Sensory integration, temporal prediction, and rule discovery reflect interdependent inference processes

Abstract

Deciphering the structure of variable sensory input is key to building an accurate model of one’s environment. Humans can accumulate evidence from sequences of stimuli to estimate their sensory statistics, predict the timing of upcoming stimuli, but also discover rules governing sequence generation. However, whether these three forms of inference operate independently or synergistically remains untested. Here, we report selective interactions between sensory integration, temporal prediction, and rule discovery in humans. Participants were exposed to rhythmic sequences of 10 stimuli governed or not by a latent rule—a predictable change in stimulus statistics after five stimuli—and then asked to predict the 10th stimulus from incomplete sequences. Individual differences in sensory integration timescale for rule-free sequences predicted efficient rule discovery. Conversely, discovering the latent rule shaped the timescale and format of sensory integration for rule-based sequences. Tampering with the rhythmicity of stimulus presentation impaired rule discovery without affecting sensory integration accuracy. Selective perturbations of recurrent neural networks trained in the same conditions confirmed these specific interactions. Together, these findings provide insights into the flexibility of human inferences based on variable yet predictable sensory input.