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

Associative Learning01:27

Associative Learning

1.9K
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...
1.9K
Factors Influencing Attraction I: Proximity01:22

Factors Influencing Attraction I: Proximity

389
Proximity plays a fundamental role in shaping interpersonal attraction by increasing opportunities for interaction and fostering familiarity. Research consistently demonstrates that individuals are more likely to form social bonds with those who are physically closer to them, whether in residential settings, workplaces, or educational institutions. This effect is largely driven by the increased frequency of encounters, which facilitates the development of friendships and romantic...
389
Observational Learning01:12

Observational Learning

1.2K
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
1.2K
Purposive Learning01:22

Purposive Learning

585
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...
585
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

3.7K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
3.7K
Cognitive Learning01:21

Cognitive Learning

1.6K
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...
1.6K

You might also read

Related Articles

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

Sort by
Same author

Structure Is Information: Structural Identifiability Mappings for Machine Learning With Partially Observed Dynamical Systems.

IEEE transactions on cybernetics·2026
Same author

Endocrine and metabolic determinants of cardiometabolic risk in mild autonomous cortisol secretion.

EBioMedicine·2026
Same author

AI-guided patient stratification improves outcomes and efficiency in the AMARANTH Alzheimer's Disease clinical trial.

Nature communications·2025
Same author

Whole-genome phenotype prediction with machine learning: open problems in bacterial genomics.

Bioinformatics (Oxford, England)·2025
Same author

Linear simple cycle reservoirs at the edge of stability perform Fourier decomposition of the input driving signals.

Chaos (Woodbury, N.Y.)·2025
Same author

Machine learning reveals sex differences in distinguishing between conduct-disordered and neurotypical youth based on emotion processing dysfunction.

BMC psychiatry·2025
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: Apr 4, 2026

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
11:18

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Published on: June 1, 2015

11.2K

Indefinite Proximity Learning: A Review.

Frank-Michael Schleif1, Peter Tino2

  • 1University of Birmingham, School of Computer Science, B15 2TT, Birmingham, U.K. schleif@cs.bham.ac.uk.

Neural Computation
|August 28, 2015
PubMed
Summary
This summary is machine-generated.

This study surveys learning with nonmetric proximity data, which classical algorithms struggle with. It systematizes approaches and provides experimental results, offering practical solutions for large-scale data analysis.

More Related Videos

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

8.2K
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

7.4K

Related Experiment Videos

Last Updated: Apr 4, 2026

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
11:18

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Published on: June 1, 2015

11.2K
Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

8.2K
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

7.4K

Area of Science:

  • Machine Learning
  • Data Science
  • Computational Statistics

Background:

  • Data representation is crucial for efficient data analysis.
  • Classical methods often use metric-based similarities/dissimilarities (inner products, distances).
  • Nonmetric proximity measures are common but challenging for standard learning algorithms.

Purpose of the Study:

  • To provide a comprehensive survey of learning with nonmetric proximities.
  • To systematize and discuss various approaches for handling nonmetric data.
  • To address the practical challenge of large-scale proximity learning.

Main Methods:

  • Introduction to formalism for nonmetric spaces and proximity data.
  • Systematization and comparative discussion of different algorithmic approaches.
  • Experimental evaluation of algorithms on standard datasets, focusing on classification tasks.

Main Results:

  • A structured overview of methods for learning with nonmetric proximities.
  • Comparative analysis of algorithms regarding complexity and generalization.
  • Experimental validation of approaches, including considerations for large-scale applications.

Conclusions:

  • Nonmetric proximity data requires specialized treatments beyond classical metric-based methods.
  • The survey offers a framework for understanding and applying diverse algorithms.
  • The study highlights the importance and feasibility of large-scale nonmetric proximity learning for practical data analysis.