Abstract
Costs (e.g. energetic expenditure) and benefits (e.g. food) are central determinants of behavior. In ecology and economics, they are combined to form a utility function which is maximized to guide choices. This principle is widely used in neuroscience as a normative model of decision and action, but current versions of this model fail to consider how decisions are actually converted into actions (i.e. the formation of trajectories). Here, we describe an approach where decision making and motor control are optimal, iterative processes derived from the maximization of the discounted, weighted difference between expected rewards and foreseeable motor efforts. The model accounts for decision making in cost/benefit situations, and detailed characteristics of control and goal tracking in realistic motor tasks. As a normative construction, the model is relevant to address the neural bases and pathological aspects of decision making and motor control.
Highlights
Consider a simple living creature that needs to move in its environment to collect food for survival
We show that the model accounts for decision making in cost/benefit situations, and control in realistic motor tasks
The decisions are based on the costs and benefits of potential actions, and the chosen actions are executed through the proper control of body segments
Summary
Consider a simple living creature that needs to move in its environment to collect food for survival (foraging problem; [1]). Standard theories of decision making [8] rely on value-based processes (e.g. maximization of expected benefit), and fail to integrate the cost of physical actions into decisions [9]. Modern theories of motor control are cast in the framework of optimal control theory, and propose to elaborate motor commands using a cost-based process (e.g. minimization of effort), irrespective of the value of actions [10,11]. An interesting exception is the model proposed by Trommershauser et al [12,13,14] which casts into a Bayesian framework the observation that at least one aspect of motor control (intrinsic motor variability) is optimally integrated into decision making processes
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