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Neural encoding of perceived patch value during competitive and hazardous virtual foraging

Psychology

Neural encoding of perceived patch value during competitive and hazardous virtual foraging

B. Silston, T. Wise, et al.

This groundbreaking research by Brian Silston and colleagues unveils how humans make foraging decisions in virtual environments, cleverly balancing competition and predation risks. Discover the fascinating strategies individuals employ to optimize outcomes in these complex scenarios.... show more
Abstract
Natural observations suggest that in safe environments, organisms avoid competition to maximize gain, while in hazardous environments the most effective survival strategy is to congregate with competition to reduce the likelihood of predatory attack. We probed the extent to which survival decisions in humans follow these patterns, and examined the factors that determined individual-level decision-making. In a virtual foraging task containing changing levels of competition in safe and hazardous patches with virtual predators, we demonstrate that human participants inversely select competition avoidant and risk diluting strategies depending on perceived patch value (PPV), a computation dependent on reward, threat, and competition. We formulate a mathematically grounded quantification of PPV in social foraging environments and show using multivariate fMRI analyses that PPV is encoded by mid-cingulate cortex (MCC) and ventromedial prefrontal cortices (vMPFC), regions that integrate action and value signals. Together, these results suggest humans utilize and integrate multidimensional information to adaptively select patches highest in PPV, and that MCC and vMPFC play a role in adapting to both competitive and predatory threats in a virtual foraging setting.
Publisher
Nature Communications
Published On
Sep 16, 2021
Authors
Brian Silston, Toby Wise, Song Qi, Xin Sui, Peter Dayan, Dean Mobbs
Tags
foraging decisions
competition avoidance
predation risk
virtual environments
perceived patch value
multivariate fMRI
decision-making
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