Abstract
Despite a large body of studies has demonstrated that stimuli matching working memory contents capture attention, an important limitation of these previous studies is that too much simplified and artificial stimuli were used. The present study investigated whether working memory contents capture attention in a setting that closely resembles real-world environment. In the experiment, participants performed a task of searching for a target object in a cluttered visual display, while maintaining a visual object in working memory. To create a setting similar to natural, real-world environment, we used images taken from IKEA's online catalogue. This catalogue included images of real-world, indoor scenes, such as living room, dining room, kitchen, and kids room. To create a real-world search task, we inserted a clock or a calendar in these images as the search target. Notably, the images of individual objects included in each scene were also available. These individual object images were served as memory samples. There were two different types of trials. In the memory-matching distractor trials, a memory sample was presented in the visual search display, whereas in the memory-nonmatching distractor trials, no memory object appeared in the search display. Importantly, participants were not informed about the presence of memory-matching stimulus in the search display. The results showed that the presence of memory-matching distractor significantly slowed search RT, F(1,112)=14.45, p< .001, suggesting that memory-matching stimuli captured attention in a real-world search. Notably, a significant RT difference emerged even when only the first pair of memory-matching distractor trials were compared with the first pair of memory–nonmatching distractor trials, p < .005. Given that participants did not expect the presentation of the memory-matching distractors a priori, this finding suggests that the observed capture took place, regardless of participants' intention. Taken together, memory-matching stimuli capture attention automatically in real-world visual search.
Meeting abstract presented at VSS 2017