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A confirmation bias in perceptual decision-making due to hierarchical approximate inference

Richard D. Lang, Ankani Chattoraj, Jeffrey M. Beck, Jacob L. Yates, Ralf M. Haefner
doi: https://doi.org/10.1101/440321
Richard D. Lang
1Brain and Cognitive Sciences, University of Rochester, Rochester, NY 14627, USA
2Computer Science, University of Rochester, Rochester, NY 14627, USA
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  • For correspondence: rlange@ur.rochester.edu rhaefne2@ur.rochester.edu
Ankani Chattoraj
1Brain and Cognitive Sciences, University of Rochester, Rochester, NY 14627, USA
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Jeffrey M. Beck
3Department of Neurobiology, Duke University, Durham, NC 27708, USA
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Jacob L. Yates
1Brain and Cognitive Sciences, University of Rochester, Rochester, NY 14627, USA
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Ralf M. Haefner
1Brain and Cognitive Sciences, University of Rochester, Rochester, NY 14627, USA
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  • For correspondence: rlange@ur.rochester.edu rhaefne2@ur.rochester.edu
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Abstract

Human decisions are known to be systematically biased. A prominent example of such a bias occurs during the temporal integration of sensory evidence. Previous empirical studies differ in the nature of the bias they observe, ranging from favoring early evidence (primacy), to favoring late evidence (recency). Here, we present a unifying framework that explains these biases and makes novel neurophysiological predictions. By explicitly modeling both the approximate and the hierarchical nature of inference in the brain, we show that temporal biases depend on the balance between “sensory information” and “category information” in the stimulus. Finally, we present new data from a human psychophysics task that confirm that temporal biases can be robustly changed within subjects as predicted by our models.

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Posted October 11, 2018.
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A confirmation bias in perceptual decision-making due to hierarchical approximate inference
Richard D. Lang, Ankani Chattoraj, Jeffrey M. Beck, Jacob L. Yates, Ralf M. Haefner
bioRxiv 440321; doi: https://doi.org/10.1101/440321
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A confirmation bias in perceptual decision-making due to hierarchical approximate inference
Richard D. Lang, Ankani Chattoraj, Jeffrey M. Beck, Jacob L. Yates, Ralf M. Haefner
bioRxiv 440321; doi: https://doi.org/10.1101/440321

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