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Sensory Cue Integration$
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Julia Trommershäuser, Konrad Kording, and Michael S. Landy

Print publication date: 2011

Print ISBN-13: 9780195387247

Published to Oxford Scholarship Online: September 2012

DOI: 10.1093/acprof:oso/9780195387247.001.0001

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Generative Probabilistic Modeling: Understanding Causal Sensorimotor Integration

Generative Probabilistic Modeling: Understanding Causal Sensorimotor Integration

Chapter:
(p.63) CHAPTER 4 Generative Probabilistic Modeling: Understanding Causal Sensorimotor Integration
Source:
Sensory Cue Integration
Author(s):

Sethu Vijayakumar

Timothy Hospedales

Adrian Haith

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780195387247.003.0004

This chapter argues that many aspects of human perception are best explained by adopting a modeling approach in which experimental subjects are assumed to possess a full generative probabilistic model of the task they are faced with, and that they use this model to make inferences about their environment and act optimally given the information available to them. It applies this generative modeling framework in two diverse settings—concurrent sensory and motor adaptation, and multisensory oddity detection—and shows, in both cases, that the data are best described by a full generative modeling approach.

Keywords:   perception, generative modeling, concurrent sensory, motor adaptation, multisensory oddity detection

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