Periodic Reporting for period 4 - Human Decisions (The Neural Determinants of Perceptual Decision Making in the Human Brain)
Reporting period: 2019-11-01 to 2020-04-30
In theme 2 of this project we sought to establish how choice confidence is represented in the brain. We showed that the same neural evidence accumulation signals that contribute to our choices, continue to accumulate evidence after the choice has been made and this 'post-decisional' accumulation plays a critical role in facilitating error detection (Murphy et al, 2015, eLife). We capitalised on these discoveries to provide new, more detailed insights into the impact of natural aging on metacognition (Harty et al 2017, Neuroimage). Further studies examining relationships between laboratory based tests of choice confidence and real-world metrics of cognitive performance have been completed and a manuscript is being prepared.
In theme 3 of this project we examined in detail how natural aging impacts on decision making processes. A review of the literature by our group established that this has been a relatively understudies area and, in particular, neurophysiological analyses were lacking (Dully et al, 2018, Beh Brain Res). We ran a multi-experiment study on a group of older and younger adults who performed a set of perceptual decision making tasks. We fit a conventional model of decision making to their behavioural data and found effects that accorded with those commonly reported in the literature - most prominently that elderly individuals respond more slowly on cognitive tasks due to implementing a more conservative decision making strategy. However, accompanying neurophysiological analyses produced conflicting results, finding no differences in the quantity of evidence old versus young adults required to commit to a decision. We therefore developed a novel model that took into account our neural observations and it yielded important new insights. First, it indicated that older adults on average tended to accumulate evidence less efficiently on some, but not all, perceptual tasks. Most interestingly, the model also highlighted that older adults were better at maintaining attention throughout the task. Thus the model pointed to both positive and negative aging effects on perceptual decision making. The results were reported in McGovern et al (2018, Nat Hum Beh).
An overarching goal of the action has been to use neurophysiological human brain recordings to empirically test and refine the predictions of computational models. Until recently, work of this kind has been the sole preserve of invasive brain recording studies but owing to methodological advances achieved by the PI we do now have the ability to isolate non-invasive brain signals that can be directly related to specific neural computations underpinning decision formation. This paves the way for a powerful new neurally-informed modelling approach in humans, in which neural signal observations can mediate between alternative model variants otherwise difficult to discern through behavioural fits alone, and where necessary, guide the construction of new models that capture key realities of neural implementation that are overlooked in existing models . We have pioneered this approach in two recent papers that have yielded new insights into the impact of natural aging (McGovern et al 2018, Nat Hum Beh) and prior knowledge (Kelly et al in press, Nat Hum Beh) on decision making.
In so doing the work has also uncovered new mechanistic accounts of several long-studied human brain signals )including the P3b, Pe, CNV and the N2pc). At the same time, by adopting a mathematical and theoretical framework that has been well establishing in non-human neurophysiology research, the work has served to bridge longstanding methodological and conceptual gaps between human and non-human research (O'Connell et al 2018, Trends Neurosci).
The isolation of non-invasive target selection signals highlighted a potentially powerful new diagnostic metric for clinical disorders impacting on visuospatial orienting. An important and unique feature of the target selection signals identified is that they can be measured independently over each hemisphere, a capability which has potentially significant benefits for research on clinical brain disorders associated with selective attention impairments (e.g. unilateral neglect following stroke).