Segregation and integration of sensory features by flexible temporal characteristics of independent neural representations

Abstract

AbstractSegregation and integration are two fundamental yet competing computations in cognition. For example, in serial speech processing, stable perception necessitates the sequential establishment of perceptual representations to remove irrelevant features for achieving invariance. Whereas multiple features need to combine to create a coherent percept. How to simultaneously achieve seemingly contradicted computations of segregation and integration in a serial process is unclear. To investigate their neural mechanisms, we used loudness and lexical tones as a research model and employed a novel multilevel oddball paradigm with Electroencephalogram (EEG) recordings to explore the dynamics of mismatch negativity (MMN) responses to their deviants. When two types of deviants were presented separately, distinct topographies of MMNs to loudness and tones were observed at different latencies (loudness earlier), supporting the sequential dynamics of independent representations for two features. When they changed simultaneously, the latency of responses to tones became shorter and aligned with that to loudness, while the topographies remained independent, yielding the combined MMN as a linear additive of single MMNs of loudness and tones. These results suggest that neural dynamics can be temporally synchronized to distinct sensory features and balance the computational demands of segregation and integration, grounding for invariance and feature binding in serial processing.