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

Introducing Social Perception01:29

Introducing Social Perception

281
Perceiving others accurately is fundamental to effective communication and relationship-building. Social perception, a key concept in social psychology, refers to the cognitive processes through which individuals gather and interpret information about others to understand their actions, intentions, and motivations. This process extends beyond spoken words and overt behaviors, incorporating subtle nonverbal cues and contextual factors.Nonverbal Cues and Their SignificanceNonverbal cues play a...
281
Perception01:28

Perception

949
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...
949
Gestalt Principles of Perception01:21

Gestalt Principles of Perception

989
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...
989
Sensory Perception: Organization of the Somatosensory System01:11

Sensory Perception: Organization of the Somatosensory System

10.9K
The somatosensory system is the central and peripheral nervous system component that senses and processes touch, pressure, pain, temperature, and body position or proprioception. The process of sensation takes place at three levels:
The receptor level:
The receptor level is the first stage of sensation. It involves the detection of a stimulus by specialized sensory receptors. The stimulus must arrive within the receptor's receptive field. Next, the receptor converts the energy of the...
10.9K
Factors Affecting Perception01:25

Factors Affecting Perception

2.6K
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...
2.6K
Gestalt Psychology01:14

Gestalt Psychology

1.3K
Gestalt psychology, founded by Max Wertheimer, Kurt Koffka, and Wolfgang Kohler, emphasizes the importance of understanding perception as an organized whole. Developed as a counter to Wilhelm Wundt's structuralism, this approach posits that our perceptions are more than just the sum of sensory parts; they are comprehensive wholes where the relationships between parts define the perception. The principle "The whole is greater than the sum of its parts" encapsulates this view,...
1.3K

You might also read

Related Articles

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

Sort by
Same author

Distilling noise characteristics and prior expectations in multisensory causal inference.

PLoS computational biology·2026
Same author

Clarifying the conceptual dimensions of representation in neuroscience.

Nature reviews. Neuroscience·2026
Same author

A megastudy of behavioral interventions to catalyze public, political, and financial climate advocacy.

PNAS nexus·2026
Same author

Dynamics of working memory drift and information flow across the cortical hierarchy.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Looking deeper into the algorithms underlying human planning.

Trends in cognitive sciences·2025
Same author

Neural mechanisms of resource allocation in working memory.

Science advances·2025
Same journal

Geographical psychology: Spatial variation in psychological phenomena and their consequences.

Trends in cognitive sciences·2026
Same journal

Multi-brain neurofeedback: what are we training for?

Trends in cognitive sciences·2026
Same journal

The developing vocal self.

Trends in cognitive sciences·2026
Same journal

Searching beyond decrements: Attentional guidance across the adult lifespan.

Trends in cognitive sciences·2026
Same journal

Looking into working memory through micro eye movements.

Trends in cognitive sciences·2026
Same journal

Timescapes of non-human experience.

Trends in cognitive sciences·2026
See all related articles

Related Experiment Video

Updated: Jan 7, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.9K

Organizing probabilistic models of perception.

Wei Ji Ma1

  • 1Department of Neuroscience, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA. wjma@bcm.edu

Trends in Cognitive Sciences
|September 18, 2012
PubMed
Summary
This summary is machine-generated.

This review clarifies the role of probability in perception models. It examines whether Bayesian approaches equate to optimality and distinguishes them from signal detection theory, aiding understanding of neural computation.

More Related Videos

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

10.2K
Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

12.2K

Related Experiment Videos

Last Updated: Jan 7, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.9K
Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

10.2K
Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

12.2K

Area of Science:

  • Cognitive Science
  • Neuroscience
  • Computational Psychology

Background:

  • Probabilistic models have been central to understanding perception for over 100 years.
  • Existing literature presents ambiguities regarding Bayesian approaches, optimality, and their relation to classic models like signal detection theory (SDT).
  • Questions arise on whether near-optimal inference findings imply neural computation using probability distributions.

Purpose of the Study:

  • To disentangle complex probabilistic concepts within perception research.
  • To critically evaluate the relationship between Bayesian inference and optimal performance.
  • To differentiate modern Bayesian models from traditional signal detection theory frameworks.

Main Methods:

  • Comprehensive literature review of probabilistic concepts in perception.
  • Conceptual analysis to distinguish between Bayesian and optimal inference.
  • Classification of empirical evidence supporting or refuting probabilistic neural computation.

Main Results:

  • Identifies key conceptual distinctions between Bayesian and optimal frameworks.
  • Highlights differences and similarities between Bayesian models and signal detection theory.
  • Categorizes evidence regarding the neural implementation of probability distributions.

Conclusions:

  • Clarifies that being Bayesian is not necessarily equivalent to being optimal.
  • Establishes that recent Bayesian models offer distinct perspectives compared to classic SDT.
  • Provides a framework for interpreting empirical findings on neural probabilistic computation.