Heuristics from bounded meta-learned inference.
Open accessAbstract
Numerous researchers have put forward heuristics as models of human decision-making. However, where such heuristics come from is still a topic of ongoing debate. In this work, we propose a novel computational model that advances our understanding of heuristic decision-making by explaining how different heuristics are discovered and how they are selected. This model-called bounded meta-learned inference (BMI)-is based on the idea that people make environment-specific inferences about which strategies to use while being efficient in terms of how they use computational resources. We show that our approach discovers two previously suggested types of heuristics-one reason decision-making and equal weighting-in specific environments. Furthermore, the model provides clear and precise predictions about when each heuristic should be applied: Knowing the correct ranking of attributes leads to one reason decision-making, knowing the directions of the attributes leads to equal weighting, and not knowing about either leads to strategies that use weighted combinations of multiple attributes. In three empirical paired comparison studies with continuous features, we verify predictions of our theory and show that it captures several characteristics of human decision-making not explained by alternative theories. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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Meta-learned models discover heuristic decision-making strategies used by people.
“discover heuristic decision-making strategies used by people (Binz, Gershman, Schulz, & Endres, 2022)”
From Meta-learned models of cognition (2024) · cites this paper as Binz, Gershman, Schulz, & Endres, 2022
Meta-learned models can capture a wide range of empirically observed phenomena that could not be explained otherwise.
“It has been shown that meta-learned models can capture a wide range of empirically observed phenomena that could not be explained otherwise.”
From Meta-learned models of cognition (2024) · cites this paper as Binz, Gershman, Schulz, & Endres, 2022
Meta-learning has been previously used to discover algorithms with a limited computational budget that show human-like cognitive biases.
“Meta-learning has been previously used to discover algorithms with a limited computational budget that show human-like cognitive biases as we have already alluded to earlier.”
From Meta-learned models of cognition (2024) · cites this paper as Binz et al. (2022)
Binz et al. designed a neural network architecture that produced weights of a probit regression model, which were then used to cluster applied strategies into different categories, enabling identification of which strategy was used by their meta-learned model in a particular situation.
“Binz et al. (2022) relied on this approach and designed a neural network architecture that produced weights of a probit regression model which were then used to cluster applied strategies into different categories. Doing so enabled them to identify which strategy was used by their meta-learned model in a particular situation.”
From Meta-learned models of cognition (2024) · cites this paper as Binz et al. (2022)
Binz et al. (2022) found that their model discovered two previously suggested heuristics in specific environments and made precise prognoses about when these heuristics should be applied.
“Their model discovered two previously suggested heuristics in specific environments and made precise prognoses about when these heuristics should be applied.”
From Meta-learned models of cognition (2024) · cites this paper as Binz et al. (2022)
In Binz et al. (2022), knowing the correct ranking of features led to one-reason decision-making, knowing the directions of features led to an equal weighting heuristic, and not knowing about either led to strategies using weighted combinations of features.
“In particular, knowing the correct ranking of features led to one reason decision-making, knowing the directions of features led to an equal weighting heuristic, and not knowing about either of them led to strategies that use weighted combinations of features”
From Meta-learned models of cognition (2024) · cites this paper as Binz et al. (2022)
Cites
Reinforcement learning, fast and slow
Botvinick, M., Ritter, S., Wang, J. X., Kurth-Nelson, Z., Blundell, C., & Hassabis, D. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2019.02.006 · Cited by 667 · see citing works
Simplicity: a unifying principle in cognitive science?
Chater, N., & Vitányi, P. (2003) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(02)00005-0 · Cited by 494 · see citing works
Efficient compression in color naming and its evolution
Zaslavsky, N., Kemp, C., Regier, T., & Tishby, N. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1800521115 · Cited by 178 · see citing works
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A foundation model to predict and capture human cognition
Binz, M., Akata, E., Bethge, M., Brändle, F., Callaway, F., Coda-Forno, J., Dayan, P., Demircan, C., Eckstein, M. K., Éltető, N., et al. (2025) · Nature · doi:10.1038/s41586-025-09215-4 · Cited by 119 · see citing works
Self-citationMeta-learned models of cognition
Binz, M., Dasgupta, I., Jagadish, A. K., Botvinick, M., Wang, J. X., & Schulz, E. (2024) · Behavioral and Brain Sciences · doi:10.1017/s0140525x23003266 · Cited by 38 · see citing works
Self-citation
- Authors
- Binz, M., Gershman, S. J., Schulz, E., & Endres, D.
- Venue
- Psychological Review
- Published
- 2022-10
- Volume
- 129
- Issue
- 5
- Pages
- 1042-1077
- ISSN
- 0033-295X (ISSN-L)
- 1939-1471
- OpenAlex
- W4205243556
- Cited by
- 31
- References
- 3
- Citations by year
- 2026: 4 · 2025: 3 · 2024: 10 · 2023: 6 · 2022: 6 · 2021: 1 · 2020: 1
- FWCI
- 4.24
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Psychological Review (publishedVersion, other-oa)
- PubMed (publishedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- PsyArXiv (OSF Preprints) (submittedVersion, cc-by)
- PsyArXiv (OSF Preprints) (submittedVersion)
- Funders
- Deutsche Forschungsgemeinschaft (290878970-GRK 2271)
- Max Planck Society
- Jacobs Foundation
- National Science Foundation (CCF-1231216)
- Topics
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Categorization, perception, and language
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- heuristics, heuristic decision making, equal weighting, feature weighting, attribute ranking, strategy selection, computational resource efficiency, paired-comparison judgments, continuous attributes
- MeSH terms
- Heuristics, Decision Making, Humans
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