Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

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

Sort by

Same author

A recurrent neural network model for a decision-making task based on sequential evidence accumulation.

Cognitive neurodynamics·2026

Same author

Dynamical analysis of a mean firing rate model in Parkinson's disease.

Cognitive neurodynamics·2026

Same author

Application of neurodynamics theory in the study of neural circuits in major depressive disorder: a review on neural energy approaches.

Cognitive neurodynamics·2026

Same author

A Domain Generalization Method for EEG Based on Domain-Invariant Feature and Data Augmentation.

Cyborg and bionic systems (Washington, D.C.)·2026

Same author

Motivational state determines error-sensitive learning modes in visual perceptual learning.

Cerebral cortex (New York, N.Y. : 1991)·2026

Same author

Dynamic mode decomposition of resting-state fMRI revealing abnormal brain region features in schizophrenia.

Frontiers in computational neuroscience·2026

Search research articles

Related Experiment Video

Updated: Jul 5, 2026

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Reward prediction based on stimulus categorization in primate lateral prefrontal cortex.

Xiaochuan Pan¹, Kosuke Sawa, Ichiro Tsuda

¹Brain Science Institute, Tamagawa University, Tamagawagakuen 6-1-1, Machida, Tokyo 194-610, Japan.

Nature Neuroscience

|May 27, 2008

Summary

Animals adapt by learning reward associations. Prefrontal cortex neurons categorize stimuli to predict rewards, even for unexperienced items within a category, aiding environmental adaptation.

More Related Videos

Experience is Instrumental in Tuning a Link Between Language and Cognition: Evidence from 6- to 7- Month-Old Infants' Object Categorization

Experience is Instrumental in Tuning a Link Between Language and Cognition: Evidence from 6- to 7- Month-Old Infants' Object Categorization

Published on: April 19, 2017

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

Related Experiment Videos

Last Updated: Jul 5, 2026

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Experience is Instrumental in Tuning a Link Between Language and Cognition: Evidence from 6- to 7- Month-Old Infants' Object Categorization

Experience is Instrumental in Tuning a Link Between Language and Cognition: Evidence from 6- to 7- Month-Old Infants' Object Categorization

Published on: April 19, 2017

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

Area of Science:

Neuroscience
Cognitive Science
Animal Behavior

Background:

Adaptation to new environments requires goal-directed behaviors.
Understanding how the brain predicts rewards is crucial for adaptive strategies.

Purpose of the Study:

Investigate how prefrontal neurons integrate associations to predict rewards.
Examine neural mechanisms underlying adaptive goal-directed behavior.

Main Methods:

Used a sequential paired-association task with an asymmetric reward schedule.
Recorded from neurons in the lateral prefrontal cortex during the task.

Main Results:

Identified two types of reward-related neurons in the lateral prefrontal cortex.
One neuron type predicted reward irrespective of stimulus features; another encoded category-specific reward value.
Category-specific neurons predicted reward for unassociated stimuli if another stimulus in the same category was rewarded.

Conclusions:

Prefrontal neurons represent reward information based on stimulus category.
This category-based reward representation facilitates prediction for novel stimuli.
Findings suggest a mechanism for flexible reward learning and adaptation in changing environments.