Abstract: Why do some mental activities feel harder than others? The answer to this question is surprisingly controversial. Current theories propose that cognitive effort affords a computational benefit, such as instigating a switch from an activity with low reward value to a different activity with higher reward value. By contrast, in this presentation I relate cognitive effort to the fact that brain neuroanatomy and neurophysiology render some neural states more energy-efficient than others. I introduce the concept of the "controllosphere," an energy-inefficient region of neural state space associated with high control, which surrounds the better known "intrinsic manifold," an energy-efficient subspace associated with low control. Integration of control-theoretic principles with classic neurocomputational models of cognitive control suggests that dorsolateral prefrontal cortex (DLPFC) implements a controller that can drive the system state into the controllosphere, anterior cingulate cortex (ACC) implements an observer that monitors changes of state of the controlled system, and cognitive effort reflects a mismatch between DLPFC and ACC energies for control and observation. On this account, cognitive effort scales with the energetic demands of the DLPFC control signal, especially when the consequences of the control are unobservable by ACC. Further, I propose that neural transitions through the controllosphere lead to a buildup of neural waste. Cognitive effort therefore prevents against neural damage by discouraging extended periods of high control.
Bio: I am a cognitive neuroscientist interested in the neural mechanisms that support cognitive control and decision making. Much of my career has focused on figuring out the function of anterior cingulate cortex, a poorly understood brain area that is involved in many cognitive tasks. My laboratory is presently funded by an ERCAdvanced Grant to investigate the neurocognitive mechanisms of cognitive effort. This project applies control-theoretic principles to neural network models of cognitive phenomena, which we then test in humans using a combination of electroencephalography, magnetoencephalography, and functional magnetic resonance imaging techniques.
Invited by: Fabienne COLLETTE