Abstract:
System and method for analyzing multiple participants&#39; eye-movements over a visual display to determine which features on the display universally attract the most attention, or are the most distracting.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This Application is a non-provisional application claiming priority to provisional application 61/526,808 filed on Aug. 24, 2011, under 35 USC 119(e). The entire disclosure of the provisional application is incorporated herein by reference. 
    
    
     BACKGROUND 
     Methods and systems disclosed herein relate generally to display features that could be distracting. Most eye-trackers come equipped with software to analyze the eye-movements of individual participants, including fixations and saccades (eye movements between fixations). What is needed is a method that combines and compiles fixations of multiple participants. 
     SUMMARY 
     The system and method of the present embodiment analyze multiple participants&#39; eye-movements (specifically, fixations) over a visual display (e.g., anything displayed on a computer screen) to determine which features on the display universally attract the most attention, or are the most distracting. Eye movement data are generally recorded by an eye-tracking device as either fixations (when visual attention is focused on an item in the field of view) or saccades (when there is eye movement—and therefore a change in visual attention—from one fixation to another). A saccade is detected when eye movement velocity is more than a predetermined speed (e.g., 30 degrees of visual angle per second), and a fixation is detected when eye movement velocity is less than that speed. When a region of interest is fixated upon (which subtends approximately 2 degrees of visual angle), that region is brought into focus and, the observer may be attending to and attempting to perceive and understand the information there. By recording and then clustering many observers&#39; fixations over a common display, the regions of the display that are universally attracting people&#39;s attention can be analyzed. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a pictorial representation of a digital aeronautical chart overlaid with locations of fixations from 24 participants; 
         FIG. 2  is the chart of  FIG. 1  with all fixations within 0.5° visual angle of each other clustered together; 
         FIG. 3  is the chart of  FIG. 1  showing fixations color-coded by participant; 
         FIG. 4  is the chart of  FIG. 1  with the “most visited” cluster of fixations highlighted and all other clusters grayed out; 
         FIG. 5  is the chart of  FIG. 1  with only the “most visited” cluster of fixations plotted thereon; 
         FIG. 6  is the chart of  FIG. 1  with the “most visited” cluster expanded to represent at least 2° of visual angle, revealing the most distracting feature of the chart (the red town); 
         FIG. 7  is a flowchart of the method of the present embodiment; 
         FIG. 8  is a schematic block diagram of the system of the present embodiment; and 
         FIG. 9  is a schematic block diagram of the cluster processor of the present embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     The problems set forth above as well as further and other problems are solved by the present teachings. These solutions and other advantages are achieved by the various embodiments of the teachings described herein below. 
     The invention is a computer system and method for determining distracting features on an electronic visual display. The system and method cluster multiple observers&#39; fixations, and track various information for each fixation, including (as a minimum) the screen location (X and Y) of the fixation and a unique index number representing the participant who made the fixation. Any other information that was measured in association with the fixation can also be tracked. Counts, averages, standard deviations, and other statistical analyses of the information for each cluster of fixations can be determined. This additional information could include, but is not limited to including, the length (dwell time) of the clustered fixations, the direction and length of previous or following saccades, the amount of clutter immediately surrounding the fixation (as measured by various clutter models), and the average salience of features immediately surrounding the fixation (as measured by various saliency models). The system and method of the present embodiment can be used to filter all the fixation clusters by number of observers, such that only clusters containing at least a pre-selected minimum number (or a maximum number, for a given display) of observers&#39; fixations are analyzed. One method of viewing the resulting clusters is to save them as shapefiles and view/analyze them with ARCINFO® or ARCGIS®. 
     In the present embodiment, fixations for twenty-four observers are included. The following fixations can be, but are not required to be, excluded from clustering: (1) first fixation for each observer/map (center point fixation); (2) all fixations after each observer completed any assigned tasks (e.g., if this was a target detection task, omit all fixations after the observer detected the target); and (3) all fixations for a trial suspected of eye-tracker drift. Clusters are created using, for example, but not limited to, a circular expansion of size five pixels (diameter=10 pix, or 0.5° visual angle). Thus, in the present embodiment, the furthest that two fixations could be separated and still be clustered together would be 0.5°. Clusters containing fixations from at least a pre-selected number of different observers, for example, but not limited to, six or 25% of the observer pool in the exemplary configuration, are shown. In this example, the largest number of observers that were represented in a single cluster was six. After removing the smallest clusters (with, for example, but not limited to, &lt;6 observers&#39; fixations), a border of pre-selected pixel width, for example, but not limited to, fifteen pixels, is added to the remaining clusters, resulting in a minimum of a forty pixel diameter (2° visual angle) per cluster, to make it easier to see what feature is being viewed. All values denoted as “pre-selected” could be constant, computed, retrieved from electronic storage, or user-selected, for example. 
     Referring now to  FIG. 1 , an exemplary chart is shown upon which the method of the present embodiment is pictorially illustrated. In this example, all fixations  125  for twenty-four observers are included, except (1) the first fixation for each observer/map (center point fixation), (2) all fixations after each observer completed an assigned task (e.g., clicking on a randomly placed target feature, not shown in this image), and (3) all fixations for a trial suspected of eye-tracker drift. 
