In visual search tasks, observers look for targets among distractors. In the lab, this often takes the form of multiple searches for a simple shape that may or may not be present among other items scattered at random on a computer screen (e.g., Find a red T among other letters that are either black or red.). In the real world, observers may search for multiple classes of target in complex scenes that occur only once (e.g., As I emerge from the subway, can I find lunch, my friend, and a street sign in the scene before me?). This article reviews work on how search is guided intelligently. I ask how serial and parallel processes collaborate in visual search, describe the distinction between search templates in working memory and target templates in long-term memory, and consider how searches are terminated.
This paper introduces the "hybrid foraging" paradigm. In typical visual search tasks, observers search for one instance of one target among distractors. In hybrid search, observers search through visual displays for one instance of any of several types of target held in memory. In foraging search, observers collect multiple instances of a single target type from visual displays. Combining these paradigms, in hybrid foraging tasks observers search visual displays for multiple instances of any of several types of target (as might be the case in searching the kitchen for dinner ingredients or an X-ray for different pathologies). In the present experiment, observers held 8-64 target objects in memory. They viewed displays of 60-105 randomly moving photographs of objects and used the computer mouse to collect multiple targets before choosing to move to the next display. Rather than selecting at random among available targets, observers tended to collect items in runs of one target type. Reaction time (RT) data indicate searching again for the same item is more efficient than searching for any other targets, held in memory. Observers were trying to maximize collection rate. As a result, and consistent with optimal foraging theory, they tended to leave 25-33% of targets uncollected when moving to the next screen/patch. The pattern of RTs shows that while observers were collecting a target item, they had already begun searching memory and the visual display for additional targets, making the hybrid foraging task a useful way to investigate the interaction of visual and memory search.
The concept of a preattentive feature has been central to vision and attention research for about half a century. A preattentive feature is a feature that guides attention in visual search and that cannot be decomposed into simpler features. While that definition seems straightforward, there is no simple diagnostic test that infallibly identifies a preattentive feature. This paper briefly reviews the criteria that have been proposed and illustrates some of the difficulties of definition.
When searching real-world scenes, human attention is guided by knowledge of the plausible size of target object (if an object is six feet tall, it isn't your cat). Computer algorithms typically do not do this, but perhaps they should.
Purpose: We conducted a driving simulator study to investigate the effects of monitoring intersection cross traffic on gaze behaviors and responses to pedestrians by drivers with hemianopic field loss (HFL). Methods: Sixteen HFL and sixteen normal vision (NV) participants completed two drives in an urban environment. At 30 intersections, a pedestrian ran across the road when the participant entered the intersection, requiring a braking response to avoid a collision. Intersections with these pedestrian events had either (1) no cross traffic, (2) one approaching car from the side opposite the pedestrian location, or (3) two approaching cars, one from each side at the same time. Results: Overall, HFL drivers made more (p < 0.001) and larger (p = 0.016) blind- than seeing-side scans and looked at the majority (>80%) of cross-traffic on both the blind and seeing sides. They made more numerous and larger gaze scans (p < 0.001) when they fixated cars on both sides (compared to one or no cars) and had lower rates of unsafe responses to blind- but not seeing-side pedestrians (interaction, p = 0.037). They were more likely to demonstrate compensatory blind-side fixation behaviors (faster time to fixate and longer fixation durations) when there was no car on the seeing side. Fixation behaviors and unsafe response rates were most similar to those of NV drivers when cars were fixated on both sides. Conclusion: For HFL participants, making more scans, larger scans and safer responses to pedestrians crossing from the blind side were associated with looking at cross traffic from both directions. Thus, cross traffic might serve as a reminder to scan and provide a reference point to guide blind-side scanning of drivers with HFL. Proactively checking for cross-traffic cars from both sides could be an important safety practice for drivers with HFL.
Importance: Individuals with homonymous hemianopia (HH) are permitted to drive in some jurisdictions. They could compensate for their hemifield vision loss by scanning toward the blind side. However, some drivers with HH do not scan adequately well to the blind side when approaching an intersection, resulting in delayed responses to hazards. Objective: To evaluate whether auditory reminder cues promoted proactive scanning on approach to intersections. Design, Setting, and Participants: This cross-sectional, single-visit driving simulator study was conducted from October 2018 to May 2019 at a vision rehabilitation research laboratory. A volunteer sample of individuals with HH without visual neglect are included in this analysis. This post hoc analysis was completed in July and August 2020. Main Outcomes and Measures: Participants completed drives with and without scanning reminder cues (a single tone from a speaker on the blind side). Scanning was quantified by the percentage of intersections at which an early large scan was made (a scan with a head movement of at least 20° made before 30 m from the intersection). Responses to motorcycle hazards at intersections were quantified by the time to the first fixation and the time to the horn-press response. Results: Sixteen individuals were recruited and completed the study. Two were subsequently excluded from analyses. Thus, data from 14 participants (median [IQR] age, 54 [36-66] years; 13 men [93%]) were included. Stroke was the primary cause of the HH (10 participants [71%]). Six (43%) had right-sided HH. Participants were more likely to make an early large scan to the blind side in drives with vs without cues (65% vs 45%; difference, 20% [95% CI, 5%-37%]; P < .001). When participants made an early large scan to the blind side, they were faster to make their first fixation on blind-side motorcycles (mean [SD], 1.77 [1.34] vs 3.88 [1.17] seconds; difference, -2.11 [95% CI, -2.46 to -1.75] seconds; P < .001) and faster to press the horn (mean [SD], 2.54 [1.19] vs 4.54 [1.37] seconds; difference, -2.00 [95% CI, -2.38 to -1.62] seconds; P < .001) than when they did not make an early scan. Conclusions and Relevance: This post hoc analysis suggests that auditory reminder cues may promote proactive scanning, which may be associated with faster responses to hazards. This hypothesis should be considered in future prospective studies.