A Behavioral and Neural Evaluation of Prospective Decision-Making under Risk

Making the best choice when faced with a chain of decisions requires a person to judge both anticipated outcomes and future actions. Although economic decision-making models account for both risk and reward in single-choice contexts, there is a dearth of similar knowledge about sequential choice. Classical utility-based models assume that decision-makers select and follow an optimal predetermined strategy, regardless of the particular order in which options are presented. An alternative model involves continuously reevaluating decision utilities, without prescribing a specific future set of choices. Here, using behavioral and functional magnetic resonance imaging (fMRI) data, we studied human subjects in a sequential choice task and use these data to compare alternative decision models of valuation and strategy selection. We provide evidence that subjects adopt a model of reevaluating decision utilities, in which available strategies are continuously updated and combined in assessing action values. We validate this model by using simultaneously acquired fMRI data to show that sequential choice evokes a pattern of neural response consistent with a tracking of anticipated distribution of future reward, as expected in such a model. Thus, brain activity evoked at each decision point reflects the expected mean, variance, and skewness of possible payoffs, consistent with the idea that sequential choice evokes a prospective evaluation of both available strategies and possible outcomes

How choice reveals and shapes expected hedonic outcome

Humans tend to modify their attitudes to align with past action. For example, after choosing between similarly valued alternatives, people rate the selected option as better than they originally did, and the rejected option as worse. However, it is unknown whether these modifications in evaluation reflect an underlying change in the physiological representation of a stimulus' expected hedonic value and our emotional response to it. Here, we addressed this question by combining participants' estimations of the pleasure they will derive from future events, with brain imaging data recorded while they imagined those events, both before, and after, choosing between them. Participants rated the selected alternatives as better after the decision stage relative to before, whereas discarded alternatives were valued less. Our functional magnetic resonance imaging findings reveal that postchoice changes in preference are tracked in caudate nucleus activity. Specifically, the difference in blood oxygenation level-dependent (BOLD) signal associated with the selected and rejected stimuli was enhanced after a decision was taken, reflecting the choice that had just been made. This finding suggests that the physiological representation of a stimulus' expected hedonic value is altered by a commitment to it. Furthermore, before any revaluation induced by the decision process, our data show that BOLD signal in this same region reflects the choices we are likely to make at a later time

The neurobiology of reference-dependent value computation

A key focus of current research in neuroeconomics concerns how the human brain computes value. Although, value has generally been viewed as an absolute measure (e.g., expected value, reward magnitude), much evidence suggests that value is more often computed with respect to a changing reference point, rather than in isolation. Here, we present the results of a study aimed to dissociate brain regions involved in reference-independent (i.e., “absolute”) value computations, from those involved in value computations relative to a reference point. During functional magnetic resonance imaging, subjects acted as buyers and sellers during a market exchange of lottery tickets. At a behavioral level, we demonstrate that subjects systematically accorded a higher value to objects they owned relative to those they did not, an effect that results from a shift in reference point (i.e., status quo bias or endowment effect). Our results show that activity in orbitofrontal cortex and dorsal striatum track parameters such as the expected value of lottery tickets indicating the computation of reference-independent value. In contrast, activity in ventral striatum indexed the degree to which stated prices, at a within-subjects and between-subjects level, were distorted with respect to a reference point. The findings speak to the neurobiological underpinnings of reference dependency during real market value computations

Associations between aversive learning processes and transdiagnostic psychiatric symptoms revealed by large-scale phenotyping

Background: Aversive learning processes are a candidate source of dysfunction in psychiatric disorders. Here symptom expression in a range of conditions is linked to altered threat perception, manifesting particularly in uncertain environments. How precise computational mechanisms that support aversive learning, and uncertainty estimation, relate to the presence of specific psychiatric symptoms remains undetermined. Methods: 400 subjects completed a novel online game-based aversive learning task, requiring avoidance of negative outcomes, in conjunction with completing measures of common psychiatric symptoms. We used a probabilistic computational model to measure distinct processes involved in learning, in addition to inferred estimates of safety likelihood and uncertainty. We tested for associations between learning processes and traditional psychiatric constructs alongside transdiagnostic factors using linear models. We used partial least squares regression to identify components of psychopathology grounded in both aversive learning behaviour and symptom self-report. Results: State anxiety and a transdiagnostic compulsivity-related factor were associated with enhanced learning from safety. However, data-driven analysis using partial least squares regression indicated the presence of two separable components across our behavioural and questionnaire data: one linked enhanced safety learning and lower estimated uncertainty to physiological anxiety, compulsivity, and impulsivity; the other linked enhanced threat learning and heightened uncertainty estimation to symptoms of depression and social anxiety. Conclusions: Our findings implicate aversive learning processes under uncertainty to the expression of psychiatric symptoms that cut across traditional diagnostic boundaries. These relationships are more complex than previously conceptualised. Future research should focus on understanding the neural mechanisms underlying alterations in aversive learning and how these lead to the development of symptoms and disorder

