Nathaniel Daw

In patch foraging tasks, animals must decide whether to remain with a depleting resource or to leave it in search of a potentially better source of reward. In such tasks, animals consistently follow the general predictions of optimal foraging theory (the marginal value theorem; MVT): to leave a patch when the reward rate in the current patch depletes to the average reward rate across patches. Prior studies implicate an important role for the anterior cingulate cortex (ACC) in foraging decisions based on MVT: within single trials, ACC activity increases immediately preceding foraging decisions, and across trials, these dynamics are modulated as the value of staying in the patch depletes to the average reward rate...

Previous research has stressed the importance of uncertainty for controlling the speed of learning, and how such control depends on the learner inferring the noise properties of the environment, especially volatility: the speed of change. However, learning rates are jointly determined by the comparison between volatility and a second factor, moment-to-moment stochasticity. Yet much previous research has focused on simplified cases corresponding to estimation of either factor alone. Here, we introduce a learning model, in which both factors are learned simultaneously from experience, and use the model to simulate human and animal data across many seemingly disparate neuroscientific and behavioral phenomena...

When people encounter items that they believe will help them gain reward, they later remember them better than others. A recent model of emotional memory, the emotional context maintenance and retrieval model (eCMR), predicts that these effects would be stronger when stimuli that predict high and low reward can compete with each other during both encoding and retrieval. We tested this prediction in two experiments. Participants were promised £1 for remembering some pictures, but only a few pence for remembering others...

A goal of computational psychiatry is to ground symptoms in basic mechanisms. Theory suggests that avoidance in anxiety disorders may reflect dysregulated mental simulation, a process for evaluating candidate actions. If so, these covert processes should have observable consequences: choices reflecting increased and biased deliberation. In two online general population samples, we examined how self-report symptoms of social anxiety disorder predict choices in a socially framed reinforcement learning task, the patent race, in which the pattern of choices reflects the content of deliberation...

It is thought that the brain's judicious reuse of previous computation underlies our ability to plan flexibly, but also that inappropriate reuse gives rise to inflexibilities like habits and compulsion. Yet we lack a complete, realistic account of either. Building on control engineering, here we introduce a model for decision making in the brain that reuses a temporally abstracted map of future events to enable biologically-realistic, flexible choice at the expense of specific, quantifiable biases. It replaces the classic nonlinear, model-based optimization with a linear approximation that softly maximizes around (and is weakly biased toward) a default policy...

Cognitive fatigue and boredom are two phenomenological states that reflect overt task disengagement. In this article, we present a rational analysis of the temporal structure of controlled behavior, which provides a formal account of these phenomena. We suggest that in controlling behavior, the brain faces competing behavioral and computational imperatives, and must balance them by tracking their opportunity costs over time. We use this analysis to flesh out previous suggestions that feelings associated with subjective effort, like cognitive fatigue and boredom, are the phenomenological counterparts of these opportunity cost measures, instead of reflecting the depletion of resources as has often been assumed...

A variety of behavioral and neural phenomena suggest that organisms evaluate outcomes not on an absolute utility scale, but relative to some dynamic and context-sensitive reference or scale. Sometimes, as in foraging tasks, this results in sensible choices; in other situations, like choosing between options learned in different contexts, irrational choices can result. We argue that what unites and demystifies these various phenomena is that the brain's goal is not assessing utility as an end in itself, but rather comparing different options to choose the better one...

To make effective decisions, people need to consider the relationship between actions and outcomes. These are often separated by time and space. The neural mechanisms by which disjoint actions and outcomes are linked remain unknown. One promising hypothesis involves neural replay of nonlocal experience. Using a task that segregates direct from indirect value learning, combined with magnetoencephalography, we examined the role of neural replay in human nonlocal learning. After receipt of a reward, we found significant backward replay of nonlocal experience, with a 160-millisecond state-to-state time lag, which was linked to efficient learning of action values...

BACKGROUND: Anorexia nervosa is a severe illness with a high mortality rate, driven in large part by severe and persistent restriction of food intake. A critical challenge is to identify brain mechanisms associated with maladaptive eating behavior and whether they change with treatment. This study tested whether food choice-related caudate activation in anorexia nervosa changes with treatment. METHODS: Healthy women (n = 29) and women hospitalized with anorexia nervosa (n = 24), ages 18 to 40 years, completed a Food Choice Task during fMRI scanning at two timepoints...

The sparsity of permanent seismic instrumentation in marine environments often limits the availability of subsea information on geohazards, including active fault systems, in both time and space. One sensing resource that provides observational access to the seafloor environment are existing networks of ocean bottom fiber optic cables; these cables, coupled to modern distributed acoustic sensing (DAS) systems, can provide dense arrays of broadband seismic observations capable of recording both seismic events and the ambient noise wavefield...

