February 2011
Volume 52, Issue 2
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Eye Movements, Strabismus, Amblyopia and Neuro-ophthalmology  |   February 2011
Effect of Stimulus Type on the Eye Movements of Children
Author Affiliations & Notes
  • Elizabeth L. Irving
    From the School of Optometry, University of Waterloo, Waterloo, Canada; and
    the Vision Science Research Program, Toronto Western Research Institute, University Health Network, Toronto, Canada.
  • Esther G. González
    the Vision Science Research Program, Toronto Western Research Institute, University Health Network, Toronto, Canada.
  • Linda Lillakas
    the Vision Science Research Program, Toronto Western Research Institute, University Health Network, Toronto, Canada.
  • Jonathan Wareham
    From the School of Optometry, University of Waterloo, Waterloo, Canada; and
  • Tara McCarthy
    From the School of Optometry, University of Waterloo, Waterloo, Canada; and
  • Corresponding author: Elizabeth L. Irving, School of Optometry, University of Waterloo, 200 University Avenue West, Waterloo, Ontario, Canada N2L3G1; elirving@sciborg.uwaterloo.ca
Investigative Ophthalmology & Visual Science February 2011, Vol.52, 658-664. doi:10.1167/iovs.10-5480
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      Elizabeth L. Irving, Esther G. González, Linda Lillakas, Jonathan Wareham, Tara McCarthy; Effect of Stimulus Type on the Eye Movements of Children. Invest. Ophthalmol. Vis. Sci. 2011;52(2):658-664. doi: 10.1167/iovs.10-5480.

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      © 2017 Association for Research in Vision and Ophthalmology.

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Abstract

Purpose.: The authors investigated whether pictures elicit superior response rates and eye movement dynamics on saccade and pursuit tasks than do dots or spots of light and whether the need for more interesting stimuli is age dependent.

Methods.: Using video eye tracking, horizontal eye movements were investigated in children and adults using dots and small colored pictures as stimuli. Saccade data were obtained from 61 people and pursuit data from 53 people, ages 3 to 30 years, with no known ocular, ocular motor, neurologic, or systemic disease. Saccadic stimuli were randomly presented in steps ranging in size from 5° to 30°. Pursuits at four velocities (5°/s, 10°/s, 20°/s, and 30°/s) were tested using step ramp stimuli.

Results.: Picture targets result in age-dependent improvements in ocular motor responses compared with dots. With the exception of saccadic accuracy, the youngest children are most affected by the type of target. Adults are affected very little. For pictures, saccadic response rates (t (60) = 4.30, P < 0.001), saccadic peak velocities (t (60) = 2.24, P = 0.03), saccadic accuracy (t (59) = 2.34, P = 0.02), and closed-loop pursuit gains (F (3,50) = 2.86, P = 0.046) are higher. Saccadic error rates (t (60) = 3.91, P < 0.001) and saccadic latencies (t (59) = 9.5, P < 0.001) are lower with pictures.

Conclusions.: Stimulus characteristics can affect response rates and eye movement dynamics, particularly in young children. To avoid underestimation of eye movement performance in young children, it is important to use meaningful targets. Furthermore, when comparing the ocular motor performance of children across studies one must consider the type of stimuli used.

