Modelling sensory attenuation as Bayesian causal inference across two datasets.

ABSTRACT:

Introduction

To interact with the environment, it is crucial to distinguish between sensory information that is externally generated and inputs that are self-generated. The sensory consequences of one's own movements tend to induce attenuated behavioral- and neural responses compared to externally generated inputs. We propose a computational model of sensory attenuation (SA) based on Bayesian Causal Inference, where SA occurs when an internal cause for sensory information is inferred.

Methods

Experiment 1investigates sensory attenuation during a stroking movement. Tactile stimuli on the stroking finger were suppressed, especially when they were predictable. Experiment 2 showed impaired delay detection between an arm movement and a video of the movement when participants were

SUBMITTER: Eckert AL 

PROVIDER: S-EPMC11761661 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature