Faculty Bibliography

BACKGROUND: Video-based noninvasive eye trackers are an extremely useful tool for many areas of research. Many open-source eye trackers are available but current open-source systems are not designed to track eye movements with the temporal resolution required to investigate the mechanisms of oculomotor behavior. Commercial systems are available but employ closed source hardware and software and are relatively expensive, limiting wide-spread use. NEW METHOD: Here we present Oculomatic, an open-source software and modular hardware solution to eye tracking for use in humans and non-human primates. RESULTS: Oculomatic features high temporal resolution (up to 600Hz), real-time eye tracking with high spatial accuracy (<0.5 degrees ), and low system latency ( approximately 1.8ms, 0.32ms STD) at a relatively low-cost. COMPARISON WITH EXISTING METHOD(S): Oculomatic compares favorably to our existing scleral search-coil system while being fully non invasive. CONCLUSIONS: We propose that Oculomatic can support a wide range of research into the properties and neural mechanisms of oculomotor behavior.

Economists, psychologists, and neuroscientists have long been interested in methods that elicit individuals' true valuations of goods. In this paper, we take 1 of the most popular of such mechanisms, the Becker-DeGroot-Marschak (BDM) procedure, and study the nature of the dependence of the valuations obtained using the BDM procedure on the distribution of prices presented to subjects when the method is implemented. In a within-subject design with products with a high market value, we show that this effect of price distribution occurs quite frequently, significantly impacts reported valuations, and is unlikely to be caused by misconceptions about the BDM procedure. This effect is largest when pricing distributions show a large peak close to an individual's average valuation of the good. A simple nonincentive-compatible subjective rating of the desirability of goods can be used to predict the likelihood that pricing distributions will influence BDM valuations; valuations for goods that subjects report to most want to purchase are most likely to be influenced by distributional structure. Our results challenge some of the dominant theoretical models of how BDM-like valuation procedures relate to standard notions of utility and shed light on how to interpret the data obtained using the BDM method.

Background: Impulsivity is a core feature of substance use disorders. Temporal discounting (TD) paradigms provide a modelbased approach to studying the dynamics of impulsive decisionmaking as drug-addicted individuals undergo treatment. Here we examine (1) how TD changes as opioid use disorder (OUD) subjects stabilize on maintenance therapy; and (2) how TD is predicted by (or is predictive of) relevant clinical outcomes. Methods: 30 individuals initiating treatment for OUD and 29 matched community controls (CC) were assessed weekly (up to 15 weeks) on a TD task. Drug use was monitored by urine toxicology and chart review. We analyzed the data with a hyperbolic discounting model and derived subject-specific parameters forTD rate, and the non-parametric proportion of immediate choices. Results: OUD subjects showed higher TD rates than CC (Means: 0.039 versus 0.139 respectively, p = 0.005). Although this measure had high test-retest reliability, OUD subjects exhibited more variability across the repeated measures. Subjects in the initial phase of treatment showed a progressive decrease of TD (p = 0.007). Recent heroin use predicted subjects' level of impulsivity: positive use in the previous week correlated with a significantly higher proportion of immediate choices (p = 0.02). We did not And a predictive effect of TD on heroin use the following week. Conclusions: These results suggest that TD greatly fluctuates in treatment-seeking heroin users, in contrast to its stability in CC. TD is both sensitive to the initial phase of treatment for OUD and to recent heroin use, but not predictive of future use in this population

Positive mood can affect a person's tendency to gamble, possibly because positive mood fosters unrealistic optimism. At the same time, unexpected positive outcomes, often called prediction errors, influence mood. However, a linkage between positive prediction errors-the difference between expected and obtained outcomes-and consequent risk taking has yet to be demonstrated. Using a large data set of New York City lottery gambling and a model inspired by computational accounts of reward learning, we found that people gamble more when incidental outcomes in the environment (e.g., local sporting events and sunshine) are better than expected. When local sports teams performed better than expected, or a sunny day followed a streak of cloudy days, residents gambled more. The observed relationship between prediction errors and gambling was ubiquitous across the city's socioeconomically diverse neighborhoods and was specific to sports and weather events occurring locally in New York City. Our results suggest that unexpected but incidental positive outcomes influence risk taking.

It is well known that hormones affect both brain and behavior, but less is known about the extent to which hormones affect economic decision-making. Numerous studies demonstrate gender differences in attitudes to risk and loss in financial decision-making, often finding that women are more loss and risk averse than men. It is unclear what drives these effects and whether cyclically varying hormonal differences between men and women contribute to differences in economic preferences. We focus here on how economic rationality and preferences change as a function of menstrual cycle phase in women. We tested adherence to the Generalized Axiom of Revealed Preference (GARP), the standard test of economic rationality. If choices satisfy GARP then there exists a well-behaved utility function that the subject's decisions maximize. We also examined whether risk attitudes and loss aversion change as a function of cycle phase. We found that, despite large fluctuations in hormone levels, women are as technically rational in their choice behavior as their male counterparts at all phases of the menstrual cycle. However, women are more likely to choose risky options that can lead to potential losses while ovulating; during ovulation women are less loss averse than men and therefore more economically rational than men in this regard. These findings may have market-level implications: ovulating women more effectively maximize expected value than do other groups.

