Humans are able to recognize familiar individuals based on certain facial features, gait and even voice tones. Most animals can do the same but they depend primarily on scent to ferret out friend from foe. But can fish identify those they know from among schools of extremely similar looking schoolmates?
That was the question researchers from the Osaka City University, Japan, tried to answer in monitoring a school of brightly-colored daffodil cichlid fish. Although these fish from Africa and along the rocky coastlines of the countries Burundi, the Democratic Republic of the Congo, Tanzania and Zambia, may all look the alike to us, it turns out they can actually tell each other apart based on certain facial coloring patterns, reported the university in its news release
In the recent study, led by Professor Masanori Kohda from the Osaka City University, several fish were exposed to digital models picturing four different combinations of familiar and unfamiliar face and body colorations. Typically, daffodil cichlid (Neolamprologus pulcher) sport a tan-to-yellow bodies with blue-tipped fins. Researchers revealed that their test subjects spent more time examining digital models with unfamiliar faces – and doing so from a further distance away – then they did when presented with models of familiar faces. This suggests that, like humans and other animals, the fish took longer to assess strangers and kept their distance.
Animal interactions within group settings are based primarily on the ability to distinguish between familiar individuals – those they can trust, versus those they may need to protect themselves, such as strangers. This study adds insight to the phenomena, suggesting some fish are able to recognize familiar individuals from among scores of subjects within a school based solely on facial coloring patterns
What's more, researchers found cichlid fish do so rapidly. They are able to discriminate between the familiar and unfamiliar within 0.5 seconds – a recognition speed comparable to that of humans.
Their study was recently published in the journal PLOS ONE.
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