Research in computational psychiatry has sought to understand the basis of compulsive behavior by relating it to basic psychological and neural mechanisms: specifically, goal-directed versus habitual control. These psychological categories have been further identified with formal computational algorithms, model-based and model-free learning, which helps to provide quantitative tools to distinguish them. Computational psychiatry may be particularly useful for examining phenomena in individuals with anorexia nervosa (AN), whose self-starvation appears both excessively goal directed and habitual...

BACKGROUND: Restriction of food intake is a central feature of anorexia nervosa (AN) and other eating disorders, yet also occurs in the absence of psychopathology. The neural mechanisms of restrictive eating in health and disease are unclear. METHODS: This study examined behavioral and neural mechanisms associated with restrictive eating among individuals with and without eating disorders. Dietary restriction was examined in four groups of women ( n = 110): healthy controls, dieting healthy controls, patients with subthreshold (non-low weight) AN, and patients with AN...

Deciding which options to engage, and which to forego, requires developing accurate beliefs about the overall distribution of prospects. Here we adapt a classic prey selection task from foraging theory to examine how individuals keep track of an environment's reward rate and adjust choices in response to its fluctuations. Preference shifts were most pronounced when the environment improved compared to when it deteriorated. This is best explained by a trial-by-trial learning model in which participants estimate the reward rate with upward vs...

Sound principles of statistical inference dictate that uncertainty shapes learning. In this work, we revisit the question of learning in volatile environments, in which both the first and second-order statistics of observations dynamically evolve over time. We propose a new model, the volatile Kalman filter (VKF), which is based on a tractable state-space model of uncertainty and extends the Kalman filter algorithm to volatile environments. The proposed model is algorithmically simple and encompasses the Kalman filter as a special case...

Appraising sequential offers relative to an unknown future opportunity and a time cost requires an optimization policy that draws on a learned estimate of an environment's richness. Converging evidence points to a learning asymmetry, whereby estimates of this richness update with a bias toward integrating positive information. We replicate this bias in a sequential foraging (prey selection) task and probe associated activation within the sympathetic branch of the autonomic system, using trial-by-trial measures of simultaneously recorded cardiac autonomic physiology...

Dopamine (DA) responses are synonymous with the 'reward prediction error' of reinforcement learning (RL), and are thought to update neural estimates of expected value. A recent study by Dabney et al. enriches this picture, demonstrating that DA neurons track variability in rewards, providing a readout of risk in the brain.

Compulsive behaviors (e.g., addiction) can be viewed as an aberrant decision process where inflexible reactions automatically evoked by stimuli (habit) take control over decision making to the detriment of a more flexible (goal-oriented) behavioral learning system. These behaviors are thought to arise from learning algorithms known as "model-based" and "model-free" reinforcement learning. Gambling disorder, a form of addiction without the confound of neurotoxic effects of drugs, showed impaired goal-directed control but the way in which problem gamblers (PG) orchestrate model-based and model-free strategies has not been evaluated...

Distributed fiber-optic sensing technology coupled to existing subsea cables (dark fiber) allows observation of ocean and solid earth phenomena. We used an optical fiber from the cable supporting the Monterey Accelerated Research System during a 4-day maintenance period with a distributed acoustic sensing (DAS) instrument operating onshore, creating a ~10,000-component, 20-kilometer-long seismic array. Recordings of a minor earthquake wavefield identified multiple submarine fault zones. Ambient noise was dominated by shoaling ocean surface waves but also contained observations of in situ secondary microseism generation, post-low-tide bores, storm-induced sediment transport, infragravity waves, and breaking internal waves...

Recent findings have shown that full-term infants engage in top-down sensory prediction, and these predictions are impaired as a result of premature birth. Here, we use an associative learning model to uncover the neuroanatomical origins and computational nature of this top-down signal. Infants were exposed to a probabilistic audiovisual association. We find that both groups (full term, preterm) have a comparable stimulus-related response in sensory and frontal lobes and track prediction error in their frontal lobes...

Importance: Dimensional definitions of transdiagnostic mental health problems have been suggested as an alternative to categorical diagnoses, having the advantage of capturing heterogeneity within diagnostic categories and similarity across them and bridging more naturally psychological and neural substrates. Objective: To examine whether a self-reported compulsivity dimension has a stronger association with goal-directed and related higher-order cognitive deficits compared with a diagnosis of obsessive-compulsive disorder (OCD)...