The sensation of cognitive effort is integral to everyday human experience, is a core construct in psychological theories of behavior,
and contributes to multiple psychiatric disorders like ADHD and depression. However, despite decades of study the neural origin and
functional purpose of cognitive effort remain poorly understood. This constitutes one of the most important outstanding questions
in cognitive neuroscience and a major challenge to mental health research. Recently, I proposed a new, mathematically formal
theory of cognitive effort that draws together concepts from dynamical systems analysis, linear control theory (LCT), artificial neural
networks (ANNs), cognitive psychology and neurophysiology. The theory relates cognitive effort to the fact that brain neuroanatomy
and neurophysiology render some neural states more energy-efficient than others. In particular, I introduced the concept of the
"controllosphere," an energy-inefficient region of neural state space associated with high cognitive control, and proposed that cognitive
effort serves to move the system state out of this high-energy region. The goal of this project is to test the controllosphere theory. I will use
ANNs to simulate human behavioral and neural data collected in tasks that require mental effort, and apply LCT to these models to make
formal predictions about which neural representations are more or less effortful. I will then test these predictions using a wide variety of
empirical techniques in a series of experiments with human participants. The establishment of a verified formal theory of cognitive effort
will fill an important gap in the cognitive neuroscience of cognitive control, strongly impact clinical practice, and provide important
insights for the development of novel artificial intelligence systems that balance computational function against energy use.