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Type Journal Article
Scope Discipline-based scholarship
Title Dissociating neural learning signals in human sign- and goal-trackers
Organization Unit
Authors
  • 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
  • Quentin J M Huys
Item Subtype Original Work
Refereed Yes
Status Published in final form
Language
  • English
Journal Title Nature Human Behaviour
Publisher Nature Publishing Group
Geographical Reach international
ISSN 2397-3374
Volume 4
Number 2
Page Range 201 - 214
Date 2020
Abstract Text 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.
Digital Object Identifier 10.1038/s41562-019-0765-5
Other Identification Number merlin-id:18813
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Keywords Classical conditioning, human behaviour, learning algorithms, reward