Faculty Bibliography

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.

Work in behaving primates indicates that midbrain dopamine neurons encode a prediction error, the difference between an obtained reward and the reward expected. Studies of dopamine action potential timing in the alert and anesthetized rat indicate that dopamine neurons respond in tonic and phasic modes, a distinction that has been less well characterized in the primates. We used spike train models to examine the relationship between the tonic and burst modes of activity in dopamine neurons while monkeys were performing a reinforced visuo-saccadic movement task. We studied spiking activity during four task-related intervals; two of these were intervals during which no task-related events occurred, whereas two were periods marked by task-related phasic activity. We found that dopamine neuron spike trains during the intervals when no events occurred were well described as tonic. Action potentials appeared to be independent, to occur at low frequency, and to be almost equally well described by Gaussian and Poisson-like (gamma) processes. Unlike in the rat, interspike intervals as low as 20 ms were often observed during these presumptively tonic epochs. Having identified these periods of presumptively tonic activity, we were able to quantitatively define phasic modulations (both increases and decreases in activity) during the intervals in which task-related events occurred. This analysis revealed that the phasic modulations of these neurons include both bursting, as has been described previously, and pausing. Together bursts and pauses seemed to provide a continuous, although nonlinear, representation of the theoretically defined reward prediction error of reinforcement learning.

Electrophysiological studies in monkeys have identified effector-related regions in the posterior parietal cortex (PPC). The lateral intraparietal area, for example, responds preferentially for saccades, whereas the parietal reach region responds preferentially for arm movements. However, the degree of effector selectivity actually observed is limited; each area contains neurons selective for the nonpreferred effector, and many neurons in both areas respond for both effectors. We used functional magnetic resonance imaging to assess the degree of effector preference at the population level, focusing on topographically organized regions in the human PPC [visual area V7, intraparietal sulcus 1 (IPS1), and IPS2]. An event-related design adapted from monkey experiments was used. In each trial, an effector cue preceded the appearance of a spatial target, after which a Go signal instructed subjects to produce the specified movement with the specified effector. Our results show that the degree of effector specificity is limited in many cortical areas and transitions gradually from saccade to reach preference as one moves through the hierarchy of areas in the occipital, parietal, and frontal cortices. Saccade preference was observed in visual cortex, including early areas and V7. IPS1 exhibited balanced activation to saccades and reaches, whereas IPS2 showed a weak but significant preference for reaches. In frontal cortex, areas near the central sulcus showed a clear and absolute preference for reaches, whereas the frontal eye field showed little or no effector selectivity. Although these results contradict many theoretical conclusions about effector specificity, they are compatible with the complex picture arising from electrophysiological studies and also with previous imaging studies that reported mostly overlapping saccade- and arm-related activation. The results are also compatible with theories of efficient coding in cortex.

The author's laboratory studies the neural basis of learning and decision-making using a combination of techniques from neuroscience, economics and psychology. Behavioral studies of decision-making in both human and non-human primates guide physiological experiments using single neuron recording and functional magnetic resonance imaging. The goal of the laboratory is to achieve an interdisciplinary understanding of choice behavior that transcends the understanding of this phenomenon available to any single scholarly discipline.

In a previous study, we identified three cortical areas in human posterior parietal cortex that exhibited topographic responses during memory-guided saccades [visual area 7 (V7), intraparietal sulcus 1 (IPS1), and IPS2], which are candidate homologs of macaque parietal areas such as the lateral intraparietal area and parietal reach region. Here, we show that these areas exhibit sustained delay-period activity, a critical physiological signature of areas in macaque parietal cortex. By varying delay duration, we disambiguated delay-period activity from sensory and motor responses. Mean time courses in the parietal areas were well fit by a linear model comprising three components representing responses to (1) the visual target, (2) the delay period, and (3) the eye movement interval. We estimated the contributions of each component: the response amplitude during the delay period was substantially smaller (<30%) than that elicited by the transient visual target. All three parietal regions showed comparable delay-period response amplitudes, with a trend toward larger responses from V7 to IPS1 and IPS2. Responses to the cue and during the delay period showed clear lateralization with larger responses to trials in which the target was placed in the contralateral visual field, suggesting that both of these components contributed to the topography we measured.

Advances in cognitive neuroscience now allow us to use physiological techniques to measure and assess mental states under a growing set of circumstances. The implication of this growing ability has not been lost on the western legal community. If biologists can accurately measure mental state, then legal conflicts that turn on the true mental states of individuals might well be resolvable with techniques ranging from electroencephalography to functional magnetic resonance imaging. Therefore, legal practitioners have increasingly sought to employ cognitive neuroscientific methods and data as evidence to influence legal proceedings. This poses a risk, because these scientific methodologies have largely been designed and validated for experimental use only. Their subsequent use in legal proceedings is an application for which they were not intended, and for which those methods are inadequately tested. We propose that neurobiologists, who might inadvertently contribute to this situation, should be aware of how their papers will be read by the legal community and should play a more active role in educating and engaging with that community.

Posterior parietal cortex (PPC) is thought to play a critical role in decision making, sensory attention, motor intention, and/or working memory. Research on the PPC in non-human primates has focused on the lateral intraparietal area (LIP) in the intraparietal sulcus (IPS). Neurons in LIP respond after the onset of visual targets, just before saccades to those targets, and during the delay period in between. To study the function of posterior parietal cortex in humans, it will be crucial to have a routine and reliable method for localizing specific parietal areas in individual subjects. Here, we show that human PPC contains at least two topographically organized regions, which are candidates for the human homologue of LIP. We mapped the topographic organization of human PPC for delayed (memory guided) saccades using fMRI. Subjects were instructed to fixate centrally while a peripheral target was briefly presented. After a further 3-s delay, subjects made a saccade to the remembered target location followed by a saccade back to fixation and a 1-s inter-trial interval. Targets appeared at successive locations "around the clock" (same eccentricity, approximately 30 degrees angular steps), to produce a traveling wave of activity in areas that are topographically organized. PPC exhibited topographic organization for delayed saccades. We defined two areas in each hemisphere that contained topographic maps of the contra-lateral visual field. These two areas were immediately rostral to V7 as defined by standard retinotopic mapping. The two areas were separated from each other and from V7 by reversals in visual field orientation. However, we leave open the possibility that these two areas will be further subdivided in future studies. Our results demonstrate that topographic maps tile the cortex continuously from V1 well into PPC.

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.

We studied the choice behavior of 2 monkeys in a discrete-trial task with reinforcement contingencies similar to those Herrnstein (1961) used when he described the matching law. In each session, the monkeys experienced blocks of discrete trials at different relative-reinforcer frequencies or magnitudes with unsignalled transitions between the blocks. Steady-state data following adjustment to each transition were well characterized by the generalized matching law; response ratios undermatched reinforcer frequency ratios but matched reinforcer magnitude ratios. We modelled response-by-response behavior with linear models that used past reinforcers as well as past choices to predict the monkeys' choices on each trial. We found that more recently obtained reinforcers more strongly influenced choice behavior. Perhaps surprisingly, we also found that the monkeys' actions were influenced by the pattern of their own past choices. It was necessary to incorporate both past reinforcers and past choices in order to accurately capture steady-state behavior as well as the fluctuations during block transitions and the response-by-response patterns of behavior. Our results suggest that simple reinforcement learning models must account for the effects of past choices to accurately characterize behavior in this task, and that models with these properties provide a conceptual tool for studying how both past reinforcers and past choices are integrated by the neural systems that generate behavior.