Paul W. Glimcher

Neural correlates of decision variables in parietal cortex

Decision theory proposes that humans and animals decide what to do in a given situation by assessing the relative value of each possible response. This assessment can be computed, in part, from the probability that each action will result in a gain and the magnitude of the gain expected. Here we show that the gain (or reward) a monkey can expect to realize from an eye-movement response modulates the activity of neurons in the lateral intraparietal area, an area of primate cortex that is thought to transform visual signals into eye-movement commands. We also show that the activity of these neurons is sensitive to the probability that a particular response will result in a gain. When animals can choose freely between two alternative responses, the choices subjects make and neuronal activation in this area are both correlated with the relative amount of gain that the animal can expect from each response. Our data indicate that a decision-theoretic model may provide a powerful new framework for studying the neural processes that intervene between sensation and action.

The neural correlates of subjective value during intertemporal choice

Neuroimaging studies of decision-making have generally related neural activity to objective measures (such as reward magnitude, probability or delay), despite choice preferences being subjective. However, economic theories posit that decision-makers behave as though different options have different subjective values. Here we use functional magnetic resonance imaging to show that neural activity in several brain regions—particularly the ventral striatum, medial prefrontal cortex and posterior cingulate cortex—tracks the revealed subjective value of delayed monetary rewards. This similarity provides unambiguous evidence that the subjective value of potential rewards is explicitly represented in the human brain.

Midbrain dopamine neurons encode a quantitative reward prediction error signal

The midbrain dopamine neurons are hypothesized to provide a physiological correlate of the reward prediction error signal required by current models of reinforcement learning. We examined the activity of single dopamine neurons during a task in which subjects learned by trial and error when to make an eye movement for a juice reward. We found that these neurons encoded the difference between the current reward and a weighted average of previous rewards, a reward prediction error, but only for outcomes that were better than expected. Thus, the firing rate of midbrain dopamine neurons is quantitatively predicted by theoretical descriptions of the reward prediction error signal used in reinforcement learning models for circumstances in which this signal has a positive value. We also found that the dopamine system continued to compute the reward prediction error even when the behavioral policy of the animal was only weakly influenced by this computation.

The Neurobiology of Decision: Consensus and Controversy

We review and synthesize recent neurophysiological studies of decision making in humans and nonhuman primates. From these studies, the basic outline of the neurobiological mechanism for primate choice is beginning to emerge. The identified mechanism is now known to include a multicomponent valuation stage, implemented in ventromedial prefrontal cortex and associated parts of striatum, and a choice stage, implemented in lateral prefrontal and parietal areas. Neurobiological studies of decision making are beginning to enhance our understanding of economic and social behavior as well as our understanding of significant health disorders where peoples behavior plays a key role.

The root of all value: a neural common currency for choice

How do humans make choices between different types of rewards? Economists have long argued on theoretical grounds that humans typically make these choices as if the values of the options they consider have been mapped to a single common scale for comparison. Neuroimaging studies in humans have recently begun to suggest the existence of a small group of specific brain sites that appear to encode the subjective values of different types of rewards on a neural common scale, almost exactly as predicted by theory. We have conducted a meta analysis using data from thirteen different functional magnetic resonance imaging studies published in recent years and we show that the principle brain area associated with this common representation is a subregion of the ventromedial prefrontal cortex (vmPFC)/orbitofrontal cortex (OFC). The data available today suggest that this common valuation path is a core system that participates in day-to-day decision making suggesting both a neurobiological foundation for standard economic theory and a tool for measuring preferences neurobiologically. Perhaps even more exciting is the possibility that our emerging understanding of the neural mechanisms for valuation and choice may provide fundamental insights into pathological choice behaviors like addiction, obesity and gambling.

Understanding dopamine and reinforcement learning: The dopamine reward prediction error hypothesis

A number of recent advances have been achieved in the study of midbrain dopaminergic neurons. Understanding these advances and how they relate to one another requires a deep understanding of the computational models that serve as an explanatory framework and guide ongoing experimental inquiry. This intertwining of theory and experiment now suggests very clearly that the phasic activity of the midbrain dopamine neurons provides a global mechanism for synaptic modification. These synaptic modifications, in turn, provide the mechanistic underpinning for a specific class of reinforcement learning mechanisms that now seem to underlie much of human and animal behavior. This review describes both the critical empirical findings that are at the root of this conclusion and the fantastic theoretical advances from which this conclusion is drawn.

