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.
- Robinson, P.A., Rennie, C.J., Rowe, D.L., O'Connor, S.C., Gordon,
E. (2005). Multiscale brain
modelling. Philos Trans R Soc B 360(1457): 1043–1050.
- Machens, C.K., Romo, R., Brody, C.D.
(2005). Flexible control of mutual inhibition: a neural model of
two-interval discrimination. Science 307 (5712): 1121–1124.
- Clithero,
J.A., Tankersley, D., Huettel, S.A. (2008)
Foundations of neuroeconomics: From philosophy to practice. PLoS Biol 6(11).
- Glimcher , P. ( 2003 ). Decisions, Uncertainty
and the Brain: The Science of Neuroeconomics. Cambridge, MA : MIT Press.
- Wang, X.J. (2008). Decision making in
recurrent neuronal circuits. Neuron
60: 215-234.
- Damasio, A.R.
(1994). Descarte's Error:
Emotion, reason and the human brain. New York: Picador.
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