  Dyadic and small group collaboration is an evolutionary advantageous
behaviour and the need for such collaboration is a regular occurrence in day to
day life. In this paper we estimate the perceived personality traits of
individuals in dyadic and small groups over thin-slices of interaction on four
multimodal datasets. We find that our transformer based predictive model
performs similarly to human annotators tasked with predicting the perceived
big-five personality traits of participants. Using this model we analyse the
estimated perceived personality traits of individuals performing tasks in small
groups and dyads. Permutation analysis shows that in the case of small groups
undergoing collaborative tasks, the perceived personality of group members
clusters, this is also observed for dyads in a collaborative problem solving
task, but not in dyads under non-collaborative task settings. Additionally, we
find that the group level average perceived personality traits provide a better
predictor of group performance than the group level average self-reported
personality traits.