Value Representations in the Primate Striatum during Matching Behavior

Choosing the most valuable course of action requires knowing the outcomes associated with the available alternatives. The striatum may be important for representing the values of actions. We examined this in monkeys performing an oculomotor choice task. The activity of phasically active neurons (PANs) in the striatum covaried with two classes of information: action-values and chosen-values. Action-value PANs were correlated with value estimates for one of the available actions, and these signals were frequently observed before movement execution. Chosen-value PANs were correlated with the value of the action that had been chosen, and these signals were primarily observed later in the task, immediately before or persistently after movement execution. These populations may serve distinct functions mediated by the striatum: some PANs may participate in choice by encoding the values of the available actions, while other PANs may participate in evaluative updating by encoding the reward value of chosen actions.

Neural Representation of Subjective Value Under Risk and Ambiguity

Risk and ambiguity are two conditions in which the consequences of possible outcomes are not certain. Under risk, the probabilities of different outcomes can be estimated, whereas under ambiguity, even these probabilities are not known. Although most people exhibit at least some aversion to both risk and ambiguity, the degree of these aversions is largely uncorrelated across subjects, suggesting that risk aversion and ambiguity aversion are distinct phenomena. Previous studies have shown differences in brain activations for risky and ambiguous choices and have identified neural mechanisms that may mediate transitions from conditions of ambiguity to conditions of risk. Unknown, however, is whether the value of risky and ambiguous options is necessarily represented by two distinct systems or whether a common mechanism can be identified. To answer this question, we compared the neural representation of subjective value under risk and ambiguity. fMRI was used to track brain activation while subjects made choices regarding options that varied systematically in the amount of money offered and in either the probability of obtaining that amount or the level of ambiguity around that probability. A common system, consisting of at least the striatum and the medial prefrontal cortex, was found to represent subjective value under both conditions.

Physiological utility theory and the neuroeconomics of choice.

Over the past half century economists have responded to the challenges of Allais [Econometrica (1953) 53], Ellsberg [Quart. J. Econ. (1961) 643] and others raised to neoclassicism either by bounding the reach of economic theory or by turning to descriptive approaches. While both of these strategies have been enormously fruitful, neither has provided a clear programmatic approach that aspires to a complete understanding of human decision making as did neoclassicism. There is, however, growing evidence that economists and neurobiologists are now beginning to reveal the physical mechanisms by which the human neuroarchitecture accomplishes decision making. Although in their infancy, these studies suggest both a single unified framework for understanding human decision making and a methodology for constraining the scope and structure of economic theory. Indeed, there is already evidence that these studies place mathematical constraints on existing economic models. This article reviews some of those constraints and suggests the outline of a neuroeconomic theory of decision.

Comparing Apples and Oranges: Using Reward-Specific and Reward-General Subjective Value Representation in the Brain

The ability of human subjects to choose between disparate kinds of rewards suggests that the neural circuits for valuing different reward types must converge. Economic theory suggests that these convergence points represent the subjective values (SVs) of different reward types on a common scale for comparison. To examine these hypotheses and to map the neural circuits for reward valuation we had food and water-deprived subjects make risky choices for money, food, and water both in and out of a brain scanner. We found that risk preferences across reward types were highly correlated; the level of risk aversion an individual showed when choosing among monetary lotteries predicted their risk aversion toward food and water. We also found that partially distinct neural networks represent the SVs of monetary and food rewards and that these distinct networks showed specific convergence points. The hypothalamic region mainly represented the SV for food, and the posterior cingulate cortex mainly represented the SV for money. In both the ventromedial prefrontal cortex (vmPFC) and striatum there was a common area representing the SV of both reward types, but only the vmPFC significantly represented the SVs of money and food on a common scale appropriate for choice in our data set. A correlation analysis demonstrated interactions across money and food valuation areas and the common areas in the vmPFC and striatum. This may suggest that partially distinct valuation networks for different reward types converge on a unified valuation network, which enables a direct comparison between different reward types and hence guides valuation and choice.