Most visual functions develop with age. 1 Saccades (the rapid eye movements used to shift gaze from one object to another) and pursuit (the eye movement used to follow a moving target) have both been shown to be poorer in children than adults and to develop with age. The most frequently studied parameters in saccadic development are latency, accuracy, and peak velocity. Saccadic latency has been well studied. Virtually all studies find that saccadic latencies are longer in infants and children 2 10 than in young adults. Most studies find that saccadic accuracy is relatively unaffected by age, 6,11 15 although in infants hypometria is often reported 2,16 18 and hypermetria is rare. 16 Fioravanti et al. 19 found that adults undershoot all targets whereas children overshoot small targets and undershoot large ones and that this effect was larger for younger children than older ones. Irving et al. 9 show an interaction between saccade size and age such that older persons have considerable undershooting of large saccades but not small ones whereas the saccadic accuracy of children is not affected by saccade size. Saccadic velocity results in the literature vary from lower velocity in children 9,12 to no difference 5,6 to higher velocity in children than in adults. 19,20 Like the saccadic system, the smooth pursuit system develops between infancy and adulthood. Closed-loop gain is the most common parameter used to quantify the quality of smooth pursuit in children. Although the presence of smooth pursuit has been reported by 1 week of age, 21,22 tracking of moving objects by infants is initially largely saccadic. 23 26 Pursuit develops first for slow target motions, and closed-loop gain is low. 25,26 With increasing age, the accuracy (closed-loop gain) improves, and faster velocities can be tracked. 23,25,27 This development continues through childhood and well into adolescence. 28 34 Katsanis et al. 35 report that pursuit has reached adult levels by late adolescence (17–18 years) (see Ref. 36 for review). Open-loop pursuit gain has also been studied. Fukushima et al. 37 found no significant difference in either open- or closed-loop gain between adults and 7- to 15-year-old children and no significant difference in open loop gain between the different target speeds they studied. Ross et al. 29 found no effect of age on open-loop pursuit in children between 8 and 15 years. Finally, two studies have found pursuit latency in infants to be higher than that in adults and to decrease with age. 23,27  
The study of children's eye movements is difficult for several reasons. One of the problems is keeping a child's attention focused on the task long enough for them to actually do it. Another is ensuring that instructions are simple enough to be understood by the participants. Frequently, the stimulus used in a laboratory to study eye movements is a small dot or a spot of light. Compared with the wide array of stimuli to which we are exposed in the real world, these stimuli are not only uninteresting but unrealistic. In the infant eye movement literature, studies use a variety of means to attract attention to the target. Faces, 38,39 faces embedded in stripes, 21 cartoon characters, 25 toys, 23 and auditory stimuli in conjunction with visual stimuli 39 have all been used. None of these studies evaluate the effectiveness of these strategies in comparison to other, presumably less interesting, targets. 
Stimulus choices (or limitations) may have an effect on the measurements obtained. Hainline et al. 40 found peak velocity to be different between two groups of infants depending on the stimulus used. Peak velocities in a group of infants viewing textures were found to be similar to those of a group of adults, whereas the peak velocities of those viewing geometric shapes were considerably lower. Rosander and von Hofsten 24 used targets of varying size to study smooth pursuit in infants and found no effect of size on pursuit gain. In another study, Rütsche et al. 33 used squares of randomly changing colors to measure pursuit in children younger than 6 years. They report doing this “to achieve better attentiveness of the children” (p. 1407) but did not test the effectiveness of this assumption. Given that attention has been shown to be linked to eye movements 41,42 and, in the case of saccades, necessary for the saccades to occur, 41,43 46 if the targets used improve attention, it stands to reason that execution of the eye movement could be improved. 
We ask these questions: Would a more interesting stimulus—one that has color, distinct shape, and potentially conveys meaning—result in more children responding more of the time to the stimulus? If so, would this result in improvements in the measures of eye movement dynamics? The purpose of this article was to investigate, using a video-based eye tracking system, whether stimulus characteristics affect performance on eye movement tasks. Specifically, we attempted to determine whether pictures elicit superior response rates and eye movement dynamics on saccade and pursuit tasks in children than do conventional dots or spots of light. We also investigated whether the need for “more interesting stimuli” is age dependent. 
Methods
Participants
Saccade data were obtained from 61 people and pursuit data were obtained from 53 people, all ranging in age from 3 to 30 years, approximately equally distributed with regard to sex. The younger participants were recruited from flyers posted in the School of Optometry Clinic at the University of Waterloo, and the older participants were members of the school's student body and responded to posters displayed within the school. Participants had no known ocular, ocular motor, neurologic, or systemic disease and were not taking any medications as determined by self-report or parental report. Sixteen children were excluded because we were unable to get satisfactory calibration data. 
Informed consent was obtained from participants 18 years of age and older and from the parents or guardians of participants younger than 18 years of age. Verbal assent was obtained from those participants for whom someone else provided consent. All procedures adhered to the tenets of the Declaration of Helsinki and were approved by the University of Waterloo's Ethics Review Board. 
Eye Movement Recording
Simultaneous binocular horizontal eye position records were collected from participants. Eye movements were recorded with a binocular CCD video-based eye tracker (Series 2020; El-Mar, Downsview, ON, Canada), which has been used successfully to measure eye movement dynamics in children. 9,32,47,48 The system is free from drift and has a maximum resolution of 6 min arc, 120-Hz sampling rate, and linear range of at least ±30° and ±25° in the horizontal and vertical meridian, respectively. Participants were seated, their heads steadied by a chinrest, 2 m from the projection screen used to present the stimuli. The eye tracker was calibrated for each participant (for calibration procedures, see Irving et al. 9 ). 
After calibration, participants were instructed to follow either a white dot or a colored picture as it stepped (saccades) or moved smoothly (pursuit) across a dark screen. The pictures presented were animal characters, either full body or head only, in cartoon format, and as such all had faces. There were no inanimate objects or pictures of scenes. Pictures were just large enough to be resolvable by participants at the 2-m viewing distance subject to the restrictions imposed by the resolution of the projector (LCD Data Projector, model LVP-X300U; Mitsubishi, Cypress, CA). To achieve this, the overall angular subtense varied from 0.3° to 1°, depending on the image projected. Before data collection, it was determined that the participant recognized each of the pictures to be presented. Saccadic stimuli were randomly presented, without gaps or overlaps, and in step sizes ranging from 5° to 30° (5°, 10°, 15°, 20°, 25°, 30°) in the horizontal direction. Four pursuit velocities (5°/s, 10°/s, 20°/s, 30°/s) were tested along the horizontal meridian using step ramp stimuli. Within a block of trials, step ramps were randomly varied with respect to the four velocities and the direction of the ramp (leftward or rightward). Saccade data were collected before pursuit data for technical reasons related to the presentation of the pursuit stimuli. To avoid delays in data collection (having to restart the system or recalibrate the participant), pursuit stimuli were presented last for all age groups. The order of stimulus presentation (dot vs. picture) was randomized for the collection of saccade and pursuit recordings, and the picture used was selected at random for any given trial. 
Data Analysis
Based on the quality of the eye tracking, the better eye of each participant was selected for analysis. Eye position traces were analyzed offline using custom software. Saccadic response rate, error rate, peak velocity, latency and accuracy (amplitude gain), and pursuit gain were the dependent variables. Age groupings and number of participants in each group for each of the parameters analyzed are shown in Table 1. For those conditions for which the data were further sorted by stimulus size, and the number of participants varied from the overall group, the revised number is provided in the Sorted columns. 
Table 1.
 
