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Using Machine Learning to Evaluate and Enhance Models of Probabilistic Inference
Date created: 2023-04-20 07:11 AM | Last Updated: 2024-06-25 05:55 AM
Identifier: DOI 10.17605/OSF.IO/FA4JW
Category: Project
Description: This page provides data and materials for the article: Glöckner, A., Jekel, M., & Lisovoj, D. (in press). Using machine learning to evaluate and enhance models of probabilistic inference. Decision.
Abstract
Probabilistic inference constitutes a class of choice tasks in which individuals rely on probabilistic cues to choose the option that is best on a given criterion. We apply a machine learning approach to a dataset of 62,311 choices in randomly generated probabilistic inference tasks to evaluate existing models and identify directions for further improvements. A generic multi-layer neural …
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