Characterization of the neural mechanisms supporting the implementation of cognitive control in human decision making

Humans are particularly adept at modifying their behavior in accordance with changing environmental demands. Through various mechanisms of cognitive control, individuals are able to tailor actions to fit complex short- and long-term goals. The research described in this thesis uses functional magnetic resonance imaging to characterize the neural correlates of cognitive control at two levels of complexity: response inhibition and self-control in intertemporal choice. First, we examined changes in neural response associated with increased experience and skill in response inhibition; successful response inhibition was associated with decreased neural response over time in the right ventrolateral prefrontal cortex, a region widely implicated in cognitive control, providing evidence for increased neural efficiency with learned automaticity. We also examined a more abstract form of cognitive control using intertemporal choice. In two experiments, we identified putative neural substrates for individual differences in temporal discounting, or the tendency to prefer immediate to delayed rewards. Using dynamic causal models, we characterized the neural circuit between ventromedial prefrontal cortex, an area involved in valuation, and dorsolateral prefrontal cortex, a region implicated in self-control in intertemporal and dietary choice, and found that connectivity from dorsolateral prefrontal cortex to ventromedial prefrontal cortex increases at the time of choice, particularly when delayed rewards are chosen. Moreover, estimates of the strength of connectivity predicted out-of-sample individual rates of temporal discounting, suggesting a neurocomputational mechanism for variation in the ability to delay gratification. Next, we interrogated the hypothesis that individual differences in temporal discounting are in part explained by the ability to imagine future reward outcomes. Using a novel paradigm, we imaged neural response during the imagining of primary rewards, and identified negative correlations between activity in regions associated the processing of both real and imagined rewards (lateral orbitofrontal cortex and ventromedial prefrontal cortex, respectively) and the individual temporal discounting parameters estimated in the previous experiment. These data suggest that individuals who are better able to represent reward outcomes neurally are less susceptible to temporal discounting. Together, these findings provide further insight into role of the prefrontal cortex in implementing cognitive control, and propose neurobiological substrates for individual variation.

Neural and computational representations of decision variables

These studies explore how, where, and when representations of variables critical to decision-making are represented in the brain. In order to produce a decision, humans must first determine the relevant stimuli, actions, and possible outcomes before applying an algorithm that will select an action from those available. When choosing amongst alternative stimuli, the framework of value-based decision-making proposes that values are assigned to the stimuli and that these values are then compared in an abstract “value space” in order to produce a decision. Despite much progress, in particular regarding the pinpointing of ventromedial prefrontal cortex (vmPFC) as a region that encodes the value, many basic questions remain. In Chapter 2, I show that distributed BOLD signaling in vmPFC represents the value of stimuli under consideration in a manner that is independent of the type of stimulus it is. Thus the open question of whether value is represented in abstraction, a key tenet of value-based decision-making, is confirmed. However, I also show that stimulus-dependent value representations are also present in the brain during decision-making and suggest a potential neural pathway for stimulus-to-value transformations that integrates these two results.

More broadly speaking, there is both neural and behavioral evidence that two distinct control systems are at work during action selection. These two systems compose the “goal-directed system”, which selects actions based on an internal model of the environment, and the “habitual” system, which generates responses based on antecedent stimuli only. Computational characterizations of these two systems imply that they have different informational requirements in terms of input stimuli, actions, and possible outcomes. Associative learning theory predicts that the habitual system should utilize stimulus and action information only, while goal-directed behavior requires that outcomes as well as stimuli and actions be processed. In Chapter 3, I test whether areas of the brain hypothesized to be involved in habitual versus goal-directed control represent the corresponding theorized variables.

The question of whether one or both of these neural systems drives Pavlovian conditioning is less well-studied. Chapter 4 describes an experiment in which subjects were scanned while engaged in a Pavlovian task with a simple non-trivial structure. After comparing a variety of model-based and model-free learning algorithms (thought to underpin goal-directed and habitual decision-making, respectively), it was found that subjects’ reaction times were better explained by a model-based system. In addition, neural signaling of precision, a variable based on a representation of a world model, was found in the amygdala. These data indicate that the influence of model-based representations of the environment can extend even to the most basic learning processes.

Knowledge of the state of hidden variables in an environment is required for optimal inference regarding the abstract decision structure of a given environment and therefore can be crucial to decision-making in a wide range of situations. Inferring the state of an abstract variable requires the generation and manipulation of an internal representation of beliefs over the values of the hidden variable. In Chapter 5, I describe behavioral and neural results regarding the learning strategies employed by human subjects in a hierarchical state-estimation task. In particular, a comprehensive model fit and comparison process pointed to the use of "belief thresholding". This implies that subjects tended to eliminate low-probability hypotheses regarding the state of the environment from their internal model and ceased to update the corresponding variables. Thus, in concert with incremental Bayesian learning, humans explicitly manipulate their internal model of the generative process during hierarchical inference consistent with a serial hypothesis testing strategy.

Contributions of parietal cortex to reach planning

Sensory-motor circuits course through the parietal cortex of the human and monkey brain. How parietal cortex manipulates these signals has been an important question in behavioral neuroscience. This thesis presents experiments that explore the contributions of monkey parietal cortex to sensory-motor processing, with an emphasis on the area's contributions to reaching. First, it is shown that parietal cortex is organized into subregions devoted to specific movements. Area LIP encodes plans to make saccadic eye movements. A nearby area, the parietal reach region (PRR), plans reaches. A series of experiments are then described which explore the contributions of PRR to reach planning. Reach plans are represented in an eye-centered reference frame in PRR. This representation is shown to be stable across eye movements. When a sequence of reaches is planned, only the impending movement is represented in PRR, showing that the area is more related to movement planning than to storing the memory of reach targets. PRR resembles area LIP in each of these properties: the two areas may provide a substrate for hand-eye coordination. These findings yield new perspectives on the functions of the parietal cortex and on the organization of sensory-motor processing in primate brains.

Neural Prosthetics and Parietal Cortex

In the last decade, research efforts into directly interfacing with the neurons of individuals with motor deficits have increased. The goal of such research is clear: Enable individuals affected by paralysis or amputation to regain control of their environments by manipulating external devices with thought alone. Though the motor cortices are the usual brain areas upon which neural prosthetics depend, research into the parietal lobe and its subregions, primarily in non-human primates, has uncovered alternative areas that could also benefit neural interfaces. Similar to the motor cortical areas, parietal regions can supply information about the trajectories of movements. In addition, the parietal lobe also contains cognitive signals like movement goals and intentions. But, these areas are also known to be tuned to saccadic eye movements, which could interfere with the function of a prosthetic designed to capture motor intentions only. In this thesis, we develop and examine the functionality of a neural prosthetic with a non-human primate model using the superior parietal lobe to examine the effectiveness of such an interface and the effects of unconstrained eye movements in a task that more closely simulates clinical applications. Additionally, we examine methods for improving usability of such interfaces.

The parietal cortex is also believed to contain neural signals relating to monitoring of the state of the limbs through visual and somatosensory feedback. In one of the world’s first clinical neural prosthetics based on the human parietal lobe, we examine the extent to which feedback regarding the state of a movement effector alters parietal neural signals and what the implications are for motor neural prosthetics and how this informs our understanding of this area of the human brain.