Neural surprise in somatosensory Bayesian learning.

Sam Gijsen,Miro Grundei,Dirk Ostwald,Robert T Lange,Felix Blankenburg,Philipp Schwartenbeck

doi:10.1371/journal.pcbi.1008068

Abstract

Tracking statistical regularities of the environment is important for shaping human behavior and perception. Evidence suggests that the brain learns environmental dependencies using Bayesian principles. However, much remains unknown about the employed algorithms, for somesthesis in particular. Here, we describe the cortical dynamics of the somatosensory learning system to investigate both the form of the generative model as well as its neural surprise signatures. Specifically, we recorded EEG data from 40 participants subjected to a somatosensory roving-stimulus paradigm and performed single-trial modeling across peri-stimulus time in both sensor and source space. Our Bayesian model selection procedure indicates that evoked potentials are best described by a non-hierarchical learning model that tracks transitions between observations using leaky integration. From around 70ms post-stimulus onset, secondary somatosensory cortices are found to represent confidence-corrected surprise as a measure of model inadequacy. Indications of Bayesian surprise encoding, reflecting model updating, are found in primary somatosensory cortex from around 140ms. This dissociation is compatible with the idea that early surprise signals may control subsequent model update rates. In sum, our findings support the hypothesis that early somatosensory processing reflects Bayesian perceptual learning and contribute to an understanding of its underlying mechanisms.

Highlights

The world is governed by statistical regularities, such that a single drop of rain on the skin might predict further tactile sensations through imminent rainfall
Despite the importance for behavior and survival, much remains unknown about how these dependencies are learned, for somatosensation
As surprise signalling about novel observations indicates a mismatch between one’s beliefs and the world, it has been hypothesized that surprise computation plays an important role in perceptual learning

Summary

Introduction

The world is governed by statistical regularities, such that a single drop of rain on the skin might predict further tactile sensations through imminent rainfall. More recent accounts of perception and perceptual learning, including predictive coding [2, 3] and the free energy principle [4], propose that these models are continuously updated in light of new sensory evidence using Bayesian inference. Under such a view, the generative model is composed of a likelihood function of sensory input given external causes and a prior probability distribution over causes [4, 5]. Such a description of Bayesian perceptual learning has been successfully used to explain aspects of learning in the auditory [7, 8, 9], visual [10, 11, 12], as well as somatosensory domain [13]

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Conclusion