We note that most current neuroscientific methods provide only coarse information about mechanism. The dominant technique, fMRI, provides temporal information about the relative metabolic demand of populations of hundreds of thousands of neurons [ 43 ]. Models based on these methods must make simplifying assumptions: e. New methods in fMRI, notably those that characterize how information shapes connections between regions [ 99 ], promise to create integrated models that include both a description of information flow among brain regions and the effects of behavior upon the connections between those regions.
Arguments Against Neuroeconomics
Integrating techniques within single studies, such as fMRI and genetics [ 27 , ], will be critical for producing mechanistically complete and biologically plausible explanations of behaviors. Neuroeconomics is at a crossroads, poised to demonstrate that neuroscience can provide the same types of benefits it has long received from the social sciences. Ideas from game theory and expected utility theory can explain the responses of individual neurons to incoming information [ 2 ]. Similarly, aspects of utility theory can be used to describe the activity of populations of neurons within the brain's reward system [ ].
There is also an opportunity for the axiomatic approach of decision theory to explain decision-making mechanisms [ 20 ], such as building from the response properties of dopaminergic neurons [ ].
Without comparable examples of neuroscience data contributing to economic models, critics could argue that neuroeconomics research is a brain-centric enterprise that incorporates ideas from the social sciences without reciprocation [ 16 , 17 ]. We agree that neuroeconomic research has indeed been brain-centric, but stress that it need not remain so. The core criticisms of neuroeconomics constrain the practice of this field, but do not render it meaningless. Clear foundational principles remain. Neuroeconomics via Mechanistic Convergence can more efficiently direct the course of future behavioral studies.
As an historical parallel, economists have explored psychological concepts e. Furthermore, neuroeconomics can facilitate the creation and testing of models that adhere to Biological Plausibility.
Uncertainty rules | Nature
Social scientists and neuroscientists should not treat decision-making phenomena as irreducible and mechanism-independent. Instead, the joint investigation of brain and behavior will lead to greater success than either discipline could achieve in isolation.
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We thank the members of the Duke Center for Neuroeconomic Studies for discussions that motivated this essay. Neuroeconomics: Promise Unfulfilled? Arguments Against Neuroeconomics Behavioral Sufficiency. Emergent Phenomenon.
Foundations of Neuroeconomics Mechanistic Convergence in experiments. Biological Plausibility in models. Conclusion Neuroeconomics is at a crossroads, poised to demonstrate that neuroscience can provide the same types of benefits it has long received from the social sciences. Acknowledgments We thank the members of the Duke Center for Neuroeconomic Studies for discussions that motivated this essay. References 1. J Econ Lit 9— View Article Google Scholar 2. Glimcher PW Decisions, uncertainty, and the brain : the science of neuroeconomics. Trends Cogn Sci — View Article Google Scholar 4.
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Decision theory, reinforcement learning, and the brain
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Cogn Neuropsychol 21— Samuelson PA A note on the pure theory of consumer's behaviour. Economica 5: 61— Houthakker HS Revealed preference and the utility function. Economica — Psychol Bull — This new edition features five sections designed to serve as both classroom-friendly introductions to each of the major subareas in neuroeconomics, and as advanced synopses of all that has been accomplished in the last two decades in this rapidly expanding academic discipline.
The first of these sections provides useful introductions to the disciplines of microeconomics, the psychology of judgment and decision, computational neuroscience, and anthropology for scholars and students seeking interdisciplinary breadth. The second section provides an overview of how human and animal preferences are represented in the mammalian nervous systems. Chapters on risk, time preferences, social preferences, emotion, pharmacology, and common neural currencies-each written by leading experts-lay out the foundations of neuroeconomic thought. The third section contains both overview and in-depth chapters on the fundamentals of reinforcement learning, value learning, and value representation.
The fourth section, "The Neural Mechanisms for Choice," integrates what is known about the decision-making architecture into state-of-the-art models of how we make choices.
The final section embeds these mechanisms in a larger social context, showing how these mechanisms function during social decision-making in both humans and animals. The book provides a historically rich exposition in each of its chapters and emphasizes both the accomplishments and the controversies in the field. A clear explanatory style and a single expository voice characterize all chapters, making core issues in economics, psychology, and neuroscience accessible to scholars from all disciplines. The volume is essential reading for anyone interested in neuroeconomics in particular or decision making in general.
Basic Methods from Neoclassical Economics 2. Experimental Economics and Experimental Game Theory 3. Estimation and Testing of Computational Models 5. Computational Neuroscience 6. Experimental Methods in Cognitive Neuroscience 7. Computation of value in simple choices 9. Valuation for Risky and Uncertain Choices Valuation, Intertemporal Choice and Self Control Neuroeconomics of Social Preferences The Study of Emotion in Decision Making Valuation and Common Neural Currencies The pharmacology of economic and social decision-making Section 3: Learning and Valuation