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Measuring model flexibility with parameter space partitioning: an introduction and application example.

Mark A Pitt¹, Jay I Myung, Maximiliano Montenegro

¹Department of Psychology, Ohio State University.

Cognitive Science

|May 19, 2011

Summary

This summary is machine-generated.

This study introduces parameter space partitioning (PSP) to assess cognitive model flexibility. PSP helps understand why models fit data well or poorly, comparing speech perception models like TRACE and ARTphone.

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Area of Science:

Cognitive Science
Computational Neuroscience
Psychology

Background:

Model evaluation in cognitive science relies heavily on empirical data fitting.
Understanding a model's flexibility is crucial for interpreting its performance.
Existing methods for model comparison and flexibility analysis can be limited.

Purpose of the Study:

To review methods for comparing cognitive models and analyzing their flexibility.
To introduce and demonstrate Parameter Space Partitioning (PSP) as a general-purpose method.
To compare the flexibility of two connectionist models of speech perception.

Main Methods:

Review of existing model comparison and flexibility analysis techniques.
Introduction and detailed explanation of Parameter Space Partitioning (PSP).
Application of PSP to compare the TRACE and ARTphone models of speech perception.

Main Results:

Parameter Space Partitioning (PSP) offers a robust framework for analyzing cognitive model flexibility.
Design differences between TRACE and ARTphone significantly impact their data-fitting potential.
PSP successfully elucidates how specific model architectures influence performance.

Conclusions:

Parameter Space Partitioning (PSP) is a valuable tool for evaluating and comparing cognitive models.
Understanding model flexibility is key to advancing computational models of cognition.
The study highlights the utility of PSP in analyzing connectionist models of speech perception.