During value-based decision-making, individuals consider the various options and select the one that provides the maximum subjective value. Although the brain integrates abstract information to compute and compare these values, the only behavioral outcome is often the decision itself. However, if the options are visual stimuli, during deliberation the brain moves the eyes from one stimulus to the other. Previous work suggests that saccade vigor, i.e., peak velocity as a function of amplitude, is greater if reward is associated with the visual stimulus. This raises the possibility that vigor during the free viewing of options may be influenced by the valuation of each option. Here, humans chose between a small, immediate monetary reward and a larger but delayed reward. As the deliberation began, vigor was similar for the saccades made to the two options but diverged 0.5 s before decision time, becoming greater for the preferred option. This difference in vigor increased as a function of the difference in the subjective values that the participant assigned to the delayed and immediate options. After the decision was made, participants continued to gaze at the options, but with reduced vigor, making it possible to infer timing of the decision from the sudden drop in vigor. Therefore, the subjective value that the brain assigned to a stimulus during decision-making affected the motor system via the vigor with which the eyes moved toward that stimulus. SIGNIFICANCE STATEMENT: We find that, as individuals deliberate between two rewarding options and arrive at a decision, the vigor with which they make saccades to each option reflects a real-time evaluation of that option. With deliberation, saccade vigor diverges between the two options, becoming greater for the option that the individual will eventually choose. The results suggest a shared element between the network that assigns value to a stimulus during the process of decision-making and the network that controls vigor of movements toward that stimulus.

Empirical decision-making in diverse species deviates from the predictions of normative choice theory, but why such suboptimal behavior occurs is unknown. Here, we propose that deviations from optimality arise from biological decision mechanisms that have evolved to maximize choice performance within intrinsic biophysical constraints. Sensory processing utilizes specific computations such as divisive normalization to maximize information coding in constrained neural circuits, and recent evidence suggests that analogous computations operate in decision-related brain areas. These adaptive computations implement a relative value code that may explain the characteristic context-dependent nature of behavioral violations of classical normative theory. Examining decision-making at the computational level thus provides a crucial link between the architecture of biological decision circuits and the form of empirical choice behavior.

Until now, most large-scale studies of humans have either focused on very specific domains of inquiry or have relied on between-subjects approaches. While these previous studies have been invaluable for revealing important biological factors in cardiac health or social factors in retirement choices, no single repository contains anything like a complete record of the health, education, genetics, environmental, and lifestyle profiles of a large group of individuals at the within-subject level. This seems critical today because emerging evidence about the dynamic interplay between biology, behavior, and the environment point to a pressing need for just the kind of large-scale, long-term synoptic dataset that does not yet exist at the within-subject level. At the same time that the need for such a dataset is becoming clear, there is also growing evidence that just such a synoptic dataset may now be obtainable-at least at moderate scale-using contemporary big data approaches. To this end, we introduce the Kavli HUMAN Project (KHP), an effort to aggregate data from 2,500 New York City households in all five boroughs (roughly 10,000 individuals) whose biology and behavior will be measured using an unprecedented array of modalities over 20 years. It will also richly measure environmental conditions and events that KHP members experience using a geographic information system database of unparalleled scale, currently under construction in New York. In this manner, KHP will offer both synoptic and granular views of how human health and behavior coevolve over the life cycle and why they evolve differently for different people. In turn, we argue that this will allow for new discovery-based scientific approaches, rooted in big data analytics, to improving the health and quality of human life, particularly in urban contexts.

Many decisions involve weighing immediate gratification against future consequences. In such intertemporal choices, people often choose smaller, immediate rewards over larger delayed rewards. It has been proposed that emotional responses to immediate rewards lead us to choose them at our long-term expense. Here we utilize an objective measure of emotional arousal-pupil dilation-to examine the role of emotion in these decisions. We show that emotional arousal responses, as well as choices, in intertemporal choice tasks are reference-dependent and reflect the decision-maker's recent history of offers. Arousal increases when less predictable rewards are better than expected, whether those rewards are immediate or delayed. Furthermore, when immediate rewards are less predictable than delayed rewards, participants tend to be patient. When delayed rewards are less predictable, immediate rewards are preferred. Our findings suggest that we can encourage people to be more patient by changing the context in which intertemporal choices are made.