title: Speed/accuracy trade-off between the habitual and the goal-directed processes.
abstract: Instrumental responses are hypothesized to be of two kinds: habitual and goal-directed, mediated by the sensorimotor and the associative cortico-basal ganglia circuits, respectively. The existence of the two heterogeneous associative learning mechanisms can be hypothesized to arise from the comparative advantages that they have at different stages of learning. In this paper, we assume that the goal-directed system is behaviourally flexible, but slow in choice selection. The habitual system, in contrast, is fast in responding, but inflexible in adapting its behavioural strategy to new conditions. Based on these assumptions and using the computational theory of reinforcement learning, we propose a normative model for arbitration between the two processes that makes an approximately optimal balance between search-time and accuracy in decision making. Behaviourally, the model can explain experimental evidence on behavioural sensitivity to outcome at the early stages of learning, but insensitivity at the later stages. It also explains that when two choices with equal incentive values are available concurrently, the behaviour remains outcome-sensitive, even after extensive training. Moreover, the model can explain choice reaction time variations during the course of learning, as well as the experimental observation that as the number of choices increases, the reaction time also increases. Neurobiologically, by assuming that phasic and tonic activities of midbrain dopamine neurons carry the reward prediction error and the average reward signals used by the model, respectively, the model predicts that whereas phasic dopamine indirectly affects behaviour through reinforcing stimulus-response associations, tonic dopamine can directly affect behaviour through manipulating the competition between the habitual and the goal-directed systems and thus, affect reaction time.
authors.affiliation: School of Management and Economics, Sharif University of Technology, Tehran, Iran. mohammadmahdi.keramati@ens.fr
authors.forename: Mehdi
authors.initials: M
authors.lastname: Keramati
authors.forename: Amir
authors.initials: A
authors.lastname: Dezfouli
authors.forename: Payam
authors.initials: P
authors.lastname: Piray
mesh_terms: D000465:Algorithms; D000818:Animals; D001522:Behavior, Animal; D002755:Choice Behavior / Q000502:physiology*; D003198:Computer Simulation; D003657:Decision Making / Q000502:physiology*; D004298:Dopamine / Q000502:physiology; D006040:Goals; D006801:Humans; D007858:Learning / Q000502:physiology*; D008390:Markov Chains; D018782:Maze Learning; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology; D051381:Rats; D012054:Reinforcement, Psychology; D015203:Reproducibility of Results
chemical_list: D004298:Dopamine