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- W4385422324 abstract "Computational models of behavior have revolutionized social sciences. Most computational models are developed top-down; researchers start with a theory and then embody it in mathematical formalisms. This approach is often limited by the predispositions of individual researchers, and often provides limited predictive accuracy. Conversely, bottom-up approaches using machine learning achieve high levels of accuracy but produce models that are relatively uninterpretable. To address these limitations, we propose the use of equation discovery algorithms – currently used mainly in physics and biology – to develop models that can improve predictability without sacrificing interpretability. We evaluated our approach by challenging it to uncover novel insights into one of psychology’s oldest and most well-developed frameworks: reinforcement learning. Specifically, we collected behavioral data from a learning task and used an equation discovery algorithm to uncover, bottom-up, an interpretable and novel model of how people learn from rewards and punishments. The uncovered model, a Quadratic Q-Weighted model, generalized beyond its training set, provided new insights into learning, and outperformed existing models at predicting behavior across eight of nine published datasets. By meeting the high bar of shedding new light on human reinforcement learning, equation discovery algorithms emerge as a promising approach for model development in social sciences." @default.
- W4385422324 created "2023-08-01" @default.
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- W4385422324 date "2023-07-31" @default.
- W4385422324 modified "2023-09-27" @default.
- W4385422324 title "Identifying Learning Models from Human Behavior using Bottom-Up Equation Discovery" @default.
- W4385422324 doi "https://doi.org/10.31234/osf.io/65jqh" @default.
- W4385422324 hasPublicationYear "2023" @default.
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