3.4.1.1 DtD task: Etiology
The Dot-to-Dot task developed serendipitously from other activities initiated by Professor Jon M. Kerridge (School of Computing at Edinburgh Napier University). As part of a public engagement at the Edinburgh Science Festival in 2006, Prof. Kerridge created a system using infrared detectors of movement to follow young children as they tried to ‘walk their name’ (see Figure 3.3). This was inspired by the Watching Walkers project, in which Prof. Kerridge was already involved1. Volunteers were asked to write their name on a piece of paper, without lifting the pencil from the paper using joined-up-writing. They were then asked to walk in the area covered by the detectors using their name as a pattern for their walk. At the end of their name, they left the area and a label was printed for volunteers to take away. During the 10-day festival, 5000 labels were printed, some of them showing interesting patterns. In discussion with some of the children, he discovered that those who found the task difficult often also had problems with reading, writing, and/or spelling.
Figure 3.3 The walking area with the sensors mounted 3 meters above the floor. On the left are presented examples of two of the children’s ‘walked’ names
Figure 3.0.3 Figure 3.3 The walking area with the sensors mounted 3 meters above the floor. On the left are presented examples of two of the children’s ‘walked’ names
From these observations, it was hypothesised that measuring a series of dots being traced or drawn by an individual could be informative of potential visual and motor problems. The DtD task is an entirely new task that may be related to motor skills as well as to high level processing. It is believed that the difficulty to fixate one’s eyes on the screen while moving the stylus on the tablet distinguishes this task from
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standard drawing tasks where the eyes gaze just ahead of the hand holding a pen. This ability to dissociate the gaze and the hand may be related to divided attention. As such it is likely to reveal any developmental delay in control of sensorimotor processing, which as discussed earlier in chapter 2 may be compromised in individuals with dyslexia. It was proposed then that one could use this technology to identify possible dyslexia, which is believed to be underpinned by sensorimotor deficits. It is important to emphasise that the task is not related to any language and phonological processing therefore it is unlikely to be interpreted under phonological deficit theory. A feasibility study was conducted in one of the primary schools (School 1), where all of the children were assessed on the prototype of the DtD task. Then, Prof. Kerridge spoke to the class teachers. There was 84% agreement between the data collected (observations of the patterns) and the teachers’ informal appraisal of children’s reading and / or writing skills.
Further investigation was undertaken using university students diagnosed with dyslexia and those who self-reported no reading problems. At that point no other information regarding the participants, for instance their age, was collected. Forty- six participants took part. This time, basic quantitative scores were generated by the software. Table 3.3 presenting the group comparison is provided on the next page.e
3.3 Group comparisons from the pilot study
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Anecdotal evidence and early pilot studies appeared to suggest that the DtD task could distinguish between adults with and without dyslexia and between children with reading problems as reported by their teachers. However, more controlled research was needed in order to: (1) see if the DtD task could objectively and reliably indicate and predict a risk of dyslexia; and (2) identify the cognitive and perceptual factors associated with performance on the DtD task. These are the core purposes of the current thesis.
3.4.1.2 The DtD task: Apparatus
The equipment used for the DtD task comprises a PC display monitor connected to a laptop computer system. Participants use a stylus to “draw” a line on a graphics tablet connected to the laptop (Figure 3.4).
Group comparisons from the pilot study
Dyslexic N Mean SD t-test Dominant Hand:
Time No 27 15.541 5.465 t(44)=.724,
Yes 19 14.300 6.081 p=.473 First sector max.
error No 27 12.333 4.625 t(44)=2.620, Yes 19 16.437 5.999 p=.012* Total error No 27 5943.985 1811.307 t(44)=1.076, Yes 19 6519.163 1745.058 p=.288 SD2 No 27 135.422 60.423 t(44)=-.858, Yes 19 152.205 71.797 p=.395 Non-dominant hand: Time No 27 15.244 5.958 t(44)=.705, Yes 19 16.047 7.218 p=.485
First sector max. error No 27 13.967 4.709 t(23.96)=1.718, Yes 19 18.111 9.742 p=.099 Total error No 27 6427.959 1819.020 t(44)=-.683, Yes 19 6827.600 2135.028 p=.498 SD2 No 27 152.270 60.922 t(44)=-.126, Yes 19 154.947 83.195 p=.900
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Figure 3.4 Experimental set up for the “Dot to Dot” task showing the display monitor, touch-screen tablet, and a stylus.
Figure 3.0.4 Experimental set up for the “Dot to Dot” task showing the display monitor, touch-screen tablet, and a stylus.
The person administering the DtD task uses the laptop to navigate around the software. The monitor displays two specific areas of which the participant has to be aware. The upper area shows the pattern as it is currently drawn. The lower grey area shows the position of the stylus on the graphics tablet (see Figure 3.5).
Figure 3.5 Example of a display screen during a trial.
Figure 3.0.5 Example of a display screen during a trial.
As the stylus was moved, the red square changes to a black dot and the movement of the stylus was shown in the upper area. If the participant lifted the stylus from the surface of the graphics tablet the red square appeared to allow them to replace the stylus in the correct place.
