Many neuroscientists have studied the development of Individual Recognition in the natural world, which is the ability of one organism to recognize another and change their behavior in response. For instance, the simplest type of individual recognition (called binary individual recognition) allows an individual to tell the difference between one group and another. This could broadly be understood as an organism’s ability to classify individuals, even those they have never met, as being members of the in-group or the out-group. Depending on their classification, an organism will be able to make a more informed decision about what to do next (attack/run/hide/follow/nothing).
Researchers investigating this subject typically do so on a rather myopic scale, focusing more heavily on how a particular form of reception works rather than on the community-wide implications. This approach helps us to understand an organism’s capability to do something, but it doesn’t tell us what that means or how an organism uses this capability.
This is the point where ecology comes into play.
Researchers Gokcekus et al. (2021) published an article that argues for combining our current understanding of individual recognition (IR) with our understanding of behavior and animal interactions. By doing so, we can take a social network approach to neuroscience that allows us to bridge the gap between animal perception (visual, auditory, olfactory, etc.) and animal culture (behavior, hierarchies, relationships).
The authors offered us the following examples of IR in the natural world, which they expand on the significance of throughout the paper:
- Zebra finches can recognize conspecifics through both sight and sound
- Chimpanzees can do the same but through the scent of an individual’s urine
- African lions can combine multiple cues to identify another individual by matching their roar and their appearance
This paper was highly communicable, especially in how they continuously asked questions, made connections, and presented ideas for further research (see figure). In fact, this is what makes the paper so engaging and impactful. They present several ‘arguments’ for their claim by explaining the potential implications of IR to animal territoriality, competition, and reproduction, which reveals the evolutionary significance of their approach.
At the end of the article, I understand their main examples and believe they can be (simply) explained in the following ways:
- Territoriality: IR can be used to determine whether an individual is familiar or unfamiliar. This means they don’t need to immediately respond with aggression or displays of dominance, which are costly and could lead to unnecessary competition. The cost of territory defense can then be decreased through what they call the ‘dear-enemy effect,’ which is when familiar (or just nearby) individuals are able to resolve or prevent conflict and mutually benefit.
- Competition: IR, depending on its level of specificity, can be used to inform an individual about whether or not they would lose a fight. I imagine it with the example, “that person over there looks normal, but I remember seeing them take down a lion with their bare hands yesterday, so I should probably give them space.” Similarly, an individual could take the information received from a trustworthy source (social transmission), and the example could instead be, “that person looks normal, but my friend told me that they took down a lion yesterday…”
- Reproduction: IR can be used in the contexts of mate recognition and parental care. If a species is monogamous and practices biparental care, it would be incredibly beneficial to recognize both their mate and their offspring. This makes IR advantageous by ensuring the survival of a future generation. Without IR, they could risk mating with an individual that is not reliable, or they could mistake their offspring with another and then fail to pass on their genes.
Gokcekus, S., Firth, J. A., Regan, C., & Sheldon, B. C. (2021). Recognising the key role of individual recognition in social networks. Trends in Ecology & Evolution 36(11): 1024-1035