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Observational Learning

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...

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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 bonus...

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Related Experiment Video

Updated: Jun 2, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Learning to learn causal models.

Charles Kemp¹, Noah D Goodman, Joshua B Tenenbaum

¹Department of Psychology, Carnegie Mellon University Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology.

Cognitive Science

|May 14, 2011

Summary

This summary is machine-generated.

Humans rapidly learn about new causal systems by leveraging prior knowledge through a hierarchical Bayesian framework. This approach organizes learned causal models into categories, accelerating future causal learning.

Related Experiment Videos

Last Updated: Jun 2, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Area of Science:

Cognitive Science
Psychology
Artificial Intelligence

Background:

Humans continuously encounter and learn about multiple causal systems throughout their lives.
Understanding individual causal systems is complex, and efficient learning across systems is crucial.

Purpose of the Study:

To present a hierarchical Bayesian framework explaining accelerated causal learning.
To investigate how learning about multiple causal systems enhances understanding of new systems.

Main Methods:

Developed a hierarchical Bayesian framework to model causal learning.
The framework learns individual causal models and a shared causal schema.
Empirically tested the framework's predictions in four experiments.

Main Results:

The framework successfully models how commonalities among causal systems are captured in a causal schema.
Demonstrated that this schema facilitates rapid learning of causal models for new objects.
Experimental data confirmed accelerated learning of novel object causal powers.

Conclusions:

The proposed hierarchical Bayesian framework provides a robust explanation for human causal learning acceleration.
This model offers a better account of empirical data compared to alternative causal learning models.