Periodic Reporting for period 2 - IndDecision (A neurally-informed behavioural modeling framework for examining individual and group difference in perceptual decision making)
Período documentado: 2022-07-01 hasta 2023-12-31
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.
Over the course of its first 18 months the project has yielded a number of important developments and insights. A first goal of the project was to address a major limitation of current models of decision making which is that they do not account for the role that neural noise can play in shaping our decisions. This is particularly problematic because well known that neural noise levels change as we age and also differ in a number of prominent clinical populations. Consequently, models that fail to account for neural noise are likely missing critical parts of the picture when accounting for individual differences in decision making behaviour. Our team has conducted several investigations of new methods for directly estimating neural noise in such a manner that those estimates can then directly inform models. Our first effort in this direction was to apply a method originally developed in the field of psychophsics in which neural noise is estimated by progressively increasing the noisiness of a visual stimulus until a detectable impact on behaviour is observed - the logic being that higher levels of stimulus noise will be required to affect the behaviour of individuals with high neural noise. We successfully incorporated these estimates into a mathematical decision model and found the resultant model offered new insights into the brain changes that accompany extended training on a visual task. Specifically, we found that neural noise levels decreased as a function of training, resulting in improved accuracy and faster reaction times. In a separate study, we used an alternative, complementary approach in which we devised a novel model that could account for variations in neural noise across individuals and found that older subjects did not exhibit any differences in noise levels compared to younger subjects, a result that contradicted our expectations.
Another key goal of the project is to improve our understanding of how prior knowledge and expectations influence our decision making. According to predictive processing accounts, expectations fundamentally shape all neural activity yet several electrophysiological studies in humans have reported no effect of prior knowledge on early visual responses. We reasoned that this might be because the visual responses being analysed were not necessarily relevant to the task the participant was performing. We therefore devised a new study in which participants were required to discriminate the contrast levels of two visual stimuli and extracted a measure of contrast sensitivity in visual cortex using novel EEG methods. At the outset of certain trials, participants were presented with a cue which indicated what the correct choice was likely to be. In line with previous work, we found that the cues had a strong influence on choice behaviour, with participants more likely to report the cued direction and doing so more rapidly. Examining the visual contrast responses in the EEG, we found no effect of these cues in the early periods of testing but an effect did emerge after several hours of exposure to the task with visual contrast responses being biased in favour of the cued choice. This is an important observation because it suggests that early visual responses can be shaped by expectations while also providing an explanation for the failure of previous studies to observe such effects - our testing session was far longer than those of previous studies, thus allowing to pick out this slowly emerging effect.
Another important area of progress for this project has been in determining how we make decisions in contexts in which we cannot predict when relevant information will present itself. In particular, we have examined tasks in which we must continuously monitor the visual environment for an unpredictable target, such as when driving down a narrow country at night time and monitoring the bend ahead for oncoming headlamps. Our EEG and comuptational modelling analyses highlight that in these contexts we continually accumulate sensory information until a sufficient level of confidence has been reached that a target is present. To avoid frequent false alarms, our brains allow older samples of information to be forgotten and, in addition, the quantity of evidence required for us to report a target is continually adjusted in the brain such that we are willing to report targets based on less information during periods where targets are more expected (e.g. if a target has not been seen for a long time).
Finally, the project has made significant progress in elucidating the neural mechanisms that underpin metacognition - the cognitive operations that allow us to evaluate the accuracy of our choices. Our EEG methods make it possible to track the evidence accumulation process that informs our decisions on a milisecond by milisecond basis. Here, we have found that this same process actually continues to evolve even after we have committed to a choice and this allows newly encountered information to influence our subsequent confidence reports.
