Dissociating neural learning signals in human sign- and goal-trackers

Daniel J. Schad (), Michael A. Rapp, Maria Garbusow, Stephan Nebe, Miriam Sebold, Elisabeth Obst, Christian Sommer, Lorenz Deserno, Milena Rabovsky, Eva Friedel, Nina Romanczuk-Seiferth, Hans-Ulrich Wittchen, Ulrich S. Zimmermann, Henrik Walter, Philipp Sterzer, Michael N. Smolka, Florian Schlagenhauf, Andreas Heinz, Peter Dayan and Quentin J. M. Huys
Additional contact information
Daniel J. Schad: University of Potsdam
Michael A. Rapp: University of Potsdam
Maria Garbusow: Charité—Universitätsmedizin Berlin
Stephan Nebe: Technische Universität Dresden
Miriam Sebold: University of Potsdam
Elisabeth Obst: Technische Universität Dresden
Christian Sommer: Technische Universität Dresden
Lorenz Deserno: Max Planck Institute for Human Cognitive and Brain Sciences
Milena Rabovsky: University of Potsdam
Eva Friedel: Charité—Universitätsmedizin Berlin
Nina Romanczuk-Seiferth: Charité—Universitätsmedizin Berlin
Hans-Ulrich Wittchen: Technische Universität Dresden
Ulrich S. Zimmermann: Technische Universität Dresden
Henrik Walter: Charité—Universitätsmedizin Berlin
Philipp Sterzer: Charité—Universitätsmedizin Berlin
Michael N. Smolka: Technische Universität Dresden
Florian Schlagenhauf: Charité—Universitätsmedizin Berlin
Andreas Heinz: Charité—Universitätsmedizin Berlin
Peter Dayan: University College London
Quentin J. M. Huys: University of Zürich

Nature Human Behaviour, 2020, vol. 4, issue 2, 201-214

Abstract: Abstract Individuals differ in how they learn from experience. In Pavlovian conditioning models, where cues predict reinforcer delivery at a different goal location, some animals—called sign-trackers—come to approach the cue, whereas others, called goal-trackers, approach the goal. In sign-trackers, model-free phasic dopaminergic reward-prediction errors underlie learning, which renders stimuli ‘wanted’. Goal-trackers do not rely on dopamine for learning and are thought to use model-based learning. We demonstrate this double dissociation in 129 male humans using eye-tracking, pupillometry and functional magnetic resonance imaging informed by computational models of sign- and goal-tracking. We show that sign-trackers exhibit a neural reward prediction error signal that is not detectable in goal-trackers. Model-free value only guides gaze and pupil dilation in sign-trackers. Goal-trackers instead exhibit a stronger model-based neural state prediction error signal. This model-based construct determines gaze and pupil dilation more in goal-trackers.

Date: 2020
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DOI: 10.1038/s41562-019-0765-5

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