Computation noise in human learning and decision-making: origin, impact, function
Computation noise in human learning and decision-making: origin, impact, function
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DOI：
10.1016/j.cobeha.2021.02.018
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发表时间：
2021-03-12
影响因子：
5
通讯作者：
Wyart, Valentin
中科院分区：
文献类型：
--
作者：
Findling, Charles;Wyart, Valentin
跨领域建模的认知过程作为统计推断，对人类智能构成了困难但普遍的挑战。除了感觉错误和探索性选择之外，最近的研究还发现，认知推理有限的计算精度是在不确定性下做出的感知和奖励引导决策的可变性和次优性的一个令人惊讶的巨大贡献者。这篇重点综述讨论了心理学和神经科学领域的理论和实验证据，这些证据结合在一起，为这种“计算噪声”的起源、影响和功能提供了重要的见解。用于学习和决策。超越内部噪声作为神经功能和认知的性能限制约束的经典描述，我们概述了计算噪声对于不利条件下的自适应行为可能带来的好处，并强调了未来研究的开放问题。
cognitive process modeled across domains as statistical inference, constitutes a difficult yet ubiquitous challenge for human intelligence. Beside sensory errors and exploratory choices, recent research has identified the limited computational precision of cognitive inference as a surprisingly large contributor to the variability and suboptimality of perceptual and reward-guided decisions made under uncertainty. This focused review discusses the theoretical and experimental evidence scattered across psychology and neuroscience which, taken together, provides key insights into the origin, impact and function of this ?computation noise? for learning and decision-making. Moving beyond the classical description of internal noise as performance-limiting constraint on neural function and cognition, we outline the possible emergent benefits of computation noise for adaptive behavior in adverse conditions and highlight open questions for future research.