Rapid brain responses independently predict gain maximization and loss minimization during economic decision making.

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2013-04-17

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Abstract

Success in many decision-making scenarios depends on the ability to maximize gains and minimize losses. Even if an agent knows which cues lead to gains and which lead to losses, that agent could still make choices yielding suboptimal rewards. Here, by analyzing event-related potentials (ERPs) recorded in humans during a probabilistic gambling task, we show that individuals' behavioral tendencies to maximize gains and to minimize losses are associated with their ERP responses to the receipt of those gains and losses, respectively. We focused our analyses on ERP signals that predict behavioral adjustment: the frontocentral feedback-related negativity (FRN) and two P300 (P3) subcomponents, the frontocentral P3a and the parietal P3b. We found that, across participants, gain maximization was predicted by differences in amplitude of the P3b for suboptimal versus optimal gains (i.e., P3b amplitude difference between the least good and the best gains). Conversely, loss minimization was predicted by differences in the P3b amplitude to suboptimal versus optimal losses (i.e., difference between the worst and the least bad losses). Finally, we observed that the P3a and P3b, but not the FRN, predicted behavioral adjustment on subsequent trials, suggesting a specific adaptive mechanism by which prior experience may alter ensuing behavior. These findings indicate that individual differences in gain maximization and loss minimization are linked to individual differences in rapid neural responses to monetary outcomes.

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10.1523/JNEUROSCI.4242-12.2013

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San Martín, René, Lawrence G Appelbaum, John M Pearson, Scott A Huettel and Marty G Woldorff (2013). Rapid brain responses independently predict gain maximization and loss minimization during economic decision making. J Neurosci, 33(16). pp. 7011–7019. 10.1523/JNEUROSCI.4242-12.2013 Retrieved from https://hdl.handle.net/10161/13524.

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Scholars@Duke

Pearson

John Pearson

Assistant Professor of Neurobiology

My research focuses on the application of machine learning methods to the analysis of brain data and behavior. In particular, we are interested in the process by which organisms like songbirds learn complex motor skills without external reinforcement, the way simple information processing principles can explain the organization of early sensory systems, and designing new computational methods that allow us to analyze neural data and change experiments in real time. 

Huettel

Scott Huettel

Professor in the Department of Psychology and Neuroscience

Research in my laboratory investigates the brain mechanisms underlying economic and social decision making; collectively, this research falls into the field of “decision neuroscience” or "neuroeconomics". My laboratory uses fMRI to probe brain function, behavioral assays to characterize individual differences, and other physiological methods (e.g., eye tracking, pharmacological manipulation, genetics) to link brain and behavior. Concurrent with research on basic processes, my laboratory has also investigated the application of new analysis methods for fMRI data, including functional connectivity analyses, pattern classification analyses, and combinatoric multivariate approaches. We have also been applying computational methods to problems in behavioral economics and consumer decision making.  

I have also been very active in outreach, mentorship, and educational activities; as examples, I am lead author on the textbook Functional Magnetic Resonance Imaging (Sinauer Associates; 3rd edition in 2014), I teach Fundamentals of Decision Science, Decision Neuroscience and Neuroethics, and many of my postdoctoral and graduate trainees now lead research laboratories of their own.

Woldorff

Marty G. Woldorff

Professor in Psychiatry and Behavioral Sciences

Dr. Woldorff's main research interest is in the cognitive neuroscience of attention. At each and every moment of our lives, we are bombarded by a welter of sensory information coming at us from a myriad of directions and through our various sensory modalities -- much more than we can fully process. We must continuously select and extract the most important information from this welter of sensory inputs. How the human brain accomplishes this is one of the core challenges of modern cognitive neuroscience. Dr. Woldorff uses a combination of electrophysiological (ERP, MEG) and functional neuroimaging (fMRI) methods to study the time course, functional neuroanatomy, and mechanisms of attentional processes. This multimethodological approach is directed along several main lines of research: (1) The influence of attention on sensory and perceptual processing; (2) Cognitive and attentional control mechanisms; (3) The role of attention in multisensory environments; (4) The interactive relationship between attention and reward; and (5) The role of attention in perceptual awareness.


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