Number of Participants per Condition
Table 1.
 
Number of Participants per Condition
Age (y) Saccades Pursuit
Response Rate Error Rate Velocity Latency Amplitude Gain Gain
Overall Sorted Overall Sorted Overall/Sorted
3 5 5 5 4 2 4 3 5
4 3 3 3 3 1 3 2 4
5 5 5 5 5 4 5 5 5
6 6 6 6 6 5 6 6 5
7 7 7 7 7 7 7 7 5
8 5 5 5 5 5 5 5 5
9 5 5 5 5 5 5 5 5
10 7 7 7 7 7 7 7 5
11 6 6 6 6 6 6 6 5
12 4 4 4 4 3 4 3 4
Adults* 8 8 8 8 7 8 8 5
Total 61 61 61 60 52 60 57 53
Response Rate and Error Rate.
For each trial we determined whether a saccade was made in response to the stimulus. Response rate was calculated by counting the number of responses for the two stimulus types (dots and pictures). Percentages were calculated by dividing the total number of responses made by the number of stimulus presentations (×100). Error rates were a subset of response rate and consisted of saccades made in the wrong direction. The total number of direction errors was counted, divided by the number of responses, and multiplied by 100 to calculate the percentage. 
Peak Velocity.
Velocity data were generated by differentiating eye position data with respect to time. Saccades were marked automatically using a velocity threshold technique and were verified manually. Peak velocities were determined for approximately 42 saccades (dependent on the response rate and the quality of the eye tracking) for a variety of amplitudes and were plotted as a function of saccadic amplitude for each stimulus type. This was then fitted with an exponential function for each participant, and the parameters of the function (V max—asymptotic peak velocity; K—slope at the origin) were determined. 9 Parameters of the function were then averaged across subjects for each age group and stimulus type (dots or pictures). 
Latency.
Saccadic latency was calculated for each eye movement that met the following criteria: it was the first saccade after the stimulus, it was in the correct direction, and it occurred between 100 and 1000 ms after stimulus onset. Once a saccade met these criteria, the time of stimulus onset was subtracted from the saccade onset time to determine its latency. Mean ± SD was calculated for all the included saccades. Saccades with latencies outside 2 SD from the mean were considered not to have occurred in response to the stimulus despite having met the previous criteria and were removed. Mean ± SD was recalculated, and the saccadic latencies were averaged across subjects for each age group and each stimulus type (dots or pictures). In a second analysis, latencies for each participant were sorted by stimulus size (≤5°, >5° to ≤10°, >10° to ≤20°, >20°) and mean ± SD was calculated, and those in each bin that were more that 2 SD away from the mean were deleted. Again, the mean ± SD was recalculated and averaged across subjects for each age group and stimulus type. 
Accuracy.
The determination of saccadic accuracy used criteria and a methodology similar to those used for latency except that the amplitude of the first saccade after the stimulus was divided by the magnitude of the stimulus yielding the saccadic amplitude gain. The overall mean ± SD was calculated for each participant, and saccades that were more that 2 SD away from the mean were deleted; mean ± SD was recalculated. In the second analysis, amplitude gains were sorted by stimulus size (≤5°, >5° to ≤10°, >10° to ≤20°, >20°), mean ± SD was calculated, and saccades in each bin that were more that 2 SD away from the mean were deleted. Finally, mean ± SD was recalculated and averaged across subjects for each age group and stimulus type. 
Pursuit.
Pursuit eye movement velocities were generated by differentiating eye position data with respect to time. For each step ramp presented, those periods of pursuit eye movements that were free of saccades, tracking errors, or both were marked. A weighted average velocity was calculated for each step ramp as follows: The average velocity for each marked section of a step ramp was multiplied by the number of eye position samples in that section. For ramps of a particular velocity, these totals were added, and the sum was divided by the total number of eye position samples obtained. Pursuit gain was calculated by dividing the average eye movement velocity by the stimulus velocity. Mean pursuit velocities for the four stimulus velocities were averaged across subjects for each age group and stimulus type (dots or pictures). Mean ± SD was calculated. 
Statistical Analysis
For the analysis of response rate, peak velocity, and amplitude gain, difference scores were computed by subtracting the corresponding values for the dots stimuli from those for the pictures stimuli. For the analysis of error rate and latency, the values for the pictures were subtracted from those of the dots. The F approximations of Wilks' λ in multivariate analysis are reported here, but the same results were found using univariate tests with the Geisser-Greenhouse conservative F statistic. Within-groups comparisons were performed using repeated-measures t-tests. For multiple comparisons, family-wise error was controlled using Holm's sequential Bonferroni approach. An α level was set at 0.05 for all statistical tests. To analyze the effect of the saccade stimulus step size on the difference scores (pictures–dots; n = 57), saccadic amplitude gain and latency difference scores for stimulus step sizes of ≤5°, >5° to ≤10°, >10° to ≤20°, and >20° were subjected to multivariate analysis of covariance (MANCOVA), with stimulus size as the within-subjects factor and age as the covariate. 
A priori computation of the sample size required for an ANOVA with 11 age groups and two repeated measures (dots vs. pictures), a medium effect size (0.25), α = 0.05, and power = 0.8 yielded a total sample of 209 for the between-subjects factor. This sort of power analysis is important for research in which comparisons among all groups—for instance, 3 year olds against 6 year olds—are of interest, but such comparisons are beyond the scope of the present investigation. 49  
Results
Saccades
Response Rate and Error Rate.
Overall, there was a significant difference in response rates between pictures and dots (t (60) = 4.30, P < 0.001). Correlation analysis of the difference scores showed that the dots-pictures difference decreased with age (r (59) = −0.49, P < 0.001). Figure 1 (top left) shows the mean response rates of the dots and pictures as a function of age averaged across stimulus size. There was also a significant difference in the overall error rate between pictures and dots (t (60) = 3.91, P < 0.001). This difference also decreased with age (r (59) = −0.40, P = 0.001). Figure 1 (bottom left) shows the mean error rates (percentages of the total number of responses) as a function of age. 
Figure 1.
 
