Search research articles

Search research articles

Related Experiment Videos

Feedback effects on cost-benefit learning in perceptual categorization.

W T Maddox¹, C J Bohil

¹Department of Psychology, University of Texas, Austin 78712, USA. maddox@psy.utexas.edu

Memory & Cognition

|August 16, 2001

Summary

This summary is machine-generated.

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

Sex differences in cognitive performance and alcohol consumption in High Alcohol-Drinking (HAD-1) rats.

Behavioural brain research·2020

Same author

Sex-specific ultrasonic vocalization patterns and alcohol consumption in high alcohol-drinking (HAD-1) rats.

Physiology & behavior·2017

Same author

Toward a dual-learning systems model of speech category learning.

Frontiers in psychology·2014

Same author

Neural correlates of rule-based and information-integration visual category learning.

Cerebral cortex (New York, N.Y. : 1991)·2006

Same author

Separating perceptual processes from decisional processes in identification and categorization.

Perception & psychophysics·2002

Same author

On the relation between base-rate and cost-benefit learning in simulated medical diagnosis.

Journal of experimental psychology. Learning, memory, and cognition·2001

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

Optimal classifier feedback significantly improved decision criterion learning in perceptual tasks, unlike objective feedback. A hybrid model best explained learning over time, showing reduced accuracy focus with experience.

Area of Science:

Cognitive Psychology
Decision Science
Machine Learning

Background:

Decision criterion learning is crucial for optimizing performance in tasks with varying costs and benefits.
Feedback displays significantly influence how individuals learn and adapt their decision-making strategies.
Understanding learning dynamics in perceptual categorization is key for designing effective training protocols.

Purpose of the Study:

To investigate the impact of different feedback displays on decision criterion learning.
To compare objective versus optimal classifier feedback and immediate versus delayed feedback.
To test computational models explaining learning in perceptual categorization with unequal cost-benefits.

Main Methods:

Two experiments were conducted using a perceptual categorization task with unequal cost-benefits.

Related Experiment Videos

Experiment 1 employed a factorial design varying feedback timing and type (objective vs. optimal classifier).

Experiment 2 utilized a within-subjects design to test model-based hypotheses (flat-maxima, reward-accuracy competition, hybrid).

Main Results:

Immediate versus delayed feedback had no significant effect on learning.
Optimal classifier feedback led to significant performance improvements over blocks.
Objective feedback resulted in relatively stable performance.
Model-based analyses indicated a hybrid model was necessary to capture later learning stages.

Conclusions:

Optimal classifier feedback is more effective than objective feedback for decision criterion learning.
Learning dynamics are complex, often requiring hybrid models that integrate different learning assumptions.
Feedback characteristics influence the strategic adjustments individuals make in response to cost-benefit structures.