Effect of Visual Field Location on Global Motion Perception: A Developmental Study

Abstract

Previous work has shown that motion perception in school-age children is similar to that of adults for fast speeds but is immature at slow speeds for stimuli presented in the central visual field. This study examined whether visual field location affects this developmental pattern. We measured left/right and up/down global motion direction discrimination for fast and slow speeds in 7- to 10-year-old children and in adults with stimuli presented to upper, central, or lower visual fields. For left/right direction discrimination, children showed significantly higher (worse) coherence thresholds than adults for slow, but not fast, speeds in the central visual field. In the upper and lower visual fields, children showed significantly higher coherence thresholds than adults for both speeds. For up/down direction discrimination, children showed similar performance to adults for the central visual field. In the upper and lower visual fields, children performed significantly worse than adults; this finding was speed-tuned only for the lower visual field. Thus, children show immature global motion perception in the periphery even when performance in central vision is adult-like. These results enrich our understanding of motion perception development in children with typical vision.

Keywords

global motion perception psychophysics visual field visual development motion speed

Performance on many visual perception tasks can differ depending on the region of visual field tested (see Christman & Niebauer, 1997; Danckert & Goodale, 2001; Karim & Kojima, 2010, for reviews). For some aspects of perception, behavioral studies have found better performance (referred to as an advantage) in the upper visual field than in the lower visual field. For other aspects of perception, such as motion, a clear pattern of visual field differences has not emerged. Discrepant findings may be due to differences in tasks and stimulus parameters, as summarized later. In addition, very little is known about the development of visual field differences in motion perception. Our goal was to determine the effect of a range of stimulus parameters on visual field differences in global motion perception in adults and in typically-developing children, and to consider the implications of the results for understanding of motion perception development.

Global motion perception is often studied psychophysically using random-dot stimuli. Studies in adults have shown lower coherence thresholds (i.e., better performance) in the lower visual field than in the upper visual field for global motion detection (Edwards & Badcock, 1993; Levine & McAnany, 2005; Raymond, 1994; Zito et al., 2016). A lower visual field advantage has also been found for other aspects of motion processing including motion-defined form discrimination (McMullen et al., 2009), motion-in-depth detection (Regan et al., 1986), motion segmentation (Lakha & Humphreys, 2005), and multiple-object tracking (He et al., 1996). Finally, a study using electroencephalography showed that amplitudes associated with motion-related evoked responses (the N2 component) and the automatic detection of motion-direction changes (a visual mismatch negativity) were higher in the lower visual field, indicating a lower visual field advantage in processing of motion information (Amenedo et al., 2007).

Other studies, however, found similar performance in the upper and lower visual fields for global motion detection (Van de Grind et al., 1993), direction discrimination (Bosworth & Dobkins, 2002; Dobkins & Bosworth, 2001; Giaschi et al., 2007; Wojciechowski et al., 1995), and velocity discrimination (McColgin, 1960). Finally, an upper visual field advantage has been found for motion-in-depth discrimination (Levine & McAnany, 2005) and for global motion direction discrimination in older adults (mean age = 66.6 years; Wojciechowski et al., 1995).

Interpretation of these previous results is complicated by the finding that visual field differences in motion perception depend on the speed and direction of motion (Giaschi et al., 2007; Raymond, 1994). Some studies found a lower visual field advantage for faster speeds of motion (>4.8 deg/s; Raymond, 1994; Van de Grind et al., 1993), but not all studies using faster speeds of motion have reported a lower field advantage (Bosworth, & Dobkins, 2002; Giaschi et al., 2007; Wojciechowski et al., 1995). Moreover, a consistent pattern of upper/lower field differences has not emerged across studies using vertical (up/down; Giaschi et al., 2007; Raymond, 1994; Wojciechowski et al., 1995; Zito et al., 2016) versus horizontal motion axes (left/right; Bosworth & Dobkins, 2002; Dobkins & Bosworth, 2001; Wojciechowski et al., 1995).

