Dataset Information

Validating model-based Bayesian integration using prior-cost metamers.

ABSTRACT: There are two competing views on how humans make decisions under uncertainty. Bayesian decision theory posits that humans optimize their behavior by establishing and integrating internal models of past sensory experiences (priors) and decision outcomes (cost functions). An alternative hypothesis posits that decisions are optimized through trial and error without explicit internal models for priors and cost functions. To distinguish between these possibilities, we introduce a paradigm that probes the sensitivity of humans to transitions between prior-cost pairs that demand the same optimal policy (metamers) but distinct internal models. We demonstrate the utility of our approach in two experiments that were classically explained by Bayesian theory. Our approach validates the Bayesian learning strategy in an interval timing task but not in a visuomotor rotation task. More generally, our work provides a domain-general approach for testing the circumstances under which humans explicitly implement model-based Bayesian computations.

SUBMITTER: Sohn H

PROVIDER: S-EPMC8237636 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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Validating model-based Bayesian integration using prior-cost metamers.

Sohn Hansem H Jazayeri Mehrdad M

Proceedings of the National Academy of Sciences of the United States of America 20210601 25

There are two competing views on how humans make decisions under uncertainty. Bayesian decision theory posits that humans optimize their behavior by establishing and integrating internal models of past sensory experiences (priors) and decision outcomes (cost functions). An alternative hypothesis posits that decisions are optimized through trial and error without explicit internal models for priors and cost functions. To distinguish between these possibilities, we introduce a paradigm that probes ...[more]

PMID: 34161261

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Project description:BackgroundGiven the rising number of asthma cases and the increasing costs of health care, prevention may be the best cure. Decisions regarding the implementation of prevention programmes in general and choosing between unifaceted and multifaceted strategies in particular are urgently needed. Existing trials on the primary prevention of asthma are, however, insufficient on their own to inform the decision of stakeholders regarding the cost-effectiveness of such prevention strategies. Decision analytic modelling synthesises available data for the cost-effectiveness evaluation of strategies in an explicit manner. Published reports on model development should provide the detail and transparency required to increase the acceptability of cost-effectiveness modelling. But, detail on the explicit steps and the involvement of experts in structuring a model is often unevenly reported. In this paper, we describe a procedure to structure and validate a model for the primary prevention of asthma in children.MethodsAn expert panel was convened for round-table discussions to frame the cost-effectiveness research question and to select and structure a model. The model's structural validity, which indicates how well a model reflects the reality, was determined through descriptive and parallel validation. Descriptive validation was performed with the experts. Parallel validation qualitatively compared similarity between other published models with different decision problems.ResultsThe multidisciplinary input of experts helped to develop a decision-tree structure which compares the current situation with screening and prevention. The prevention was further divided between multifaceted and unifaceted approaches to analyse the differences. The clinical outcome was diagnosis of asthma. No similar model was found in the literature discussing the same decision problem. Structural validity in terms of descriptive validity was achieved with the experts and was supported by parallel validation.ConclusionsA decision-tree model developed with experts in round-table discussions benefits from a systematic and transparent approach and the multidisciplinary contributions of the experts. Parallel validation provides a feasible alternative to validating novel models. The process of structuring and validating a model presented in this paper could be a useful guide to increase transparency, credibility, and acceptability of (future, novel) models when experts are involved.

Dataset Information

Validating model-based Bayesian integration using prior-cost metamers.

Publications

Validating model-based Bayesian integration using prior-cost metamers.

Similar Datasets

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