CRCNS: Neural decision mechanisms: from value-encoding to preference reversal

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 DESCRIPTION (provided by applicant): Decision-making is one of the most central cognitive functions. Since the early days of the 20th century a body of mathematical work developed the modern axiomatic approach to rationality in choice behavior. These normative models revolutionized economics and mathematical psychology by describing the properties of choices consistent with maximizing an ordered, internal representation of value, termed utility. Experimental research, however, has demonstrated a wide set of "non-rational" behaviors (preference reversals) that deviate from these normative theories. A number of computational models where developed to account for the observed non-rationalities. Most of these models explain behavioral preferences as the outcome of a dynamic computational process and not of a static maximization process with fixed utility and probability weighting functions. However, the cognitive and neural processes that are at the heart of preference formation are still poorly understood. We will combine behavioral data, electrophysiological recordings in humans and monkeys, and computational approaches to develop a new theory of the neural mechanisms underlying complex, multi-attribute decision-making. Intellectual Merit (provided by applicant): The overall goal of the present proposal is to understand the neural code of decision-variables (such as reward amounts and probabilities) and of the dynamic process by which these variables are integrated to form subjective values (utility) and preferences and mediate nonrational behavior. Monkey and human subjects will work in a novel behavioral task that allows us to observe the focus of attention of decision makers while they evaluate the offers and select one of them. Together with these behavioral data we will record decision-related activity in several brain areas. This data set will allow us t test the predictions of various cutting edge computational models that have been suggested to explain preference reversals, but are based on different mechanisms. We will also use the experimental findings to develop a neural mechanistic theory (Aims 1-3, below) and to account for non-rational behaviors, such as preference reversal (Aim 4). Specifically, we have the following aims: (1) Understand how the decision-variables (outcomes, amounts and probabilities) are encoded in the brain. (2) Understand how the separate decision-variables are integrated to compute the overall subjective value of choice options. (3) Investigate whether, and if yes how, attention influences the value computation of choice options. (4) Use the decision model developed in aims 1-3 to explain preference reversals. The end point of these investigations will be a new neurocomputational theory that consistently explains behavioral and neural data in our experiments. This model will integrate decision and attentional selection processes and will generate novel predictions to be tested in future research. Broader Impact (provided by applicant): Some of the most important problems of modern societies are caused by non-optimal decisions made by people. Abuse of illegal drugs, alcohol and nicotine but also the current epidemic of obesity and metabolic disease in the population can ultimately be traced back to people making decisions that are not in their objective best interest. The research proposed here studies how the variables underlying decisions are represented and computed in the primate brain, in particular by understanding situations in which optimal choices are discarded in favour of inferior ones. The project also contributes to the training of the next generation of scientists. Four PhD students will be trained; two at Johns Hopkins University and two at Tel Aviv University, and undergraduates will be part of the research groups. All PIs are strongly committed to increase participation by women and underrepresented minorities. Niebur and Stuphorn have a long track record of training minority high school students in their labs, successfully preparing them for a future college career. In addition, existing connections with Morgan State University, a historically black college in Baltimore, will be extended.