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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...

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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...

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Learned Behavior I

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Related Experiment Video

Updated: Aug 13, 2025

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

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

Published on: June 1, 2015

Continual task learning in natural and artificial agents.

Timo Flesch¹, Andrew Saxe², Christopher Summerfield¹

¹Department of Experimental Psychology, University of Oxford, Oxford, UK.

Trends in Neurosciences

|January 22, 2023

Summary

This summary is machine-generated.

Humans and animals learn tasks by changing neural representations, minimizing interference. Machine learning models help understand how the brain partitions knowledge for efficient task acquisition and coding.

Keywords:

Hebbian gating machine learning neural networks neuroimaging representational geometry

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Last Updated: Aug 13, 2025

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Area of Science:

Neuroscience
Cognitive Science
Computational Neuroscience

Background:

Understanding how the brain learns and stores new information is a fundamental question in neuroscience.
Neural representations undergo significant changes during the process of task learning.

Purpose of the Study:

To review recent research on neural task representations and their geometry.
To explore computational models explaining knowledge partitioning during learning.
To discuss the role of machine learning in understanding biological learning.

Main Methods:

Review of brain recording studies focusing on neural representations during task learning.
Analysis of the geometry and dimensionality of neural task representations in the neocortex.
Examination of computational models that leverage findings from neural recordings.

Main Results:

Neural representations change during task learning to minimize mutual interference.
The geometry and dimensionality of neural representations are key to understanding task partitioning.
Computational models incorporating machine learning principles offer insights into biological learning mechanisms.

Conclusions:

The brain likely employs strategies to partition knowledge across tasks, minimizing interference.
Machine learning concepts, such as supervised and unsupervised learning, provide valuable frameworks for neuroscientists.
Future research integrating neural data with machine learning may further elucidate the mechanisms of natural task learning and coding.