiovisual rhythm configurations, and momentary neural states jointly shape perceptual decisions by integrating trial-level EEG and behavioral data within a hierarchical Bayesian regression framework. Thirty healthy adults performed six beep-flash conditions (B0F1, B0F2, B1F1, B1F2, B2F1, and B2F2), encompassing two visual-only baseline conditions, two audiovisually congruent conditions, and two incongruent audiovisual illusion conditions, while response accuracy and reaction time (RT) were recorded together with prestimulus occipital α-band power, auditory and visual N1 amplitudes, and the centro-parietal positivity (CPP) indexing evidence accumulation. We first characterized condition effects and EEG-behavior relationships using repeated-measures ANOVAs and correlations, and then jointly modeled accuracy (Bernoulli likelihood with logit link; effects reported as odds ratios) and RT (Student-t likelihood on logRT; effects interpreted as percentage change in median RT) using trial-level hierarchical Bayesian regression, with model generalizability assessed via Pareto-smoothed importance sampling leave-one-out (PSIS-LOO) cross-validation. Behaviorally, unimodal and congruent conditions yielded faster and more accurate responses, whereas incongruent conditions-particularly the "2 beep-1 flash" configuration-produced elevated illusion rates and markedly prolonged RTs, highlighting the role of causal structure and signal reliability in cross-modal weighting. Neurally, higher trial-level prestimulus α power was reliably associated with reduced odds of a correct response, consistent with lower cortical excitability and a more conservative decision criterion; larger auditory N1 amplitudes predicted faster responses; and CPP slope closely tracked RT in descriptive analyses, consistent with its interpretation as an accumulation-to-bound signal. Model comparisons indicated that experimental conditions accounted for most of the behavioral variance, whereas EEG covariates added limited and non-robust incremental out-of-sample predictive value under the present linear additive specification. Nevertheless, prestimulus α showed a stable negative association with accuracy, and auditory N1 showed a weaker association with response speed, indicating that these neural measures remain theoretically informative even though they did not materially improve overall model generalizability.