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Full-Text Articles in Cognition and Perception
Improving Measurements Of Similarity Judgments With Machine-Learning Algorithms, Jeffrey R. Stevens, Alexis Polzkill Saltzman, Tanner Rasmussen, Leen-Kiat Soh
Improving Measurements Of Similarity Judgments With Machine-Learning Algorithms, Jeffrey R. Stevens, Alexis Polzkill Saltzman, Tanner Rasmussen, Leen-Kiat Soh
Jeffrey Stevens Publications
Intertemporal choices involve assessing options with different reward amounts available at different time delays. The similarity approach to intertemporal choice focuses on judging how similar amounts and delays are. Yet we do not fully understand the cognitive process of how these judgments are made. Here, we use machine-learning algorithms to predict similarity judgments to (1) investigate which algorithms best predict these judgments, (2) assess which predictors are most useful in predicting participants’ judgments, and (3) determine the minimum number of judgments required to accurately predict future judgments. We applied eight algorithms to similarity judgments for reward amount and time delay …
Predicting Similarity Judgments In Intertemporal Choice With Machine Learning, Jeffrey R. Stevens, Leen-Kiat Soh
Predicting Similarity Judgments In Intertemporal Choice With Machine Learning, Jeffrey R. Stevens, Leen-Kiat Soh
Jeffrey Stevens Publications
Similarity models of intertemporal choice are heuristics that choose based on similarity judgments of the reward amounts and time delays. Yet, we do not know how these judgments are made. Here, we use machine-learning algorithms to assess what factors predict similarity judgments and whether decision trees capture the judgment outcomes and process. We find that combining small and large values into numerical differences and ratios and arranging them in tree-like structures can predict both similarity judgments and response times. Our results suggest that we can use machine learning to not only model decision outcomes but also model how decisions are …