Clockwise from top left: mean (SE) of response rate, asymptotic peak velocity, latency, and error rate for each stimulus type (dots and pictures) as a function of age.
Figure 1.
 
Clockwise from top left: mean (SE) of response rate, asymptotic peak velocity, latency, and error rate for each stimulus type (dots and pictures) as a function of age.
Peak Velocity.
The overall asymptotic peak velocity for the pictures was slightly (17.68 ± 61.58°/s) but significantly (t (60) = 2.24, P = 0.03) higher than that for the dots. This difference diminished with age (r (59) = −0.31, P = 0.02) (Figs. 1 [top right], 2). 
Figure 2.
 
Difference in the asymptotic peak velocity (V max) between the two stimulus types (pictures–dots) as a function of age.
Figure 2.
 
Difference in the asymptotic peak velocity (V max) between the two stimulus types (pictures–dots) as a function of age.
Latency.
Collapsed across stimulus step size, saccadic latencies were shorter for pictures than for dots (t (59) = 9.05, P < 0.001), and this difference diminished as a function of age (r (58) = −0.60, P < 0.001) (Fig. 1, bottom right). MANCOVA yielded no effect of stimulus step size but a significant effect of age (F (1,50) = 20.51, P < 0.001) and a nonsignificant interaction between step size and age. 
Accuracy.
MANCOVA yielded no effect of stimulus size, no significant effect of age, and no significant interaction between the two factors. Overall and pooled across stimulus sizes, pictures produced a slightly (0.03 ± 0.1) but significantly (t (59) = 2.34, P = 0.02) higher amplitude gain than dots. This difference did not change as a function of age (r (58) = −0.15, P = 0.24). What appears to change as a function of age is the variability of the responses, but the small sizes of the age groups prevented us from testing this statistically (Fig. 3). 
Figure 3.
 
Difference in saccadic amplitude gain between the two stimulus types (pictures–dots) as a function of age. Variability in the gain decreased as a function of age.
Figure 3.
 
Difference in saccadic amplitude gain between the two stimulus types (pictures–dots) as a function of age. Variability in the gain decreased as a function of age.
Pursuit
As can be appreciated in Figure 4, pursuit gain was a function of stimulus type and velocity, and those relationships changed with age. 
Figure 4.
 
Mean (SE) pursuit gain as a function of age and stimulus velocity (5°/s, 10°/s, 20°/s, 30°/s) for each stimulus type (dots and pictures).
Figure 4.
 
Mean (SE) pursuit gain as a function of age and stimulus velocity (5°/s, 10°/s, 20°/s, 30°/s) for each stimulus type (dots and pictures).
Stimulus Type and Velocity.
Overall, pictures produced higher pursuit gains than did dots, particularly for the children (Fig. 4). One-way repeated-measures ANOVA of the difference scores (pursuit gainpictures − pursuit gaindots) collapsed across ages found a significant effect of stimulus velocity (F (3,50) = 2.86, P = 0.046, partial η2 = 0.15), and pairwise comparisons showed that only the largest difference (i.e., that between 5°/s and 20°/s) was significant. In other words, the difference between the gains of the pictures and the dots increased with the speed of the task and then decreased again for the fastest condition of 30°/s (Fig. 4). 
Stimulus Type and Pursuit Gain.
Figure 5 shows the mean pursuit gain for children (collapsed across age groups) and adults. For the children, only the mean of the pictures moving at 20°/s fell within the 95% confidence interval (CI) of the mean CI of the adults. 
Figure 5.
 
