Abstract
Decision making is a vital component of human and animal behavior that involves selecting between alternative options and generating actions to implement the choices. Although decisions can be as simple as choosing a goal and then pursuing it, humans and animals usually have to make decisions in dynamic environments where the value and the availability of an option change unpredictably with time and previous actions. A predator chasing multiple prey exemplifies how goals can dynamically change and compete during ongoing actions. Classical psychological theories posit that decision making takes place within frontal areas and is a separate process from perception and action. However, recent findings argue for additional mechanisms and suggest the decisions between actions often emerge through a continuous competition within the same brain regions that plan and guide action execution. According to these findings, the sensorimotor system generates concurrent action-plans for competing goals and uses online information to bias the competition until a single goal is pursued. This information is diverse, relating to both the dynamic value of the goal and the cost of acting, creating a challenging problem in integrating information across these diverse variables in real time. We introduce a computational framework for dynamically integrating value information from disparate sources in decision tasks with competing actions. We evaluated the framework in a series of oculomotor and reaching decision tasks and found that it captures many features of choice/motor behavior, as well as its neural underpinnings that previously have eluded a common explanation.
Highlights
From very simple decisions, such as selecting what to wear or choosing a place for dinner, to more complex decisions, such as trading in the stock market, we usually have to select among competing options
Once a decision is made, the action planning begins. This view is in accordance with evidence suggesting the convergence of subjective value in the orbitofrontal cortex (OFC) and ventromedial prefrontal cortex, where the best alternative is selected
Unlike classic neurodynamical models that used hard-wired associations between sensory inputs and motor outputs, we allow for plasticity in the connections between specific dynamic neural fields (DNFs) and, using reinforcement learning mechanisms, we show how action-selection is influenced by trained sensorimotor associations or changing reward contingencies
Summary
From very simple decisions, such as selecting what to wear or choosing a place for dinner, to more complex decisions, such as trading in the stock market, we usually have to select among competing options. A classic psychological theory, known as “goods-based decision making”, posits that decision making is a distinct cognitive function from perception and action and that it entails assigning values to the available goods [1,2,3,4,5] According to this theory, multiple decision determinants are integrated into a subjective economic value at the time of choice. Both the relative value of the goods and the cost of the actions to pursue these goods will change continuously (e.g., a new prey may appear or a current prey may escape from the field), and what is currently the best option may not be the best or even available in the near future In such situations, the goals dynamically compete during movement, and it is not possible to clearly separate goal decision-making from action selection
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