Neuroeconomics and The Brain--论文代写范文精选

一般来说,神经经济学研究按照下列方法:选择一个正式的模型,决定博弈论的情况,然后推导出决策。测试模型行为对象是否遵循规范标准，识别脑区和神经机制是选择行为的基础。下面的paper代写范文进一步论述。

Intoduction
  Neuroscience is the scientific study of the nervous system. Molecular, cellular, behavioral, and cognitive mechanisms are revealed through different means; functional imaging technologies study degrees of activation and locations; single-cell recordings analyze the activity of individual cells; lesion studies try to determine the function of certain brain areas by investigating brain-impaired subjects (see D'Esposito, for an accessible presentation); and computational neuroscience uses computer simulations to support or invalidate hypotheses about brain mechanisms (Eliasmith). 

  Obviously, these researches should shed light on the nature of decision-making. Recently, a conjunction between the neuroscientific study of decision-making and experimental economics has led to the creation of a new field now called neuroeconomics (Glimcher; McCabe; Zak). Although many definitions of the field exist, I will limit myself to the one given by philosopher Don Ross: “the program for understanding the neural basis of the behavioral response to scarcity” (Ross 330). Ross’s definition connects clearly this field to the traditional endeavour of economics (cf. Robbins’s characterization of economics in first section).

  Generally speaking, neuroeconomic research proceeds according to the following methodology: (1) Choosing a formal model of rationality, a decision- or a game-theoretic situation, and then deducing what decisions agents should make. (2) Testing the model behaviorally to see if subjects follow normative standards. (3)Identifying the brain areas and neural mechanisms that underlie choice behavior. (4) Explaining why subject follow/fail to follow normative standards Of course, many variations are possible at each step: the formal model may be an alternative theory, or tackle a question not addressed by rational-choice theory; subjects may be of different age, sex or cultural background, or they may be subjects who incur cerebral impairment, cognitive deficit, etc. Neuroeconomics research thus proceeds by comparing formal models with behavioral data, and by identifying neural structures causally involved in economic behavior.

 Homo Neuroeconomicus 
  Neuroeconomics explains decision-making as the product of brain processes involved in the representation, anticipation, valuation and selection of choice opportunities. It breaks down the whole process of decision into mechanistic components: certain brain areas may represent the value of the outcome of an action before decision, other ones may represent the value of the action per se, and yet other ones may represent these values at the time of the decision. Although such dispersion of data may appear confusing, economic psychology provides us with a useful framework for understanding the mechanics of rationality at the neural level in a coherent manner. Kahneman and his collaborators suggest that the concept of utility should be divided in subspecies (Kahneman, Wakker and Sarin). 

  While decision utility is important (the expected gains and losses, or cost and benefits), decision-makers also value experienced utility (the hedonic, pleasant or unpleasant affect), predicted utility (the anticipation of experienced utility) and remembered utility (how experienced utility is remembered after the decision, e.g. as regretting or rejoicing). Neuroeconomics should identify neural structures and processes associated with these variables or, if necessary, suggest another typology. This distributed account of utility, as I will call it here, is a useful tool for organizing the numerous findings in this burgeoning field.

  Neuroeconomics and the distributed account of utility can, for instance, provide a more precise explanation of loss-aversion, a robust finding in psychology. Subjects usually give to the loss of $10 a higher impact than a $10 gain. Tversky and Kahneman attribute this aversion to a bias in the representation of the values of gain and loss (Tversky and Kahneman). Instead of postulating abstract cognitive heuristics, neuroeconomics explains loss-aversion as the interaction of neural structures involved in the anticipation, registration and computation of the hedonic affect of a risky decision. 

  The amygdala, a structure involved in fear, emotional learning and memory modulation, registers the emotional impact of the loss; the ventromedial prefrontal cortex predicts that a loss will result in a given affective impact; and midbrain dopaminergic neurons compute the probability and magnitude of the loss (Naqvi, Shiv and Bechara; Tom et al.). Subjects are thus loss-averse because they tend to have or already had a negative response to losses (experienced utility). When they expect a loss to occur (decision utility), they anticipate their affective reaction (predicted utility). They might be also attempting to minimize their post-decision feeling of regret (remembered utility).

  Similar researches can also illuminate ambiguity-aversion. In many experimental settings, subjects have a strong preference for risky prospects (those for which the probabilities are known) over ambiguous one (those for which the probabilities are unknown). For instance, let’s imagine two decks of 20 cards. There are 10 red cards and 10 blue cards in the first one (risky deck), while there is an unknown proportion of blue to red cards in the second one (ambiguous deck). Agents win $1 each time they pick a red card. Despite a 50–50 chance of winning in both cases, subjects have a marked preference for the risky deck. According to decision theory, there is no reason to prefer one deck to another, but neuroeconomic studies showed that in this case of decision under ambiguity, a stronger activation is found in many areas, especially the amygdala (Huettel et al.). Although decision theory treats ambiguity as a special case of risky decision-making, ambiguous and risky decision-making are supported by two distinct mechanisms. It thus seems that ambiguity-aversion happens because people have a stronger negative affective reaction to ambiguity than risk.

  One of the most robust finding in neuroeconomics concerns decision utility, the calculation of cost and benefits. According to many findings, this process is realized by dopaminergic systems, a network of structures in ‘older’ brain areas highly involved in motivation and valuation (Montague and Berns; Berridge). Dopaminergic neurons respond selectively to prediction errors, either the presence of unexpected rewards or the absence of expected rewards. In other words, they detect the discrepancy between predicted and experienced utility. Moreover, dopaminergic neurons learn from their mistakes: they learn to predict future rewarding events from prediction errors, and the product of this learning (a ‘behavioral policy’) can then bias action choice. 

  Computational neuroscience identifies a class of reinforcement learning algorithms that mirror the activity of dopaminergic activity (Niv, Duff and Dayan; R. E. Suri and W. Schultz). It is suggested that dopaminergic neurons broadcast in different brain areas a reward-prediction error signal similar to those displayed by temporal difference (TD) algorithms developed by computer scientists (Sutton and Barto "A Temporal-Difference Model of Classical Conditioning"; Sutton and Barto Reinforcement Learning : An Introduction). These algorithms are plausible descriptions of neural mechanisms of decision-making implemented in dopaminergic systems. They are not only involved in basic reward prediction, such as food, but also with abstract stimuli like art, brands, love or trust (Montague, King-Casas and Cohen 420).(paper代写)

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