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Related Concept Videos

Observational Learning01:12

Observational Learning

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 because...
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

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...
Hindsight Biases01:12

Hindsight Biases

Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now?
Associative Learning01:27

Associative Learning

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...
Cognitive Learning01:21

Cognitive Learning

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...
Neural Regulation01:37

Neural Regulation

Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.

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Related Experiment Video

Updated: Jul 7, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Predicting human interactive learning by regret-driven neural networks.

Davide Marchiori1, Massimo Warglien

  • 1Interdepartmental Center for Research Training in Economics and Management (CIFREM), University of Trento, Italy.

Science (New York, N.Y.)
|February 23, 2008
PubMed
Summary

Neural networks modeling human interactive learning in games accurately predict behavior. Regret-based feedback significantly enhances predictions, outperforming traditional economic models for understanding social learning dynamics.

Related Experiment Videos

Last Updated: Jul 7, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Area of Science:

  • Cognitive Science
  • Neuroscience
  • Game Theory

Background:

  • Human learning in social contexts is inherently interactive, with individual learning influenced by concurrent learning of others.
  • Games serve as a standard model for interactive decision-making scenarios.
  • Understanding and predicting interactive learning is crucial in social contexts.

Purpose of the Study:

  • To explore the efficacy of neural networks in modeling and predicting human interactive learning within repeated game settings.
  • To assess the impact of regret-based feedback on the predictive accuracy of these models.
  • To compare the performance of regret-based neural network models against established economic models.

Main Methods:

  • Utilized simple neural networks designed to simulate learning processes.
  • Incorporated regret-based feedback mechanisms into the learning networks.
  • Tested the models across 21 diverse games featuring unique mixed-strategy equilibria.
  • Compared model predictions against observed human behavior in experimental settings.

Main Results:

  • Even basic neural networks with regret-based feedback accurately predicted human behavior in repeated games.
  • The inclusion of regret in the feedback mechanism substantially improved the neural network's predictive performance.
  • Regret-based models demonstrated superior predictive capabilities compared to conventional economic models.

Conclusions:

  • Neural networks, particularly when incorporating regret-based feedback, offer a powerful tool for modeling human interactive learning.
  • Regret is a key factor in social learning that can be effectively captured by computational models.
  • These findings suggest a promising direction for advancing our understanding of interactive decision-making and social learning through computational approaches.