Abstract
The human brain has the impressive capacity to adapt how it processes information to high-level goals. While it is known that these cognitive control skills are malleable and can be improved through training, the underlying plasticity mechanisms are not well understood. Here, we develop and evaluate a model of how people learn when to exert cognitive control, which controlled process to use, and how much effort to exert. We derive this model from a general theory according to which the function of cognitive control is to select and configure neural pathways so as to make optimal use of finite time and limited computational resources. The central idea of our Learned Value of Control model is that people use reinforcement learning to predict the value of candidate control signals of different types and intensities based on stimulus features. This model correctly predicts the learning and transfer effects underlying the adaptive control-demanding behavior observed in an experiment on visual attention and four experiments on interference control in Stroop and Flanker paradigms. Moreover, our model explained these findings significantly better than an associative learning model and a Win-Stay Lose-Shift model. Our findings elucidate how learning and experience might shape people’s ability and propensity to adaptively control their minds and behavior. We conclude by predicting under which circumstances these learning mechanisms might lead to self-control failure.
Highlights
The human brain has the impressive ability to adapt how it processes information and responds to stimuli in the service of high level goals, such as writing an article [1]
Given its core reinforcement learning assumption (Table 1), the Learned Value of Control (LVOC) model predicts that 1) people should learn to attend to the circle with the predictive color and become faster at finding the target over the course of training, 2) continue to use the learned attentional control strategy in the test block and be significantly slower when the target appears in a circle of a different color during the test block, and 3) gradually unlearn their attentional bias during the test block (Fig 1c)
Our simulation results suggested that the LVOC model provides a promising step towards a mathematical theory of cognitive plasticity that can serve as a scientific foundation for designing cognitive training programs to improve people’s executive functions
Summary
The human brain has the impressive ability to adapt how it processes information and responds to stimuli in the service of high level goals, such as writing an article [1]. The mechanisms underlying this behavioral flexibility range from seemingly simple processes, such as inhibiting the impulse to browse your Facebook feed, to very complex processes such as orchestrating your thoughts to reach a solid conclusion. One of the paradigms used to study cognitive control is the Stroop task, where participants are instructed to name the hue of a color word (e.g., respond “green” when seeing the stimulus REDÞ while inhibiting their automatic tendency to read the word (“red”) [2]. Individual differences in the capacity for cognitive control are highly predictive of academic achievement, interpersonal success, and many other important life outcomes [3,4]
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