3 research outputs found
Sensory perception relies on fitness-maximizing codes
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
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Undoing in human planning
From writing to hiking, people’s real-world sequential decision-making often benefits from “undoing” (e.g. deleting sentences or backtracking). Surprisingly, undoing has not been studied in experiments on human planning. To investigate how much, when, and why people undo, we introduce a task that is a cross between the “Traveling Salesperson” and the “Knapsack” problems with an undo option. Within a length budget, subjects sequentially connect as many dots as possible on a map. On “undo” trials, they are allowed to take back actions without constraints. We find that undoing is beneficial, that subjects exhibit great individual variability in the number of undos, that undos are more frequent after errors than after correct actions, and that long response times tend to precede sequences of undos. Together, these results suggest that undo actions serve a dual role of correcting errors and of exploring alternative paths, where path evaluation benefits from full play-outs
Sensory perception relies on fitness-maximizing codes
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.ISSN:2397-337