Todd Hare (Zurich)

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Elizabeth Bowman

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Title:  Sensory perception relies on fitness-maximizing codes

It has generally been presumed that sensory information encoded by humans and other organisms should be as accurate as their biological limitations allow. However, perhaps counter-intuitively, accurate sensory representations do 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. Initially, we applied predictions of this model to neural responses from large monopolar cells (LMCs) in the blowfly retina. We found that neural codes that maximize reward expectation—and not accurate sensory representations—account for retinal LMC activity. Behavioural experiments in humans revealed that sensory encoding strategies are flexibly adopted 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 fMRI data revealed that novel behavioral 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 insects, humans, and machines.