Mismatch Negativity and Stimulus-Preceding Negativity in Paradigms of Increasing Auditory Complexity: A Possible Role in Predictive Coding

Francisco J Ruiz-Martínez,Carlos M Gómez,Antonio Arjona

doi:10.3390/e23030346

Abstract

The auditory mismatch negativity (MMN) has been considered a preattentive index of auditory processing and/or a signature of prediction error computation. This study tries to demonstrate the presence of an MMN to deviant trials included in complex auditory stimuli sequences, and its possible relationship to predictive coding. Additionally, the transfer of information between trials is expected to be represented by stimulus-preceding negativity (SPN), which would possibly fit the predictive coding framework. To accomplish these objectives, the EEG of 31 subjects was recorded during an auditory paradigm in which trials composed of stimulus sequences with increasing or decreasing frequencies were intermingled with deviant trials presenting an unexpected ending. Our results showed the presence of an MMN in response to deviant trials. An SPN appeared during the intertrial interval and its amplitude was reduced in response to deviant trials. The presence of an MMN in complex sequences of sounds and the generation of an SPN component, with different amplitudes in deviant and standard trials, would support the predictive coding framework.

Highlights

Francesco Donnarumma, Our brain processes perceptual information to create an adaptive response to the environment
Predictive coding assumes that the brain implements a hierarchical generative model of its sensory inputs, which it optimizes by minimizing prediction errors between top-down predictions and bottom-up streams of sensory inputs
The gray shaded areas indicate the latencies in exists whicha exists a significant difference

Summary

Introduction

Francesco Donnarumma, Our brain processes perceptual information to create an adaptive response to the environment. The predictive error would cause an increase in neural activity, whose objective is to update the model and improve its compatibility with the environment [2,3,4,5]. This approach is formally expressed in the so-called Bayesian Brain Hypothesis [2], and it has been applied to different fields as sensory processing [6,7], sensory-motor activity [8,9,10], language [11], or attention [12]

Methods

Results

Conclusion