Computation noise promotes zero-shot adaptation to uncertainty during decision-making in artificial neural networks.

ABSTRACT: Random noise in information processing systems is widely seen as detrimental to function. But despite the large trial-to-trial variability of neural activity, humans show a remarkable adaptability to conditions with uncertainty during goal-directed behavior. The origin of this cognitive ability, constitutive of general intelligence, remains elusive. Here, we show that moderate levels of computation noise in artificial neural networks promote zero-shot generalization for decision-making under uncertainty. Unlike networks featuring noise-free computations, but like human participants tested on similar decision problems (ranging from probabilistic reasoning to reversal learning), noisy networks exhibit behavioral hallmarks of optimal inference in uncertain conditions entirely unseen during tr

SUBMITTER: Findling C 

PROVIDER: S-EPMC11524185 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature