Imprecise neural computations as a source of adaptive behaviour in volatile environments

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. Findling et al. present the Weber-imprecision model of decision-making, which operates on imprecise representations of uncertainty. It produces efficient adaptive behaviour regardless of environmental volatility and fits human behaviour better than optimal adaptive models.

Automated summary

Research suggests that simple, low-level inferences about uncertainty may be more effective in developing adaptive behavior in volatile environments than complex computations or high-level inferences about volatility. The Weber-imprecision model, based on imprecise representations of uncertainty, demonstrates near-optimal adaptive behavior regardless of environmental volatility and fits human behavior better than other optimal adaptive models.