Premium
Affect Intensity and Individual Differences In Informational Style
Author(s) -
Larsen Randy J.,
Billings Douglas W.,
Cutler Susan E.
Publication year - 1996
Publication title -
journal of personality
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.082
H-Index - 144
eISSN - 1467-6494
pISSN - 0022-3506
DOI - 10.1111/j.1467-6494.1996.tb00819.x
Subject(s) - psychology , affect (linguistics) , feeling , diener , cognition , generalization , developmental psychology , arousal , cognitive style , style (visual arts) , cognitive psychology , social psychology , categorization , life satisfaction , communication , mathematical analysis , philosophy , mathematics , epistemology , neuroscience , history , archaeology
Although individuals differ widely in the typical intensity of their affective experience, the mechanisms that create or maintain these differences are unclear. Larsen, Diener, and Cropanzano (1987) examined the hypothesis that individual differences in affect intensity (AI) are related to how people interpret emotional stimuli. They found that high AI individuals engaged in more personalizing and generalizing cognitions while construing emotional stimuli than low AI individuals. The present study extends these findings by examining cognitive activity during a different task‐the generation of information to communicate about life events. Participants provided free‐response descriptions of 16 life events. These descriptions were content coded for five informational style variables. It was found that the descriptive information generated by high AI participants contained significantly more references to emotional arousal, more focus on feelings, and more generalization compared to participants low in AI. These results are consistent with the notion that specific cognitive activity may lead to, or at least be associated with, dispositional affect intensity. In addition, the informational style variables identified in this study were stable over time and consistent across situations. Although men and women differ in AI, this difference becomes insignificant after controlling for informational style variation. Overall results are discussed