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Errors as cost-optimizing decisions? Redefining the origin and nature of human decision errors in light of associated neural computations

Total Cost €


EC-Contrib. €






 OPTIMIZERR project word cloud

Explore the words cloud of the OPTIMIZERR project. It provides you a very rough idea of what is the project "OPTIMIZERR" about.

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Project "OPTIMIZERR" data sheet

The following table provides information about the project.


Organization address
address: RUE DE TOLBIAC 101
city: PARIS
postcode: 75654

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country France [FR]
 Total cost 1˙497˙125 €
 EC max contribution 1˙497˙125 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2017-STG
 Funding Scheme ERC-STG
 Starting year 2018
 Duration (year-month-day) from 2018-05-01   to  2023-04-30


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 


 Project objective

Making decisions, from simple perceptual judgments to complex policy-making orientations, often requires to combine multiple pieces of ambiguous or conflicting information. In such uncertain conditions, human decisions exhibit a suboptimal variability whose origin remains poorly understood. Dominant psychological theories attribute the resulting errors to imperfections at the peripheries of an otherwise optimal inference process, and consider them essentially as failures of human cognition. Instead, my research program seeks to redefine decision errors not as cognitive failures, but as cognitive compromises which optimize a trade-off between the expected accuracy of a decision and the cost associated with neural computations required to reach this accuracy. I hypothesize that human decision errors arise to a large part from the limited computational precision of probabilistic inference, and that humans adapt this precision to the cognitive demands imposed by their environment - by increasing it when it is deemed necessary or decreasing it when they can rely on 'cheaper' sources of information to decide. I propose to test this original research hypothesis using a combination of computational modeling and multimodal functional neuroimaging of human decision-making. The degree of generality of the obtained findings will be assessed by bridging research across two types of decisions historically studied separately: perceptual decisions and reward-guided decisions. I will also test the clinical relevance of the hypothesized 'accuracy-cost' trade-off for two psychiatric conditions associated with dysfunctions of decision-making under uncertainty: 1. the emergence of false beliefs in schizophrenia, and 2. the repetitive checking behavior observed in obsessive-compulsive disorders. Together, the proposed research will shed light on previously unsuspected cognitive pressures which shape virtually every human decision, and identify associated neural computations.


year authors and title journal last update
List of publications.
2019 Charles Findling, Vasilisa Skvortsova, Rémi Dromnelle, Stefano Palminteri, Valentin Wyart
Computational noise in reward-guided learning drives behavioral variability in volatile environments
published pages: 2066-2077, ISSN: 1097-6256, DOI: 10.1038/s41593-019-0518-9
Nature Neuroscience 22/12 2020-01-29

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The information about "OPTIMIZERR" are provided by the European Opendata Portal: CORDIS opendata.

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