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Related Experiment Video

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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

Decisional separability, model identification, and statistical inference in the general recognition theory framework.

Noah H Silbert¹, Robin D Thomas

¹Center for Advanced Study of Language, University of Maryland, College Park, MD, USA. nsilbert@umd.edu

Psychonomic Bulletin & Review

|October 24, 2012

Summary

This summary is machine-generated.

New research reveals significant issues with statistical methods used in General Recognition Theory (GRT) for analyzing perceptual and decisional interactions. These problems can lead to incorrect conclusions about how we perceive and categorize information.

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Area of Science:

Cognitive Psychology
Perception and Recognition
Psychometric Modeling

Background:

The General Recognition Theory (GRT) framework is widely used to study multidimensional identification and categorization.
Existing inferential machinery within GRT has known limitations in detecting perceptual and decisional interactions.
Previous work has highlighted some issues, but the extent of these problems has been underestimated.

Purpose of the Study:

To identify and analyze previously unrecognized problems in the statistical methods used within the GRT framework.
To investigate the identifiability of decisional separability failures in the Gaussian GRT model.
To examine the relationships between different tests of perceptual and decisional interactions.

Main Methods:

Analytic derivations to formally assess the properties of GRT models.
Simulation-based analyses to evaluate the performance of statistical tests under various conditions.
Examination of two common response selection models within the GRT framework.

Main Results:

Failure of decisional separability is not identifiable in the Gaussian GRT model with common response selection models.
Previously unnoticed formal implicational relationships exist between distinct tests of perceptual and decisional interactions.
Tests using marginal signal detection parameters exhibit unacceptably high rates of Type I statistical errors (false positives).

Conclusions:

The identified problems have broad implications for research on perceptual and decisional interactions in multidimensional identification.
Current statistical approaches may lead to erroneous conclusions regarding the separability of perceptual and decisional processes.
New recommendations are needed for robustly testing dimensional relationships in the full-factorial identification paradigm.