A cognitive–ecological approach to temporal self-appraisals.

Abstract

We investigate self-appraisals over time using a cognitive-ecological approach. We assume that ecologically, negative person attributes are more diverse than positive ones, while positive person attributes are more frequent than negative ones. We combine these ecological properties with the cognitive process of similarity-and differences-based social comparisons to predict temporal self-appraisals. The resulting cognitive-ecological model predicts that people should evaluate similarities with themselves over time positively, whereas differences would be evaluated more negatively. However, because positive attributes are reinforced over time relative to negative attributes, we predicted an asymmetry to emerge such that distinct attributes of the past self (past differences) would be most negative. Six experiments (total N = 1,796) and an integrative data analysis confirmed the cognitive-ecological model's predictions for temporal self-appraisals. However, we found no evidence for motivated self-perception across time. We discuss the implications of these findings for temporal self-appraisal theory and other aspects of self and identity.

Differentiation in social perception: Why later-encountered individuals are described more negatively.

“…The combination of the two ecological assumptions that people typically possess more positive than negative attributes and that there are a larger number of possible negative attributes implies that the probability for a given positive attribute to be present in a person is higher than the probability for a given negative attribute to be present in a person [p(pos i ) > p(neg i )]. 1 Thus, positive attributes tend to be shared among individuals, while an individual's negative attributes tend to be distinct (for a formalization, see Alves et al, 2017aAlves et al, , 2018Baldwin et al, 2023).…”

The Formation of Negative Attitudes Toward Novel Groups

“…If one sampled distinct attributes of persons or groups, the resulting attribute sample would be negatively biased. More detailed formalizations and simulations of this common-good phenomenon can be found elsewhere (Alves et al, 2017; see also Baldwin et al, 2024;Koch et al, 2024).…”