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

Introduction to Learning01:18

Introduction to Learning

524
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...
524
Associative Learning01:27

Associative Learning

557
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...
557
Cognitive Learning01:21

Cognitive Learning

508
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...
508
Observational Learning01:12

Observational Learning

297
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...
297
Purposive Learning01:22

Purposive Learning

199
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...
199
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

779
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...
779

You might also read

Related Articles

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

Sort by
Same author

Spatial and seasonal changes in levels of perfluoroalkyl acids (PFAAs) in surface water and sediment from a typical paper-recycling area in Vietnam.

Environmental geochemistry and health·2026
Same author

Toward a science of human-AI teaming for decision making: A complementarity framework.

PNAS nexus·2026
Same author

A Linearized Alternating Direction Multiplier Method for Federated Matrix Completion Problems.

IEEE transactions on neural networks and learning systems·2026
Same author

Emergent cooperative decision-making in triadic Prisoner's Dilemmas: Effects of incentives and information.

Acta psychologica·2025
Same author

Federated learning with randomized alternating direction method of multipliers and application in training neural networks.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

Personalized Model-Driven Interventions for Decisions From Experience.

Topics in cognitive science·2024

Related Experiment Video

Updated: Sep 6, 2025

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

7.7K

SpeedyIBL: A comprehensive, precise, and fast implementation of instance-based learning theory.

Thuy Ngoc Nguyen1, Duy Nhat Phan1, Cleotilde Gonzalez2

  • 1Carnegie Mellon University, Social and Decision Sciences, 5000 Forbes Ave., Pittsburgh, 15213, PA, USA.

Behavior Research Methods
|June 29, 2022
PubMed
Summary
This summary is machine-generated.

Instance-based learning theory (IBLT) is updated for complex decision-making. A new implementation, SpeedyIBL, overcomes computational limits, enhancing human decision modeling in dynamic, multi-agent environments.

Keywords:
Cognitive modelsDecision from experienceInstance-based learningPython instance-based learning library

More Related Videos

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.5K
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

677

Related Experiment Videos

Last Updated: Sep 6, 2025

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

7.7K
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.5K
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

677

Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Instance-based learning theory (IBLT) explains human decision-making from experience in dynamic tasks.
  • Existing computational models based on IBLT successfully predict human decisions but face limitations in complex environments.
  • The original IBLT description lacks precision for advanced applications, and prior models struggle with computational scaling.

Purpose of the Study:

  • To present a precise, updated theoretical framework for Instance-based Learning Theory (IBLT).
  • To introduce SpeedyIBL, an advanced computational implementation of IBLT designed for complex individual and multi-agent decision-making.
  • To address and overcome the 'curse of exponential growth' in memory-based computations of IBL models.

Main Methods:

  • Developed an updated theoretical formulation of IBLT's core components.
  • Implemented SpeedyIBL, leveraging parallel computation and vectorization to optimize computational efficiency.
  • Evaluated SpeedyIBL's performance against existing IBLT implementations using decision games of increasing complexity.

Main Results:

  • SpeedyIBL effectively handles a diverse range of individual and multi-agent decision-making problems.
  • The new implementation significantly speeds up computation time compared to previous IBL models, mitigating the curse of exponential growth.
  • IBLT demonstrates broad applicability across various decision-making tasks, with SpeedyIBL showing marked improvements as task complexity increases.

Conclusions:

  • The updated IBLT framework and SpeedyIBL implementation significantly enhance the capability to model human decision-making in complex scenarios.
  • SpeedyIBL offers a computationally efficient solution for applying IBLT to large-scale and dynamic decision-making problems.
  • The open-sourced SpeedyIBL library facilitates broader research in computational cognitive science and artificial intelligence.