Machine learning and human adaptability: towards a hierarchical model of executive cognition and brain function
Aldo Faisal (Bioengineering/Computing)
Adam Hampshire (Brain Sciences)
The term ‘executive cognition’ refers to a general class of psychological processes that are closely associated with the frontal lobes and that are fundamental to human adaptability.
Executive cognition enables us to rapidly identify the most appropriate set of actions when faced with novel situations, to efficiently organise those actions into complex goal orientated behaviours, and to modify/override established behaviours when environmental conditions change.
Impairments of executive cognition are of great clinical relevance because they are a prominent symptom in a raft of neurological and psychiatric patient populations. Despite being the focus of much research, our current theoretical understanding of executive cognition and its relationship to frontal lobe functional organisation is at best rudimentary and operate on proxy measures of executive function.
The aim of this MRes/PhD project will be to impact on these issues by applying the rigorous mathematical framework of Bayesian Decision theory and Reinforcement learning in the context of behavioural and neuroimaging experiments that probe human executive function. On a practical level, the project will determine the potential utility of these models for providing more sensitive detection and finer grained classification of cognitive impairments in psychiatric and neurological populations.