Bayesian multisensory perception

Humans ’ multisensory perception , from integration to segregation , follows Bayesian inference

A Bayesian Nonparametric Approach to Multisensory Perception

We propose a Bayesian nonparametric model of multisensory perception based upon the Indian buffet process. The model includes a set of latent variables that learn multisensory features from unisensory data. The model is highly flexible because it makes few statistical assumptions. In particular, the number of latent multisensory features is not fixed a priori. Instead, this number is estimated ...

ʻComputational modeling of multisensory object perceptionʼ

Computational modeling largely based on advances in artificial intelligence and machine learning has helped furthering the understanding of some of the principles and mechanisms of multisensory object perception. Furthermore, this theoretical work has led to the development of new experimental paradigms and to important new questions. The last 20 years have seen an increasing emphasis on models...

Hierarchical Log-Spherical Inference Grid – An Unconventional Approach to Robotic Perception and Action

In this text, an unconventional paradigm for robotic multisensory perception and action will be presented in the form of a generalisation of a framework devised in previous work by the authors, the Bayesian Volumetric Map (BVM). The BVM, a log-spherical inference grid providing an egocentric and probabilistic representation of spatial information, was designed to bridge multisensory perception ...

Cortical Hierarchies Perform Bayesian Causal Inference in Multisensory Perception

To form a veridical percept of the environment, the brain needs to integrate sensory signals from a common source but segregate those from independent sources. Thus, perception inherently relies on solving the "causal inference problem." Behaviorally, humans solve this problem optimally as predicted by Bayesian Causal Inference; yet, the underlying neural mechanisms are unexplored. Combining ps...

A Concept Learning Approach to Multisensory Object Perception

This paper presents a computational model of concept learning using Bayesian inference for a grammatically structured hypothesis space, and test the model on multisensory (visual and haptics) recognition of 3D objects. The study is performed on a set of artificially generated 3D objects known as fribbles, which are complex, multipart objects with categorical structures. The goal of this work is...