title: Dual reward prediction components yield Pavlovian sign- and goal-tracking.
abstract: Reinforcement learning (RL) has become a dominant paradigm for understanding animal behaviors and neural correlates of decision-making, in part because of its ability to explain Pavlovian conditioned behaviors and the role of midbrain dopamine activity as reward prediction error (RPE). However, recent experimental findings indicate that dopamine activity, contrary to the RL hypothesis, may not signal RPE and differs based on the type of Pavlovian response (e.g. sign- and goal-tracking responses). In this study, we address this discrepancy by introducing a new neural correlate for learning reward predictions; the correlate is called "cue-evoked reward". It refers to a recall of reward evoked by the cue that is learned through simple cue-reward associations. We introduce a temporal difference learning model, in which neural correlates of the cue itself and cue-evoked reward underlie learning of reward predictions. The animal's reward prediction supported by these two correlates is divided into sign and goal components respectively. We relate the sign and goal components to approach responses towards the cue (i.e. sign-tracking) and the food-tray (i.e. goal-tracking) respectively. We found a number of correspondences between simulated models and the experimental findings (i.e. behavior and neural responses). First, the development of modeled responses is consistent with those observed in the experimental task. Second, the model's RPEs were similar to dopamine activity in respective response groups. Finally, goal-tracking, but not sign-tracking, responses rapidly emerged when RPE was restored in the simulated models, similar to experiments with recovery from dopamine-antagonist. These results suggest two complementary neural correlates, corresponding to the cue and its evoked reward, form the basis for learning reward predictions in the sign- and goal-tracking rats.
authors.affiliation: Lab for Integrated Theoretical Neuroscience, RIKEN BSI, Wako, Japan; Dept. of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama, Japan.
authors.forename: Sivaramakrishnan
authors.initials: S
authors.lastname: Kaveri
authors.affiliation: Lab for Integrated Theoretical Neuroscience, RIKEN BSI, Wako, Japan.
authors.forename: Hiroyuki
authors.initials: H
authors.lastname: Nakahara
mesh_terms: D000818:Animals; D001522:Behavior, Animal / Q000502:physiology; D003198:Computer Simulation; D003216:Conditioning, Operant / Q000502:physiology*; D003463:Cues; D006040:Goals; D007858:Learning / Q000502:physiology; D008962:Models, Theoretical; D051381:Rats; D012201:Reward