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Abstract In many group‐living animals, survival and reproductive success depend on the formation of long‐term social bonds, yet it remains largely unclear why particular pairs of groupmates form social bonds and not others. Can social bond formation be reliably predicted from each individual's immediately observable traits and behaviors at first encounter? Or is social bond formation hard to predict due to the impacts of shifting social preferences on social network dynamics? To begin to address these questions, we asked how well long‐term cooperative relationships among vampire bats were predicted by how they interacted during their first encounter as introduced strangers. In Study 1, we found that the first 6 h of observed interactions among unfamiliar bats co‐housed in small cages did not clearly predict the formation of allogrooming or food‐sharing relationships over the next 10 months. In Study 2, we found that biologger‐tracked first contacts during the first 4–24 h together in a flight cage did not strongly predict allogrooming rates over the next 4 months. These results corroborate past evidence that social bonding in vampire bats is not reducible to the individual traits or behaviors observed at first encounter. Put simply, first impressions are overshadowed by future social interactions.
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The Effectiveness of Multi-Label Classification and Multi-Output Regression in Social Trait Recognition
First impressions make up an integral part of our interactions with other humans by providing an instantaneous judgment of the trustworthiness, dominance and attractiveness of an individual prior to engaging in any other form of interaction. Unfortunately, this can lead to unintentional bias in situations that have serious consequences, whether it be in judicial proceedings, career advancement, or politics. The ability to automatically recognize social traits presents a number of highly useful applications: from minimizing bias in social interactions to providing insight into how our own facial attributes are interpreted by others. However, while first impressions are well-studied in the field of psychology, automated methods for predicting social traits are largely non-existent. In this work, we demonstrate the feasibility of two automated approaches—multi-label classification (MLC) and multi-output regression (MOR)—for first impression recognition from faces. We demonstrate that both approaches are able to predict social traits with better than chance accuracy, but there is still significant room for improvement. We evaluate ethical concerns and detail application areas for future work in this direction.
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- Award ID(s):
- 1757929
- PAR ID:
- 10327147
- Date Published:
- Journal Name:
- Sensors
- Volume:
- 21
- Issue:
- 12
- ISSN:
- 1424-8220
- Page Range / eLocation ID:
- 4127
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/s21124127
