PT  - JOURNAL ARTICLE
AU  - Charles Findling
AU  - Nicolas Chopin
AU  - Etienne Koechlin
TI  - Imprecise neural computations as source of human adaptive behavior in volatile environments
AID  - 10.1101/799239
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 799239
4099  - http://biorxiv.org/content/early/2019/10/11/799239.short
4100  - http://biorxiv.org/content/early/2019/10/11/799239.full
AB  - Everyday life features uncertain and ever-changing situations. In such environments, optimal adaptive behavior requires higher-order inferential capabilities to grasp the volatility of external contingencies. These capabilities however involve complex and rapidly intractable computations, so that we poorly understand how humans develop efficient adaptive behaviors in such environments. Here we demonstrate this counterintuitive result: simple, low-level inferential processes involving imprecise computations conforming to the psychophysical Weber Law actually lead to near-optimal adaptive behavior, regardless of the environment volatility. Using volatile experimental settings, we further show that such imprecise, low-level inferential processes accounted for observed human adaptive performances, unlike optimal adaptive models involving higher-order inferential capabilities, their biologically more plausible, algorithmic approximations and non-inferential adaptive models like reinforcement learning. Thus, minimal inferential capabilities may have evolved along with imprecise neural computations as contributing to near-optimal adaptive behavior in real-life environments, while leading humans to make suboptimal choices in canonical decision-making tasks.