Sensory perception relies on fitness-maximizing codes
Year: 2023
Type: article
Abstract: Sensory information encoded by humans and other organisms is generally presumed to be as accurate as their biological limitations allow. However, perhaps counterintuitively, accurate sensory representations may not necessarily maximize the organism's chances of survival. To test this hypothesis, we developed a unified normative framework for fitness-maximizing encoding by combining theoretical insights from neuroscience, computer science, and economics. Behavioural experiments in humans revealed that sensory encoding strategies are flexibly adapted to promote fitness maximization, a result confirmed by deep neural networks with information capacity constraints trained to solve the same task as humans. Moreover, human functional MRI data revealed that novel behavioural goals that rely on object perception induce efficient stimulus representations in early sensory structures. These results suggest that fitness-maximizing rules imposed by the environment are applied at early stages of sensory processing in humans and machines.
Source: Nature Human Behaviour
Institutions: University of Zurich, ETH Zurich
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $11,690
Awards: Spectroscopic characterization of carbon radicals, ions, and cluster ions, Enhancing functional connectivity in prefrontal networks to test and improve self-control mechanisms in decision-making, G434337789, The neural basis of interactions between goods, Mechanisms for social learning, G7779572749, G7805815234, G8189315002
OpenAlex ID: W4367176309