Spatiotemporal precision of neuroimaging in psychiatry

Aberrant patterns of cognition, perception, and behaviour seen in psychiatric disorders are thought to be driven by a complex interplay of neural processes that evolve at a rapid temporal scale. Understanding these dynamic processes in vivo in humans has been hampered by a trade-off between the spatial and temporal resolution inherent to current neuroimaging technology. A recent trend in psychiatric research has been the use of high temporal resolution imaging, particularly magnetoencephalography (MEG), often in conjunction with sophisticated machine learning decoding techniques. Developments here promise novel insights into the spatiotemporal dynamics of cognitive phenomena, including domains relevant to psychiatric illness such as reward and avoidance learning, memory, and planning. This review considers recent advances afforded by exploiting this increased spatiotemporal precision, with specific reference to applications the seek to drive a mechanistic understanding of psychopathology and the realisation of preclinical translation

Integration of Retinal and Extraretinal Information across Eye Movements

Visual perception is burdened with a highly discontinuous input stream arising from saccad- ic eye movements. For successful integration into a coherent representation, the visuomo- tor system needs to deal with these self-induced perceptual changes and distinguish them from external motion. Forward models are one way to solve this problem where the brain uses internal monitoring signals associated with oculomotor commands to predict the visual consequences of corresponding eye movements during active exploration. Visual scenes typically contain a rich structure of spatial relational information, providing additional cues that may help disambiguate self-induced from external changes of perceptual input. We rea- soned that a weighted integration of these two inherently noisy sources of information should lead to better perceptual estimates. Volunteer subjects performed a simple percep- tual decision on the apparent displacement of a visual target, jumping unpredictably in sync with a saccadic eye movement. In a critical test condition, the target was presented together with a flanker object, where perceptual decisions could take into account the spatial dis- tance between target and flanker object. Here, precision was better compared to control conditions in which target displacements could only be estimated from either extraretinal or visual relational information alone. Our findings suggest that under natural conditions, inte- gration of visual space across eye movements is based upon close to optimal integration of both retinal and extraretinal pieces of informatio

Decision-Making, Pro-variance Biases and Mood-Related Traits

In value-based decision-making there is wide behavioural variability in how individuals respond to uncertainty. Maladaptive responses to uncertainty have been linked to a vulnerability to mental illness, for example, between risk aversion and affective disorders. Here, we examine individual differences in risk sensitivity when subjects confront options drawn from different value distributions, where these embody the same or different means and variances. In simulations, we show that a model that learns a distribution using Bayes' rule and reads out different parts of the distribution under the influence of a risk-sensitive parameter (Conditional Value at Risk, CVaR) predicts how likely an agent is to prefer a broader over a narrow distribution (pro-variance bias/risk-seeking) under the same overall means. Using empirical data, we show that CVaR estimates correlate with participants' pro-variance biases better than a range of alternative parameters derived from other models. Importantly, across two independent samples, CVaR estimates and participants' pro-variance bias negatively correlated with trait rumination, a common trait in depression and anxiety. We conclude that a Bayesian-CVaR model captures individual differences in sensitivity to variance in value distributions and task-independent trait dispositions linked to affective disorders

Explaining enhanced logical consistency during decision making in autism

The emotional responses elicited by the way options are framed often results in lack of logical consistency in human decision making. In this study, we investigated subjects with autism spectrum disorder (ASD) using a financial task in which the monetary prospects were presented as either loss or gain. We report both behavioral evidence that ASD subjects show a reduced susceptibility to the framing effect and psycho-physiological evidence that they fail to incorporate emotional context into the decision-making process. On this basis, we suggest that this insensitivity to contextual frame, although enhancing choice consistency in ASD, may also underpin core deficits in this disorder. These data highlight both benefits and costs arising from multiple decision processes in human cognition

The neural basis of metacognitive ability

Ability in various cognitive domains is often assessed by measuring task performance, such as the accuracy of a perceptual categorization. A similar analysis can be applied to metacognitive reports about a task to quantify the degree to which an individual is aware of his or her success or failure. Here, we review the psychological and neural underpinnings of metacognitive accuracy, drawing on research in memory and decision-making. These data show that metacognitive accuracy is dissociable from task performance and varies across individuals. Convergent evidence indicates that the function of the rostral and dorsal aspect of the lateral prefrontal cortex (PFC) is important for the accuracy of retrospective judgements of performance. In contrast, prospective judgements of performance may depend upon medial PFC. We close with a discussion of how metacognitive processes relate to concepts of cognitive control, and propose a neural synthesis in which dorsolateral and anterior prefrontal cortical subregions interact with interoceptive cortices (cingulate and insula) to promote accurate judgements of performance

β-adrenergic modulation of oddball responses in humans

Detection of salient or motivationally significant stimuli is of adaptive importance. The neurophysiological correlates of this detection have been extensively studied in 'oddball' paradigms. Much theoretical data supports the role of noradrenergic systems in generating oddball responses. We combine psychopharmacology and functional neuroimaging to demonstrate modulation of neuronal responses to oddball nouns by the β-adrenergic antagonist propranolol. Critically, responses in regions implicated in oddball detection, namely right ventrolateral prefrontal cortex and temporoparietal junction (TPJ), were abolished by propranolol. Thus, oddball responses depend on modulatory adrenergic inputs, mediated via β-adrenergic receptors