Our annual conference, Neuroeconomics: Decision Making and the Brain aims to promote global interdisciplinary discussion on topics lying at the intersection of the brain and decision sciences in hopes of advancing both theory and research. The meeting is attended by scholars of all levels from all areas of neuroeconomic research including the fields of economics, psychology, and neural science, as well as by leaders in fields such as finance and medicine. The meeting's format, consisting of general talk sessions, poster sessions, organized receptions and group meals, provides ample opportunities for networking and off-line discussions. Further networking opportunities are provided by other events like our all-attendee banquet.
Time and Location
Poster Session Assignments and Abstract:
Kavli Lecture Information Page:
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September 25: Consumer Neuroscience Satellite Symposium - organized by Milica Mormann, University of Miami, Hilke Plassmann, INSEAD & Ecole Normale Superieure & Carolyn Yoon, University of Michigan. Please register for the event separately here.
This year we continue to enhance our scientific content with the 5th Annual Kavli Foundation Plenary Lecture, given by Colin Camerer, PhD, Robert Kirby Professor of Behavioral Finance and Economics at the California Institute of Technology and a 2013 MacArthur Fellow*. We will begin the conference with four Kavli Foundation workshop tutorials on current advances in neuroeconomics, presented by Matthew Botvinick, PhD, Associate Professor, Department of Psychology and Princeton Neuroscience Institute, Princeton University; Russell Poldrack, PhD, Director of the Imaging Research Center and Professor of Psychology and Neurobiology, The University of Texas at Austin; Michael Woodford, PhD, John Bates Clark Professor of Political Economy, Columbia University, and Elke Weber, PhD, Jerome A. Chazen Professor of International Business and Professor of Psychology, Columbia University.
Kavli Workshops in the Foundations of Neuroeconomics
|Neuroscience Workshop||Social and Decision Science Workshop|
|Using neuroimaging to infer mental states: A guided tour through the minefield|
The University of Texas at Austin
One of the most common uses of neuroimaging is to infer what kind of psychological state a person is in during a particular decision making task, known as “reverse inference". This enterprise is crucial to neuroeconomics, but the journey to effective inferences is littered with land mines that must be avoided. I will outline the problems with informal reverse inference, and will show how these problems can be overcome through the use of decoding techniques from machine learning along with large-scale databases that can support formal reverse inference. I will also discuss how pattern similarity analyses can be used to understand neural representational spaces, and how differences between univariate and multivariate analyses can be interpreted.
|Psychophysical Aspects of Choice Behavior|
The lecture will discuss consequences for choice behavior of limits on the accuracy of subjective coding of the features of a choice situation, such as the attributes of the options available in the current choice set. It will be argued that such limits can explain aspects of behavior that may appear to be anomalies from the standpoint of rational choice theory, including stochasticity of choice, focusing illusions, context-dependent choice, and violations of the predictions of expected utility maximization. It will be shown how methods from the literature on sensory perception, such as signal detection theory, can be applied to the analysis of value-based choice. Finally, implications of the hypothesis of efficient coding, as a specific theory of the nature of the errors in subjective coding will be discussed, both for perceptual phenomena and for choice behavior. Alternative versions of the efficient coding hypothesis, from both the neuroscience and economics literatures, will be compared.
|Hierarchical reinforcement learning and the neural basis of choice|
Matthew M. Botvinick
This workshop will provide an overview of recent research investigating the role of hierarchical structure in reward based decision-making. We will begin with a tutorial introduction to hierarchical reinforcement learning (HRL), a computational framework that expands the scope of standard reinforcement learning to include temporally extended behaviors. From there, we will look at fMRI studies that have tested an initial set of predictions from HRL. On a formal level, we probe the question of why (and when) hierarchy is beneficial to adaptive behavior, and on the neuroscientific level we will relate HRL to the broader notion of efficient coding.
|Preference: Choice Primitive or Constructed Value?|
The realization that preferences are often constructed at the time of decision rather than simply recalled is arguably psychology’s most important and successful export to behavioral economics. It explains a broad range of violations of economic rationality postulates and lies at the basis of choice architecture, the modification of normatively irrelevant features of the choice environment that can change preferences. I will review theoretical frameworks (including Prospect Theory, Decision Field Theory and other drift diffusion models, and Query Theory) that detail the how and why of preference construction and empirical evidence supporting hypothesized processes.