THE NEUROECONOMICS IN THE CONTEXT OF INTEGRATIVE NEUROSCIENCE

The brain's large-scale organizational principles are elucidated by many fields, including the neurophysiology, clinical practice, biology, cognitive sciences. Integrative neuroscience attempts to consolidate these observations through unified descriptive models and databases of behavioral measures and recordings. These are the basis for some quantitative modeling of large-scale brain activity [1]. Statistics and computational neuroscience form the bases of the quantitative analysis for those models. Actually, computational neuroscience is an approach to understanding the information content of neural signals by modeling the nervous system at many different structural scales, including the biophysical, the circuit and the system levels. Computer simulations of neurons and neural networks are complementary to traditional techniques in neuroscience. Computational modeling of higher cognitive functions has only recently begun with experimental data that comes primarily from single-unit recording in primates. The frontal lobe and parietal lobe act as integrators of information from multiple sensory modalities. There are some tentative ideas regarding how simple mutually inhibitory functional circuits in these areas may carry out biologically relevant computation [2].

We define neuroeconomics as the convergence of the neural and social sciences, applied to the understanding and prediction of decisions about rewards, such as money, food, information acquisition, physical pleasure or pain, and social interactions. Neuroscience introduces technological approaches, including brain imaging, lesion studies, molecular biology, pharmacology, and electrophysiology. Economics adds conceptual principles (e.g., rationality and utility), statistical techniques, and rigorous modeling. Psychology provides evidence for decision biases such as heuristics, framing effects, and emotional influences. Finally, genetics, computer science, and philosophy contribute to neuroeconomic research [3].

The central assumption of this discipline is that by combining both theoretical and empirical tools from neuroscience, psychology and economics into a single approach, the resulting synthesis will provide insights valuable to all three parent disciplines. Studies conducted to date seem to support that conclusion. Theories from economics and psychology have already begun to restructure our neurobiological understanding of decision making, and a number of recent neurobiological findings are beginning to suggest constraints on theoretical models of choice developed in both economic and psychological domains [4].

While economics describes choices and economic decisions of large groups of individuals (macroeconomics) or of individual households or firms (microeconomics), the neuroeconomics analyzes the mental processes that accompany personal choices. The neuroeconomics arose from the observation that economic agents do not always behave according to the model of rationality (maximum effect with minimum means) that underlies classical economics. This event, that was explored by behavioral economics, has led to investigate asymmetries in attitude towards irrational behavior as losses or gains, or the influence of unbiased considerations (fairness) in decisions that should concern only the rational sphere. The neuroeconomics therefore seeks to explain the heterogeneity of behaviors observed in situations where economics provides a rather homogeneous behavior. These findings have stimulated the search for biological basis of these attitudes through experimental studies of the brain’s activity (e.g., by techniques such as magnetic resonance imaging) during decisional (economic) tasks and so computational models that try to reproduce the cortical connections involved in the thinking process have been built. The main result of the neuroeconomics is that the economic value is effectively represented in different regions of the brain, particularly in the orbital cortex and in the medial prefrontal cortex. The encoding of the economic value in those areas has been supported by several experiments, either from single-neuron studies in primates or from neuronal populations in human subjects. Neurophysiological studies have showed that neuronal populations in the orbital cortex encode the value of the different alternatives and of the final choice [5]. Deficit in the choice behavior have been correlated to lesions in these areas and patients with damage to the ventromedial prefrontal cortex have been reported to have difficulty making advantageous decisions [6]. These observations indicate that the economical choices rely on encoded values in the orbital and medial prefrontal cortex. However, the mechanisms by which the different values are compared during the decisional process are to be investigated. There exist about this issue two general hypotheses. The first hypothesis considers that the values are compared irrespective of the actions in the prefrontal areas (abstract representation) and so the final decision drives the subsequent action. The second hypothesis assumes that the values are initially associated to many possible actions and the final decision takes place in the premotor areas through a selective process among the possible actions.

Behavioral phenomena such as the hyperbolic discounting (http://economics.about.com/library/glossary/bldef-hyperbolic-discounting.htm)  whereby individuals show unbalanced preferences to immediate instead of long term returns, have been usually interpreted as the effect of the competition between two decisional systems. The first system is fast, impulsive, metabolically parsimonious. The second system is slow, long term referenced, metabolically expensive and typically characterizes human beings. By this dual representation, the limbic areas would role in the impulsive choices, while the prefrontal areas would role in the forward-looking choices. On the contrary, the unitary hypothesis considers a single decisional system and a single representation of the value of the alternatives. 

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