Computational Precision of Mental Inference as Critical Source of Human Choice Suboptimality
Computational Precision of Mental Inference as Critical Source of Human Choice Suboptimality
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DOI：
10.1016/j.neuron.2016.11.005
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发表时间：
2016-12-21
期刊：
影响因子：
16.2
通讯作者：
Koechlin, Etienne
中科院分区：
文献类型：
--
作者：
Drugowitsch, Jan;Wyart, Valentin;Koechlin, Etienne
在不确定的环境中做出决策通常需要结合来自外部线索的多条模糊信息。在这种情况下，人类的选择类似于最优贝叶斯推理，但通常表现出较大的次优变异性，其起源仍然知之甚少。特别是，这种次优选择可能源于心理推理的不完善，而不是外围阶段，如感觉处理和反应选择。在这里，我们解离这三个来源的次优人类选择的基础上结合多个模糊的线索。使用一种新的定量方法确定的起源和结构的选择变异性，我们表明，不完善的推理单独导致占主导地位的部分次优选择。此外，这种次优性的三分之二似乎来自于实现推理的神经计算的有限精度，而不是来自贝叶斯最优推理的系统偏差。这些发现为人类在不确定环境中的选择的准确性和最终可预测性设定了上限。
Making decisions in uncertain environments often requires combining multiple pieces of ambiguous information from external cues. In such conditions, human choices resemble optimal Bayesian inference, but typically show a large suboptimal variability whose origin remains poorly understood. In particular, this choice suboptimality might arise from imperfections in mental inference rather than in peripheral stages, such as sensory processing and response selection. Here, we dissociate these three sources of suboptimality in human choices based on combining multiple ambiguous cues. Using a novel quantitative approach for identifying the origin and structure of choice variability, we show that imperfections in inference alone cause a dominant fraction of suboptimal choices. Furthermore, two-thirds of this suboptimality appear to derive from the limited precision of neural computations implementing inference rather than from systematic deviations from Bayes-optimal inference. These findings set an upper bound on the accuracy and ultimate predictability of human choices in uncertain environments.