
Adaptive valuation of social images by Rhesus Macaques
In a finding that deepens our understanding of animal social cognition, researchers at Duke University Medical Center have demonstrated for the first time that monkeys, like humans, value information according to its social content. People readily pay to see powerful or sexually attractive individuals, and, according to this new study, monkeys will also "pay" to view these kinds of images.
Both economics and evolutionary theory predict that animals should selectively acquire information about others that is most useful for guiding behavior. In most monkey social groups, behavior is structured by kinship, dominance, and reproductive status, suggesting that social information should be valued according to these attributes. While previous studies had shown that monkeys would work to see other monkeys, no one knew whether the value they placed on seeing other individuals was related to the social relevance of those individuals.
In the new work, researchers Robert Deaner, Amit Khera and Michael Platt, all of Duke University Medical Center, tested this hypothesis by measuring how much fruit juice monkeys would accept or forgo to see photographs of familiar monkeys, permitting the researchers to compare monkeys’ valuation of different types of social information. Male monkeys "paid" in juice to view female hindquarters or high-ranking monkeys’ faces, but required "overpayment" to view low-ranking monkeys’ faces. Despite living in a captive colony, the value monkeys placed on information about potential sexual partners and powerful individuals matched the relative importance of these individuals for behavioral success in the wild. This study demonstrates that monkeys assess visual information by its social value and provides the first experimental evidence that they spontaneously discriminate between images of others based on the social rank or classification of individuals.
Heidi Hardman | Source: EurekAlert!
Further information: www.cell.com
www.current-biology.com/content/article/abstract?
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