There is some evidence that visual field differences are present during childhood, specifically in children aged 8 to 13 years (Silva et al., 2014), but this has not been studied for motion perception. Previous studies in central vision have shown that speed and motion axis may affect the maturation of motion perception in children. For example, global motion direction discrimination of school-age children was similar to that of adults for fast speeds (30 deg/s) but was immature for slow speeds (1 deg/s; Meier & Giaschi, 2017). Similar speed tuning has been found in developmental disorders that disrupt motion perception (e.g., amblyopia; Meier et al., 2016). In addition, direction discrimination along a vertical motion axis (Ellemberg et al., 2002; Parrish et al., 2005) has been shown to mature at an earlier age than direction discrimination along a horizontal axis (Meier & Giaschi, 2014, 2017; Narasimhan & Giaschi, 2012). It is not known how visual field location interacts with speed or axis in the development of motion perception.

A number of explanations have been proposed to interpret visual field differences (Karim & Kojima, 2010). For example, Previc (1990) argued that because the lower visual field is associated with stimuli that are nearby (referred to as peripersonal space), this region requires a global analysis of object motion including that of arms and hands, while the upper visual field is associated with stimuli that are farther away (referred to as extrapersonal space) and are searched for and recognized by local perceptual mechanisms. Moreover, peripersonal and extrapersonal space may be disproportionately represented by dorsal (lower visual field) and ventral (upper visual field) processing streams, respectively (Previc, 1990, 1998).

The neural basis for visual field differences in performance may start with the retinotopic organization of the posterior visual cortex, in which the lower visual field is represented in dorsal retinotopic regions and the upper visual field is represented in ventral retinotopic regions, and also include higher, more specialized regions of the dorsal and ventral visual streams (see Gilaie-Dotan, 2016, for a review). For example, neuroimaging studies have consistently reported that the dorsal regions V2d and V3d show a lower visual field bias, and ventral regions V2v, V3v, and hV4 show an upper visual field bias (Kravitz et al., 2013; Larsson & Heeger, 2006; Smith et al., 2001). Moreover, greater dorsal stream activity was reported in participants that performed a line bisection task in the lower visual field, whereas greater ventral stream activity was observed when the same task was performed in the upper visual field (Bjoertomt et al., 2002; Weiss et al., 2000). Taken together, stimuli in the lower visual field may preferentially activate the dorsal processing pathway more than the ventral processing pathway, and vice versa. In support of this, studies have found an upper visual field advantage for shape perception tasks and a lower visual advantage for motion perception tasks (McMullen et al., 2009; Zito et al., 2016). By extension, the dorsal stream responds well to motion stimuli, while the ventral stream responds well to form and shape stimuli (reviewed in Goodale & Milner, 1992; Kravitz et al., 2011; Nassi & Callaway, 2009). This may have implications for understanding visual developmental disorders for which disruptions to motion perception are often attributed to dorsal stream vulnerability (Braddick et al., 2003; Simmers et al., 2003; see Atkinson, 2017, for a review).

There is, however, evidence against a strict dichotomy that assigns motion processing solely to the dorsal stream. For example, some motion perception tasks activate regions in the ventral stream (Braddick et al., 2001; Gilaie-Dotan et al., 2013; Helfrich et al., 2013; Meier et al., 2018). Moreover, it has been suggested that at least two distinct speed-tuned motion systems exist: one for fast speeds and the other for slow speeds (Edwards et al., 1998; Joshi & Falkenberg, 2015; Khuu & Badcock, 2002; Manning et al., 2012; Thompson et al., 2006). These fast and slow motion systems have been linked to the dorsal and ventral streams, respectively (Lorteije et al., 2008; Zhuo et al., 2003), although this suggestion is contentious (Van Boxtel & Erkelens, 2006). The well-established motion processing region MT/V5 (Tootell et al., 1995; Zeki et al., 1991) is traditionally placed in the dorsal processing stream, but it may be better understood as an early visual area prior to the division of dorsal and ventral streams (Milner & Goodale, 2006; Schenk et al., 2000; Schenk & McIntosh, 2010). By extension, Gilaie-Dotan (2016) has proposed that MT/V5 is at the first tier of the visual hierarchy (alongside V1) as part of a functionally distinct motion processing pathway that can feed into both ventral and dorsal processing streams. This lateral motion pathway is predicted to have full visual field sensitivity (Gilaie-Dotan, 2016).

