Faculty Bibliography

Since Odysseus committed to resisting the Sirens, mechanisms to limit self-control failure have been a central feature of human behavior. Psychologists have long argued that the use of self-control is an effortful process and, more recently, that its failure arises when the cognitive costs of self-control outweigh its perceived benefits. In a similar way, economists have argued that sophisticated choosers can adopt "precommitment strategies" that tie the hands of their future selves in order to reduce these costs. Yet, we still lack an empirical tool to quantify and demonstrate the cost of self-control. Here, we develop and validate an economic decision-making task to quantify the subjective cost of self-control by determining the monetary cost a person is willing to incur in order to eliminate the need for self-control. We find that humans will pay to avoid having to exert self-control in a way that scales with increasing levels of temptation and that these costs appear to be modulated both by motivational incentives and stress exposure. Our psychophysical approach allows us to index moment-to-moment self-control costs at the within-subject level, validating important theoretical work across multiple disciplines and opening avenues of self-control research in healthy and clinical populations.

Consumer valuations are shaped by choice sets, exemplified by patterns of substitution between alternatives as choice sets are varied. Building on recent neuroeconomic evidence that valuations are transformed during the choice process, we incorporate the canonical divisive normalization computation into a discrete choice model and characterize how choice behaviour depends on both size and composition of the choice set. We then examine evidence for such behaviour from two choice experiments that vary the size and composition of the choice set. We find that divisive normalization more accurately captures observed behaviour than alternative models, including an example range normalization model. These results are robust across experimental paradigms. Finally, we demonstrate that Divisive Normalization implements an efficient means for the brain to represent valuations given neurobiological constraints, yielding the fewest choice errors possible given those constraints.

Importance/UNASSIGNED:Opioid addiction is a major public health problem. Despite availability of evidence-based treatments, relapse and dropout are common outcomes. Efforts aimed at identifying reuse risk and gaining more precise understanding of the mechanisms conferring reuse vulnerability are needed. Objective/UNASSIGNED:To use tools from computational psychiatry and decision neuroscience to identify changes in decision-making processes preceding opioid reuse. Design, Setting, and Participants/UNASSIGNED:A cohort of individuals with opioid use disorder were studied longitudinally at a community-based treatment setting for up to 7 months (1-15 sessions per person). At each session, patients completed a risky decision-making task amenable to computational modeling and standard clinical assessments. Time-lagged mixed-effects logistic regression analyses were used to assess the likelihood of opioid use between sessions (t to t + 1; within the subsequent 1-4 weeks) from data acquired at the current session (t). A cohort of control participants completed similar procedures (1-5 sessions per person), serving both as a baseline comparison group and an independent sample in which to assess measurement test-retest reliability. Data were analyzed between January 1, 2018, and September 5, 2019. Main Outcomes and Measures/UNASSIGNED:Two individual model-based behavioral markers were derived from the task completed at each session, capturing a participant's current tolerance of known risks and ambiguity (partially unknown risks). Current anxiety, craving, withdrawal, and nonadherence were assessed via interview and clinic records. Opioid use was ascertained from random urine toxicology tests and self-reports. Results/UNASSIGNED:Seventy patients (mean [SE] age, 44.7 [1.3] years; 12 women and 58 men [82.9% male]) and 55 control participants (mean [SE] age, 42.4 [1.5] years; 13 women and 42 men [76.4% male]) were included. Of the 552 sessions completed with patients (mean [SE], 7.89 [0.59] sessions per person), 252 (45.7%) directly preceded opioid use events (mean [SE], 3.60 [0.44] sessions per person). From the task parameters, only ambiguity tolerance was significantly associated with increased odds of prospective opioid use (adjusted odds ratio, 1.37 [95% CI, 1.07-1.76]), indicating patients were more tolerant specifically of ambiguous risks prior to these use events. The association of ambiguity tolerance with prospective use was independent of established clinical factors (adjusted odds ratio, 1.29 [95% CI, 1.01-1.65]; P = .04), such that a model combining these factors explained more variance in reuse risk. No significant differences in ambiguity tolerance were observed between patients and control participants, who completed 197 sessions (mean [SE], 3.58 [0.21] sessions per person); however, patients were more tolerant of known risks (B = 0.56 [95% CI, 0.05-1.07]). Conclusions and Relevance/UNASSIGNED:Computational approaches can provide mechanistic insights about the cognitive factors underlying opioid reuse vulnerability and may hold promise for clinical use.

