Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Multi-input and Multi-variable systems

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...

Perceptual Constancy

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...

Sensory Modalities

Sensory Modalities

Sensation typically is the process by which the sensory receptors and sense organs detect stimuli from the internal and external environment and transmit this information to the central nervous system for processing.
General senses refer to the broad category of sensory information detected by receptors in the body and can be further grouped into somatic and visceral senses. Somatic sensations include touch, pressure, temperature, and pain and are essential for navigating our environment and...

Hindsight Biases

Hindsight Biases

Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now?

Difference from Background: Limit of Detection

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...

Perceiving Loudness, Pitch, and Location

Perceiving Loudness, Pitch, and Location

The human brain perceives pitch through two primary mechanisms reflected in place theory and frequency theory. Each mechanism describes how sound waves are interpreted as specific pitches by the brain, offering insights into the intricate processes of auditory perception.
Place theory, or place coding, suggests that different pitches are heard because various sound waves activate specific locations along the cochlea's basilar membrane. The brain determines the pitch of a sound by identifying...

You might also read

Related Articles

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

Sort by

Same author

Bridging clinical narratives and structured phenotypes with large language models and sentence transformers.

Journal of genetics and genomics = Yi chuan xue bao·2026

Same author

Self-Supervised Multimodal Learning: A Survey.

IEEE transactions on pattern analysis and machine intelligence·2024

Same author

Adaptive assistive robotics: a framework for triadic collaboration between humans and robots.

Royal Society open science·2023

Same author

A Haptic Sleeve as a Method of Mechanotactile Feedback Restoration for Myoelectric Hand Prosthesis Users.

Frontiers in rehabilitation sciences·2022

Same author

HapFIC: An Adaptive Force/Position Controller for Safe Environment Interaction in Articulated Systems.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2021

Same author

Meta-Learning in Neural Networks: A Survey.

IEEE transactions on pattern analysis and machine intelligence·2021

Same journal

Invaders taking over-Mollusc faunal change in volcanic barrier lakes of the Albertine Rift biodiversity hotspot.

PloS one·2026

Same journal

AI-driven molecular diversification and ligand-based optimization of macitentan derivatives targeting VEGFR1 and endothelin signaling pathways.

PloS one·2026

Same journal

Performance patterns and records in the world aquatics masters championships: Where do the most frequently represented nations among the top-ten masters swimmers come from?

PloS one·2026

Same journal

Modeling diurnal Temperature-Rainfall relationships under multicollinearity using PLS-SEM: A case study of Ghana.

PloS one·2026

Same journal

Organizational culture, social capital, and emergency capacity in primary healthcare institutions: A cross-sectional structural equation modeling study comparing ordinary and older communities.

PloS one·2026

Same journal

Impact of kidney function on the metabolome in the general population.

PloS one·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 26, 2026

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Multisensory oddity detection as bayesian inference.

Timothy Hospedales¹, Sethu Vijayakumar

¹Institute of Perception, Action and Behaviour, School of Informatics, University of Edinburgh, Edinburgh, United Kingdom. t.hospedales@ed.ac.uk

|January 16, 2009

Summary

This summary is machine-generated.

Structure inference, a Bayesian approach, successfully explains multisensory perception, outperforming maximum likelihood integration (MLI) in oddity detection tasks.

More Related Videos

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

A Two-interval Forced-choice Task for Multisensory Comparisons

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

Related Experiment Videos

Last Updated: Jun 26, 2026

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

A Two-interval Forced-choice Task for Multisensory Comparisons

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

Area of Science:

Cognitive Neuroscience
Computational Neuroscience
Psychology

Background:

The brain optimally integrates multisensory information for accurate world perception.
Maximum likelihood integration (MLI) models human sensory combination but fails with uncertain stimulus correspondence.
Bayesian inference models, specifically structure inference, have succeeded where MLI falters in direct stimulus estimation.

Purpose of the Study:

To investigate causal uncertainty in multisensory oddity detection using a Bayesian framework.
To demonstrate that Bayesian ideal observers treat oddity detection as a structure inference problem.
To provide a unified explanation for multisensory perception across and within modalities.

Main Methods:

Examined causal uncertainty in multisensory oddity detection.
Applied Bayesian structure inference to model oddity detection.
Validated the model against experimental data from multisensory oddity detection tasks.

Main Results:

Bayesian structure inference successfully models multisensory oddity detection.
This approach provides a quantitative explanation for experimental results where MLI failed.
A unified treatment of within- and cross-modal multisensory perception was achieved.

Conclusions:

Structure inference offers a powerful framework for understanding multisensory perception under causal uncertainty.
This Bayesian approach unifies explanations for diverse multisensory oddity detection scenarios.
Structure inference may be a fundamental principle for perceptual information integration in the brain.