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Constructing and Visualizing Models using Mime-based Machine-learning Framework

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Comparing machine learning and artificial neural network models in psychological research: a ROC-based analysis.

Marie-Luise Leitner¹, Martin Arendasy¹

¹Department of Psychology, University of Graz, Graz, Austria.

Frontiers in Psychology

|March 9, 2026

Summary

This summary is machine-generated.

Traditional machine learning models, like logistic regression and random forest, outperform artificial neural networks for psychological selection tasks. These classical methods offer more stable and interpretable results in applied assessment contexts.

Keywords:

ROC (receiver operating characteristic)artificial neural network decision tree feature importance logistic regression machine learning noise overfitting

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Area of Science:

Psychological Assessment
Machine Learning
Artificial Intelligence

Background:

Data-driven methods are increasingly used in psychological assessment.
The comparative performance of artificial neural networks versus traditional machine learning models in selection contexts is not well-established.
Limited comparative evidence exists using real-world psychological datasets and ROC-based evaluation.

Purpose of the Study:

To compare the effectiveness of traditional machine learning models against artificial neural networks in applied psychological selection.
To evaluate model performance using accuracy and receiver operating characteristic (ROC) analysis on a real-world dataset.

Main Methods:

Compared logistic regression, decision tree, and random forest models with a feedforward artificial neural network.
Utilized a dataset of 4,155 university entrance examination applicants.
Evaluated models using accuracy and Area Under the Curve (AUC) from ROC analysis.

Main Results:

Logistic regression yielded the highest predictive performance (accuracy = 0.973, AUC = 0.99), closely followed by random forest (accuracy = 0.961, AUC = 0.98).
The artificial neural network achieved lower discriminative ability (AUC = 0.87) and showed signs of overfitting.
Biology, chemistry, and numerical reasoning were identified as key predictors.

Conclusions:

Traditional machine learning models offer more stable, interpretable, and robust performance for medium-sized, structured psychological datasets compared to shallow neural networks.
Model selection and inductive bias are crucial in applied psychological research.
Classical machine learning approaches remain valuable for selection and assessment contexts.