Mean (SE) pursuit gain of the children (12 years and younger) against the 95% CIs of the means of the adults (16–30 years).
Figure 5.
 
Mean (SE) pursuit gain of the children (12 years and younger) against the 95% CIs of the means of the adults (16–30 years).
Age.
Pearson r correlations evaluating the relationship between age and pursuit gain showed that, for the pictures, pursuit gain increased with age only at the fastest velocity (30°/s) (r (51) = 0.47, P < 0.001). For the dots, on the other hand, pursuit gain increased with age at all velocities (5°/s, r (51) = 0.43, P = 0.001; 10°/s, r (51) = 0.41, P = 0.002; 20°/s, r (51) = 0.47, P < 0.001; 30°/s, r (51) = 0.46, P < 0.001). 
Discussion and Conclusions
The main findings of this study are that young children respond better to targets that are potentially of some interest to them, that there are improvements in the characteristics of the ocular motor response with these targets, and that the observed differences are age dependent. The youngest children are most affected by the type of target, and adults are affected very little, if at all. Improvements are found for saccadic response and error rates, saccadic latencies, saccadic velocities, and closed-loop pursuit gains. The only parameter studied that did not show an improvement with the picture targets was saccadic accuracy. 
Values for saccadic latency for the dots stimuli were similar to those found in the literature, 4 6 indicating that the sample behaved similarly to previously studied samples. Also similar to findings in the previous literature, no effect of saccadic amplitude gain was found for the age range sampled. 6,9,12 Adult pursuit gains were lower than those found in the literature, 50 but this more likely reflected differences in the methodology (unpredictable step-ramps vs. predictable sinusoids) than an aberration in the sample. 
Differences in the physical attributes of the two stimuli could be responsible for the performance differences observed. Although the individual elements of the pictures were smaller than the dots, the overall size of the pictures was larger because of the resolution limitations of the projection system. Unless targets are very small, 51 there are few or no differences in saccadic accuracy, latency, or precision. 52 With extended targets, the saccade is directed toward the center of symmetrical targets and to some weighted average of the center of nonsymmetrical targets (center of gravity effect), and overall saccadic accuracy is unaffected. 53 55 With regard to pursuit, it has been argued that very large targets will evoke an optokinetic response, and one is no longer strictly measuring pursuit. 56 Results for pursuit gains in response to targets of different sizes vary. 24,56 58 Our targets were all large enough to be easily seen but small enough not to evoke an optokinetic response; therefore, we do not think stimulus size is a likely explanation. 
There were also differences in luminance and contrast between our dot and picture targets. Previous studies 51,59 62 suggest that as long as targets are well above the perceptual threshold, luminance and contrast have little effect on the dynamics of saccades and smooth pursuit. The targets used in the present study were all suprathreshold and easily visible, making luminance and contrast differences an unlikely explanation for the observed differences between the target types. Stimulus background was not an issue in the present study because both the dots and the pictures were presented on the same black background. Based on evidence from previous studies, 51,63 if the color differences of the stimulus were responsible for the differences between the pictures and the dots, one would expect performance, if different, to be worse when using pictures than when using dots. Clearly this was not the case. Finally, although the pictures had different physical attributes than the dots, the attributes of both pictures and dots were similar for all ages. Therefore, the physical attributes of the target could not be the explanation for the changes seen with age. 
We postulate that the effect of the pictures is related to issues of attention. Various aspects of visual attention have been shown to develop with age across the age range of our study. 64 Shagass et al. 65 not only have shown a relationship between pursuit eye movement performance and attention, they have also shown that defective pursuit can be improved by increasing the engagement of attention. They found that the larger the baseline deficit, the greater the improvement. Richard and Holley, 66 using heart rate as a measure of attention, found eye movement performance to be related to attention in infants. They also found an improvement in eye movement performance with age (8–26 weeks) and attributed this improvement to developmental changes in attention. Murray and Giggey 67 have suggested that when a stimulus is informative, it becomes difficult to ignore. Rewards and punishments that alter motivational levels have also been shown to affect saccadic latency and peak velocity, with adolescents more significantly affected by motivational level than adults. 68,69 We, therefore, argue that our picture stimuli were more interesting and had more relevance to our young participants than did dots and that this was responsible for the observed increase in performance. 
