Ecole Normale Supérieure, PSL Research University, Paris, France.
ENSAE ParisTech, Saclay, France.
Nat Hum Behav. 2021 Jan;5(1):99-112. doi: 10.1038/s41562-020-00971-z. Epub 2020 Nov 9.
In everyday life, humans face environments that feature uncertain and volatile or changing situations. Efficient adaptive behaviour must take into account uncertainty and volatility. Previous models of adaptive behaviour involve inferences about volatility that rely on complex and often intractable computations. Because such computations are presumably implausible biologically, it is unclear how humans develop efficient adaptive behaviours in such environments. Here, we demonstrate a counterintuitive result: simple, low-level inferences confined to uncertainty can produce near-optimal adaptive behaviour, regardless of the environmental volatility, assuming imprecisions in computation that conform to the psychophysical Weber law. We further show empirically that this Weber-imprecision model explains human behaviour in volatile environments better than optimal adaptive models that rely on high-level inferences about volatility, even when considering biologically plausible approximations of such models, as well as non-inferential models like adaptive reinforcement learning.
在日常生活中，人类面临着不确定、不稳定或变化的环境。有效的自适应行为必须考虑到不确定性和波动性。以前的自适应行为模型涉及到对波动性的推断，这些推断依赖于复杂且通常难以处理的计算。由于这些计算在生物学上可能不太可信，因此不清楚人类如何在这种环境中发展出有效的自适应行为。在这里，我们展示了一个违反直觉的结果：简单的、低层次的、仅限于不确定性的推断可以产生近乎最优的自适应行为，而不管环境的不稳定性如何，前提是计算的不精确性符合心理物理学的韦伯定律。我们进一步通过实验证明，与依赖于对波动性的高级推断的最优自适应模型相比，这种基于韦伯不精确性的模型能够更好地解释人类在不稳定环境中的行为，即使考虑到对这些模型的生物学上合理的近似以及像自适应强化学习这样的非推断模型也是如此。
