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

Survival Tree01:19

Survival Tree

178
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
178
Associative Learning01:27

Associative Learning

644
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...
644
Cognitive Learning01:21

Cognitive Learning

695
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...
695
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

875
Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
875
Purposive Learning01:22

Purposive Learning

218
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
218
Introduction to Learning01:18

Introduction to Learning

593
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
593

You might also read

Related Articles

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

Sort by
Same author

Latent subdimensions of anxiety and depression differentially influence exertion of effort in pursuit of reward versus avoidance of threat.

Translational psychiatry·2026
Same author

Neural signatures of model-based and model-free reinforcement learning across prefrontal cortex and striatum.

eLife·2026
Same author

Uncertainty for better and worse.

Current opinion in neurobiology·2026
Same author

Dopamine dynamics in human anterior cingulate cortex during Pavlovian-instrumental conflict.

bioRxiv : the preprint server for biology·2026
Same author

Modality-general sensitivity of pupil responses to regularity violations.

Cognitive, affective & behavioral neuroscience·2026
Same author

Metacognitive efficiency in learned value-based choice.

PLoS computational biology·2026
Same journal

Corrigendum to 'Consonant, vowel, and tone cues in early wordform recognition: Evidence from Cantonese-learning infants' [Cognition 275 (2026) 106624].

Cognition·2026
Same journal

Identifying distinct sources of whole number interference in children's decimal comparison: the role of numerical magnitude and inhibitory control.

Cognition·2026
Same journal

Evidence for abstract spatial concept learning in young animals.

Cognition·2026
Same journal

Blurred lines or clear boundaries? Synchrony and social dominance shape domain-specific self-other processing.

Cognition·2026
Same journal

Knowability predicts curiosity and learning.

Cognition·2026
Same journal

Throwing good effort after bad: Evidence for a sunk-cost effect in cognitive effort-based decision-making.

Cognition·2026
See all related articles

Related Experiment Video

Updated: Oct 8, 2025

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
07:31

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Published on: February 8, 2019

6.7K

When unsupervised training benefits category learning.

Franziska Bröker1, Bradley C Love2, Peter Dayan3

  • 1Max Planck Institute for Biological Cybernetics, Tübingen, Germany; Gatsby Computational Neuroscience Unit, London, UK.

Cognition
|December 26, 2021
PubMed
Summary
This summary is machine-generated.

Unsupervised learning can help or hinder human learning depending on how well a person’s internal understanding aligns with the task. Misalignment can strengthen incorrect assumptions, impacting category learning.

Keywords:
CategorisationRepresentationSemi-supervised learning

More Related Videos

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

12.0K
Experience is Instrumental in Tuning a Link Between Language and Cognition: Evidence from 6- to 7- Month-Old Infants' Object Categorization
05:35

Experience is Instrumental in Tuning a Link Between Language and Cognition: Evidence from 6- to 7- Month-Old Infants' Object Categorization

Published on: April 19, 2017

6.8K

Related Experiment Videos

Last Updated: Oct 8, 2025

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
07:31

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Published on: February 8, 2019

6.7K
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

12.0K
Experience is Instrumental in Tuning a Link Between Language and Cognition: Evidence from 6- to 7- Month-Old Infants' Object Categorization
05:35

Experience is Instrumental in Tuning a Link Between Language and Cognition: Evidence from 6- to 7- Month-Old Infants' Object Categorization

Published on: April 19, 2017

6.8K

Area of Science:

  • Cognitive Science
  • Machine Learning
  • Human-Computer Interaction

Background:

  • Humans often learn with limited feedback, suggesting semi-supervised learning (integrating unsupervised and supervised data).
  • Previous studies on semi-supervised learning in humans show conflicting results regarding the benefits of unsupervised information.

Purpose of the Study:

  • To investigate the role of representational alignment in human semi-supervised learning.
  • To determine if unsupervised learning can improve or impair performance based on the match between internal and task-defined representations.

Main Methods:

  • An experiment was designed where participants initially categorized stimuli based on a salient, irrelevant dimension.
  • Feedback was withdrawn at different learning stages to assess the impact of unsupervised learning under varying degrees of representational alignment.
  • Performance was measured to see if unsupervised learning aided or hindered learning when internal representations were sufficiently or insufficiently aligned with the task.

Main Results:

  • Unsupervised learning demonstrated opposing effects on human learning outcomes.
  • The degree of alignment between subjects' internal representations and the task's requirements critically influenced the impact of unsupervised learning.
  • Momentary performance alone is insufficient to predict the effects of unsupervised learning.

Conclusions:

  • The alignment between internal and external representations is crucial for successful human semi-supervised learning.
  • Unsupervised learning can be detrimental if it reinforces incorrect assumptions due to poor representational alignment.
  • Understanding and assessing subjects' representational spaces is key to predicting and improving category learning in various contexts, including educational systems.