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

Law of Effect01:06

Law of Effect

1.6K
B.F. Skinner, a prominent figure in behavioral psychology, introduced operant conditioning by emphasizing the role of consequences in shaping behavior. This theory builds upon the law of effect proposed by Edward Thorndike, which posits that behaviors followed by satisfying outcomes are likely to be repeated. In contrast, those followed by unsatisfying outcomes are less likely to recur.
Edward Thorndike's foundational work involved studying learning in animals, particularly using puzzle...
1.6K
Reinforcement01:23

Reinforcement

343
Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
343
Reinforcement Schedules01:24

Reinforcement Schedules

242
Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
242
Purposive Learning01:22

Purposive Learning

207
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...
207
Associative Learning01:27

Associative Learning

579
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...
579
Observational Learning01:12

Observational Learning

314
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...
314

You might also read

Related Articles

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

Sort by
Same author

Collective emotion regulation.

The American psychologist·2026
Same author

Learning affect norms: Implications for predictions, experiences, and social judgments.

Journal of experimental psychology. General·2026
Same author

Challenging the mechanism for the implicit association test.

Nature human behaviour·2026
Same author

From conflict to control: Responsiveness to food-related conflict predicts healthy eating.

Appetite·2026
Same author

How malicious AI swarms can threaten democracy.

Science (New York, N.Y.)·2026
Same author

Neural general circulation models for modeling precipitation.

Science advances·2026

Related Experiment Video

Updated: Sep 13, 2025

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

10.8K

Data-driven equation discovery reveals nonlinear reinforcement learning in humans.

Kyle J LaFollette1,2, Janni Yuval3, Roey Schurr4

  • 1Department of Psychological Sciences, Case Western Reserve University, Cleveland, OH 44106.

Proceedings of the National Academy of Sciences of the United States of America
|July 31, 2025
PubMed
Summary
This summary is machine-generated.

A new Quadratic Q-Weighted model improves reinforcement learning (RL) predictions by incorporating nonlinear dynamics and negativity biases. This advanced computational model offers better insights into human learning and decision-making compared to traditional linear approaches.

Keywords:
dynamical systemsmachine learningnonlinear modelingreinforcement learning

More Related Videos

Investigating Motor Skill Learning Processes with a Robotic Manipulandum
07:52

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Published on: February 12, 2017

8.8K
Pavlovian Conditioned Approach Training in Rats
06:57

Pavlovian Conditioned Approach Training in Rats

Published on: February 4, 2016

11.1K

Related Experiment Videos

Last Updated: Sep 13, 2025

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

10.8K
Investigating Motor Skill Learning Processes with a Robotic Manipulandum
07:52

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Published on: February 12, 2017

8.8K
Pavlovian Conditioned Approach Training in Rats
06:57

Pavlovian Conditioned Approach Training in Rats

Published on: February 4, 2016

11.1K

Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Behavioral Economics

Background:

  • Reinforcement learning (RL) models are crucial for understanding human decision-making.
  • Traditional RL models often use linear updates for reward expectations, which may oversimplify complex human behavior.
  • There is a need for more nuanced computational models to capture the intricacies of learning and reward processing.

Purpose of the Study:

  • To develop and validate a novel computational model of reinforcement learning (RL) that addresses limitations of traditional linear models.
  • To explore the application of equation discovery algorithms in uncovering new RL models from behavioral data.
  • To investigate the role of nonlinear dynamics and negativity biases in human reward prediction errors.

Main Methods:

  • Utilized equation discovery algorithms, a method adapted from physics and biology, to identify potential RL models.
  • Proposed a new model, the Quadratic Q-Weighted model, based on differential equations capturing linear and nonlinear functions.
  • Tested the model's generalizability and predictive accuracy against classical RL models using nine published datasets.

Main Results:

  • The Quadratic Q-Weighted model demonstrated superior predictive accuracy over traditional models in eight out of nine published datasets.
  • The model revealed that reward prediction errors follow nonlinear dynamics and exhibit negativity biases.
  • Findings indicate an underweighting of rewards when expectations are low and an overweighting of reward absence when expectations are high.

Conclusions:

  • The Quadratic Q-Weighted model offers a more accurate and interpretable approach to modeling human learning and decision-making.
  • This study highlights the power of integrating behavioral tasks with advanced computational methods for discovering cognitive patterns.
  • The findings represent a significant advancement in developing broadly applicable and insightful computational models of human cognition.