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

Search research articles

Related Experiment Videos

Analyzing recognition performance with sparse data.

Ching-Fan Sheu¹, Yuh-Shiow Lee, Pei-Ying Shih

¹Department of Psychology, National Chung Cheng University, Chia-Yi, Taiwan. psycfs@ccu.edu.tw

Behavior Research Methods

|August 14, 2008

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

Using Machine Learning to Develop a Short-Form Measure Assessing 5 Functions in Patients With Stroke.

Archives of physical medicine and rehabilitation·2022

Same author

Effects of luminance contrast and character size on reading speed.

Vision research·2019

Same author

A functional generalized F-test for signal detection with applications to event-related potentials significance analysis.

Biometrics·2019

Same author

Latino-American Mothers' Perspectives on Feeding Their Young Children: A Qualitative Study.

The American journal of occupational therapy : official publication of the American Occupational Therapy Association·2019

Same author

Feeding Patterns and Parental Perceptions of Feeding Issues of Preterm Infants in the First 2 Years of Life.

The American journal of occupational therapy : official publication of the American Occupational Therapy Association·2019

Same author

Teaching Theory in Occupational Therapy Using a Cooperative Learning: A Mixed-Methods Study.

Journal of allied health·2018

This study introduces a new signal detection model to reliably analyze recognition performance, even with limited data per subject. The method accounts for individual differences, improving group comparisons in recognition studies.

Area of Science:

Cognitive psychology
Psychometrics
Signal detection theory

Background:

Recognition performance studies often use small sample sizes, limiting the reliability of traditional d' analysis.
Individual variability in hit and false alarm rates can obscure true group effects.

Purpose of the Study:

To develop a robust signal detection model that accommodates subject-specific random variables.
To enable reliable estimation of population d' effects by pooling data across subjects and conditions.
To analyze the impact of emotional stimuli on recognition memory.

Main Methods:

Incorporation of subject-specific random variables into signal detection models.
Estimation of population d' by pooling information across subjects and experimental conditions.

Related Experiment Videos

Validation through a simulation study and application to the emotional Stroop task.

Main Results:

The proposed method provides a reliable estimation of d' even with limited observations per subject.
The model effectively accounts for inter-individual heterogeneity in response biases.
Analysis of the emotional Stroop task demonstrated the method's utility in real-world cognitive research.

Conclusions:

The novel signal detection approach enhances the reliability of recognition performance analysis.
This method offers a valuable tool for researchers studying group differences in cognitive tasks.
The findings have implications for understanding emotional influences on memory and perception.