Biology subjects

Daw, N.

Publications and source records attributed to Daw, N..

6 recordsLinked to original sources

bioRxiv2018

Hierarchical Bayesian inference for concurrent model fitting and comparison for group studies

Computational modeling plays an important role in modern neuroscience research. Much previous research has relied on statistical methods, separately, to address two problems that are actually interdependent. First, given a particular computational model, Bayesian hierarchical techniques have been used to estimate individual variation in parameters over a population of subjects, leveraging their population-level distributions. Second, candidate models are themselves compared, and individual variation in the expressed model estimated, according to the fits of the models to each subject. The interdependence between these two problems arises because the relevant population for estimating parameters of a model depends on which other subjects express the model. Here, we propose a hierarchical Bayesian inference (HBI) framework for concurrent model comparison, parameter estimation and inference at the population level, combining previous approaches. We show that this framework has important advantages for both parameter estimation and model comparison theoretically and experimentally. The parameters estimated by the HBI show smaller errors compared to other methods. Model comparison by HBI is robust against outliers and is not biased towards overly simplistic models. Furthermore, the fully Bayesian approach of HBI enables researchers to quantify uncertainty in group parameter estimates, for each candidate model separately, and to perform statistical tests on parameters of a population.

neuroscience↗

bioRxiv2017

How Cognitive and Reactive Fear Circuits Optimize Escape Decisions in Humans

Flight initiation distance (FID), the distance at which an organism flees from an approaching threat, is an ecological metric of cost-benefit functions of escape decisions. We adapted the FID paradigm to investigate how fast or slow attacking virtual predators constrain escape decisions. We show that rapid escape decisions rely on reactive fear circuits in the periaqueductal gray and midcingulate cortex (MCC), while protracted escape decisions, defined by larger buffer zones, were associated with cognitive fear circuits which include posterior cingulate cortex, hippocampus and the ventromedial prefrontal cortex, circuits implicated in strategic avoidance and behavioral flexibility. Using a Bayesian Decision Model, we further show that optimization of escape decisions under rapid flight were localized to the MCC, a region involved in adaptive motor control, while the hippocampus is implicated in optimizing decisions that update and control slower escape initiation. These results demonstrate an unexplored link between defensive survival circuits and their role in adaptive escape decisions.\n\nSignificanceHumans, like other animals, have evolved a set of circuits whose primary function is survival. In the case of predation, these circuits include reactive fear circuits involved in fast and immediate escape decisions and cognitive fear circuits that are involved in the conscious feeling of threat as well as slow strategic escape. Using neuroimaging combined with computational modeling, we support this differentiation of fear circuits by showing that fast escape decisions are elicited by the periaqueductal gray and MCC, regions involved in reactive flight. Conversely, slower escape decisions rely on the hippocampus, posterior cingulate cortex and prefrontal cortex, a circuit implicated in behavioral flexibility. These results support the role of the defensive survival circuitry in escape decisions and a separation of fear into reactive and cognitive circuits.

neuroscience↗

bioRxiv2017

The Opportunity Cost of Time Modulates Cognitive Effort

A spate of recent work demonstrates that humans seek to avoid the expenditure of cognitive effort, much like physical effort or economic resources. Less is clear, however, about the circumstances dictating how and when people decide to expend cognitive effort. Here we adopt a popular theory of opportunity costs and response vigor and to elucidate this question. This account, grounded in Reinforcement Learning, formalizes a trade-off between two costs: the harder work assumed necessary to emit faster actions and the opportunity cost inherent in acting more slowly (i.e., the delay that results to the next reward and subsequent rewards). Recent work reveals that the opportunity cost of time--operationalized as the average reward rate per unit time, theorized to be signaled by tonic dopamine levels, modulates the speed with which a person responds in a simple discrimination tasks. We extend this framework to cognitive effort in a diverse range of cognitive tasks, for which 1) the amount of cognitive effort demanded from the task varies from trial to trial and 2) the putative expenditure of cognitive effort holds measureable consequences in terms of accuracy and response time. In the domains of cognitive control, perceptual decision-making, and task-switching, we found that subjects tuned their level of effort exertion in accordance with the experienced average reward rate: when the opportunity cost of time was high, subjects made more errors and responded more quickly, which we interpret as a withdrawal of cognitive effort. That is, expenditure of cognitive effort appeared to be modulated by the opportunity cost of time. Further, and consistent with our account, the strength of this modulation was predicted by individual differences in efficacy of cognitive control. Taken together, our results elucidate the circumstances dictating how and when people expend cognitive effort.

