Faculty Bibliography

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.

Craving is thought to be a specific desire state that biases choice toward the desired object, be it chocolate or drugs. A vast majority of people report having experienced craving of some kind. In its pathological form craving contributes to health outcomes in addiction and obesity. Yet despite its ubiquity and clinical relevance we still lack a basic neurocomputational understanding of craving. Here, using an instantaneous measure of subjective valuation and selective cue exposure, we identify a behavioral signature of a food craving-like state and advance a computational framework for understanding how this state might transform valuation to bias choice. We find desire induced by exposure to a specific high-calorie, high-fat/sugar snack good is expressed in subjects' momentary willingness to pay for this good. This effect is selective but not exclusive to the exposed good; rather, we find it generalizes to nonexposed goods in proportion to their subjective attribute similarity to the exposed ones. A second manipulation of reward size (number of snack units available for purchase) further suggested that a multiplicative gain mechanism supports the transformation of valuation during laboratory craving. These findings help explain how real-world food craving can result in behaviors inconsistent with preferences expressed in the absence of craving and open a path for the computational modeling of craving-like phenomena using a simple and repeatable experimental tool for assessing subjective states in economic terms.

Normalization is a common cortical computation widely observed in sensory perception, but its importance in perception of reward value and decision making remains largely unknown. We examined (1) whether normalized value signals occur in the orbitofrontal cortex (OFC) and (2) whether changes in behavioral task context influence the normalized representation of value. We record medial OFC (mOFC) single neuron activity in awake-behaving monkeys during a reward-guided lottery task. mOFC neurons signal the relative values of options via a divisive normalization function when animals freely choose between alternatives. The normalization model, however, performed poorly in a variant of the task where only one of the two possible choice options yields a reward and the other was certain not to yield a reward (so called: "forced choice"). The existence of such context-specific value normalization may suggest that the mOFC contributes valuation signals critical for economic decision making when meaningful alternative options are available.

Measuring temporal discounting through the use of intertemporal choice tasks is now the gold standard method for quantifying human choice impulsivity (impatience) in neuroscience, psychology, behavioral economics, public health and computational psychiatry. A recent area of growing interest is individual differences in discounting levels, as these may predispose to (or protect from) mental health disorders, addictive behaviors, and other diseases. At the same time, more and more studies have been dedicated to the quantification of individual attitudes towards risk, which have been measured in many clinical and non-clinical populations using closely related techniques. Economists have pointed to interactions between measurements of time preferences and risk preferences that may distort estimations of the discount rate. However, although becoming standard practice in economics, discount rates and risk preferences are rarely measured simultaneously in the same subjects in other fields, and the magnitude of the imposed distortion is unknown in the assessment of individual differences. Here, we show that standard models of temporal discounting -such as a hyperbolic discounting model widely present in the literature which fails to account for risk attitudes in the estimation of discount rates- result in a large and systematic pattern of bias in estimated discounting parameters. This can lead to the spurious attribution of differences in impulsivity between individuals when in fact differences in risk attitudes account for observed behavioral differences. We advance a model which, when applied to standard choice tasks typically used in psychology and neuroscience, provides both a better fit to the data and successfully de-correlates risk and impulsivity parameters. This results in measures that are more accurate and thus of greater utility to the many fields interested in individual differences in impulsivity.

The population of people above 65 years old continues to grow and there is mounting evidence that as humans age, they are more likely to make errors. However, the specific effect of neuroanatomical aging on the efficiency of economic decision-making is poorly understood. We used whole brain voxel based morphometry (VBM) analysis to determine where reduction of gray matter volume in healthy female and male adults over the age of 65 correlates with a classic measure of economic irrationality: violations of the Generalized Axiom of Revealed Preference (GARP). All participants were functionally normal with Mini-Mental State Examination scores ranging between 26 and 30. While our elders showed the previously reported decline in rationality compared to younger subjects, chronological age per se did not correlate with rationality measures within our population of elders. Instead, reduction of gray matter density in ventrolateral prefrontal cortex correlates tightly with irrational behavior. Interestingly, using a large fMRI sample and meta-analytic tool with Neurosynth, we found that this brain area shows strong co-activation patterns with nearly all of the value-associated regions identified in previous studies.These findings point towards a neuroanatomic locus for economic rationality in the aging brain, and highlight the importance of understanding both anatomy and function in the study of aging, cognition, and decision-making. Significance Statement Age is a crucial factor in decision making, with older individuals making more errors in choices. Using whole brain voxel based morphometry (VBM) analysis, we found that reduction of gray matter density in ventrolateral prefrontal cortex correlates with economic irrationality: reduced gray matter volume in this area correlates with the frequency and severity of violations of the Generalized Axiom of Revealed Preference (GARP). Furthermore, this brain area strongly co-activates with other reward-associated regions identified with Neurosynth. These findings point towards a role for neuroscientific discoveries in shaping long-standing economic views of decision-making.