the consequence - the choice of optimal behavior occurs at the level of specialized
neural networks. In general, the neuroeconomic approach is relatively mechanic
critical: neural networks have the ability to regulate the evaluation of all “for” and “pro-
tiv "when choosing a behavior. According to this approach, a neuron, or neuron
alternatives and makes the best choice. In the first neuroeconomic
simple animal models have been used.
making decisions. For example, monkeys were trained to follow the gaze of moving
or another object (Sugrue et al., 2005). Experimenters tried to detect
or left. A cloud of moving dots appeared on the screen in front of the monkey;
which the experimenter manipulated. Sometimes the movement was absolutely
left). Studies have shown that monkeys, stimulated for the correct
the implementation of the task fruit juice, able to quickly identify the dominant direction
follow the movement of the points and follow them with a look. Increasing or decreasing the number
points moving in the same direction, the researchers manipulated the degree
the complexity of the task and demonstrated that in carrying out the task the activity of the neuro
new intraparietal sulcus (lateral intraparietal area, LIP) gradually increased
The rons have accumulated information up to a certain threshold for making a decision - re-
move through this threshold triggered eye movement. Neuroscientists have suggested relatively
simple decision making model: processing and storing information about alternatives
movement directions occur at the level of specialized neuron detectors,
movement of visual images left or right independently of each other.
ha For example, the more points it moves to the left, the more active the neural system becomes.
us, detecting movement to the left. Now let us imagine that these detectors turn out to be
inhibitory (overwhelming) effects on each other: as a result, the most active
Neural neurons, for example, those specializing in moving to the left, will slow down (
influence) the neurons coding the movement in the opposite direction (to the right), activity
which is less at a given time. So we are dealing with elementary
algorithm of comparison of alternatives, where the strongest survives and wins due to
suppressing a competitor. In general, "left-sided" neurons launch the translation command.
view to the left, restraining (suppressing, slowing down), while using alternatives
options. In this model, LIP neurons are a decision-making substrate
comparing visual information and opting for one of the alternatives.
In recent years, several models of neural networks have been proposed that provide
decision making (Bogacz, 2007), which are based on the idea that
generation is generated when the difference between the alternatives reaches a certain
threshold value. In experiments with monkeys (Glimcher, 2003), it was shown
that decision making can be confidently predicted based on the activity of LIP neurons:
after the activity of the neuron encoding decision-making reaches the threshold
level, this decision is inevitable. Moreover, affecting certain
neurons, for example, by electric discharge, can be affected by the monkey’s
Sewing Thus, for the first time, it was possible to demonstrate the existence of neurons that
elementary decision making.
In animal experiments, food, as a rule, is the reward
the Interestingly, the magnitude of the reinforcement is proportional to the activity of the LIP neurons.
Moreover, the activity of neurons LIP simultaneously reflects the magnitude of the expected sub-
fasteners, and the likelihood of its receipt. Thus, the neuroeconomic studies
approach came close to the concept of the expected utility of the alternative (expected
utility) - one of the key concepts in economic theory. According to this
concepts, among all possible alternatives or behavioral scenarios
Boron must be made in favor of the alternative with the highest expected utility.
stew. Indeed, as was shown in neuroeconomic experiments, in active
LIP neurons exhibit the same patterns that can be predicted
zany classical economic theory. Of course, a simplified view of what
only LIP neurons are actively involved in the decision-making process, it would be wrong.
A number of other areas of the brain are also involved in the process of estimating expected benefits.
these are the so-called dopaminergic regions, the
by the neurotransmitter dopamine. In humans, the basal ganglia (striatum) and the lower
The areas of the frontal cortex (orbitofrontal cortex) play a key role in evaluating the utility of
pa at the time of the decision and thus govern our decisions (Rangel et
al., 2008).
Neuroeconomics offers its rather mechanistic model for the assessment of
subjective utility, according to which neural networks have the ability to
ability to compare available alternatives. In this model, the input to the neural network
A sensory, motivational, cognitive, or any other signal arrives, and
course we get the result of the comparison in favor of the most optimal solution. Such
the model has received a somewhat illogical name diffuse (the term “diffuse”
comes from the statistical concept of the same name). Currently this simple
the model is considered the basic neuroeconomic neural model of the theory of adoption
solutions (Fig. 1).
If we consider the problem of decision making from the point of view of neuroeconomics and
suggest that our decisions can be predicted based on the activity
neural networks, the disclosure of the corresponding neural mechanisms will open
new horizons in understanding the nature of human rational behavior
Fig. 1. A schematic representation of the sequence of processes underlying the adoption of elements
tare (perceptual) solutions (adapted with changes; see: Bogacz, 2007). In the first stage, the brain re-
step sensor signals with coded information about available alternatives; lines schematically
the dynamics of the activity of sensory neurons is indicated. At first, sensory information centers around
one of two alternatives. Since the sensory input of the nervous system is characterized by a high level of noise,
Sometimes, at certain times, the second alternative may be preferable, but the noise
obviously complicates the operation of choice. In the second stage, information is integrated during
a certain period of time. From stage to stage, noise is reduced. At the third stage, the extraction takes place
information and its comparison with the selected criterion. The whole process is similar to the work of a traffic light at a crossroads,
traffic control: the system is waiting to receive enough information for
making a final decision or continuing further integration until it’s accumulated
the required number of arguments in favor of one of the alternatives
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NEUROECONOMICS
Terms: NEUROECONOMICS