The goal of this study was to determine the effects of age, speed, motion axis, and visual field location on global motion perception. Direction discrimination thresholds were obtained for children (7–10 years) and adults with stimuli moving at slow (1 deg/s) or fast (30 deg/s) speeds along a vertical or horizontal axis in the central, upper, or lower visual field. Testing these parameters at the same time may resolve the discrepant visual field findings in adult studies and for the first time extend our knowledge of visual field differences for motion perception to children. In addition, this would allow us to determine whether the known speed-tuned immaturities in left/right discrimination (Meier & Giaschi, 2014, 2017, 2019) are consistent with the proposed visual field bias of dorsal (lower), ventral (upper), or lateral (full field) motion pathways. For example, higher thresholds for the children in the lower visual field would be consistent with dorsal stream immaturity. Alternatively, higher thresholds at slow speeds in the upper visual field would be consistent with immaturity in the ventral stream. No difference in the pattern of results as a function of visual field location would be consistent with immaturity at the level of the proposed lateral motion pathway (Gilaie-Dotan, 2016). Given the earlier maturation for up/down motion discrimination, age effects might be observed for left/right motion only.

Methods

The study was conducted in accordance with the Code of Ethics of the World Medical Association (Declaration of Helsinki) and approved by the Children’s and Women’s Research Ethics Board at the University of British Columbia.

Power Analysis

A series of power analyses were conducted using G*Power (Faul et al., 2007) with the F test family, analysis of variance (ANOVA): Repeated measures statistical test to determine a target sample size for this study. To replicate the previously reported main effects of age (f = 0.71), speed (f = 0.64), and their interaction (f = 0.48) for left/right direction discrimination in the central visual field (Meier & Giaschi, 2017) with power of 0.80, it was estimated that a total of 12, 12, and 20 total participants were necessary. We ran an independent pilot study in a small group of experienced psychophysical observers (n = 5) to obtain preliminary effect size estimates for the main effect of visual field location (f = 0.41) and determined a minimum of 22 adult participants would be necessary to detect this effect. Given no estimates are available for developmental effects, we used a conventional medium effect size of f = 0.25 as a minimum meaningful size for the age by visual field location to determine that a total of 54 participants would be necessary to detect a significant age by location interaction. Thus, we opted to collect data from 54 participants total (27 in each age-group).

Participants

Children (aged 7–10 years) and adults (aged 18–30 years) were recruited to participate in this study. This age range for the children was selected based on an expectation of poorer direction discrimination at the slow relative to the fast speed according to our previous work (Meier & Giaschi, 2014, 2017), combined with an adequate ability to maintain eccentric fixation (Fukushima et al., 2000; Giaschi et al., 2015). A total of 37 adults and 46 children participated. For each participant, visual acuity of each eye was assessed with the Regan (1988) high-contrast letter chart and stereoacuity was measured with the Randot Preschool Stereoacuity Test (Stereo Optical Co., Inc.). Participants with visual acuity in either eye worse than 0.15 logMAR (1.4 arcmin resolution; equivalent to 20/28 Snellen) or stereoacuity worse than 60 arcsec (Birch et al., 2008) were excluded from analysis. Four children were excluded for poor visual acuity and stereoacuity. An additional two children and one adult were excluded for difficulties maintaining fixation on the fixation target. Finally, four children withdrew from the study prior to completion. Thus, data from a total of 36 adults (M_age = 22.6, 28 females) and 36 children (M_age = 8.9, 13 females) were included in the analysis.

Informed consent was obtained from the adults and from the parents of all children who participated in the study. Written and verbal assent were obtained from the children.

Apparatus

Stimulus presentation was controlled using MATLAB R2015a (version 8.5.0.197613; The Mathworks, Inc.) with the Psychtoolbox extension 3.0.12 (Brainard, 1997, Kleiner et al., 2007; Pelli, 1997). An Intel Core i7 Macintosh Macbook Pro was used to run the program.

