UBC Theses and Dissertations
To thine own self be true : the links between psychological adjustment and expressive accuracy Human, Jacqueline Lauren
Well-adjusted, happy people appear to be judgeable: their personalities tend to be seen more accurately than the personalities of less adjusted individuals (Colvin, 1993a, 1993b). The mechanisms behind this effect, however, are not well understood. One possibility is that well-adjusted individuals are not more judgeable at all; instead, they may have greater self-knowledge that makes them appear to be more easily understood. Studies 1 and 2 address this question by utilizing trait observability to disentangle self-knowledge from judgeability. Across these two round-robin studies of new acquaintances, well-adjusted individuals were seen with greater distinctive self-other agreement, but more so on low rather than highly observable traits. Thus, well-adjusted individuals provide new acquaintances with greater information regarding their less observable traits, enhancing others’ knowledge and thus distinctive self-other agreement. In sum, these studies indicate that well-adjusted individuals are indeed more judgeable. But how does adjustment facilitate judgeability? Across two video perceptions studies (Studies 3 and 4), I examined several potential mechanisms through which adjustment could promote judgeability at three stages of the Realistic Accuracy Model (RAM; Funder, 1995): 1) cue relevance, 2) cue availability, and 3) cue detection. In both studies, well-adjusted individuals were more judgeable because they provided others with more relevant cues: specifically, well-adjusted individuals behaved more in line with their distinctive personalities, which in turn led them to be seen more accurately. In contrast, neither cue availability nor detection could sufficiently account for the link between adjustment and judgeability. In sum, well-adjusted individuals are more judgeable because to their own selves, they are true.
Item Citations and Data
Attribution-NonCommercial-NoDerivatives 4.0 International