Decision-making is one of the most central cognitive functions of importance at practically all levels of society. In many real-world decisions, which of the available alternatives is chosen is influenced by many different attributes. Such multi-attribute decisions are complex because they require the integration and comparison of many pieces of information. For instance, selecting the bundle of goods that maximizes value given a budget constraint in a supermarket that only stocks 100 different goods requires checking approximately 10^30 possible combinations. For this reason, humans do not use rational choice theory in all their decisions. In addition to having to combine the influence of all the different attributes, another complexity is that one alternative is often preferable on one set of attributes, but another is preferred on others. Making a choice then requires a trade-off, which further complicates the decision process. However, the cognitive and neural processes that are at the heart of preference formation are still poorly understood. This complexity is thought to tax limited cognitive resources in humans who therefore can pay attention only to a limited set of information, on which the decision is then based. In addition, task history often systematically changes decision biases. This research program takes advantage of the opportunity to obtain direct recordings from individual's brains while they perform such complex decision. It will study these activity patterns to determine whether they can be explained via mathematical models of decision making. Understanding which attributes are considered during decision making, and how they are weighted could explain decision making in typical and a-typical populations. Furthermore this integrative research program forms an opportunity to expose engineering students to dynamical systems and control theories in an interdisciplinary context.
This project combines behavioral data, neural recordings in humans (patients undergoing epilepsy evaluation) implanted with multiple depth electrodes covering many cortical and subcortical brain areas, and computational approaches to develop a new theory of the neural mechanisms underlying multi-attribute decision-making in complex environments. This is a unique opportunity to study brain circuits simultaneously across multiple brain areas while humans make these decisions. The overall goal of the present proposal is to understand the neural circuit involved in (1) representing the relevant decision variables, (2) integrating these variables to form subjective values, and (3) selecting one of the options in multi-attribute decisions. Participants, with implanted electrodes, will work in a novel behavioral task that makes it possible to observe their focus of attention while they evaluate the offers and select one of them. Data will constrain cutting edge computational models of multi-attribute decision making that will combine: (i) a procedural model of the decision in each trial, and (ii) a latent variable model of biasing influence on decision-making resulting from past trial history. The computational models will make it possible to identify neuronal activity that represents task-relevant variables and the dynamic flow of information across the different elements of the identified neural circuit.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
