scholarly journals What eye-tracking can tell us about multiple-target visual search

2012 ◽  
Vol 12 (9) ◽  
pp. 1010-1010 ◽  
Author(s):  
M. S. Cain ◽  
S. H. Adamo ◽  
S. R. Mitroff
Keyword(s):  
Visual Search ◽  
Eye Tracking ◽  
Multiple Target

scholarly journals Beta and theta oscillations differentially support free versus forced control over multiple-target search

2018 ◽  
Author(s):  
Joram van Driel ◽  
Eduard Ort ◽  
Johannes J. Fahrenfort ◽  
Christian N. L. Olivers
Keyword(s):  
Visual Search ◽  
Eye Tracking ◽  
Search Display ◽  
Neural Mechanisms ◽  
Search Performance ◽  
Multiple Target ◽  
Proactive Control ◽  
Reactive Control ◽  
Target Search ◽  
Time Frequency

AbstractMany important situations require human observers to simultaneously search for more than one object. Despite a long history of research into visual search, the behavioral and neural mechanisms associated with multiple-target search are poorly understood. Here we test the novel theory that the efficiency of looking for multiple targets critically depends on the mode of cognitive control the environment affords to the observer. We used an innovative combination of EEG and eye tracking while participants searched for two targets, within two different contexts: Either both targets were present in the search display and observers were free to prioritize either one of them, thus enabling proactive control over selection; or only one of the two targets would be present in each search display, which requires reactive control to reconfigure selection when the wrong target is prioritized. During proactive control, both univariate and multivariate signals of beta-band (15–35 Hz) power suppression prior to display onset predicted switches between target selections. This signal originated over midfrontal and sensorimotor regions and has previously been associated with endogenous state changes. In contrast, imposed target selections requiring reactive control elicited prefrontal power enhancements in the delta/theta-band (2–8 Hz), but only after display onset. This signal predicted individual differences in associated oculomotor switch costs, reflecting reactive reconfiguration of target selection. The results provide compelling evidence that multiple target representations are differentially prioritized during visual search, and for the first time reveal distinct neural mechanisms underlying proactive and reactive control over multiple-target search.Significance StatementSearching for more than one object in complex visual scenes can be detrimental for search performance. While perhaps annoying in daily life, this can have severe consequences in professional settings such as medical and security screening. Previous research has not yet resolved whether multiple-target search involves changing priorities in what people attend to, and how such changes are controlled. We approached these questions by concurrently measuring cortical activity and eye movements using EEG and eye tracking, while observers searched for multiple possible targets. Our findings provide the first unequivocal support for the existence of two modes of control during multiple-target search, which are expressed in qualitatively distinct time-frequency signatures of the EEG both before and after visual selection.

scholarly journals Looking ahead: When do you find the next item in foraging visual search?

2020 ◽  
Author(s):  
Anna Kosovicheva ◽  
Abla Alaoui-Soce ◽  
Jeremy Wolfe
Keyword(s):  
Visual Search ◽  
Eye Tracking ◽  
Real World ◽  
Final Experiment ◽  
Color Wheel ◽  
Visual Tasks ◽  
Search Experiment ◽  
Fixation Locations

Many real-world visual tasks involve searching for multiple instances of a target (e.g., picking ripe berries). What strategies do observers use when collecting items in this type of search? Do they wait to finish collecting the current item before starting to look for the next target, or do they search ahead for future targets? We utilized behavioral and eye tracking measures to distinguish between these two possibilities in foraging search. Experiment 1 used a color wheel technique in which observers searched for T shapes among L shapes while all items independently cycled through a set of colors. Trials were abruptly terminated, and observers reported both the color and location of the next target that they intended to click. Using observers’ color reports to infer target-finding times, we demonstrate that observers found the next item before the time of the click on the current target. We validated these results in Experiment 2 by recording fixation locations around the time of each click. Experiment 3 utilized a different procedure, in which all items were intermittently occluded during the trial. We then calculated a distribution of when targets were visible around the time of each click, allowing us to infer when they were most likely found. In a fourth and final experiment, observers indicated the locations of multiple future targets after the search was abruptly terminated. Together, our results provide converging evidence to demonstrate that observers can find the next target before collecting the current target and can typically forage 1-2 items ahead.

Effects of Image Segmentation and Target Number on Visual Search: An Eye-Tracking Study

2018 ◽  
Vol 62 (1) ◽  
pp. 1987-1991
Author(s):  
Kaifeng Liu ◽  
Calvin Ka-lun Or
Keyword(s):  
Image Segmentation ◽  
Visual Search ◽  
Eye Tracking ◽  
Repeated Measures ◽  
Fixation Duration ◽  
Search Time ◽  
Search Performance ◽  
Target Number ◽  
Lower Accuracy ◽  
Time Accuracy

This is an eye-tracking study examining the effects of image segmentation and target number on visual search performance. A two-way repeated-measures computer-based visual search test was used for data collection. Thirty students participated in the test, in which they were asked to search for all of the Landolt Cs in 80 arrays of closed rings. The dependent variables were search time, accuracy, fixation count, and average fixation duration. Our principal findings were that some of the segmentation methods significantly improved accuracy, and reduced search time, fixation count, and average fixation duration, compared with the no-segmentation condition. Increased target number was found to be associated with longer search time, lower accuracy, more fixations, and longer average fixation duration. Our study indicates that although visual search tasks with multiple targets are relatively difficult, the visual search accuracy and efficiency can potentially be improved with the aid of image segmentation.

scholarly journals Lack of Free Choice Reveals the Cost of Having to Search for More Than One Object

2017 ◽  
Vol 28 (8) ◽  
pp. 1137-1147 ◽  
Author(s):  
Eduard Ort ◽  
Johannes J. Fahrenfort ◽  
Christian N. L. Olivers
Keyword(s):  
Cognitive Control ◽  
Eye Tracking ◽  
Crucial Role ◽  
Free Choice ◽  
The Other ◽  
Multiple Target ◽  
Proactive Control ◽  
Switch Costs ◽  
The Cost ◽  
Reactive Mechanism

It is debated whether people can actively search for more than one object or whether this results in switch costs. Using a gaze-contingent eye-tracking paradigm, we revealed a crucial role for cognitive control in multiple-target search. We instructed participants to simultaneously search for two target objects presented among distractors. In one condition, both targets were available, which gave the observer free choice of what to search for and allowed for proactive control. In the other condition, only one of the two targets was available, so that the choice was imposed, and a reactive mechanism would be required. No switch costs emerged when target choice was free, but switch costs emerged reliably when targets were imposed. Bridging contradictory findings, the results are consistent with models of visual selection in which only one attentional template actively drives selection and in which the efficiency of switching targets depends on the type of cognitive control allowed for by the environment.

Computer vision enhances mobile eye-tracking to expose expert cognition in natural-scene visual-search tasks

2014 ◽  
Author(s):  
Tommy P. Keane ◽  
Nathan D. Cahill ◽  
John A. Tarduno ◽  
Robert A. Jacobs ◽  
Jeff B. Pelz
Keyword(s):  
Computer Vision ◽  
Visual Search ◽  
Eye Tracking ◽  
Natural Scene ◽  
Search Tasks ◽  
Mobile Eye Tracking
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