Search

Background: The initial preference task (IPT) is an implicit measure that has featured prominently in the literature and enjoys high popularity because it offers to provide an unobtrusive and objective assessment of self-esteem that is easy to administer. However, its use for self-esteem assessment may be limited because of weak associations with direct personality measures. Moreover, moderator effects of sample- and study-related variables need investigation to determine the value of IPT-based assessments of self-esteem. Methods: Conventional and grey-literature database searches, as well as screening of reference lists of obtained articles, yielded a total of 105 independent healthy adult samples (N = 17,777) originating from 60 studies. Summary effect estimates and subgroup analyses for potential effect moderators (e.g., administration order, algorithm, rating type) were calculated by means of meta-analytic random- and mixed-effects models. Moreover, we accounted for potential influences of publication year, publication status (published vs. not), and participant sex in a weighted stepwise hierarchical multiple meta-regression. We tested for dissemination bias through six methods. Results: There was no noteworthy correlation between IPT-based implicit and explicit self-esteem (r = .102), indicating conceptual independence of these two constructs. Effects were stronger when the B-algorithm was used for calculation of IPT-scores and the IPT was administered only once, whilst all other moderators did not show significant influences. Regression analyses revealed a somewhat stronger (albeit non-significant) effect for men. Moreover, there was no evidence for dissemination bias or a decline effect, although effects from published studies were numerically somewhat stronger than unpublished effects. Discussion: We show that there is no noteworthy association between IPT-based implicit and explicit self-esteem, which is broadly consistent with dual-process models of implicit and explicit evaluations on the one hand, but also casts doubt on the suitability of the IPT for the assessment of implicit self-esteem on the other hand. [ABSTRACT FROM AUTHOR]

Visual complexity is relevant for many areas ranging from improving usability of technical displays or websites up to understanding aesthetic experiences. Therefore, many attempts have been made to relate objective properties of images to perceived complexity in artworks and other images. It has been argued that visual complexity is a multidimensional construct mainly consisting of two dimensions: A quantitative dimension that increases complexity through number of elements, and a structural dimension representing order negatively related to complexity. The objective of this work is to study human perception of visual complexity utilizing two large independent sets of abstract patterns. A wide range of computational measures of complexity was calculated, further combined using linear models as well as machine learning (random forests), and compared with data from human evaluations. Our results confirm the adequacy of existing two-factor models of perceived visual complexity consisting of a quantitative and a structural factor (in our case mirror symmetry) for both of our stimulus sets. In addition, a non-linear transformation of mirror symmetry giving more influence to small deviations from symmetry greatly increased explained variance. Thus, we again demonstrate the multidimensional nature of human complexity perception and present comprehensive quantitative models of the visual complexity of abstract patterns, which might be useful for future experiments and applications. [ABSTRACT FROM AUTHOR]