Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Perceptual Constancy01:12

Perceptual Constancy

Perceptual constancy is the ability to recognize that objects remain consistent and unchanged even when their appearance varies due to changes in sensory input. There are four main types of perceptual constancy: size constancy, shape constancy, color constancy, and brightness constancy.
Size constancy is the recognition that an object remains the same size, even when its image on the retina changes. For instance, a bus is perceived to be large enough to carry people, even if it looks tiny from...
Gestalt Principles of Perception01:21

Gestalt Principles of Perception

Gestalt principles provide a framework for understanding how humans perceive objects as unified wholes within their context. These principles are essential in explaining the cognitive processes that make sense of complex visual stimuli by organizing them into coherent groups. One fundamental principle is proximity, which posits that objects located close to each other are perceived as a collective group. For instance, when dots are positioned near one another, the visual system interprets them...
Perception01:28

Perception

Perception is a fundamental psychological process that enables individuals to organize, interpret, and consciously experience sensory information. This process is crucial for understanding and interacting with the world around us. It includes both bottom-up and top-down processing, each playing a distinct role in how we perceive our environment.
Bottom-up processing begins at the sensory level, where receptors detect external environmental stimuli. These could include the tactile sensation of...
Factors Affecting Perception01:25

Factors Affecting Perception

Perception is influenced by perceptual set, context, motivation, and emotion. Perceptual set, or perceptual expectancy, refers to the tendency to perceive things in a particular way, influenced by previous experiences and expectations. This phenomenon affects the interpretation of stimuli, creating a set of mental tendencies and assumptions that impact sensory perceptions of sound, taste, touch, and sight.
An illustrative example of a perceptual set is the scenario where an airline pilot told...
Subliminal Perception01:15

Subliminal Perception

Subliminal perception refers to the processing of sensory information that occurs below the level of conscious awareness. Researchers study subliminal perception by presenting a stimulus, such as a word or image, very quickly, typically around 50 milliseconds. This rapid presentation is often followed by another stimulus, such as a pattern of dots or lines, which blocks further mental processing of the initial stimulus. As a result, if participants cannot identify the initial stimulus better...
Parallel Processing01:20

Parallel Processing

The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Conniving With Continuations: Representing Goals in a Domain-Specific Language of Thought.

Topics in cognitive science·2026
Same author

Neural representation of action symbols in primate frontal cortex.

Nature·2026
Same author

Fast efficient coding and sensory adaptation in gain-adaptive recurrent networks.

Nature communications·2026
Same author

Human-level learning of complex novel tasks as theory-based modelling, exploration and planning.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026
Same author

Gradient Descent as Loss Landscape Navigation: a Normative Framework for Deriving Learning Rules.

Advances in neural information processing systems·2026
Same author

Probabilistic forecasting guides dynamic decisions.

Psychological review·2026
Same journal

A Model-Free Reinforcement Learning Implementation of Decision Making Under Uncertainty by Sequential Sampling.

Neural computation·2026
Same journal

DROP: Distributional and Regular Optimism and Pessimism for Reinforcement Learning.

Neural computation·2026
Same journal

Hierarchical Active Inference Using Successor Representations.

Neural computation·2026
Same journal

W-Kernel and Its Principal Space for Frequentist Evaluation of Bayesian Estimators.

Neural computation·2026
Same journal

A Hidden Markov Model-Inspired Sequence Classification Method for Hyperdimensional Computing.

Neural computation·2026
Same journal

Sparse Graphical Modeling for Electrophysiological Phase-Based Connectivity Using Circular Statistics.

Neural computation·2026
See all related articles

Related Experiment Video

Updated: May 28, 2026

A Two-interval Forced-choice Task for Multisensory Comparisons
07:13

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

Multistability and perceptual inference.

Samuel J Gershman1, Edward Vul, Joshua B Tenenbaum

  • 1Department of Psychology and Neuroscience Institute, Princeton University, Princeton, NJ, USA. sjgershm@princeton.edu

Neural Computation
|October 26, 2011
PubMed
Summary
This summary is machine-generated.

The visual system approximates complex probability distributions using samples, leading to perceptual multistability. This process, akin to Markov chain Monte Carlo (MCMC) sampling, explains phenomena like binocular rivalry.

More Related Videos

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

Testing Sensory and Multisensory Function in Children with Autism Spectrum Disorder
09:13

Testing Sensory and Multisensory Function in Children with Autism Spectrum Disorder

Published on: April 22, 2015

Related Experiment Videos

Last Updated: May 28, 2026

A Two-interval Forced-choice Task for Multisensory Comparisons
07:13

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

Testing Sensory and Multisensory Function in Children with Autism Spectrum Disorder
09:13

Testing Sensory and Multisensory Function in Children with Autism Spectrum Disorder

Published on: April 22, 2015

Area of Science:

  • Computational Neuroscience
  • Cognitive Science
  • Bayesian Brain Hypothesis

Background:

  • Ambiguous images pose challenges for the visual system in representing uncertainty.
  • Bayesian ideal observers should compute posterior probability distributions over causes, but this is often intractable.

Purpose of the Study:

  • To propose that the visual system approximates posterior distributions using samples.
  • To explain perceptual multistability as a result of this sample-based approximation.

Main Methods:

  • Analysis of perceptual multistability through the lens of approximate Bayesian inference.
  • Modeling multistability as a dynamic sample-generating process using Markov chain Monte Carlo (MCMC).
  • Examination of binocular rivalry as a case study for MCMC sampling.

Main Results:

  • Perceptual multistability arises from a stochastic diffusion process exploring the posterior distribution.
  • Experimental phenomena in binocular rivalry (switching, patchy percepts, fusion, traveling waves) are explained by MCMC sampling.
  • Spiking neuron dynamics may implement sample-based approximations in the brain.

Conclusions:

  • The brain may use sample-based approximations for Bayesian inference.
  • Perceptual multistability is a consequence of this approximate Bayesian computation.
  • This framework offers a unified explanation for various phenomena in visual perception.