Does gender affect the processing of object classification in natural scenes

Abstract

Humans can quickly and efficiently extract information from a complex natural scene. Rapid detection of animals is such an example, which is fast and accurate. We can see that animals have gender differences, and human beings also have gender differences, and they all appear in our real life. Therefore, we will use a two-alternative forced-choice paradigm (2AFC) to investigate the gender differences between the two targets. In our experiment, we balanced the various factors that could be taken into account and subjected the images to histogram equalization. We analyzed the reaction time of the subjects to stimuli of the target gender (male or female). We report two main findings. First, when the type of target (human or animal) was not considered, subjects had faster reaction times to male targets than to female targets. Second, gender differences were only significant for animals when the kind of object (human or animal) was considered.