Faculty Bibliography

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.

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.

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.

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.

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.

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.

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 central goal of modern science that evolved during the Enlightenment was the empirical reduction of uncertainty by experimental inquiry. Although there have been challenges to this view in the physical sciences, where profoundly indeterminate events have been identified at the quantum level, the presumption that physical phenomena are fundamentally determinate seems to have defined modern behavioral science. Programs like those of the classical behaviorists, for example, were explicitly anchored to a fully deterministic worldview, and this anchoring clearly influenced the experiments that those scientists chose to perform. Recent advances in the psychological, social, and neural sciences, however, have caused a number of scholars to begin to question the assumption that all of behavior can be regarded as fundamentally deterministic in character. Although it is not yet clear whether the generative mechanisms for human and animal behavior will require a philosophically indeterminate approach, it is clear that behavioral scientists of all kinds are beginning to engage the issues of indeterminacy that plagued physics at the beginning of the twentieth century.

Economics, psychology, and neuroscience are converging today into a single, unified discipline with the ultimate aim of providing a single, general theory of human behavior. This is the emerging field of neuroeconomics in which consilience, the accordance of two or more inductions drawn from different groups of phenomena, seems to be operating. Economists and psychologists are providing rich conceptual tools for understanding and modeling behavior, while neurobiologists provide tools for the study of mechanism. The goal of this discipline is thus to understand the processes that connect sensation and action by revealing the neurobiological mechanisms by which decisions are made. This review describes recent developments in neuroeconomics from both behavioral and biological perspectives.