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Feature Specific Prediction Errors and Surprise across Macaque Fronto-Striatal Circuits during Attention and Learning

Mariann Oemisch, Stephanie Westendorff, Marzyeh Azimi, Seyed Ali Hassani, Salva Ardid, Paul Tiesinga, Thilo Womelsdorf
doi: https://doi.org/10.1101/266205
Mariann Oemisch
1Department of Biology, Centre for Vision Research, York University, 4700 Keele Street, Toronto, Ontario M6J 1P3, Canada.
2Department of Neurobiology, Yale University School of Medicine, New Haven, CT 06510.
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Stephanie Westendorff
1Department of Biology, Centre for Vision Research, York University, 4700 Keele Street, Toronto, Ontario M6J 1P3, Canada.
3Institute of Neurobiology, University of Tübingen, 72076 Tübingen, Germany.
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Marzyeh Azimi
1Department of Biology, Centre for Vision Research, York University, 4700 Keele Street, Toronto, Ontario M6J 1P3, Canada.
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Seyed Ali Hassani
1Department of Biology, Centre for Vision Research, York University, 4700 Keele Street, Toronto, Ontario M6J 1P3, Canada.
6Department of Psychology, Vanderbilt University, Nashville, TN 37240
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Salva Ardid
4Center for Computational Neuroscience and Neural Technology (CompNet), Department of Mathematics and Statistics, Boston University, Boston, Massachusetts 02215.
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Paul Tiesinga
5Donders Institute for Brain, Cognition and Behaviour Radboud University Nijmegen, 6525 EN Nijmegen, Netherlands.
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Thilo Womelsdorf
1Department of Biology, Centre for Vision Research, York University, 4700 Keele Street, Toronto, Ontario M6J 1P3, Canada.
6Department of Psychology, Vanderbilt University, Nashville, TN 37240
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Summary

Prediction errors signal unexpected outcomes indicating that expectations need to be adjusted. For adjusting expectations efficiently prediction errors need to be associated with the precise features that gave rise to the unexpected outcome. For many visual tasks this credit assignment proceeds in a multidimensional feature space that makes it ambiguous which object defining features are relevant. Here, we report of a potential solution by showing that neurons in all areas of the medial and lateral fronto-striatal networks encode prediction errors that are specific to separate features of attended multidimensional stimuli, with the most ubiquitous prediction error occurring for the reward relevant features. These feature specific prediction error signals (1) are different from a non-specific prediction error signal, (2) arise earliest in the anterior cingulate cortex and later in lateral prefrontal cortex, caudate and ventral striatum, and (3) contribute to feature-based stimulus selection after learning. These findings provide strong evidence for a widely-distributed feature-based eligibility trace that can be used to update synaptic weights for improved feature-based attention.

Highlights

  • Neural reward prediction errors carry information for updating feature-based attention in all areas of the fronto-striatal network.

  • Feature specific neural prediction errors emerge earliest in anterior cingulate cortex and later in lateral prefrontal cortex.

  • Ventral striatum neurons encode feature specific surprise strongest for the goal-relevant feature.

  • Neurons encoding feature-specific prediction errors contribute to attentional selection after learning.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted February 15, 2018.
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Feature Specific Prediction Errors and Surprise across Macaque Fronto-Striatal Circuits during Attention and Learning
Mariann Oemisch, Stephanie Westendorff, Marzyeh Azimi, Seyed Ali Hassani, Salva Ardid, Paul Tiesinga, Thilo Womelsdorf
bioRxiv 266205; doi: https://doi.org/10.1101/266205
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Feature Specific Prediction Errors and Surprise across Macaque Fronto-Striatal Circuits during Attention and Learning
Mariann Oemisch, Stephanie Westendorff, Marzyeh Azimi, Seyed Ali Hassani, Salva Ardid, Paul Tiesinga, Thilo Womelsdorf
bioRxiv 266205; doi: https://doi.org/10.1101/266205

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