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3.4.1.3 DtD task: Description of the task
Participants were asked to look at a sequence of dots on a monitor and draw a line as quickly and accurately as possible between the dots using the graphics tablet and stylus. Initially, only the first two dots in the pattern were shown with no line joining them. The participant placed the stylus on the red square and moved the stylus towards the first dot. This part of the line was not captured. The participant moved the stylus towards the second dot and as soon as they were close enough to that dot the next dot appeared and so on until the stylus moved to the final dot at the centre of the right-hand edge. The system stops the participant from moving the dot outside the grey area. If the participant moved the stylus beyond a point and the next point did not appear because they were insufficiently close to the target dot then they would have to retrace their steps so that they get close enough for the next dot to appear. Participants always started the task with their dominant hand which was determined before the task was started. The testing session started with researcher’s explanation and demonstration of the task followed with one practice trial. Participants then completed three trials for each of FirstUp (joining 9 dots) and FirstDown (8 dots) patterns. The sequence of trials was randomized by the software. The whole process was then repeated for the non-dominant hand.
Once a trial was completed, the points that make up the drawn line were sent to the software system. Typically, it took a participant between 10 to 20 seconds to complete a trial during which time about 1000-1500 data points were captured. The system detects movements of the stylus and records each movement as a new point denoting the end of the movement.
The software system then created a canonic version of the drawn pattern to enable further analysis. The drawing area is 800 pixels wide (x – direction) and 200 pixels high (y – direction). For each value of x the software determined the average value of the corresponding y-values and recorded that value. If there were no y-values for a particular value of x the system interpolated a y-value based on the closest x-values either side that had a y-value. The missing y-values were created assuming a straight line between the interpolation points. The canonic pattern thus comprises a set of 800 points in the x-direction each with a single y-value. An x-value may have several y-
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values if the participant did move the stylus sufficiently or if for example they created a loop or retraced the line to return to a dot that they had missed. A simplified diagram is presented in Figure 3.6.
Figure 3.6 Simplified diagram of the drawing area showing how the y values are derived if the drawn line is away from the target dot (black dot).
Figure 3.0.6 Simplified diagram of the drawing area showing how the y values are derived if the drawn line is away from the target dot (black dot).
A large number of different values that capture aspects of each line were calculated by the software. These values were calculated relative to the line of perfect fit (straight line joining two dots with the fewest number of pixels coloured), calculated automatically by the software (see Figure 3.7).
Figure 3.7 Example of the output from a single trial for FirstDown (top picture) and FirstUp (bottom) patterns. The grey line shows the line of best fit calculated by the software. The coloured line shows the line drawn by the participant: The blue line shows the resultant line taking the values from all the participants in a particular age (in three months categories) group. The multi-coloured line shows the line drawn by the participant. The green, yellow and red sections of the line indicate that the points fell within 1, beyond 1 and 2 SD of the mean, respectively, for the sample as a whole based upon the standard deviation derived from the analysis of the participant’s line compared with the straight line joining the points. Note that in these examples, the first sector max errors (FSME) are very large as indicated by red lines. In the top pattern also the error is in the wrong direction (opposite to the target dot).
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The following are the detailed DtD measures used in the studies: (1) Time (T) in seconds taken to draw the pattern;
(2) Total error (TE) the sum of all the errors (deviations from the perfect fit line) over the whole pattern. The smaller the value the better the performance; (3) First sector maximum error (FSME) maximum error (deviation from the
perfect fit line) between the first two dots measured as the vertical distance between the line drawn and the straight line joining the two dots. The smaller the value the better the performance;
(4) SD2 the count of the points over 2 standard deviations (red line);
(5) Time total (TT) a score combining the time and total error in order to account for the effect of time that participants took to complete the task and the accuracy (speed and accuracy trade-off);
(6) Direction Ratio (DR) direction of the first sector max error (see explanation below).
The above measures were generated by the software for the entire task (all trials averaged) for both dominant and non-dominant hand, but the average values are also calculated separately for each type of pattern: FirstUp and FirstDown. For the FirstUp and FirstDown patterns, only the first section measures (First sector maximum error and direction ratio) were of interest and only these were included in the analyses.
Direction of the first line
As half of the patterns always started from the first dot located below the starting point (FirstDown) and the other half above it (FirstUp), it was noticed during data collection that some children tended to move their stylus towards the top of the screen where the panel containing the dots and lines was located. Therefore, it was decided to investigate the direction of the first drawn line, and whether children confused the direction in any systematic way. To do this, a measure called the Direction Ratio was calculated. This variable indicated whether the maximum error in the first sector (between the first two dots) was in the same direction as the target dot. For instance, in Figure 3.7 in the top pattern (FirstDown) the maximum error is towards the top of the drawing area although the dot which needs to be joined is below the initial/starting dot. It indicates that the participant drawing the first line drew it in the opposite direction to the target dot. The bottom example in Figure 3.7, on the other
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hand, shows that the maximum error was towards the target dot. In this example, the participant struggled to draw a straight line but did not confuse the directions and aimed towards the target dot.
The ratio was derived from four different codes: two of them indicated that the participant drew the first line in the same direction as the first dot (correct direction) and the other two codes indicated the opposite direction (wrong direction). The ratio was calculated by dividing the number of completed patterns by the number of correct direction patterns. Thus a perfect score would be 1 (all patterns with correct direction) and the lowest score would be 0 (all wrong direction). The feature of calculating the direction ratio measure was added to the software in the middle of data collection; therefore, not all participants will have this measure calculated.