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，人类面临的环境具有不确定性、多变或多变的情况。有效的适应性行为必须考虑不确定性和波动性。先前的自适应行为模型涉及依赖于复杂且通常难以处理的计算的波动性推断。因为这样的计算在生物学上可能是难以置信的，所以尚不清楚人类如何在这样的环境中发展有效的适应性行为。在这里，我们展示了一个违反直觉的结果：假设计算中的不精确性符合心理物理学韦伯定律，则仅限于不确定性的简单、低级推理可以产生近乎最佳的自适应行为，而不管环境波动如何。