ArticlePNAS nexus2026
Decision-making trades off learned and perceived information.
Article in PNAS nexus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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4 authors.
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Abstract
A fundamental question in cognitive science is how information from internal memory is combined with external sensory input when making decisions. We hypothesized that previously learned and currently perceived information trade-off against each other, such that information from one source reduces the gathering and usage of information from the other. To test this hypothesis, we designed a two-armed bandit task where each arm is composed of both learned and perceived elements. We monitored participants' gathering of perceptual information using eye tracking. Participants' choices and gaze deployment showed a trade-off between the impact of learned and perceived information. The more a participant utilized internally stored learned information, the less they gathered perceptual information, and vice versa. To understand the factors underlying the trade-off, we developed a computational model of participants' information gathering. This showed that the trade-off results from the faster gathering of learned information, which makes it less valuable to invest effort in gathering additional perceptual information. Preliminary findings also suggested that an individual's tendency to primarily rely on one source of information is a stable individual trait. These findings contribute to the understanding of how humans use learning and perception in forming decisions.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.