In total, 1% of trials were missing due to Eyelink error (30 out of 3000 total trials). There were high levels of individual differences in performance on this task: the highest maximum N-back reached was 10 (1 participant), and the lowest maximum N-back reached was 2 (1 participant). The median N-back reached was 5, and the mode was 4 (Fig. 2). This wide distribution of maximum N-back achieved demonstrates the variability of subjects on this task, while simultaneously exemplifying the notion that this adaptive task can be suited to a number of participants, regardless of overall ability on the cognitive load task.
There were both learning and fatigue effects throughout the experiment, providing evidence that our task was successful in increasing cognitive load. We compared the rate of learning across each block by performing individual t-tests on the b value of our fit equation (y = a*(x-1)^b). We fit all 4 curves individually, found their average a value (0.5096), and set this as the constant a. By fitting all 4 blocks with an average constant, we were able to compare strictly the b value of each curve, or the rate of learning. Throughout each block there was a steady learning effect, and as the blocks continued, the rate of learning generally increased (Fig. 3). Compared to block 1, the rate of learning was faster in block 2 (t(29) = -6.824, p < .001) and in block 4 (t(29) = -4.276, p < .001), but not in block 3 (t(29) = -1.140, p = .1318), demonstrating a possible fatigue effect that occurs just after the halfway point in the experiment. Learning is recovered in block 4, where the rate is significantly higher than in block 3 (t(29) = -3.386, p = .001). The rate of learning was highest in block 2, where it was significantly faster than block 1 (t(29) = -6.824, p < .001), block 3 (t(29) = -6.082, p < .001), and block 4 (t(29) = -2.094, p = .023).
Response Latency:
We used a Pearson’s correlation to examine the relationship between N-back and response latency. Replicating previous studies 26–28, there was a significant correlation between increasing response latency and N-back (r(2998) = 0.292, p < .001). There was also a significant correlation between mean response time and N-back for each subject (r(328) = 0.374, p < .001) (Fig. 4). This suggests that our paradigm was successful in actively engaging working memory, as subjects demonstrated more difficulty in recalling the correct response as the N-back increased. This increase in response time is indicative of subjects having to work harder to search short term memory as difficulty of the task increases. Furthermore, when analyzing each subject individually, 26/30 subjects (86.7%) showed significant correlations (p < .05) between response latency and N-back. These results suggest that our paradigm successfully increases cognitive load and also adapts to individual differences in skill level on the task, and thus can easily accommodate the ability of different subjects.
Pupilometry:
Evidence suggests cognitive load can be measured through pupil diameter, where an increase in cognitive demand is associated with an increase in pupil size 31–34. Our results replicate this finding, with a univariate ANOVA reporting a significant interaction of pupil size and N-back (F(10) = 1.925, p = 0.038). Pupil size slightly increases as N-back increases, and then sharply drops off at an N-back of 9 or 10 (Fig. 5). This is consistent with previous reports, which have shown that pupils dilate with the increasing demands of a working memory task, and then constrict again when cognitive load capacity has been surpassed 31.
Fixations and Saccades:
We used the threshold criteria of the Eyelink 1000 to analyze the number of fixations and saccades, and durations of fixations and saccades. Standard settings on the Eyelink use a velocity threshold of 30°/s and an acceleration threshold of 8000°/s2 to determine the onset and of offset of saccades (samples below these thresholds are considered to be fixational/microsaccadic eye movements). We only counted fixations or saccades occurring within the scene region, any events falling outside the image presented were discarded (amounting to a total of 1.59% data removal). Events for each trial were taken from one eye only: the eye used was determined by smoothing the position data of each eye and comparing the smoothed data to the original binocular data, and the eye with a smaller error was used. The total number of fixations and saccades that the Eyelink recorded during a trial were recorded, and the duration of fixations and saccades were the total cumulative time spent performing each type of event. An example of a subject’s scan-path is presented in Fig. 6.
Averages across subjects for maximum N-back:
We calculated the mean number and duration of fixations and saccades made by each subject, and compared those means across the maximum N-back achieved by each subject. We hypothesized that subjects who could achieve a higher N-back were generally better at this task than subjects who maintained a lower N-back and may use different oculomotor strategies than subjects who struggle with this task. However, there was no significant differences in oculomotor parameters between any of the N-back groups. We ran a one way ANOVA with unequal sample sizes, as each maximum N-back had a different number of subjects who had achieved it. There were no significant differences between the number of fixations (F(8,21) = 0.848, p = 0.572), duration of fixations (F(8,21) = 0.693, p = 0.694) (Fig. 7A), number of saccades (F(8,21) = 0.709, p = 0.681), or duration of saccades (F(8,21) = 0.279, p = 0.966) (Fig. 7B), across all groups (post hoc analysis showed no significant differences between any two maximum groups of N-back). This suggests that subjects who performed well in this task did not use different oculomotor strategies (e.g. looking more frantically around an image with a large number of brief fixations or making fewer, longer fixations), to achieve success.
Correct vs incorrect responses:
We also analyzed oculomotor events according to subject’s responses, in order to test if there are differences in oculomotor strategies that lead to more success in this task. We performed a univariate analysis of variance (two way ANOVA) to analyze the interaction of N-back and response accuracy. There was no significant interaction between N-back and response accuracy for the number of fixations made (F(9) = 0.128, p = 0.999) (Fig. 8A), or for the duration of fixations made (F(9) = 0.385, p = 0.943) (Fig. 8B). This suggests that the ability to perform this task successfully across various N-backs is not related to the number or duration of fixations made. There were also no significant interactions between N-back and response accuracy for the number of saccades made (F(9) = 0.309, p = 0.972) (Fig. 8C), or for the duration of saccades made (F(9) = 1.350, p = 0.205) (Fig. 8D). This suggests that the ability to perform this task successfully across various N-backs is not affected by the amount or duration of saccades made.
Proportion of image looked at:
Our analysis of the number and duration of fixations and saccades showed no relationships between task performance and high or low scoring subjects. We therefore looked at the proportion of each image viewed by each subject for each trial to examine whether there were any effects of the efficiency of eye movements and fixations. We used the convhull() function in Matlab to estimate the image area falling within the polygon defined by the farthest reaching positions recorded by the Eyelink (positions that fell outside of the image region were ignored). We used this as a measure of the approximate area of the image that was viewed by the subject. Values are represented as percentages, where the area of the image viewed was divided by the total size of the image (Fig. 9).
Averages across subjects for max N-back:
A one-way ANOVA with unequal sample sizes found no significant differences across groups of maximum N-back reached (F(8) = 0.448, p = .878) (Fig. 10). This suggests that subjects who were better at this task, (those who were able to reach a higher N-back), on average, did not look at a greater proportion of the image than subjects who performed poorly at this task.
Correct vs incorrect responses:
We performed a univariate analysis of variance (two way ANOVA) to analyze the interaction of N-back and response accuracy. Once again, there was no significant interaction between N-back and response accuracy (F(9) = 0.803, p = 0.613) (Fig. 11). When looking at Fig. 11, there is a slight trend: as N-back increases, for correct responses there is a small increase in the proportion of the image viewed, whereas for incorrect responses there is a small decrease in the proportion of the image viewed. This suggests that maybe subjects are more successful when viewing more of the image, however there was no significant difference between correct and incorrect responses (F(1) = 2.946, p = 0.086).