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Strategies for Assessing Autistic-Like Behaviors in Mice

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Machine Learning for Supplementing Behavioral Assessment.

Jordan D Bailey¹, Jonathan C Baker², Mark J Rzeszutek²

¹Department of Psychology, Franciscan Missionaries of Our Lady University, Baton Rouge, LA 70808 USA.

Perspectives on Behavior Science

|January 31, 2022

Summary

This summary is machine-generated.

Machine learning (ML) models can improve behavioral assessments by predicting behavior function from indirect data. ML models demonstrated higher accuracy than the Questions About Behavioral Function (QABF) assessment, offering a proof-of-concept for enhanced functional analysis.

Keywords:

Functional analysis Indirect assessment Machine learning QABF

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Area of Science:

Behavioral science
Machine learning
Psychometrics

Background:

The Questions About Behavioral Function (QABF) shows good convergent validity but lacks agreement with experimental functional analysis.
Machine learning (ML) offers potential for improving assessment validity through data-driven predictive models.

Purpose of the Study:

To train ML models using QABF and functional analysis data to predict behavior function.
To evaluate ML model performance against the QABF and explore ML's application in functional assessment.

Main Methods:

Trained five ML algorithms on published QABF and functional analysis data using cross-validation.
Augmented data with artificial samples to train and test an artificial neural network (ANN).

Main Results:

All five ML algorithms outperformed QABF on multilabel accuracy, though false negatives persisted.
The ANN demonstrated superior accuracy across all measures compared to other models.
ML models can inform the design of conditions for functional analysis.

Conclusions:

Machine learning shows promise for enhancing the accuracy and validity of behavioral functional assessments.
This study serves as a proof-of-concept for applying ML to functional assessment, potentially improving predictions from indirect measures.