“Birds of a feather flock together.” 

The proverb plays out true for a lot of us. We attract – or hope to attract – people like us. This plausibility of our social inclination to surround ourselves with beings like us is referred to as the similarity-attraction hypothesis. I say beings… because it also seems include robots.  

We often describe people by – and identify ourselves with – personality. This is the basis for how we understand who we are, and how we understand who others are. Inherently, this is knowingness is done through the lens and expression brought about by being in relationship with others. 

Personalities are sets of distinctive characteristics among humans and exchanged between them. Goldberg indicates the personality is composed of openness, conscientiousness, agreeableness, extraversion, and neuroticism – you might have heard these referred to these factors as the “Big Five.” Levels of extroversion are particularly exposed in defining a human’s personality as it characterizes it is often associated with one’s social aptitude in various relationships. An extroverted person is generally energized by external stimulus, while an introverted person will be comparatively less socialable. It follows that it is often the case introverted people tend to have fewer relationships than extroverted counterparts as they present with less engagement in social interactions. 

Of our perceptive abilities in social interactions, verbal and non-verbal communication deliver information, emotion, and intention to us. Verbal communication delivers purposes and details; non-verbal communication supports delivery by conveying information subtly with gestures, facial movements, and eye movements. In application to levels of extroversion, it’s notable that extroverted people more actively form social relationships with facial communication, while introverted people are less likely to do so.  

Studies indicate that social rules and terminations of human-human interactions will equally apply to human-robot ones, and similar rules to engagement – aka, the prevalence of personality in building relationships – apply. “If computers and robots are able to communicate with humans, humans will respond to these computers and robots using the same elements of social interactions that they employ in human-human interactions.”

A purpose-blinded study, participants interacted with robots programmed to introverted or extroverted properties (eg passive facial movements, slow facial expressions, and small, infrequent eye contact movements for an introverted robot). The responses of participants were measured by indexes of anthropomorphism, friendliness, preference, and social presence. The results of participants’ perception of the robots were correlated directly with their personality type. Extroverted participants found a higher degree of anthropomorphism, friendliness, and social presence than intermediate or introverted participants when interacting with a robot holding extroverted properties. When introverted participants interacted with the robot similar to them, they felt higher degrees of friendliness, immersive tendency, and preference. Breaking the correlation, introverted participants did find a lower degree of presence with the introverted robot.  

In similar studies, subjects are found preferring a computer acting similar to themselves and reporting higher satisfaction with their interactions. “Birds of a feather flock together,” even if no one has feathers, one is covered in skin, and the other runs on batteries. 


 Check these out:

Park E, Jin D, del Pobil AP (2012) The Law of Attraction in Human-Robot Interaction. International Journal of Advanced Robotic Systems 9(2).  

Nass C, Steuer J, Tauber E, Reeder H (1993) Anthropomorphism, agency and ethopoeia: Computers as social actor. Proceedings of the INTERACT ’93 and CHI ’93 Conference Companion on Human Factors in Computing Systems. New York: ACM. pp. 111–112. 

Nass C, Moon Y, Fogg BJ, Reeves B, Dryer C (1995) Can computer personalities be human personalities? International Journal of Human-Computer Studies 43: 223–239. 

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