Although we are not yet certain what it is about the stimulus that affects eye movement dynamics, we are certain that stimulus characteristics can affect response rates and eye movement dynamics, particularly in young children. The level of ocular motor performance obtained for children in this study using the picture stimuli presumably corresponded more closely to performance than one could expect in real-world situations. Motor response capabilities of children are likely underestimated by the conventional stimuli used for measuring eye movements. To achieve optimum eye movement performance in young children, it is important to use targets to which they will respond. It is also important, when comparing ocular motor performance of children across studies, to consider the type of stimuli used. 
Footnotes
 Supported by Natural Sciences and Engineering Research Council of Canada Grant 203699, Canadian Foundation for Innovation Grant 3164401, Ontario Innovation Trust Grant 31645, Premium Research Excellence Award Grant 3234801, and Canada Research Chairs Grant 950-202761.
Footnotes
 Disclosure: E.L. Irving, None; E.G. González, None; L. Lillakas, None; J. Wareham, None; T. McCarthy, None
References
Daw NW . Visual Development. 2nd ed. New York: Springer Science & Business; 2005.
Aslin RN Salapatek P . Saccadic localization of visual targets by the very young human infant. Percept Psychophys. 1975;17:293–302. [CrossRef]
Cohen ME Ross LE . Latency and accuracy characteristics of saccades and corrective saccades in children and adults. J Exp Child Psychol. 1978;26:517–527. [CrossRef] [PubMed]
Fischer B Biscaldi M Gezeck S . On the development of voluntary and reflexive components in human saccade generation. Brain Res. 1997;754:285–297. [CrossRef] [PubMed]
Munoz DP Broughton JR Goldring JE Armstrong IT . Age-related performance of human subjects on saccadic eye movement tasks. Exp Brain Res. 1998;121:391–400. [CrossRef] [PubMed]
Fukushima J Hatta T Fukushima K . Development of voluntary control of saccadic eye movements, I: age-related changes in normal children. Brain Dev. 2000;22:173–180. [CrossRef] [PubMed]
Klein C . Developmental functions for saccadic eye movement parameters derived from pro- and antisaccade tasks. Exp Brain Res. 2001;139:1–17. [PubMed]
Yang Q Bucci MP Kapoula Z . The latency of saccades, vergence, and combined eye movements in children and in adults. Invest Ophthalmol Vis Sci. 2002;43:2939–2949. [PubMed]
Irving EL Steinbach MJ Lillakas L Babu RJ Hutchings N . Horizontal saccade dynamics across the human life span. Invest Ophthalmol Vis Sci. 2006;47:2478–2484. [CrossRef] [PubMed]
Karatekin C . Eye tracking studies of normative and atypical development. Dev Rev. 2007;27:283–348. [CrossRef]
Warabi T Kase M Kato T . Effect of aging on the accuracy of visually guided saccadic eye movement. Ann Neurol. 1984;16:449–454. [CrossRef] [PubMed]
Rosenhall U Björkman G Pedersen K Hanner P . Oculomotor tests in different age groups. In: Graham MD Kemink JL eds. The Vestibular System: Neurophysiologic and Clinical Research. New York: Raven Press; 1987:401–409.
Hotson JR Steinke GW . Vertical and horizontal saccades in aging and dementia. Neuro-ophthalmology. 1988;8:267–273. [CrossRef]
Huaman AG Sharpe JA . Vertical saccades in senescence. Invest Ophthalmol Vis Sci. 1993;34:2588–2595. [PubMed]
Moschner C Baloh RW . Age-related changes in visual tracking. J Gerontol. 1994;49:M235–M238. [CrossRef] [PubMed]
Harris CM Jacobs M Shawkat F Taylor D . The development of saccadic accuracy in the first seven months. Clin Vision Sci. 1993;8:85–96.
Regal DM Ashmead DH Salapatek P . The coordination of eye and head movements during early infancy: a selective review. Behav Brain Res. 1983;10:125–132. [CrossRef] [PubMed]
Salapatek P Aslin RN Simonson J Pulos E . Infant saccadic eye movements to visible and previously visible targets. Child Dev. 1980;51:1090–1094. [CrossRef] [PubMed]
Fioravanti F Inchingolo P Pensiero S Spanio M . Saccadic eye movement conjugation in children. Vision Res. 1995;35:3217–3228. [CrossRef] [PubMed]
Funk CJ Anderson ME . Saccadic eye movements and eye-head coordination in children. Percept Mot Skills. 1977;44:599–610. [CrossRef] [PubMed]
Kremenitzer JP Vaughan HGJr Kurtzberg D Dowling K . Smooth pursuit eye movements in the newborn infant. Child Dev. 1979;50:442–448. [CrossRef] [PubMed]
Lengyel D Weinacht S Charlier J Gottlob I . The development of visual pursuit during the first months of life. Graefes Arch Clin Exp Ophthalmol. 1998;236:440–444. [CrossRef] [PubMed]
Jacobs M Harris CM Shawkat F Taylor D . Smooth pursuit development in infants. Aust N Z J Ophthalmol. 1997;25:199–206. [CrossRef] [PubMed]
Rosander K von Hofsten C . Development of gaze tracking of small and large objects. Exp Brain Res. 2002;146:257–264. [CrossRef] [PubMed]
Roucoux A Culee C Roucoux M . Development of fixation and pursuit eye movements in human infants. Behav Brain Res. 1983;10:133–139. [CrossRef] [PubMed]
Shea SL Aslin RN . Oculomotor responses to step-ramp targets by young human infants. Vision Res. 1990;30:1077–1092. [CrossRef] [PubMed]
Phillips JO Finocchio DV Ong L Fuchs AF . Smooth pursuit in 1- to 4-month-old human infants. Vision Res. 1997;37:3009–3020. [CrossRef] [PubMed]
Palmer LL MacDonald JE . Monocular and binocular measurements of smooth eye-pursuit by rural children in kindergarten and grade two. Percept Mot Skills. 1990;70:608–610. [PubMed]
Ross RG Radant AD Hommer DW . Open- and closed-loop smooth-pursuit eye movements in normal children: an analysis of a step-ramp task. Dev Neuropsych. 1994;10:255–264. [CrossRef]
Accardo AP Pensiero S Da Pozzo S Perissutti P . Characteristics of horizontal smooth pursuit eye movements to sinusoidal stimulation in children of primary school age. Vision Res. 1995;35:539–548. [CrossRef] [PubMed]
Haishi K Kokubun M . Developmental trends in pursuit eye movements among preschool children. Percept Mot Skills. 1995;81:1131–1137. [CrossRef] [PubMed]