neuroscience↗

bioRxiv2017

A retrieved context model of the emotional modulation of memory

Emotion enhances episodic memory, an effect thought to be an adaptation to prioritise the memories that best serve evolutionary fitness. But viewing this effect largely in terms of prioritising what to encode or consolidate neglects broader rational considerations about what sorts of associations should be formed at encoding, and which should be retrieved later. Although neurobiological investigations have provided many mechanistic clues about how emotional arousal modulates item memory, these effects have not been wholly integrated with the cognitive and computational neuroscience of memory more generally.\n\nHere we apply the Context Maintenance and Retrieval Model (CMR, Polyn, Norman & Kahana, 2009) to this problem by extending it to describe the way people may represent and process emotional information. A number of ways to operationalise the effect of emotion were tested. The winning emotional CMR (eCMR) model reconceptualises emotional memory effects as arising from the modulation of a process by which memories become bound to ever-changing temporal and emotional contexts. eCMR provides a good qualitative fit for the emotional list-composition effect and the emotional oddball effect, illuminating how these effects are jointly determined by the interplay of encoding and retrieval processes. eCMR explains the increased advantage of emotional memories in delayed memory tests through the limited ability of retrieval to reinstate the temporal context of encoding.\n\nBy leveraging the rich tradition of temporal context models, eCMR helps integrate existing effects of emotion and provides a powerful tool to test mechanisms by which emotion affects memory in a broad range of paradigms.

neuroscience↗

bioRxiv2017

A Neural Mechanism for the Opportunity Cost of Time

Recent interest has focused on a class of decision problems in which subjects encounter options serially and must decide when to leave an option in search of a better one, rather than directly comparing simultaneously presented options. Although such problems have a rich history in animal foraging and economics, relatively little is known about their neural substrates. Suggestively, however, a separate literature has argued that the key decision variable in these tasks - the opportunity cost of time, given by the average reward rate - may also govern behavioral vigor and may be reported by tonic dopamine (DA).\n\nIn this study, we test whether this putative dopaminergic opportunity cost signal plays an analogous role in serial decisions by examining the behavior of patients with Parkinsons disease (PD), on and off their DA replacement medication, in a patch-foraging task. In these tasks, subjects decisions about when to leave a depleting resource implicitly reflect their beliefs about the opportunity cost of time spent harvesting that resource. Consistent with the opportunity cost hypothesis, umedicated patients harvested longer than matched controls, and medication remediated this deficit. These effects were not explained by motor perseveration. Our results suggest a functional role for DA, and an associated cognitive deficit in PD, in a type of decision process that may be distinct from (but related to) the neuromodulators well studied roles in behavioral invigoration and learning from rewards.\n\nSignificance StatementThis study addresses two important questions whose answers are, unexpectedly, linked. First, what is the scope of cognitive functions of the neuromodulator dopamine, whose contributions - for instance, as assessed by both the motoric and more subtle cognitive deficits of patients with PD, which depletes dopamine - range from movement to reward and decision-making? Second, what are the neural mechanisms supporting an important but understudied class of problems, in which, rather than choose among a set of alternatives (like apples and oranges), one makes serial decisions about whether to stick with an option (like a job, or a mate) or seek another? We demonstrate a novel cognitive deficit in PD that integrates this function into the web of DAs contributions.

neuroscience↗

bioRxiv2016

The successor representation in human reinforcement learning

Theories of reward learning in neuroscience have focused on two families of algorithms, thought to capture deliberative vs. habitual choice. \"Model-based\" algorithms compute the value of candidate actions from scratch, whereas \"model-free\" algorithms make choice more efficient but less flexible by storing pre-computed action values. We examine an intermediate algorithmic family, the successor representation (SR), which balances flexibility and efficiency by storing partially computed action values: predictions about future events. These pre-computation strategies differ in how they update their choices following changes in a task. SRs reliance on stored predictions about future states predicts a unique signature of insensitivity to changes in the tasks sequence of events, but flexible adjustment following changes to rewards. We provide evidence for such differential sensitivity in two behavioral studies with humans. These results suggest that the SR is a computational substrate for semi-flexible choice in humans, introducing a subtler, more cognitive notion of habit.

neuroscience↗