Participants were seated in a dimly lit room, 1 m from a BenQ XL2420T LED-backlit LCD monitor (resolution 1,920 × 1,080; refresh rate 60 Hz) that displayed the stimuli. A Gravis Gamepad Pro was used to collect responses. An eye tracker (The Eye Tribe, version 0.9.77, 60 Hz sampling rate) placed 50 cm in front of the eyes was used to monitor fixation in a subset of participants. Head position was controlled with a chinrest.

Stimuli and Experimental Conditions

The stimuli used in this experiment have been described previously (Meier & Giaschi, 2017). Each motion stimulus was a random-dot-kinematogram that consisted of 64 white dots (260 cd/m²), 1 arcmin in diameter, on a black (0.7 cd/m²) background. The dots spanned a 7.7 × 7.7 deg square area in the center of the screen, for a density of 1.1 dots/deg². Signal dots were either moving up/down or left/right, depending on the condition. Noise dots were controlled with a white noise algorithm. Dots were updated at a rate equal to the monitor refresh (Δt = 16.7 milliseconds, or 60 Hz) for a dot density over time of 66 dots/deg²/s. The fixation target spanned 0.5 × 0.5 deg and resembled a cartoon eye.

Three factors were investigated in this study: axis of signal dot movement (vertical and horizontal), speed of signal dot movement (slow: 1 deg/s and fast: 30 deg/s), and visual field location of the stimulus (upper, central, and lower visual field), for a total of 12 conditions. Speed was varied by changing the spatial displacement of the dots (Δx = 1 and 30 arcmin for the slow and fast speed, respectively). The visual field location of the stimulus was manipulated by repositioning the fixation target. For the central visual field condition, the target was placed at the center of the screen, in the middle of the stimulus. For the upper and lower visual field conditions, the center of the fixation target was positioned 4.35 deg below or above the center of the screen, such that the edge of the fixation target was 0.25 deg from the edge of the stimulus. The total stimulus duration was 600 milliseconds, regardless of condition. Participants viewed the stimuli binocularly. The condition order was blocked by motion axis, such that half of participants completed the six vertical conditions first and the other half completed the six horizontal conditions first. Within each motion axis condition, motion speed and visual field location were counterbalanced using a Latin-square paradigm to mitigate practice and order effects.

Procedure

After visual acuity and stereoacuity were assessed, participants completed the global motion direction discrimination task. The task utilized a two-alternative forced choice paradigm: The participant had to decide whether the dots were moving left or right (for the horizontal axis condition), or up or down (for the vertical axis condition). During each trial, the participant was instructed to maintain fixation on the fixation target. After the participant pressed a button on the game controller to start the trial, the motion stimulus played for 600 milliseconds. Then, the fixation target was replaced by a question mark and two cartoon doors appeared at the left and right (or the top and bottom) of the screen to prompt a response. Participants pressed a button on the controller corresponding to the door in the direction of their response. Visual and auditory feedback indicated whether a response was correct or incorrect. Finally, the fixation target appeared to begin the next trial.

A two-down, one-up staircase procedure controlled stimulus coherence level (i.e., the proportion of signal dots moving together in the same direction) such that a decrease in coherence level occurred after two correct responses or an increase in coherence level after one incorrect response. The coherence level decreased or increased in steps of 10% for the first three response reversals, after which reversals beyond this decreased or increased the coherence level by half of the previous step until a minimum step of 1%. The first trial of a staircase began with a coherence level of 100%, and reversals at coherence levels above 80% did not impact step size rules to avoid any impact of early mistakes on the range of coherence value the staircase would reach. Prior to beginning the experimental conditions, participants were run through eight trials of a practice staircase. Motion speed was 16 deg/s (Δx = 16 arcmin, Δt = 33.3 milliseconds) along whichever motion axis condition they conducted first with stimuli presented to the lower visual field. If participants made at least three incorrect responses, the practice staircase was repeated. For each of the 12 experimental conditions, the staircase was terminated after 10 response reversals or 50 trials, whichever occurred first. Staircases were not interleaved, so the motion direction, motion speed, and fixation target position remained constant for the duration of one staircase.

