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Law of Effect

Law of Effect

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...

Timing and Consequences on Behavior

Timing and Consequences on Behavior

In operant conditioning, the timing of reinforcement is crucial. For animals like rats and cats, immediate reinforcement (within a few seconds) is much more effective than delayed reinforcement. For example, a food reward for a rat needs to follow within 30 seconds of pressing a bar to be effective.
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Reinforcement

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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.
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Operant Conditioning

Operant Conditioning

Operant conditioning, a key concept in behavioral psychology, involves using reinforcement and punishment to alter the likelihood of a behavior being repeated. B.F. introduced this type of conditioning. Skinner focused on voluntary behaviors and the consequences that follow them, influencing whether these behaviors will be strengthened or diminished.
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Decision Making

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Related Experiment Video

Updated: Apr 18, 2026

Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents

Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents

Published on: September 10, 2018

Computational framework explains how animals select actions with rewarding outcomes.

Janelle Weaver¹

¹Freelance Science Writer, Carbondale, Colorado, United States of America.

|January 7, 2015

Summary

This summary is machine-generated.

A new brain model highlights the striatum's role in learning from rewards. This research replicates data and sparks new questions about neurological disorders.

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A Fully Automated and Highly Versatile System for Testing Multi-cognitive Functions and Recording Neuronal Activities in Rodents

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Published on: May 3, 2012

Area of Science:

Neuroscience
Computational Neuroscience
Behavioral Neuroscience

Background:

The striatum is crucial for reward-based learning.
Existing models do not fully capture striatal function in behavior acquisition.

Purpose of the Study:

To present a novel computational model of the striatum.
To explain how the brain learns beneficial behaviors from rewarding outcomes.

Main Methods:

Development of a new computational model.
Simulation and analysis of model behavior.
Comparison of model predictions with experimental data.

Main Results:

The model successfully replicates key experimental findings on reward learning.
The model emphasizes the critical role of the striatum in this process.

Conclusions:

The proposed model offers a new framework for understanding striatal function in learning.
This work opens avenues for investigating neurological disorders associated with striatal dysfunction.