Internal Bayesian precision modulates the neural representation of social attention: Disentangling implicit and explicit components via model-informed multivariate EEG analysis

Social attention integrates sensory cues with high-level cognitive expectations, yet the generative mechanisms through which implicit orienting and explicit belief-driven modulation interact remain poorly understood. This ambiguity complicates the distinction between specialized social modules and domain-general attentional processes. We combined a dynamic cueing task with hierarchical Bayesian modeling and model-informed multivariate EEG decoding to address this. Behavioral results revealed a computational double dissociation: symbolic arrow cues elicited heterogeneous strategies, whereas averted gaze recruited a consistent, surprise-driven computational phenotype. At the neural level, time-resolved decoding and temporal generalization revealed a critical representational shift starting approximately 400 ms post-cue. Initial activity related to physical cue features was rapidly replaced by stable neural templates of predicted spatial intent. Crucially, topographical activation patterns showed that this intentional template, characterized by a lateralized temporo-occipital distribution, emerged exclusively under high internal certainty. Furthermore, partial representational similarity analysis demonstrated that late-stage neural manifolds were overwhelmingly organized around integrated spatial goals rather than isolated sensory or motivational signals. These findings suggest that social attention is a specialized generative process, where internal certainty modulates the transformation of social perceptions into actionable top-down intentions.