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Combining meta-learned models with process models of cognition.

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Title: Combining meta-learned models with process models of cognition.
Authors: Sanborn, Adam N. (AUTHOR), Yan, Haijiang (AUTHOR), Tsvetkov, Christian (AUTHOR)
Source: Behavioral & Brain Sciences. 2024, Vol. 47, p1-58. 58p.
Subjects: Judgment (Psychology), Cognition, Stimulus & response (Psychology), Forecasting
Abstract: Meta-learned models of cognition make optimal predictions for the actual stimuli presented to participants, but investigating judgment biases by constraining neural networks will be unwieldy. We suggest combining them with cognitive process models, which are more intuitive and explain biases. Rational process models, those that can sequentially sample from the posterior distributions produced by meta-learned models, seem a natural fit. [ABSTRACT FROM AUTHOR]
Copyright of Behavioral & Brain Sciences is the property of Cambridge University Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Combining meta-learned models with process models of cognition.
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  Data: <searchLink fieldCode="AR" term="%22Sanborn%2C+Adam+N%2E%22">Sanborn, Adam N.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yan%2C+Haijiang%22">Yan, Haijiang</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tsvetkov%2C+Christian%22">Tsvetkov, Christian</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Behavioral+%26+Brain+Sciences%22">Behavioral & Brain Sciences</searchLink>. 2024, Vol. 47, p1-58. 58p.
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  Data: Meta-learned models of cognition make optimal predictions for the actual stimuli presented to participants, but investigating judgment biases by constraining neural networks will be unwieldy. We suggest combining them with cognitive process models, which are more intuitive and explain biases. Rational process models, those that can sequentially sample from the posterior distributions produced by meta-learned models, seem a natural fit. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Behavioral & Brain Sciences is the property of Cambridge University Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1017/S0140525X24000165
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      – Code: eng
        Text: English
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      – SubjectFull: Stimulus & response (Psychology)
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      – TitleFull: Combining meta-learned models with process models of cognition.
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              Text: 2024
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