Computational exploration of task and attention modulation on holistic processing and left side bias effects in face recognition: the case of face drawing experts.


Drawing artists and non-drawers are like any adult both experts at face recognition. Yet, artists have a richer learning experience with faces: they were trained in rapid sketching of faces. Zhou, Cheng, Zhang and Wong (2011) found that drawing experts showed less holistic processing (HP) for face recognition than non-drawers. Using a computational model of face recognition that did not implement motor processing, we examined whether engagement of local attention and nature of the learning task could account for the reduced HP in drawers without the influence from motor experience. We showed that compared with the non-drawer model that had a global face input (i.e., Hsiao, Shieh & Cottrell, 2008), a drawer model that incorporated both global face and local facial parts (eyes and mouth) in the input showed reduced HP, suggesting the modulation of local attention engagement. In contrast, the other drawer model that used only global face input but learned to perform an additional face part identification task did not show the reduced HP effect. In addition, both drawer models demonstrated stronger left side (right hemisphere) bias than the non-drawer model. Our data thus suggest that engagement of local attention is sufficient to account for the reduced HP in drawers, and that HP and left side bias effects can be differentially modulated by visual attention or task requirements.

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