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A recent study from the Hebrew University of Jerusalem has discovered that marmoset monkeys use distinct vocalizations to "name" each other. This behavior is similar to humans and certain other advanced social animals.
This behavior, previously thought to be exclusive to humans, bottlenose dolphins, and African elephants, indicates advanced cognitive abilities.
The study, published in the journal Science, highlights how marmosets use high-pitched calls, known as "phee calls," to communicate within their groups.
These calls are as loud as power tools and serve as "vocal labels" to address specific individuals. The researchers observed that the monkeys responded more often when their specific call or "name" was used.
Marmosets live in small family groups and are known for their cooperative social behaviors.
The research team, led by graduate student Guy Oren, recorded interactions between marmosets separated by a visual barrier.
They also analyzed the monkeys' responses to pre-recorded calls played by a computer system.
Findings revealed that marmosets could recognize when calls were directed specifically at them, demonstrating an understanding of these vocal labels.
The study also found that marmosets from the same family group shared similar sound features in their calls, similar to human dialects or accents.
This pattern was seen even among non-related adult marmosets living in the same group.
It suggests they learned these vocal cues from each other rather than through genetic inheritance.
Marmosets, although distant relatives of humans, have developed similar social traits through convergent evolution. They have developed analogous characteristics in response to similar environmental challenges.
The dense rainforests of South America, where these monkeys live, often limit visibility, making vocal communication vital for maintaining social bonds.
The research challenges previous beliefs that non-human primates could not offer insights into the evolution of human language.
The team's work was supported by advances in computational power and machine learning, which enabled a detailed statistical analysis of the marmoset calls.
Future research may leverage artificial intelligence to further explore the complexity of these communications.
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