Faculty Bibliography

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.

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.

Environmental public goods-including national parks, clean air/water, and ecosystem services-provide substantial benefits on a global scale. These goods have unique characteristics in that they are typically "nonmarket" goods, with values from both use and passive use that accrue to a large number of individuals both in current and future generations. In this study, we test the hypothesis that neural signals in areas correlated with subjective valuations for essentially all other previously studied categories of goods (ventromedial prefrontal cortex and ventral striatum) also correlate with environmental valuations. We use contingent valuation (CV) as our behavioral tool for measuring valuations of environmental public goods. CV is a standard stated preference approach that presents survey respondents with information on an issue and asks questions that help policymakers determine how much citizens are willing to pay for a public good or policy. We scanned human subjects while they viewed environmental proposals, along with three other classes of goods. The presentation of all four classes of goods yielded robust and similar patterns of temporally synchronized brain activation within attentional networks. The activations associated with the traditional classes of goods replicate previous correlations between neural activity in valuation areas and behavioral preferences. In contrast, CV-elicited values for environmental proposals did not correlate with brain activity at either the individual or population level. For a sub-population of participants, CV-elicited values were correlated with activity within the dorsomedial prefrontal cortex, a region associated with cognitive control and shifting decision strategies. The results show that neural activity associated with the subjective valuation of environmental proposals differs profoundly from the neural activity associated with previously examined goods and preference measures.

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.

Normalization is a widespread neural computation in both early sensory coding and higher-order processes such as attention and multisensory integration. It has been shown that during decision-making, normalization implements a context-dependent value code in parietal cortex. In this paper we develop a simple differential equations model based on presumed neural circuitry that implements normalization at equilibrium and predicts specific time-varying properties of value coding. Moreover, we show that when parameters representing value are changed, the solution curves change in a manner consistent with normalization theory and experiment. We show that these dynamic normalization models naturally implement a time-discounted normalization over past activity, implying an intrinsic reference-dependence in value coding of a kind seen experimentally. These results suggest that a single network mechanism can explain transient and sustained decision activity, reference dependence through time discounting, and hence emphasizes the importance of a dynamic rather than static view of divisive normalization in neural coding

Over the course of the last decade a multitude of studies have investigated the relationship between neural activations and individual human decision-making. Here we asked whether the anatomical features of individual human brains could be used to predict the fundamental preferences of human choosers. To that end, we quantified the risk attitudes of human decision-makers using standard economic tools and quantified the gray matter cortical volume in all brain areas using standard neurobiological tools. Our whole-brain analysis revealed that the gray matter volume of a region in the right posterior parietal cortex was significantly predictive of individual risk attitudes. Participants with higher gray matter volume in this region exhibited less risk aversion. To test the robustness of this finding we examined a second group of participants and used econometric tools to test the ex ante hypothesis that gray matter volume in this area predicts individual risk attitudes. Our finding was confirmed in this second group. Our results, while being silent about causal relationships, identify what might be considered the first stable biomarker for financial risk-attitude. If these results, gathered in a population of midlife northeast American adults, hold in the general population, they will provide constraints on the possible neural mechanisms underlying risk attitudes. The results will also provide a simple measurement of risk attitudes that could be easily extracted from abundance of existing medical brain scans, and could potentially provide a characteristic distribution of these attitudes for policy makers.

Over the course of the last century, economists and ethologists have built detailed models from first principles of how humans and animals should make decisions. Over the course of the last few decades, psychologists and behavioral economists have gathered a wealth of data at variance with the predictions of these economic models. This has led to the development of highly descriptive models that can often predict what choices people or animals will make but without offering any insight into why people make the choices that they do--especially when those choices reduce a decision-maker's well-being. Over the course of the last two decades, neurobiologists working with economists and psychologists have begun to use our growing understanding of how the nervous system works to develop new models of how the nervous system makes decisions. The result, a growing revolution at the interdisciplinary border of neuroscience, psychology, and economics, is a new field called Neuroeconomics. Emerging neuroeconomic models stand to revolutionize our understanding of human and animal choice behavior by combining fundamental properties of neurobiological representation with decision-theoretic analyses. In this overview, one class of these models, based on the widely observed neural computation known as divisive normalization, is presented in detail. The work demonstrates not only that a discrete class of computation widely observed in the nervous system is fundamentally ubiquitous, but how that computation shapes behaviors ranging from visual perception to financial decision-making. It also offers the hope of reconciling economic analysis of what choices we should make with psychological observations of the choices we actually do make.

There is limited data in the literature to explicitly support the notion that neurons in OFC are truly action-independent in their coding. We set out to specifically test the hypothesis that OFC value-related neurons in area 13 m of the monkey do not carry information about the action required to obtain that reward--that activity in this area represents reward values in an abstract and action-independent manner. To accomplish that goal we had two monkeys select and execute saccadic eye movements to 81 locations in the visual field for three different kinds of juice rewards. Our detailed analysis of the response fields indicates that these neurons are insensitive to the amplitude or direction of the saccade required to obtain these rewards. Our data thus validate earlier proposals that neurons of 13 m in the OFC encode subjective value independent of the saccadic action required to obtain that reward.

Normalization is a widespread neural computation, mediating divisive gain control in sensory processing and implementing a context-dependent value code in decision-related frontal and parietal cortices. Although decision-making is a dynamic process with complex temporal characteristics, most models of normalization are time-independent and little is known about the dynamic interaction of normalization and choice. Here, we show that a simple differential equation model of normalization explains the characteristic phasic-sustained pattern of cortical decision activity and predicts specific normalization dynamics: value coding during initial transients, time-varying value modulation, and delayed onset of contextual information. Empirically, we observe these predicted dynamics in saccade-related neurons in monkey lateral intraparietal cortex. Furthermore, such models naturally incorporate a time-weighted average of past activity, implementing an intrinsic reference-dependence in value coding. These results suggest that a single network mechanism can explain both transient and sustained decision activity, emphasizing the importance of a dynamic view of normalization in neural coding.

Making predictions about the rewards associated with environmental stimuli and updating those predictions through feedback is an essential aspect of adaptive behavior. Theorists have argued that dopamine encodes a reward prediction error (RPE) signal that is used in such a reinforcement learning process. Recent work with fMRI has demonstrated that the BOLD signal in dopaminergic target areas meets both necessary and sufficient conditions of an axiomatic model of the RPE hypothesis. However, there has been no direct evidence that dopamine release itself also meets necessary and sufficient criteria for encoding an RPE signal. Further, the fact that dopamine neurons have low tonic firing rates that yield a limited dynamic range for encoding negative RPEs has led to significant debate about whether positive and negative prediction errors are encoded on a similar scale. To address both of these issues, we used fast-scan cyclic voltammetry to measure reward-evoked dopamine release at carbon fiber electrodes chronically implanted in the nucleus accumbens core of rats trained on a probabilistic decision-making task. We demonstrate that dopamine concentrations transmit a bidirectional RPE signal with symmetrical encoding of positive and negative RPEs. Our findings strengthen the case that changes in dopamine concentration alone are sufficient to encode the full range of RPEs necessary for reinforcement learning.