Abstract
We address the distinction between habitual/automatic vs. goal-directed/controlled behavior, from the perspective of a computational model of the frontostriatal loops. The model exhibits a continuum of behavior between these poles, as a function of the interactive dynamics among different functionally-specialized brain areas, operating iteratively over multiple sequential steps, and having multiple nested loops of similar decision making circuits. This framework blurs the lines between these traditional distinctions in many ways. For example, although habitual actions have traditionally been considered purely automatic, the outer loop must first decide to allow such habitual actions to proceed. Furthermore, because the part of the brain that generates proposed action plans is common across habitual and controlled/goal-directed behavior, the key differences are instead in how many iterations of sequential decision-making are taken, and to what extent various forms of predictive (model-based) processes are engaged. At the core of every iterative step in our model, the basal ganglia provides a “model-free” dopamine-trained Go/NoGo evaluation of the entire distributed plan/goal/evaluation/prediction state. This evaluation serves as the fulcrum of serializing otherwise parallel neural processing. Goal-based inputs to the nominally model-free basal ganglia system are among several ways in which the popular model-based vs. model-free framework may not capture the most behaviorally and neurally relevant distinctions in this area.
Highlights
Since its inception, the field of psychology has been fascinated by the distinction between two types of behavior, one that leads us to act relatively automatically, according to well-worn habits, and another that allows us to act with intent and deliberation (James, 1890; Thorndike, 1911; Hull, 1943; Tolman, 1948)
In the remainder of this paper, we present an alternative framework based on computational models of the basal ganglia/prefrontal cortex/dopamine system, which is consistent with the overall critique of model-based vs. modelfree (MBMF) by Miller et al (2018, 2019), and provides a specific set of ways in which these brain systems can support a continuum of goal-directed, model-based forms of decision making and action selection
We have presented a computational systems-neuroscience approach to understanding the dynamics of decision making and action selection, which suggests that the classical dichotomy between habitual/automatic vs. goal-directed/controlled processing can be understood as different modes of functioning within a unitary system, operating fundamentally in a serial manner
Summary
The field of psychology has been fascinated by the distinction between two types of behavior, one that leads us to act relatively automatically, according to well-worn habits, and another that allows us to act with intent and deliberation (James, 1890; Thorndike, 1911; Hull, 1943; Tolman, 1948). Our general answer to these questions, as captured in our computational modeling framework, is that both habits and more controlled, goal-driven behaviors emerge from a shared neural system, and both operate within a common outer-loop of overall cognitive control that pervasively shapes and modulates the nature of processing performed in the inner-loops associated with specific task performance.
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