Faculty Bibliography

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.

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.

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.

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'.

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.

Adaptation is a fundamental process crucial for the efficient coding of sensory information. Recent evidence suggests that similar coding principles operate in decision-related brain areas, where neural value coding adapts to recent reward history. However, the circuit mechanism for value adaptation is unknown, and the link between changes in adaptive value coding and choice behavior is unclear. Here we show that choice behavior in nonhuman primates varies with the statistics of recent rewards. Consistent with efficient coding theory, decision-making shows increased choice sensitivity in lower variance reward environments. Both the average adaptation effect and across-session variability are explained by a novel multiple timescale dynamical model of value representation implementing divisive normalization. The model predicts empirical variance-driven changes in behavior despite having no explicit knowledge of environmental statistics, suggesting that distributional characteristics can be captured by dynamic model architectures. These findings highlight the importance of treating decision-making as a dynamic process and the role of normalization as a unifying computation for contextual phenomena in choice.