Tajik-Parvinchi DJ Lillakas L Irving E Steinbach MJ . Children's pursuit eye movements: a developmental study. Vision Res. 2003;43:77–84. [CrossRef] [PubMed]
Rütsche A Baumann A Jiang X Mojon DS . Development of visual pursuit in the first 6 years of life. Graefes Arch Clin Exp Ophthalmol. 2006;244:1406–1411. [CrossRef] [PubMed]
Salman MS Sharpe JA Lillakas L Dennis M Steinbach MJ . Smooth pursuit eye movements in children. Exp Brain Res. 2006;169:139–143. [CrossRef] [PubMed]
Katsanis J Iacono WG Harris M . Development of oculomotor functioning in preadolescence, adolescence, and adulthood. Psychophysiology. 1998;35:64–72. [CrossRef] [PubMed]
Luna B Velanova K Geier CF . Development of eye-movement control. Brain Cogn. 2008;68:293–308. [CrossRef] [PubMed]
Fukushima J Tanaka S Williams JD Fukushima K . Voluntary control of saccadic and smooth-pursuit eye movements in children with learning disorders. Brain Dev. 2005;27:579–588. [CrossRef] [PubMed]
von Hofsten C Rosander K . Development of smooth pursuit tracking in young infants. Vision Res. 1997;37:1799–1810. [CrossRef] [PubMed]
Grönqvist H Gredebäck G von Hofsten C . Developmental asymmetries between horizontal and vertical tracking. Vision Res. 2006;46:1754–1761. [CrossRef] [PubMed]
Hainline L Turkel J Abramov I Lemerise E Harris CM . Characteristics of saccades in human infants. Vision Res. 1984;24:1771–1780. [CrossRef] [PubMed]
Shepherd M Findlay JM Hockey RJ . The relationship between eye movements and spatial attention. Q J Exp Psychol A. 1986;38:475–491. [CrossRef] [PubMed]
Khurana B Kowler E . Shared attentional control of smooth eye movement and perception. Vision Res. 1987;27:1603–1618. [CrossRef] [PubMed]
Hoffman JE Subramaniam B . The role of visual attention in saccadic eye movements. Percept Psychophys. 1995;57:787–795. [CrossRef] [PubMed]
Kowler E Anderson E Dosher B Blaser E . The role of attention in the programming of saccades. Vision Res. 1995;35:1897–1916. [CrossRef] [PubMed]
Deubel H Schneider WX . Saccade target selection and object recognition: evidence for a common attentional mechanism. Vision Res. 1996;36:1827–1837. [CrossRef] [PubMed]
Peterson MS Kramer AF Irwin DE . Covert shifts of attention precede involuntary eye movements. Percept Psychophys. 2004;66:398–405. [CrossRef] [PubMed]
Irving EL Tajik-Parvinchi DJ Lillakas L González EG Steinbach MJ . Mixed pro and antisaccade performance in children and adults. Brain Res. 2009;1255:67–74. [CrossRef] [PubMed]
Salman MS Sharpe JA Lillakas L Steinbach MJ Dennis M . Smooth ocular pursuit in Chiari type II malformation. Dev Med Child Neurol. 2007;49:289–293. [CrossRef] [PubMed]
Erdfelder E Faul F Buchner A . GPOWER: a general power analysis program. Behav Res Methods Instrum Comput. 1996;28:1–11. [CrossRef]
Schalén L . Quantification of tracking eye movements in normal subjects. Acta Otolaryngol. 1980;90:404–413. [CrossRef] [PubMed]
Perron C Hallett PE . Saccades to large coloured targets stepping in open fields. Vision Res. 1995;35:263–274. [CrossRef] [PubMed]
Kowler E Blaser E . The accuracy and precision of saccades to small and large targets. Vision Res. 1995;35:1741–1754. [CrossRef] [PubMed]
Findlay JM . Global visual processing for saccadic eye movements. Vision Res. 1982;22:1033–1045. [CrossRef] [PubMed]
He P Kowler E . The role of location probability in the programming of saccades: implications for ‘center-of-gravity’ tendencies. Vision Res. 1989;29:1165–1181. [CrossRef] [PubMed]
Beauvillain C Doré K Baudouin V . The ‘center of gravity’ of words: evidence for an effect of the word-initial letters. Vision Res. 1996;36:589–603. [CrossRef] [PubMed]
Pola J Wyatt HJ . Active and passive smooth eye movements: effects of stimulus size and location. Vision Res. 1985;25:1063–1076. [CrossRef] [PubMed]
van den Berg AV Collewijn H . Human smooth pursuit: effects of stimulus extent and of spatial and temporal constraints of the pursuit trajectory. Vision Res. 1986;26:1209–1222. [CrossRef] [PubMed]
Heinen SJ Watamaniuk SNJ . Spatial integration in human smooth pursuit. Vision Res. 1998;38:3785–3794. [CrossRef] [PubMed]
Wheeless LLJr Cohen GH Boynton RM . Luminance as a parameter of the eye-movement control system. J Opt Soc Am. 1967;57:394–400. [CrossRef]
Doma H Hallett PE . Rod-cone dependence of saccadic eye-movement latency in a foveating task. Vision Res. 1988;28:899–913. [CrossRef] [PubMed]
Jaśkowski P Sobieralska K . Effect of stimulus intensity on manual and saccadic reaction time. Percept Psychophys. 2004;66:535–544. [CrossRef] [PubMed]
Carpenter RHS . Contrast, probability, and saccadic latency; evidence for independence of detection and decision. Curr Biol. 2004;14:1576–1580. [CrossRef] [PubMed]
Satgunam P Fogt N . Saccadic latencies for achromatic and chromatic targets. Vision Res. 2005;45:3356–3364. [CrossRef] [PubMed]
Schul R Townsend J Stiles J . The development of attentional orienting during the school-age years. Dev Sci. 2003;6:262–272. [CrossRef]
Shagass C Roemer RA Amadeo M . Eye-tracking performance and engagement of attention. Arch Gen Psychiatry. 1976;33:121–125. [CrossRef] [PubMed]
Richards JE Holley FB . Infant attention and the development of smooth pursuit tracking. Dev Psychol. 1999;35:856–867. [CrossRef] [PubMed]
Murray NP Giggey K . Saccadic latency and attentional control: evidence for the pre-motor theory of attention. North Am J Psychol. 2006;8:383–396.
Blaukopf CL DiGirolamo GJ . Differential effects of reward and punishment on conscious and unconscious eye movements. Exp Brain Res. 2006;174:786–792. [CrossRef] [PubMed]
Jazbec S Hardin MG Schroth E McClure E Pine DS Ernst M . Age-related influence of contingencies on a saccade task. Exp Brain Res. 2006;174:754–762. [CrossRef] [PubMed]
Figure 1.
 
