Generative Probabilistic Modeling: Understanding Causal Sensorimotor Integration

Sethu Vijayakumar, Timothy Hospedales, Adrian Haith

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

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.

Original languageEnglish (US)
Title of host publicationSensory Cue Integration
PublisherOxford University Press
ISBN (Print)9780199918379, 9780195387247
DOIs
StatePublished - Sep 20 2012

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Statistical Models

Keywords

  • Concurrent sensory
  • Generative modeling
  • Motor adaptation
  • Multisensory oddity detection
  • Perception

ASJC Scopus subject areas

  • Psychology(all)

Cite this

Generative Probabilistic Modeling : Understanding Causal Sensorimotor Integration. / Vijayakumar, Sethu; Hospedales, Timothy; Haith, Adrian.

Sensory Cue Integration. Oxford University Press, 2012.

Research output: Chapter in Book/Report/Conference proceedingChapter

Vijayakumar, Sethu ; Hospedales, Timothy ; Haith, Adrian. / Generative Probabilistic Modeling : Understanding Causal Sensorimotor Integration. Sensory Cue Integration. Oxford University Press, 2012.
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