The Drift-Diffusion Model (DDM) is the prevalent computational model of the speed-accuracy trade-off in decision making. The DDM provides an explanation of behavior by optimally balancing reaction times and error rates. However, when applied to value-based decision making, the DDM makes the stark prediction that reaction times depend only on the relative utility difference between the options and not on absolute utility magnitudes. This prediction runs counter to evidence that reaction times decrease with higher utility magnitude. Here, we ask if and how it could be optimal for reaction times to show this observed pattern. We study an algorithmic framework that balances the cost of delaying rewards against the utility of obtained rewards. We find that the functional form of the cost of delay plays a key role, with the empirically observed pattern becoming optimal under multiplicative discounting. We add to the empirical literature by testing whether utility magnitude affects reaction times using a novel methodology that does not rely on functional form assumptions for the subjects' utilities. Our results advance the understanding of how and why reaction times are sensitive to the magnitude of rewards.

Recent advances in neuroscience suggest that a utility-like calculation is involved in how the brain makes choices, and that this calculation may use a computation known as divisive normalization. While this tells us how the brain makes choices, it is not immediately evident why the brain uses this computation or exactly what behavior is consistent with it. In this paper, we address both of these questions by proving a three-way equivalence theorem between the normalization model, an information-processing model, and an axiomatic characterization. The information-processing model views behavior as optimally balancing the expected value of the chosen object against the entropic cost of reducing stochasticity in choice. This provides an optimality rationale for why the brain may have evolved to use normalization-type models. The axiomatic characterization gives a set of testable behavioral statements equivalent to the normalization model. This answers what behavior arises from normalization. Our equivalence result unifies these three models into a single theory that answers the "how", "why", and "what" of choice behavior.

Decisions under uncertainty distinguish between those made under risk (known probabilities) and those made under ambiguity (unknown probabilities). Despite widespread interest in decisions under uncertainty and the successful documentation that these distinct psychological constructs profoundly-and differentially-impact behavior, research has not been able to systematically converge on which brain regions are functionally involved in processing risk and ambiguity. We merge a lesion approach with computational modeling and simultaneous measurement of the arousal response to investigate the impact the medial prefrontal cortex (mPFC), lateral prefrontal cortex (lPFC), and amygdala have on decisions under uncertainty. Results reveal that the lPFC acts as a unitary system for processing uncertainty: Lesions to this region disrupted the relationship between arousal and choice, broadly increasing both risk and ambiguity seeking. In contrast, the mPFC and amygdala appeared to play no role in processing risk, and the mPFC only had a tenuous relationship with ambiguous uncertainty. Together, these findings reveal that only the lPFC plays a global role in processing the highly aversive nature of uncertainty.

Awareness of the human health impacts of exposure to air pollution is growing rapidly. For example, it has become evident that the adverse health effects of air pollution are more pronounced in disadvantaged populations. Policymakers in many jurisdictions have responded to this evidence by enacting initiatives that lead to lower concentrations of air pollutants, such as urban traffic restrictions. In this review, we focus on the interplay between advances in environmental exposure assessment and developments in policy. We highlight recent progress in the granular measurement of air pollutants and individual-level exposures, and how this has enabled focused local policy actions. Finally, we detail an illustrative study designed to link individual-level health-relevant exposures with economic, behavioral, biological, familial, and environmental variables.

Choice impulsivity is an important subcomponent of the broader construct of impulsivity and is a key feature of many psychiatric disorders. Choice impulsivity is typically quantified as temporal discounting, a well-documented phenomenon in which a reward's subjective value diminishes as the delay to its delivery is increased. However, an individual's proclivity to-or more commonly aversion to- risk can influence nearly all of the standard experimental tools available for measuring temporal discounting. Despite this interaction, risk preference is a behaviourally and neurobiologically distinct construct that relates to the economic notion of utility or subjective value. In this opinion piece, we discuss the mathematical relationship between risk preferences and time preferences, their neural implementation, and propose ways that research in psychiatry could, and perhaps should, aim to account for this relationship experimentally to better understand choice impulsivity and its clinical implications. This article is part of the theme issue 'Risk taking and impulsive behaviour: fundamental discoveries, theoretical perspectives and clinical implications'.