Veit Stuphorn - US grants

DESCRIPTION (provided by applicant): Cognitive control of behavior is of central importance in human daily life. The hallmark of voluntary control over behavior is the ability to change an action when it no longer serves the current behavioral goal. The long-term goal of this research project is the understanding of the neural mechanisms that underlie these processes. We will study response inhibition using the stop signal task, which manipulates the ability to inhibit a movement at different degrees of preparation by presenting an imperative stop signal. This paradigm has led to a detailed mechanistic understanding of the control of eye movements by the frontal and supplementary eye field (FEF, SEF). We hypothesize that these effector-specific inhibitory mechanisms are guided and controlled by more general effector-independent cognitive systems. Recent lesion and neuroimaging work has indicated that such higher-order signals might exist in a network of areas in the medial frontal cortex (MFC), inferior frontal cortex (IFC), and subthalamic nucleus (STN). Therefore, we will test this hypothesis by determining the functional organization of response inhibition signals in MFC, IFC, and STN in macaque monkeys that are trained to inhibit both eye and arm movements. Our first aim is to understand how the frontal cortex and basal ganglia circuit interact to inhibit responses and to regulate the level of responsiveness of the motor system. In our second aim, we will identify cognitive neural activity above the effector-specific level by showing that these signals are generally important across eye and arm movement inhibition. This study will determine the underlying neural basis of motor control and has relevance toward understanding neuropsychiatric disorders such as Attention Deficit Hyperactivity Disorder (ADHD) that could arise from alterations to the circuitry underlying response inhibition. Other forms of behavioral control might use similar neural mechanisms and thus, our research might lead to insights into self-control in general. PUBLIC HEALTH RELEVANCE Cognitive control of behavior is of central importance in human daily life. The hallmark of voluntary control over behavior is the ability to suppress or change an action when it no longer serves the current behavioral goal. The long-term goal of this research project is to understand the neural mechanisms underlying behavioral control. This study has relevance toward understanding neuropsychiatric disorders such as Attention Deficit Hyperactivity Disorder (ADHD) or impulsivity that could arise from alterations to the circuitry underlying response inhibition. Other forms of behavioral control might use similar neural mechanisms and thus, our research might lead to insights into self-control in general.

? 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.

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.

Project Summary Many everyday decisions have to be made in the face of uncertainty about the eventual outcome of the chosen action. Such decisions are strongly influenced by an individual's risk attitude. Risk attitude is flexible and depends on contextual factors, such as whether the gamble outcome represents a potential gain or a loss, and the momentary wealth level. Impairments in the ability to properly assess risk can lead to severe behavioral disorders including various addictions and pathological gambling as well as an increased tendency toward criminal behavior. Such self-defeating behavior creates an enormous medical and economic toll on the individual as well as on society. The long-term goal of this research project is to understand the neural mechanisms controlling risk attitude. Human imaging experiments and lesion studies suggested that two functionally different, but connected networks guide decisions under risk. One `risk-attitude' network monitors the contextual factors that influence risk attitude. A central node within this risk-attitude network is anterior insular cortex. The risk-attitude network signals the momentary value of seeking or avoiding risk to a second `risk-decision' network, centered on the lateral and medial frontal cortex, which represents the option and action values of risky and sure (risk-free) options and selects a particular action. Our central hypothesis is that: (1) Anterior insular cortex (AIC) monitors the behaviorally salient factors that modulate risk-attitude. The neuronal signals serve as input variables into the risky decision process. They are likely represented in a non- spatial reference frame, not linked to specific actions. These signals guide activity in lateral prefrontal cortex (LPFC). (2) LPFC uses the risk-attitude-relevant signals to estimate and compare the value of the risky and sure option. These transformed signals guide activity in supplementary eye field (SEF), the oculomotor subsection of the medial frontal cortex. (3) SEF uses the option value input from LPFC to generate action value signals that reflect the momentary contextual risk-attitude and guides the final saccade action selection process, which indicates the choice between seeking and avoiding a risky option. We have developed the token- based gambling task, an animal model of context-dependent risky decision making. In this task, the monkey has to acquire a number of tokens over multiple trials to obtain reward by making decisions under risk. The trial outcomes can either be a gain or loss of tokens. Behavioral data show a clear effect of both gain/loss context and currently owned token number on the monkey's risk attitude. Using the token-based gambling task, we can use a combination of recording (Aim 1) and reversible inactivation (Aim 2) to test whether and how neural activity in AIC, LPFC and SEF is causally involved in risk-related behavior. These experiments provide a novel approach to understanding the competition between risk-seeking and risk-avoidance behavior at the neural level. Understanding the neural basis of variations in risk-related behavior will provide a road map for precise therapeutic interventions and early diagnosis of pathological risk-seeking behaviors.