Generative Models Can Advance the Social, Behavioral, and Brain Sciences


Theories of individual differences are foundational to psychological and brain sciences, yet they are traditionally developed and tested using superficial summaries of data (e.g., mean response times) that are both (1) disconnected from our otherwise rich conceptual theories of behavior, and (2) contaminated with measurement error. Traditional approaches therefore lack the flexibility required to test increasingly complex theories of behavior. To resolve this theory-description gap, we present the generative modeling approach, which involves using background knowledge to formally specify how behavior is generated within people, and in turn how generative processes vary across people. Generative modeling shifts our focus away from estimating descriptive statistical “effects” toward estimating psychologically interpretable parameters, while simultaneously accounting for measurement error that would otherwise attenuate individual difference correlations. We demonstrate the utility of generative models in the context of the “reliability paradox”, a phenomenon wherein highly replicable group effects (e.g., Stroop effect) fail to capture individual differences (e.g., low test-retest reliability). Simulations and empirical data from the Implicit Association Test, and Stroop, Flanker, Posner, and Delay Discounting tasks show that generative models yield (1) more theoretically informative parameters, and (2) higher test-retest estimates relative to traditional approaches, illustrating their potential for enhancing theory development.

Oct 14, 2020 12:00 AM
Talk presented to the Neuroscience and Mental Health Group at the Institute of Cognitive Neuroscience, University College London
Nathaniel Haines
Nathaniel Haines
Computational Psychologist & Data Scientist, PhD

An academic Bayesian who is currently exploring the high dimensional posterior distribution of life

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