     Referring now to  FIG. 2 , all fixations  125  ( FIG. 1 ) within 0.5° visual angle of each other are clustered together. In the present embodiment, clusters  129  are created using a circular expansion of size 5 (diameter=10 pix, or 0.5° visual angle). Thus, the farthest that two fixations  125  ( FIG. 1 ) are separated and still be clustered together is 0.5°. 
     Referring now to  FIG. 3 , clusters  129  ( FIG. 2 ) are shown with color-coded fixations  130 , color-coded by observer  127  ( FIG. 8 ). Only clusters  129  ( FIG. 2 ) containing fixations  125  ( FIG. 1 ) from at least six different observers  127  ( FIG. 8 ) (25% of the observer pool) are retained. 
     Referring now to  FIG. 4 , only one processed clustered fixation  131  has fixations  125  ( FIG. 1 ) from at least six different observers. 
     Referring now to  FIG. 5 , the smallest of clusters  129  ( FIG. 2 ) (with fewer than six different observers&#39; fixations) have been removed. In the present embodiment, a fifteen-pixel border is added to the remaining of clusters  129  ( FIG. 2 ), resulting in a minimum forty-pixel diameter (2° visual angle) per isolated clustered fixation  133 , to make it easier to see the feature that is viewed by the observers. 
     Referring now to  FIG. 6 , the underlying chart  134  of  FIG. 1  corresponding to the isolated clustered fixation  133  ( FIG. 5 ), which became the subject of most observers&#39; fixations, as isolated by the method of the present embodiment is shown. 
     Referring now to  FIG. 7 , method  150  for analyzing multiple observers&#39; fixations, recorded by an eye-tracker, over a visual display to determine distracting features, can include, but is not limited to including, the steps of automatically detecting  151  fixations on an electronic display, each of the fixations being associated with an observer, automatically clustering  153  together the fixations within a pre-selected visual angle of other of the fixations, automatically isolating  155  the clustered fixations associated with at least a preselected number of different observers, automatically removing  157  the fixations that are not part of the isolated clustered fixations, automatically expanding  159  the isolated clustered fixations to represent a pre-selected output visual angle, and automatically providing  161  the expanded isolated clustered fixations as the distracting features. The pre-selected visual angle can optionally be user-selected. The pre-selected number of different observers can optionally be a pre-selected percentage of the total number of the observers. The pre-selected output visual angle can optionally be user-selected. The step of detecting fixations can include, but is not limited to including, the step of configuring an electronic automated device for detecting the fixations. Optionally, method  150  can include the step of planting targets and distractors on the display. Method  150  can optionally include the step of excluding predetermined fixations, where the predetermined fixations can include, but are not limited to including, the first fixation in each trial, the fixations following the successful completion of some task, the fixations deemed to be affected by drift of the eye tracking device, and fixations over specified features. The step of expanding can include, but is not limited to including, the step of adding a border of pre-selected pixel width to the isolated clustered fixations. The pre-selected pixel width can optionally be user-selected. 
     Optional steps can include (1) while forming each cluster, automatically calculating and maintaining a running summation and count of various measureable parameters associated with each fixation in each cluster, including, but not limited to including, (a) the number of unique observers represented by the fixations in each cluster; (b) the duration (in milliseconds) of each of the fixations in each cluster; (c) the index (i.e., location in time, per trial) of each fixation in each cluster; (d) any other measureable, user-specified parameters associated with each fixation in each cluster; (2) after forming each cluster, automatically calculating the final number of unique observers represented by the fixations in each cluster; and standard statistical measures (e.g., minimum, maximum, average, median, mode, standard deviation, etc.) for each measurable parameter calculated for the fixations in each cluster; and (3) automatically providing the clustered fixation statistics for each distracting feature. 
     The method of the present embodiment could be implemented as executable computer code configured with, for example, but not limited to: (1) default values for clustering resolution, e.g. 10, and the clustering radius, e.g. 5 (such that 2 points would be clustered together if they are 10 (or fewer) pixels apart); (2) the location of the fixations input file; (3) the location in which are to be written the output files, e.g. shapefiles; (4) a flag to indicate whether a) exact point locations are used or b) point locations are “snapped” to the nearest grid location, based on a preset resolution; (5) the resolution (in pixels) if the previous flag is set to “snap” to a grid; and (6) a flag to indicate whether or not to smooth the cluster boundaries, which a) would compress the final cluster file and b) might in some cases (e.g., for very complex cluster boundaries) produce cleaner, less jagged-looking cluster boundaries. The executable computer code could be invoked with parameters such as, for example, but not limited to, (1) a unique identifier per fixation; (2) the screen coordinates of the fixation; (3) the observer&#39;s identifier; (4) the fixation length (amount of time fixated, in milliseconds); and (5) the average clutter and saliency of the region immediately surrounding the fixation (e.g., 2° of visual angle centered on the fixation point). 
