ArticleProceedings of the National Academy of Sciences of the United States of America2020
Scaling up psychology via Scientific Regret Minimization.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed.
- AI-Discovered Cognitive Models Reveal Novel Insights into Human and Animal Learning.bioRxiv : the preprint server for biology · 2026Article
- Hybrid neural-cognitive models reveal how memory shapes human reward learning.Nature human behaviour · 2026Article
- Predicting human decisions with behavioural theories and machine learning.Nature human behaviour · 2025Article
- Sequence-to-sequence models with attention mechanistically map to the architecture of human memory search.Communications psychology · 2025Article
- Capturing the complexity of human strategic decision-making with machine learning.Nature human behaviour · 2025Article
- Data-driven equation discovery reveals nonlinear reinforcement learning in humans.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
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- Comprehensive exploration of visual working memory mechanisms using large-scale behavioral experiment.Nature communications · 2025Article
- Is Ockham's razor losing its edge? New perspectives on the principle of model parsimony.Proceedings of the National Academy of Sciences of the United States of America · 2025Review
- Automating the practice of science: Opportunities, challenges, and implications.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Automatic discovery and description of human planning strategies.Behavior research methods · 2024Article
- Using deep neural networks as a guide for modeling human planning.Scientific reports · 2023Article
- Scaling up behavioural studies of visual memory.Nature human behaviour · 2023Article
- A Model for Cognitive Personalization of Microtask Design.Sensors (Basel, Switzerland) · 2023Article
- Computational Scientific Discovery in Psychology.Perspectives on psychological science : a journal of the Association for Psychological Science · 2023Article
- Empirica: a virtual lab for high-throughput macro-level experiments.Behavior research methods · 2021Article
- Article
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3 authors.
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Abstract
Do large datasets provide value to psychologists? Without a systematic methodology for working with such datasets, there is a valid concern that analyses will produce noise artifacts rather than true effects. In this paper, we offer a way to enable researchers to systematically build models and identify novel phenomena in large datasets. One traditional approach is to analyze the residuals of models-the biggest errors they make in predicting the data-to discover what might be missing from those models. However, once a dataset is sufficiently large, machine learning algorithms approximate the true underlying function better than the data, suggesting, instead, that the predictions of these data-driven models should be used to guide model building. We call this approach "Scientific Regret Minimization" (SRM), as it focuses on minimizing errors for cases that we know should have been predictable. We apply this exploratory method on a subset of the Moral Machine dataset, a public collection of roughly 40 million moral decisions. Using SRM, we find that incorporating a set of deontological principles that capture dimensions along which groups of agents can vary (e.g., sex and age) improves a computational model of human moral judgment. Furthermore, we are able to identify and independently validate three interesting moral phenomena: criminal dehumanization, age of responsibility, and asymmetric notions of responsibility.
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