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Related Experiment Video

Updated: Jul 2, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain
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Fitting computational models to fMRI.

F Gregory Ashby1, Jennifer G Waldschmidt

  • 1Department of Psychology, University of California, Santa Barbara, California 93106, USA. ashby@psych.ucsb.edu

Behavior Research Methods
|August 14, 2008
PubMed
Summary
This summary is machine-generated.

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Computational models in psychology can now be tested using functional magnetic resonance imaging (fMRI). This study presents methods to translate neural predictions into blood-oxygen-level-dependent (BOLD) responses for fMRI analysis.

Area of Science:

  • Cognitive Neuroscience
  • Computational Psychology
  • Neuroimaging

Background:

  • Computational models in psychology predict neural activity during cognitive tasks.
  • Functional magnetic resonance imaging (fMRI) is a key tool for testing these models.
  • Methodological challenges hinder direct comparison of model predictions and fMRI data.

Purpose of the Study:

  • To address methodological problems in testing computational psychology models with fMRI.
  • To provide a framework for translating model-based neural predictions into measurable fMRI signals.
  • To enable robust validation of cognitive models using neuroimaging data.

Main Methods:

  • Developing methods to convert predicted neural activations into predicted blood-oxygen-level-dependent (BOLD) responses.

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Last Updated: Jul 2, 2026

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  • Establishing procedures for selecting relevant voxels within regions of interest.
  • Implementing techniques for comparing observed and predicted BOLD responses.

    • Main Results:

    • Successfully outlined a methodology for transforming neural predictions into BOLD signals.
    • Provided a strategy for identifying appropriate voxels for model testing.
    • Described a comparative analysis framework for observed and predicted BOLD data.

    Conclusions:

    • The described methods facilitate the empirical testing of computational psychology models using fMRI.
    • This approach bridges the gap between theoretical predictions and neuroimaging evidence.
    • Enables more rigorous validation of psychological theories through direct comparison with brain activity data.