Jove
Visualize
Contact Us

Related Concept Videos

Associative Learning01:27

Associative Learning

322
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
322
Perceptual Constancy01:12

Perceptual Constancy

371
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...
371
Chunking and Rehearsal in Sensory Memory01:22

Chunking and Rehearsal in Sensory Memory

188
Improving short-term memory can be achieved through techniques like chunking and rehearsal. Chunking involves organizing information into larger, more manageable units. This technique is particularly useful for information that exceeds the typical memory span of between five and nine items. For instance, logging into an online account with a password like "ta89vq0179gz" involves grouping letters and numbers into three chunks—ta89, vq01, and 79gz. It makes large amounts of...
188
Long-term Potentiation01:25

Long-term Potentiation

2.7K
Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
Hebbian LTP
LTP can occur when...
2.7K
Cognitive Learning01:21

Cognitive Learning

233
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
233
The Anchoring-and-Adjustment Heuristic01:25

The Anchoring-and-Adjustment Heuristic

7.2K
In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the...
7.2K

You might also read

Related Articles

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

Sort by
Same author

Differentiating high-frequency and high-severity hotspots: A robust risk-evolution-volume (REV) framework.

Accident; analysis and prevention·2026
Same author

Insights into perceptual learning.

eLife·2026
Same author

A novel QTc-RR differential biomarker for the early assessment of autonomic dysfunction in type 2 diabetes.

Frontiers in endocrinology·2026
Same author

Deep-learning-empowered programmable topolectrical circuits.

Nature communications·2026
Same author

Advancing entropy analysis for heart rate variability: clinical insights for aging and diabetes.

Frontiers in physiology·2026
Same author

Programmable optical activation functions for KANs via feed-forward integrated microring resonators.

Optics letters·2026
Same journal

A human-specific genetic modifier reconfigures large-scale cortical network dynamics underlying behavioral performance.

bioRxiv : the preprint server for biology·2026
Same journal

<i>Staphylococcus aureus</i> uses a eukaryotic-like uridyltransferase to make UDP-GlcNAc for cell wall synthesis.

bioRxiv : the preprint server for biology·2026
Same journal

Dynamic redistribution of eIF4F controls cap-dependent translation initiation.

bioRxiv : the preprint server for biology·2026
Same journal

When does additional information improve accuracy of RNA secondary structure prediction?

bioRxiv : the preprint server for biology·2026
Same journal

Normative brain-state trajectories reveal deviation from healthy aging in Alzheimer's disease.

bioRxiv : the preprint server for biology·2026
Same journal

Noradrenergic infraslow rhythm during sleep is the critical link between heart-rate dynamics and memory consolidation.

bioRxiv : the preprint server for biology·2026
See all related articles
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 Experiment Video

Updated: Jun 16, 2025

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
07:12

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Published on: April 11, 2025

308

Hierarchical Bayesian Augmented Hebbian Reweighting Model of Perceptual Learning.

Zhong-Lin Lu1, Shanglin Yang2, Barbara Dosher3

  • 1Division of Arts and Sciences, NYU Shanghai, Shanghai, China; Center for Neural Science and Department of Psychology, New York University, New York, USA; NYU-ECNU Institute of Brain and Cognitive Science, Shanghai, China.

Biorxiv : the Preprint Server for Biology
|August 16, 2024
PubMed
Summary
This summary is machine-generated.

A new hierarchical Bayesian model (HB-AHRM) simultaneously models individual and population learning curves in perceptual learning. This approach significantly speeds up analysis and enhances statistical inference across all levels.

Keywords:
Augmented Hebbian Reweighting ModelHierarchical Bayesian ModelLikelihood ApproximationPerceptual LearningPytensor

More Related Videos

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

2.5K
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

9.9K

Related Experiment Videos

Last Updated: Jun 16, 2025

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
07:12

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Published on: April 11, 2025

308
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

2.5K
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

9.9K

Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Machine Learning

Background:

  • The Augmented Hebbian Reweighting Model (AHRM) is established for modeling collective perceptual learning.
  • Existing methods often analyze individual or population data separately.

Purpose of the Study:

  • Introduce a novel hierarchical Bayesian Augmented Hebbian Reweighting Model (HB-AHRM).
  • Simultaneously model individual participant and population learning curves within a unified framework.
  • Compare HB-AHRM performance against a Bayesian Inference Procedure (BIP).

Main Methods:

  • Developed a hierarchical Bayesian framework (HB-AHRM).
  • Implemented a likelihood function approximation using feature engineering and linear regression.
  • Achieved a 20,000x speed increase in estimation procedures.
  • Computed joint posterior distributions at population, observer, and test levels.

Main Results:

  • HB-AHRM successfully models both individual and population learning curves.
  • The likelihood approximation drastically reduces computational demands.
  • Enables robust statistical inference across hierarchical levels.
  • HB-AHRM outperforms BIP in integrated modeling.

Conclusions:

  • HB-AHRM provides a powerful, unified framework for perceptual learning analysis.
  • The likelihood approximation technique has broad applicability for stochastic models.
  • This methodology facilitates accurate prediction of human performance at multiple levels.