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灵长类动物前岛叶皮层代表了前景理论所提出的经济决策变量。

Primate anterior insular cortex represents economic decision variables proposed by prospect theory.

机构信息

Department of Psychological and Brain Sciences, Johns Hopkins University, 3400N. Charles St., Baltimore, MD, 21218-2685, USA.

Zanvyl Krieger Mind/Brain Institute, 3400N. Charles St., Baltimore, MD, 21218-2685, USA.

出版信息

Nat Commun. 2022 Feb 7;13(1):717. doi: 10.1038/s41467-022-28278-9.

Abstract

In humans, risk attitude is highly context-dependent, varying with wealth levels or for different potential outcomes, such as gains or losses. These behavioral effects have been modelled using prospect theory, with the key assumption that humans represent the value of each available option asymmetrically as a gain or loss relative to a reference point. It remains unknown how these computations are implemented at the neuronal level. Here we show that macaques, like humans, change their risk attitude across wealth levels and gain/loss contexts using a token gambling task. Neurons in the anterior insular cortex (AIC) encode the 'reference point' (i.e., the current wealth level of the monkey) and reflect 'loss aversion' (i.e., option value signals are more sensitive to change in the loss than in the gain context) as postulated by prospect theory. In addition, changes in the activity of a subgroup of AIC neurons correlate with the inter-trial fluctuations in choice and risk attitude. Taken together, we show that the primate AIC in risky decision-making may be involved in monitoring contextual information used to guide the animal's willingness to accept risk.

摘要

在人类中,风险态度高度依赖于情境,会随着财富水平或不同的潜在结果(如收益或损失)而变化。这些行为效应已使用前景理论进行建模,关键假设是人类相对于参考点不对称地表示每个可用选项的价值,将其表示为收益或损失。目前尚不清楚这些计算在神经元水平上是如何实现的。在这里,我们展示了猕猴与人类一样,通过代币赌博任务改变了其在财富水平和收益/损失情境下的风险态度。前脑岛皮层(AIC)中的神经元编码“参考点”(即猴子的当前财富水平),并反映出前景理论所假设的“损失厌恶”(即,相对于收益情境,选项价值信号对损失的变化更为敏感)。此外,AIC 中一组亚群神经元的活动变化与试验间选择和风险态度的波动相关。总之,我们表明,灵长类动物 AIC 在风险决策中可能参与监测用于指导动物接受风险意愿的情境信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b165/8821715/e636d2673cb4/41467_2022_28278_Fig1_HTML.jpg

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