Dynamics of Cognitive Control
This capacity for cognitive control allows people not only to pursue goal-directed behaviors over extended periods of time, but also to rapidly and flexibly adapt behavior to novel circumstances in a fast-changing world. This flexibility requires dynamic adjustments in the allocation of control that we seek to understand through the use of well-crafted psychophysics experiments, neuroimaging studies, and neural network modeling. Where possible, we exploit the mathematical tools of control theory to construct normative models – that is, to explain how control is used to optimize behavior – and how control allocation is impacted by fundamental computational tradeoffs, such as the stability versus flexibility of representations and the use of shared (general purpose) versus separated (task-dedicated) representations in neural networks, the explore/exploit tradeoff, and how learning interacts with and shapes the balance between these tradeoffs. This work is cast within the framework of “bounded optimality,” or “resource rationality,” which assume that the human brain has evolved to optimize its computational functions subject to the constraints imposed by the tradeoffs above, and the constraints imposed by its structure and the world in which it must operate.
Another major focus in our lab is to develop cognitive models that formalize the role, implementation and dynamics of cognitive control. One part of this endeavor is to understand how control dynamics can account for the stability-flexibility trade-off in attention: the fact that stable focus on one task comes at a cost of slower switching of attention to another task. In our work (Musslick et al., 2018, 2019; Jongkees et al., 2023) we proposed that stronger allocation of control increases the weighting of relevant stimulus inputs in a decision making process (thus improving ‘stability’), but also increases the time necessary to reconfigure control when a shift to a different task is required (thus impairing ‘flexibility’). We formalized these ideas in a dynamical system, where the intensity of control allocation is determined by the gain of task-representing units (Figure 2A). Model simulations show high gain (corresponding to high intensity of control), as compared to low gain, improves stability (indexed by the maximum level of activation of the relevant task unit and minimum level of activation of the irrelevant task unit) but impairs flexible switching (indexed by the time required for task units to settle into the appropriate activation state during a task-switch; Figure 2B, C). Simulations further show the optimal intensity of control (in terms of maximizing response accuracy) depends on how much flexibility is required during a task: when task-switches are frequent the optimal intensity of control is lower, and model fitting of behavioral data indicates people adjust their control intensity in line with this model prediction. These findings provide a mechanistic account of the stability-flexibility trade-off and allow for rational predictions on optimal control intensity as a function of a task’s demand for cognitive flexibility.
Figure 2. Mechanistic modeling of the stability-flexibiltiy trade-off in attention. (A) Effect of gain modulation on the nonlinear activation function of task units, which relates the net input of a unit (e.g. task cues) to its activity state. (B-C) Phase portraits for activity of task units in response to task cues, under low gain (B) and high gain (C), showing trajectory from prior activation of one task (e.g., red dot for task 1) to the currently-relevant task (e.g., blue for task 2). Contour lines and arrows indicate the energy and shape of the attractor landscape after a task-switch from one task to the other.