ArticleProceedings of the National Academy of Sciences of the United States of America2021
Retrieval-constrained valuation: Toward prediction of open-ended decisions.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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Who cites it
10 citing papers in PubMed, 30 citations in OpenAlex.
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- Flexible Reconfigurations of Brain Networks During Decisions With Predefined Versus Self-Generated Options.Human brain mapping · 2025Article
- Incorporating Best-Worst Scaling (BWS) Questions into Focus Groups to Improve Understanding of Patient Preferences and Refine BWS Attributes.The patient · 2025Article
- Distinguishing deception from its confounds by improving the validity of fMRI-based neural prediction.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- A Theory of Mental FrameworksFrontiers in psychology · 2023Article
- Item memorability has no influence on value-based decisions.Scientific reports · 2022Article
- Neural Support for Contributions of Utility and Narrative Processing of Evidence in Juror Decision Making.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2022Article
- If you don't let it in, you don't have to get it out: Thought preemption as a method to control unwanted thoughts.PLoS computational biology · 2022Article
- Cognitive and neural principles of a memory bias on preferential choices.Current research in neurobiology · 2022Review
- The Role of the Ventromedial Prefrontal Cortex in Preferential Decisions for Own- and Other-Age Faces.Frontiers in psychology · 2022Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
Real-world decisions are often open ended, with goals, choice options, or evaluation criteria conceived by decision-makers themselves. Critically, the quality of decisions may heavily rely on the generation of options, as failure to generate promising options limits, or even eliminates, the opportunity for choosing them. This core aspect of problem structuring, however, is largely absent from classical models of decision-making, thereby restricting their predictive scope. Here, we take a step toward addressing this issue by developing a neurally inspired cognitive model of a class of ill-structured decisions in which choice options must be self-generated. Specifically, using a model in which semantic memory retrieval is assumed to constrain the set of options available during valuation, we generate highly accurate out-of-sample predictions of choices across multiple categories of goods. Our model significantly and substantially outperforms models that only account for valuation or retrieval in isolation or those that make alternative mechanistic assumptions regarding their interaction. Furthermore, using neuroimaging, we confirm our core assumption regarding the engagement of, and interaction between, semantic memory retrieval and valuation processes. Together, these results provide a neurally grounded and mechanistic account of decisions with self-generated options, representing a step toward unraveling cognitive mechanisms underlying adaptive decision-making in the real world.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.