Clay Holroyd - SEE-ACC

Description of the PI

clayholroyd.jpgClay Holroyd received B.A.s in Physics and Creative Writing from the University of California, Santa Cruz in 1991 and a PhD in Neuroscience from the University of Illinois at Urbana-Champaign in 2001. Subsequently he was a post-doctoral fellow for 3.5 years in the Department of Psychology at Princeton University. From 2004-2019 he was a faculty member in the Department of Psychology at the University of Victoria, where he was a Canada Research Chair in Cognitive Neuroscience. Since 2019 he has been a Professor in the Department of Experimental Psychology at Ghent University.

His primary scientific interest concerns the cognitive neuroscience of cognitive control and decision making. In particular, his research focuses on determining the computational function of a brain area called anterior cingulate cortex. Together with colleagues, he has proposed that anterior cingulate cortex selects and sustains extended sequences of effortful behaviour – for example, how one decides to jog up a steep mountain and then follow through with the decision. To get at this issue he follows a "converging methods" approach involving electroencephalography, functional magnetic resonance imaging, and computational modelling. Currently he is the holder of an ERC Advanced Grant to investigate this issue (SEE-ACC). He has published over 100 journal papers and chapters. He is a Member of the Royal Society of Canada College of New Scholars, Artists and Scientists.  


Description of the project

Cracking the Anterior Cingulate Code: Toward a Unified Theory of ACC Function

Anterior cingulate cortex is one of the largest riddles in cognitive neuroscience and presents a major challenge to mental health research. ACC dysfunction contributes to a wide spectrum of psychiatric and neurological disorders but no one knows what it actually does. Although more than a thousand papers are published about it each year, attempts to identify its function have been confounded by the fact that a multiplicity of tasks and events activate ACC, as if it were involved in everything.

Recently, my colleagues and I proposed a theory that reconciles many of the complexities surrounding ACC. This holds that ACC selects and motivates high-level, temporally extended behaviors according to principles of hierarchical reinforcement learning. For example, on this view ACC would be responsible for initiating and sustaining a run up a steep mountain. We have instantiated this theory in two computational models that make explicit the theory's assumptions, while yielding testable predictions. In this project I will integrate the two computational models into a unified, biologically-realistic model of ACC function, which will be evaluated using mathematical techniques from non-linear dynamical systems analysis. I will then systematically test the unified model in a series of experiments involving functional magnetic resonance imaging, electroencephalography and psychopharmacology, in both healthy human subjects and patients.

The establishment of a complete, formal account of ACC will fill an important gap in the cognitive neuroscience of cognitive control and decision making, strongly impact clinical practice, and be important for artificial intelligence and robotics research, which draws inspiration from brain-based mechanisms for cognitive control. The computational modelling work will also link high level, abstract processes associated with hierarchical reinforcement learning with low level cellular mechanisms, enabling the theory to be tested in animal models.



This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme, grant agreement no. 787307.