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

Fulltext in: http://mindmodeling.org/cogsci2013/papers/0429/paper0429.pdfDrawing 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

The prevention and detection of perpetrators

The methods for identification of potentially aggressive persons based on observation of behavior and appearance were described. The results of studies on the signals from the facial expressions and other relationships between human behavior and hostile intent were presented. The practical use of tools for the detection of deception and hostile intent was described, based among others on the Paul Ekman’s theory of basic emotions. The rules for creating the sketch of a suspect were described, it was also raised the issue of profiling offenders based on behavioral traces under analysis

Affective Man-Machine Interface: Unveiling human emotions through biosignals

As is known for centuries, humans exhibit an electrical profile. This profile is altered through various psychological and physiological processes, which can be measured through biosignals; e.g., electromyography (EMG) and electrodermal activity (EDA). These biosignals can reveal our emotions and, as such, can serve as an advanced man-machine interface (MMI) for empathic consumer products. However, such a MMI requires the correct classification of biosignals to emotion classes. This chapter starts with an introduction on biosignals for emotion detection. Next, a state-of-the-art review is presented on automatic emotion classification. Moreover, guidelines are presented for affective MMI. Subsequently, a research is presented that explores the use of EDA and three facial EMG signals to determine neutral, positive, negative, and mixed emotions, using recordings of 21 people. A range of techniques is tested, which resulted in a generic framework for automated emotion classification with up to 61.31% correct classification of the four emotion classes, without the need of personal profiles. Among various other directives for future research, the results emphasize the need for parallel processing of multiple biosignals

Show, don't tell: Non-verbal eyewitness testimony based on non-artistic face sketches

Ph.DDOCTOR OF PHILOSOPH