Clockwise from top left: mean (SE) of response rate, asymptotic peak velocity, latency, and error rate for each stimulus type (dots and pictures) as a function of age.
Figure 1.
 
Clockwise from top left: mean (SE) of response rate, asymptotic peak velocity, latency, and error rate for each stimulus type (dots and pictures) as a function of age.
Figure 2.
 
Difference in the asymptotic peak velocity (V max) between the two stimulus types (pictures–dots) as a function of age.
Figure 2.
 
Difference in the asymptotic peak velocity (V max) between the two stimulus types (pictures–dots) as a function of age.
Figure 3.
 
Difference in saccadic amplitude gain between the two stimulus types (pictures–dots) as a function of age. Variability in the gain decreased as a function of age.
Figure 3.
 
Difference in saccadic amplitude gain between the two stimulus types (pictures–dots) as a function of age. Variability in the gain decreased as a function of age.
Figure 4.
 
Mean (SE) pursuit gain as a function of age and stimulus velocity (5°/s, 10°/s, 20°/s, 30°/s) for each stimulus type (dots and pictures).
Figure 4.
 
Mean (SE) pursuit gain as a function of age and stimulus velocity (5°/s, 10°/s, 20°/s, 30°/s) for each stimulus type (dots and pictures).
Figure 5.
 
Mean (SE) pursuit gain of the children (12 years and younger) against the 95% CIs of the means of the adults (16–30 years).
Figure 5.
 
Mean (SE) pursuit gain of the children (12 years and younger) against the 95% CIs of the means of the adults (16–30 years).
Table 1.
 
Number of Participants per Condition
Table 1.
 
Number of Participants per Condition
Age (y) Saccades Pursuit
Response Rate Error Rate Velocity Latency Amplitude Gain Gain
Overall Sorted Overall Sorted Overall/Sorted
3 5 5 5 4 2 4 3 5
4 3 3 3 3 1 3 2 4
5 5 5 5 5 4 5 5 5
6 6 6 6 6 5 6 6 5
7 7 7 7 7 7 7 7 5
8 5 5 5 5 5 5 5 5
9 5 5 5 5 5 5 5 5
10 7 7 7 7 7 7 7 5
11 6 6 6 6 6 6 6 5
12 4 4 4 4 3 4 3 4
Adults* 8 8 8 8 7 8 8 5
Total 61 61 61 60 52 60 57 53
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