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[OPTIMIZERR] Errors as cost-optimizing decisions? Redefining the origin and nature of human decision errors in light of associated neural computations (759341)
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  • Open Access English
    Authors: 
    Aurélien Weiss; Valérian Chambon; Jan Drugowitsch; Valentin Wyart;
    Publisher: Cold Spring Harbor Laboratory
    Project: ANR | FrontCog (ANR-17-EURE-0017), ANR | VARICOMP (ANR-17-NEUC-0001), ANR | SUB-DECISION (ANR-14-CE13-0028), EC | OPTIMIZERR (759341), NIH | CRCNS: Leveraging decisio... (1R01MH115554-01)

    AbstractMaking accurate decisions in uncertain environments requires identifying the generative cause of sensory cues, but also the expected outcomes of possible actions. Although both cognitive processes can be formalized as Bayesian inference, they are commonly studied using different experimental frameworks, making their formal comparison difficult. Here, by framing a reversal learning task either as cue-based or outcome-based inference, we found that humans perceive the same volatile environment as more stable when inferring its hidden state by interaction with uncertain outcomes than by observation of equally uncertain cues. Multivariate patterns of magnetoencephalo-graphic (MEG) activity reflected this behavioral difference in the neural interaction between inferred beliefs and incoming evidence, an effect originating from associative regions in the temporal lobe. Together, these findings indicate that the degree of control over the sampling of volatile environments shapes human learning and decision-making under uncertainty.

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[OPTIMIZERR] Errors as cost-optimizing decisions? Redefining the origin and nature of human decision errors in light of associated neural computations (759341)
Include:
The following results are related to Canada. Are you interested to view more results? Visit OpenAIRE - Explore.
1 Research products, page 1 of 1
  • Open Access English
    Authors: 
    Aurélien Weiss; Valérian Chambon; Jan Drugowitsch; Valentin Wyart;
    Publisher: Cold Spring Harbor Laboratory
    Project: ANR | FrontCog (ANR-17-EURE-0017), ANR | VARICOMP (ANR-17-NEUC-0001), ANR | SUB-DECISION (ANR-14-CE13-0028), EC | OPTIMIZERR (759341), NIH | CRCNS: Leveraging decisio... (1R01MH115554-01)

    AbstractMaking accurate decisions in uncertain environments requires identifying the generative cause of sensory cues, but also the expected outcomes of possible actions. Although both cognitive processes can be formalized as Bayesian inference, they are commonly studied using different experimental frameworks, making their formal comparison difficult. Here, by framing a reversal learning task either as cue-based or outcome-based inference, we found that humans perceive the same volatile environment as more stable when inferring its hidden state by interaction with uncertain outcomes than by observation of equally uncertain cues. Multivariate patterns of magnetoencephalo-graphic (MEG) activity reflected this behavioral difference in the neural interaction between inferred beliefs and incoming evidence, an effect originating from associative regions in the temporal lobe. Together, these findings indicate that the degree of control over the sampling of volatile environments shapes human learning and decision-making under uncertainty.