ZAPS Lab #5 Visual Search Kelly Liu Dornsife College of Letters, Arts and Sciences, University of Southern California PSYC 301 CL: Cognitive Processes Dr. Vita Droutman 10 October 2021 ZAPS Lab #5 Visual Search Background The purpose of this lab is to examine whether the presence of multiple non-target elements affects target detection. In this experiment, participants completed two feature search tasks where they were asked to identify features, such as color and shape, in a display. During the task, researchers manipulated the nature of the target and the nature and number of the non-target elements. Search time was measured, which was the time participants took to detect the target. In the first task, participants had to identify a target among distractors that differed from the target by a single visual feature such as color. For example, participants identified a blue circle (target) surrounded by orange circles and squares (distractors). Compared to the first task, participants in the second task performed a conjunction search task where they had to identify a target that varied in two dimensions, color (orange or blue) and shape (square and triangle), surrounded by distractors. Due to the increased demand for attention in serial processing, we hypothesized that reaction times are higher for conjunction search tasks than for feature search tasks. Results There is a consistent trend between the feature search and the conjunction search. Results of the study all show that reaction times were faster during feature search tasks than during serial search tasks. During the feature search task, the average reaction time remained relatively constant and was not dependent on the presence of the target or the number of distractors. In contrast, average reaction times increased as the number of distractions increased for the conjunction search task. Apart from the reference results, the results show that reaction times were higher when the target was absent. Interpretation Consistent with the hypothesis, we found slower reaction times for the conjunction search task compared to the feature search task. A popular explanation for the different reaction times of feature and conjunction searches is the feature integration theory (FIT), proposed by Anne Treisman in 1980 (Treisman & Gelade, 1980). According to Treisman & Gelade (1980), searches are divided into parallel and serial models of attention. The first stage uses parallel processing in which participants focus on the simple distinguishable feature of color that pops out from surrounding distractors (Treisman & Gelade, 1980). Basic features are registered early, with almost no conscious effort (Treisman & Gelade, 1980). As a result, individuals complete the search task with faster reaction times than conjunction search tasks, which demand the integration of at least two features to detect targets (Treisman & Gelade, 1980). For conjunction tasks, search times increase as the number of distractors increase because attention must be directed serially to each item in the display one at a time (Treisman & Gelade, 1980). When participants were asked to locate the blue circle among blue squares and orange squares and circles, neither the color (blue) or shape (a circle) are sufficient to detect the target. Instead, participants must integrate information of both color and shape features to identify the target. Challenge Age and attention can influence search task performance. Children and older adults may have slower reaction times on visual search tasks due to age-related changes in brain physiology. Children often perform worse on search tasks because their prefrontal structures have not fully developed in the brain (Woods et al., 2013). These structures are crucial for executive functions and feature search (Woods et al., 2013). However, as the dorsolateral prefrontal cortex develops rapidly with age, researchers observe age-related improvements in search organization and search task performance (Woods et al., 2013). Older adults also exhibit slower reaction times and less accuracy in visual search tasks than younger adults (Tamura & Sato, 2020). Search time increases with age because the ability to attend to multiple stimuli and shift attention across display locations slows down (Tamura & Sato, 2020). Paper Review A 2002 study published in The Journals of Gerontology by Davis et al. (2002) investigates the effect of age on search performance. While some research suggests age impacts search performance, others have found no differences between older and younger adults. In the experiment, 15 young adults (ages 18 to 30 years) and 15 older adults (ages 65 to 78 years) were asked to identify a red disc (target) surrounded by red diamonds (distractors; Davis et al., 2002). In the simple search task, researchers hypothesized that the length of time needed to search the stimulus array threshold (SOA threshold) would be the same for both set sizes (Davis et al., 2002). In the conjunction search task, researchers predicted that the SOA threshold duration would be longer for the larger set size because more time is needed to search through more items of the larger set size (Davis et al., 2002). Moreover, set-size effects would be longer for high-accuracy performance, and that the SOA threshold durations and the set-size effects would be greater for older adults than for young adults (Davis et al., 2002). Results of the study support the hypotheses. SOA threshold durations were longer for larger set sizes, with the largest set-size effects found for high-accuracy responses (Davis et al., 2002). Older adults had longer SOA threshold durations and greater set-size effects than young adults (Davis et al., 2002). These results suggest that older adults may find the simple feature searches harder than do young adults. Older adults require more time than young adults to extract visual information from the display, resulting in longer SOA threshold durations (Davis et al., 2002). One possible explanation is that searches are less efficient for older adults because older adults have a more limited capacity for attending to information (Davis et al., 2002). Limitations were not discussed in this study. Novel Study Idea Future research should investigate the effect of playing action video games on search task performance. I am interested in whether playing video games increases the efficiency of attentional processes. Video games are visually and attentionally demanding and require players to focus on specific information while ignoring others. In many games, players must identify and kill their enemies to prevent death and advance further into the game. In the experiment, video game players and non-players perform feature search and conjunction search tasks. The amount of time taken to identify the target (search time) is measured. Since video games and visual search tasks both demand high efficiency and accuracy, video gamers should have short response times on search tasks than non-players. References Davis, E. T., Fujawa, G., & Shikano, T. (2002). Perceptual processing and search efficiency of young and older adults in a simple-feature search task: A staircase approach. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 57(4), P324–P337. https://doi.org/10.1093/geronb/57.4.P324 Madden, D. J., Turkington, T. G., Provenzale, J. M., Denny, L. L., Langley, L. K., Hawk, T. C., & Coleman, R. E. (2002). Aging and attentional guidance during visual search: Functional neuroanatomy by positron emission tomography. Psychology and Aging, 17(1), 24–43. https://doi.org/10.1037//0882-7974.17.1.24 Reisberg, D. (2018). Cognition: Exploring the science of the mind. New York: W.W. Norton. Tamura, S., & Sato, K. (2020). Age-related changes in visual search: Manipulation of colour cues based on cone contrast and opponent modulation space. Scientific Reports, 10(1), 21328. https://doi.org/10.1038/s41598-020-78303-4 Treisman, A. M., & Gelade, G. (1980). A feature-integration theory of attention. Cognitive Psychology, 12(1), 97–136. https://doi.org/10.1016/0010-0285(80)90005-5 l