     Referring now to  FIG. 8 , system  100  for analyzing multiple observers&#39; fixations, recorded by tracking device  140 , over a visual display  138  to determine distracting features  135 , can include, but is not limited to including, fixation processor  101  including, but not limited to, detector  103  automatically detecting, from tracking device data  126 , fixations  125  on visual display  138 , each fixation  125  being associated with one of a plurality of observers  127 , cluster processor  105  automatically clustering together fixations  125  within pre-selected visual angle  115  of other of fixations  125 , isolator  107  automatically isolating clustered fixations  129  associated with at least preselected number  119  of different observers  127 , remover  109  automatically removing fixations  125  that are not part of processed clustered fixations  131 , expander  111  automatically expanding isolated clustered fixations  133  to represent pre-selected output visual angle  122 , expander  111  automatically providing to chart processor  123  expanded isolated clustered fixations as distracting features  135 . Pre-selected visual angle  115  can optionally be user-selected. Pre-selected number  119  of different observers  127  can optionally be pre-selected percentage  117  of the total number of the observers  127 . Pre-selected output visual angle  115  can optionally be user-selected. Detector  103  can optionally provide configuration information  121  to tracking device  140  for detecting fixations  125 . Detector  103  can also plant targets  15  and distractors  13  on the display. Optionally, excluder  113  can exclude predetermined of fixations  125 , where the predetermined of fixations  125  can include, but are not limited to including, the first of fixations  125  in each trial, fixations  125  following the successful completion of a task, fixations  125  deemed to be affected by drift of tracking device  140 , and fixations  125  over specified features. Expander  111  can optionally add a border of pre-selected pixel width to isolated clustered fixations  133 . The pre-selected pixel width can optionally be user-selected. 
     Referring now to  FIG. 9 , cluster processor  105  can include, but is not limited to including, cluster statistics processor  137  automatically calculating and maintaining measureable parameters statistics  136  associated with each fixation  125  in each of clustered fixations  129 , including, but not limited to including, (a) the number of unique observers  127  represented by fixations  125  in each of clustered fixations  129 ; (b) the duration (in milliseconds) of each of the fixations  125  in each of clustered fixations  129 ; (c) the index (i.e., location in time, per trial) of each fixation  125  in each of clustered fixations  129 ; (d) any other measureable, user-specified parameters associated with each fixation  125  in each of clustered fixations  129 ; (2) after forming each of clustered fixations  129 , automatically calculating the final number of unique of observers  127  represented by fixations  125  in each of clustered fixations  129 ; and standard statistical measures (e.g., minimum, maximum, average, median, mode, standard deviation, etc.) for each measurable parameter calculated for the fixations  125  in each of clustered fixations  129 . Cluster processor  105  can also include distractions statistics processor  139  automatically providing the clustered fixation statistics  134  for each distracting feature  135 . 
     Embodiments of the present teachings are directed to computer systems for accomplishing the methods discussed in the description herein, and to computer readable media containing programs for accomplishing these methods. The raw data and results can be stored for future retrieval and processing, printed, displayed, transferred to another computer, and/or transferred elsewhere. Communications links can be wired or wireless, for example, using cellular communication systems, military communications systems, and satellite communications systems. In an exemplary embodiment, the software for the system is written in Fortran and C. The system operates on a computer having a variable number of CPUs. Other alternative computer platforms can be used. The operating system can be, for example, but is not limited to, WINDOWS® or LINUX®. 
     The present embodiment is also directed to software for accomplishing the methods discussed herein, and computer readable media storing software for accomplishing these methods. The various modules described herein can be accomplished on the same CPU, or can be accomplished on a different computer. In compliance with the statute, the present embodiment has been described in language more or less specific as to structural and methodical features. It is to be understood, however, that the present embodiment is not limited to the specific features shown and described, since the means herein disclosed comprise preferred forms of putting the present embodiment into effect. 
     Referring again primarily to  FIG. 7 , method  150  can be, in whole or in part, implemented electronically. Signals representing actions taken by elements of system  100  ( FIG. 8 ) and other disclosed embodiments can travel over at least one live communications network. Control and data information can be electronically executed and stored on at least one computer-readable medium. The system can be implemented to execute on at least one computer node in at least one live communications network. Common forms of at least one computer-readable medium can include, for example, but not be limited to, a floppy disk, a flexible disk, a hard disk, magnetic tape, or any other magnetic medium, a compact disk read only memory or any other optical medium, punched cards, paper tape, or any other physical medium with patterns of holes, a random access memory, a programmable read only memory, and erasable programmable read only memory (EPROM), a Flash EPROM, or any other memory chip or cartridge, or any other medium from which a computer can read. Further, the at least one computer readable medium can contain graphs in any form including, but not limited to, Graphic Interchange Format (GIF), Joint Photographic Experts Group (JPEG), Portable Network Graphics (PNG), Scalable Vector Graphics (SVG), and Tagged Image File Format (TIFF). 
     Although the present teachings have been described with respect to various embodiments, it should be realized these teachings are also capable of a wide variety of further and other embodiments.