Perceptual Decision Making
A central challenge for the field of neuroscience is understanding how our brains allow us to make reliable categorical decisions from the noisy sensory information they receive. In collaboration with Simon Kelly of University College Dublin, we have devised a number of novel paradigms that make it possible to isolate and continuously track the key information processing stages intervening between sensation and action during simple perceptual decisions in discrete human brain signals (see O’Connell, Dockree & Kelly, 2012). These techniques allow us to explore the mechanisms that influence the timing and accuracy of perceptual decision making in both clinical (e.g. mild cognitive impairment, ADHD) and non-clinical populations. We recently established an exciting multi-site collaboration with Michael Shadlen (Columbia University), Stephan Bickel (Hofstra Northwell School of Medicine), Simon Kelly (University College Dublin) and KongFatt Wong-Lin (University of Ulster) which will conduct complementary investigations in human and non-human primates to pinpoint the neural architecture underlying decisions that are abstracted from movement.
The brain possesses specialized systems for continually monitoring our performance and for adjusting our behaviour if an error is detected. However, occasionally this monitoring system fails us and an error can go unnoticed. Such failures of self-awareness can cause significant functional impairment in a range of clinical populations e.g. people with schizophrenia. Our group is currently exploring the neural processes that determine whether or not a performance error will enter consciousness and how the brain enables us to form representations of choice confidence (e.g. Murphy et al 2015).
Our research is also directed toward understanding how, why and when attention levels fluctuate. Lapses of attention are a leading cause of human error and a major focus of our work has been to develop laboratory tests that mimic real life situations and make it possible to continuously track neural signatures of spatial and non-spatial attention over time. In collaboration with Mark Bellgrove of Monash University, we are utilizing pharmacological and genetic analysis techniques to probe the neurochemical influences on visuospatial and vigilant attention.
European Research Council Consolidator Grant (2021-2026) – Inddecision
Pinpointing the mechanistic origins of inter-individual differences in decision making is a central goal of modern psychology and a considerable challenge because even elementary perceptual choices rely on a multitude of sensory, cognitive, motivational and motoric processes. For this reason, researchers have relied heavily on a set of mathematical ‘sequential sampling’ models that are designed to parse the latent psychological processes driving variations in choice behaviour. Although these models have been fruitfully employed in thousands of theoretical and neurophysiological investigations, they suffer from several limitations that particularly undermine their utility in inter-individual or -group comparisons including: A) parameter values are estimated on a relative, within-subject scale; B) the models come in many forms that can make identical behavioural predictions despite invoking fundamentally different mechanisms (‘model mimicry’); and C) they deal in abstract psychological constructs that are themselves dependent on multiple neural processes. The objective of this proposal is to address each of these issues by pioneering a ground-breaking decision modelling framework in which models are constructed and evaluated based on their ability to explain key observable aspects of the neural implementation of the human decision process in addition to its behavioural output. This ambitious goal is made possible by recent advances in non-invasive electrophysiology which enable direct observation, measurement and manipulation of the decision process as it unfolds in the human brain. Across a series of empirical investigations that will use adult aging as a testbed for studying inter-individual and -group differences, this research will yield new methods for directly comparing model parameter values across subjects, resolve prominent theoretical debates regarding decision making algorithms and gain important new insights into their susceptibility to cognitive aging.