Untangling theory as theoretical framework for understanding the functions of brain systems dedicated to economic decision-making

Abstract

The long tradition of neuroscience has focused on dissecting systems and searching for the unique functional properties of each anatomical area. This tradition creates separate fields of study within systems neuroscience: the field of study of the visual system, that which studies the motor system, that which studies the memory system, etc. The field of value-based decision-making builds upon this tradition and successfully claims new territory in the prefrontal cortex (PFC). In fact, now neuroscience as a field has a very robust understanding of the prefrontal cortex. The dominant understanding in the field posits that the information in PFC is all abstract: like utility of certain objects or preferences (Padoa-Schioppa 2011). This position holds belief that no concrete information related to sensory or motor systems exists in the PFC. In parallel, more and more studies have subdivided the prefrontal cortex by anatomy and cytoarchitecture, assigning a unique functionality to each region (Wise 2012). However, is it really true that the value-based decision-making system is unique compared to the other systems of the brain? Is it true the PFC consists of hundreds or thousands of patches, each with its own unique functionality? If not, what can be the alternative? With the dissertation, we would like to discuss counterevidence against this widely believed framework of functional specialization. We will argue that the prefrontal cortex encodes non-abstracted variables like spatial information, and there are many similarities between the different brain regions in the prefrontal cortex that have largely been ignored. There can be many reasons why the field of neuroscience, specifically the study of value-based decision-making, has been biased towards a functional specialization framework. We hypothesize that this understanding is the result of more controlled, simplistic tasks with the aim of generating reductionist explanations. Using a high-dimensional and natural task paradigm where many sensorimotor variables change and affordances vary accordingly generates evidence counter to this view. We developed a task that mimics the hunting of animals where every agent must process changing information dynamically and interactively. Our result suggests that even a single brain area, the dorsal anterior cingulate cortex (dACC), encodes position and kinematic variables that influence the subject's instantaneous decision. We further sought to characterize the computations underlying the subject's performance. We found that the subject's behavior is well explained by the subject predicting the future position of the rewarding agent by an internal model. Furthermore, we find that dACC neurons represent positional and kinematic variables that would be critical for predicting the future. These results provide evidence against the claim that brain areas usually associated with value-based decision-making brain are dedicated solely to processing unique information related to abstract value. Instead, finding kinematic information about self and other agents suggests that information is distributed across brain areas. In addition, this outcome could provide insight about the benefits of continuous and naturalistic tasks. In the final section of this manuscript, we will define the necessary components of continuous decision-making and discuss the benefits of these task paradigms.