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Scaling cognitive modeling to big data: A deep learning approach to studying individual differences in evidence
Mischa von Krause1, Stefan T Radev2
1Psychological Institute, Heidelberg University.
Psychological Methods
|June 18, 2026
Summary
This study uses deep learning and Bayesian methods to analyze large behavioral datasets. Trial-by-trial variability in drift rate, a cognitive parameter, strongly predicts socioeconomic factors.
Area of Science:
- Cognitive Science
- Computational Neuroscience
- Behavioral Data Science
Background:
- Scalable estimation of cognitive process models is advancing with Bayesian modeling and deep learning.
- Large behavioral datasets offer opportunities to study individual differences in cognition.
Purpose of the Study:
- To present a fully Bayesian workflow using amortized inference with neural networks for rapid parameter estimation and model comparison.
- To investigate the relationship between latent cognitive parameters and socioeconomic variables using big behavioral data.
Main Methods:
- Employed a fully Bayesian workflow leveraging amortized inference with neural networks.
- Analyzed a large online implicit association test dataset (N > 5,000,000).
- Estimated cognitive parameters including drift rate, boundary separation, and nondecision times.
Main Results:
- Identified small but consistent associations between cognitive model parameters and socioeconomic covariates.
- Found that trial-by-trial variability in drift rate was the strongest predictor across socioeconomic covariates.
- Demonstrated the utility of deep learning-based Bayesian estimation for large, noisy behavioral data.
Conclusions:
- Deep learning-enhanced Bayesian estimation provides robust insights from large-scale behavioral data.
- Trial-by-trial variability in drift rate is a significant factor in understanding individual differences related to socioeconomic status.
- The developed open pipeline facilitates future research in behavioral data science and cognitive modeling.
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