A Neurocomputational Model for Intrinsic Reward.
A Neurocomputational Model for Intrinsic Reward.
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DOI：
10.1523/jneurosci.0858-20.2021
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发表时间：
2021-10-27
期刊：
影响因子：
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通讯作者：
Rutledge RB
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文献类型：
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作者：
Chew B;Blain B;Dolan RJ;Rutledge RB
标准的经济指标不能完整地反映我们作为个人和社会所重视的东西。此外，规范的宏观经济措施，如GDP，并没有考虑到非市场活动（例如，烹饪，儿童保育），尽管如此，它们仍会影响福祉。在这里，我们引入了一个计算工具来测量体验的情感价值（例如，演奏乐器时没有错误）。我们继续用神经数据来验证这个工具，使用功能磁共振成像来测量男性和女性受试者的神经活动，这些受试者执行强化学习任务，其中包括主观情感状态的定期评级。学习表现决定了报酬水平（即外在奖励）。至关重要的是，这项任务还包含了技能表现部分（即内在奖励），这不会影响报酬。外在奖励和内在奖励都会影响情感动态，它们的相对影响可以在我们的计算模型中捕捉到。内在奖励对情感状态的影响大于外在奖励的个体，其腹内侧前额叶皮层（vmPFC）活动对内在奖励的影响大于外在奖励。因此，我们表明，情感动态的计算建模可以索引内在奖励相对于外在奖励的主观价值，这是一种反映行为和神经活动的“计算幸福计”，可以量化体验的情感价值。人们越来越认识到，传统的经济指标不能全面反映我们作为一个社会所重视的东西。标准的经济学方法难以准确地为非市场活动分配价值，尽管这些活动在本质上可能是有益的，这促使人们需要新的工具来衡量对个人真正重要的东西。通过神经成像和计算模型的结合，我们发现，尽管内在奖励缺乏工具价值，但它们会影响主观情感状态和腹内侧前额叶皮层（vmPFC）的活动。外在和内在奖励影响情感状态的相对程度可以预测它们对神经活动的相对影响，这证实了我们的方法在衡量个人经历和其他非市场活动的情感价值方面的实用性。
Standard economic indicators provide an incomplete picture of what we value both as individuals and as a society. Furthermore, canonical macroeconomic measures, such as GDP, do not account for non-market activities (e.g., cooking, childcare) that nevertheless impact well-being. Here, we introduce a computational tool that measures the affective value of experiences (e.g., playing a musical instrument without errors). We go on to validate this tool with neural data, using fMRI to measure neural activity in male and female human subjects performing a reinforcement learning task that incorporated periodic ratings of subjective affective state. Learning performance determined level of payment (i.e., extrinsic reward). Crucially, the task also incorporated a skilled performance component (i.e., intrinsic reward) which did not influence payment. Both extrinsic and intrinsic rewards influenced affective dynamics, and their relative influence could be captured in our computational model. Individuals for whom intrinsic rewards had a greater influence on affective state than extrinsic rewards had greater ventromedial prefrontal cortex (vmPFC) activity for intrinsic than extrinsic rewards. Thus, we show that computational modeling of affective dynamics can index the subjective value of intrinsic relative to extrinsic rewards, a “computational hedonometer” that reflects both behavior and neural activity that quantifies the affective value of experience. SIGNIFICANCE STATEMENT Traditional economic indicators are increasingly recognized to provide an incomplete picture of what we value as a society. Standard economic approaches struggle to accurately assign values to non-market activities that nevertheless may be intrinsically rewarding, prompting a need for new tools to measure what really matters to individuals. Using a combination of neuroimaging and computational modeling, we show that despite their lack of instrumental value, intrinsic rewards influence subjective affective state and ventromedial prefrontal cortex (vmPFC) activity. The relative degree to which extrinsic and intrinsic rewards influence affective state is predictive of their relative impacts on neural activity, confirming the utility of our approach for measuring the affective value of experiences and other non-market activities in individuals.