ABSTRACT
Holistic processing (HP) of faces is usually measured by the composite effect. While Weston and Perfect [2005. Effects of processing bias on the recognition of composite face halves. Psychonomic Bulletin & Review, 12, 1038–1042. doi:10.3758/BF03206440] found that priming at the local level speeded recognition of components of faces, Gao et al. [2011. Priming global and local processing of composite faces: Revisiting the processing-bias effect on face perception. Attention Perception & Psychophysics, 73, 1477–1486. doi:10.3758/s13414-011-0109-7] found that only global priming had an effect on HP of faces. The two studies used different versions of the composite task (the partial design, which is considered to be prone on bias, and the complete design). However, the two studies also differed in other respects and it is difficult to know to what extent issues with the partial design contributed to the differing conclusions. In the present study, the HP indexed by the complete design measure was augmented by global priming. In contrast, no effect was observed in the partial design index. We claim that the partial design index reflects other factors besides HP, including response bias, and conclude that HP can be understood within the context of domain-general attentional processes.
Acknowledgements
We thank very much Kevin Potter and an anonymous Reviewer for very helpful suggestions and comments.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes
1 The most common model of SDT is based on the assumption that the two evidence distributions are Gaussian and equal in variance. Some researchers reject using parametric measures because of concerns about the validity of explicit underlying assumptions, instead using nonparametric such as A for sensitivity (Zhang & Mueller, Citation2005) or B’ for criterion. Although we favored the use of parametric sensitivity and criterion measures, we always checked that the same statistical pattern was found when non- parametric measures were used.
2 This transformation was adopted to guarantee no violation of the normality assumption, given that proportion of correct responses follows a binomial distribution in which the variance is usually a direct function of the mean. Still, we always checked that the same statistical pattern was found when the ANOVA was run over the (untransformed) accuracy.
3 When considering computations based on the non-parametric sensitivity measure A, local response bias was associated with the local alignment effect in the partial design.