Another key goal of the project is to improve our understanding of how prior knowledge and expectations influence our decision making. According to predictive processing accounts, expectations fundamentally shape all neural activity yet several electrophysiological studies in humans have reported no effect of prior knowledge on early visual responses. We reasoned that this might be because the visual responses being analysed were not necessarily relevant to the task the participant was performing. We therefore devised a new study in which participants were required to discriminate the contrast levels of two visual stimuli and extracted a measure of contrast sensitivity in visual cortex using novel EEG methods. At the outset of certain trials, participants were presented with a cue which indicated what the correct choice was likely to be. In line with previous work, we found that the cues had a strong influence on choice behaviour, with participants more likely to report the cued direction and doing so more rapidly. Examining the visual contrast responses in the EEG, we found no effect of these cues in the early periods of testing but an effect did emerge after several hours of exposure to the task with visual contrast responses being biased in favour of the cued choice. This is an important observation because it suggests that early visual responses can be shaped by expectations while also providing an explanation for the failure of previous studies to observe such effects - our testing session was far longer than those of previous studies, thus allowing to pick out this slowly emerging effect.
Another important area of progress for this project has been in determining how we make decisions in contexts in which we cannot predict when relevant information will present itself. In particular, we have examined tasks in which we must continuously monitor the visual environment for an unpredictable target, such as when driving down a narrow country at night time and monitoring the bend ahead for oncoming headlamps. Our EEG and comuptational modelling analyses highlight that in these contexts we continually accumulate sensory information until a sufficient level of confidence has been reached that a target is present. To avoid frequent false alarms, our brains allow older samples of information to be forgotten and, in addition, the quantity of evidence required for us to report a target is continually adjusted in the brain such that we are willing to report targets based on less information during periods where targets are more expected (e.g. if a target has not been seen for a long time).
Finally, the project has made significant progress in elucidating the neural mechanisms that underpin metacognition - the cognitive operations that allow us to evaluate the accuracy of our choices. Our EEG methods make it possible to track the evidence accumulation process that informs our decisions on a milisecond by milisecond basis. Here, we have found that this same process actually continues to evolve even after we have committed to a choice and this allows newly encountered information to influence our subsequent confidence reports.
A key focus of the remaining years of the project will be to develop a formal modelling framework in which neural observations, such as those highlighted above, can be used to inform and constrain models of decision making. Not only would this result in models that are able to account for how decisions are made in the brain, as well as their output in behaviour, such models would be far better positioned to measure inter-individual differences in decision making and yield important insights into the origins of decision making deficits. An ongoing study has collected data from a sample of older and younger adults and through a combination of careful task design, modelling and neurophysiological analyses, we expect to be able for the first time to distinguish the influence of distinct sources of neural noise on age-related cognitive decline. Specifically, we will be able to determine whether age-related declines in decision making behaviour are attributable to inefficiencies in representing external information in sensory areas, inefficiencies in accumulating that information for a decision, inefficiencies in maintaining that information in memory and/or inefficiencies in translating the emerging decision into an appropriate motor plan. Such a model would be the first of its kind, offering an unparalleled detailed view of the brain's decision processes that would have applications for investigations of a range of clinical populations in addition to natural aging.
Another key focus of the remaining years of the project will be to build on the breakthroughs we have achieved in elucidating the neural processes underpinning metacognition. The ability to directly measure the evidence accumulation process that informs our initial choices and our subsequent confidence reports presents us with the opportunity to achieve a first in the field - to develop a model that can simultaneously account for the timing and accuracy of our confidence ratings as well as the timing and accuracy of our choices. Such a model would have very important implications for research on metacognition, in particular for studies seeking to dissociate deficits in metacognition from deficits in basic decision making.
Another key focus of the remaining years of the project will be to build on the breakthroughs we have achieved in elucidating the neural processes underpinning metacognition. The ability to directly measure the evidence accumulation process that informs our initial choices and our subsequent confidence reports presents us with the opportunity to achieve a first in the field - to develop a model that can simultaneously account for the timing and accuracy of our confidence ratings as well as the timing and accuracy of our choices. Such a model would have very important implications for research on metacognition, in particular for studies seeking to dissociate deficits in metacognition from deficits in basic decision making.