The confirmability and disconfirmability of trait concepts revisited: Does content matter?
Nicole Tausch, Jared B. Kenworthy, Miles Hewstone
Abstract
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Nicole Tausch, Jared B. Kenworthy, Miles Hewstone
Abstract
Open-access reader
M. Rothbart and B. Park (1986) demonstrated that, consistent with the common negativity bias, positive traits are difficult to confirm and easy to disconfirm, whereas the opposite is true for negative traits. This article extends their analysis by showing that trait (dis-)confirmability is moderated by trait content (warmth vs. competence). Study 1 identifies a trait sample representative of warmth and competence. Study 2 shows a strong negativity effect for warmth and a reduced (or absent) negativity effect for competence. Study 3 examines trait properties related to the behavioral range of the trait possessor and to the motivational goals of the perceiver as predictors of trait (dis-)confirmability. The theoretical and practical implications of the authors' findings are discussed, and avenues for future research are suggested.
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M. Rothbart and B. Park (1986) demonstrated that, consistent with the common negativity bias, positive traits are difficult to confirm and easy to disconfirm, whereas the opposite is true for negative traits. This article extends their analysis by showing that trait (dis-)confirmability is moderated by trait content (warmth vs. competence). Study 1 identifies a trait sample representative of warmth and competence. Study 2 shows a strong negativity effect for warmth and a reduced (or absent) negativity effect for competence. Study 3 examines trait properties related to the behavioral range of the trait possessor and to the motivational goals of the perceiver as predictors of trait (dis-)confirmability. The theoretical and practical implications of the authors' findings are discussed, and avenues for future research are suggested.
Key concepts: Psychology, Trait, Negativity effect, Social psychology, Competence (human resources), Developmental psychology, Negativity bias, Computer science