To ensure participants were maintaining fixation on the target as instructed, two research assistants seated on each side of the participant monitored the participant’s eye gaze. Because of the large saccades (4.35 deg) needed to adjust gaze from the fixation target to the stimulus for the upper and lower visual field stimulus conditions, research assistants were able to detect when a participant was not looking at the fixation target. Assistants would provide verbal feedback to the participant if they observed the participant’s eyes leave the target. To ensure that the research assistants were effectively monitoring participants’ ability to maintain fixation, a subset of participants (11 children and 9 adults) completed the experiment while the eye tracker also monitored participants’ gaze. If the participant was not looking at the fixation stimulus for the entire duration of a staircase, it was discounted and the condition was repeated. This happened only once for a 9-year old child.

Data Analysis

A coherence threshold was calculated for each of the 12 conditions by fitting a Weibull function to participants’ accuracy as a function of coherence via a maximum-likelihood minimization bootstrap procedure (Watson & Pelli, 1983) implemented in the Palamedes toolbox (Prins & Kingdom, 2018) to obtain the coherence threshold (α) corresponding to 82% correct. The thresholds could range from 1 (i.e., 100% coherent motion) to 0 (i.e., 0% coherent motion). Guess (γ) and lapse (δ) rate were fixed to 0.5 and 0.01, respectively; the slope (β) was free to vary, with an initial guess of 3.5. The Monte Carlo goodness-of-fit test implemented in Palamedes (Wichmann & Hill, 2001) was assessed for each threshold. When this test failed (p < .05), data were inspected and refit after removing one early mistake at a high coherence level or one trial reflecting a coherence level that was presented only once. This occurred for 3.2% of all staircases from the children, and 2.3% of all staircases from the adults. In all cases, fit was improved, so all 36 adults and 36 children had a complete set of data from all 12 conditions.

To examine group differences between adults and children, a four-way ANOVA was first conducted on coherence thresholds. The ANOVA used the between-subjects factor age-group (adults, children) and three within-subject factors speed (1 deg/s and 30 deg/s), motion axis (horizontal and vertical), and visual field location (lower, central, and upper). Because of the significant four-way interaction (see “Results” section), we simplified the analysis by running two separate three-way ANOVAs, one for each motion axis (horizontal and vertical). Each ANOVA used the between-subjects factor age-group, and two within-subject factors speed and visual field location. Significant interactions were probed with simple main effects analyses; significant main effects were followed up with Tukey’s method for pairwise comparisons (α = .05).

To validate participants’ ability to maintain gaze on the fixation target using the eye gaze data collected with the eye tracker, we calculated the proportion of gaze samples that fell within a 1.5 × 1.5 deg box centered on the fixation target. This value was first calculated per trial, averaged across all trials in a condition, and then averaged across all conditions per participant.

Results

Participant Characteristics

Mean visual acuity for the children was −0.06 logMAR (standard deviation [SD] = 0.07, range = −0.25 to 0.05 logMAR) and −0.05 logMAR (SD = 0.06, range = −0.16 to 0.09 logMAR) in the right and left eyes, respectively (approximately 20/17 Snellen equivalent). Mean visual acuity for the adults was −0.07 logMAR (SD = 0.08, range = −0.18 to 0.13 logMAR) and −0.07 logMAR (SD = 0.07, range = −0.18 to 0.14 logMAR) in the right and left eyes, respectively. There were no significant differences between age groups in monocular acuity, right eye: t(70) = 0.32, p = .44, left eye: t(70) = 0.94, p = .96. All participants had stereoacuity of 40 arcsec.

The proportion of gaze samples that fell within the fixation target area was high and was similar for the children (M = 0.91, SD = 0.02) and the adults (M = 0.93, SD = 0.01) for whom an eye tracker was used, t(18) = 1.64, p = .19, d = 0.35. Prior to our main analysis, for each condition of the experiment, we compared the coherence thresholds of the 11 children who conducted eye tracking and the 25 who did not; and the coherence thresholds of the 9 adults who conducted eye tracking and the 27 who did not. There were no significant differences between coherence thresholds for participants whose gaze was monitored by the research assistants only, and those whose gaze was monitored by the research assistants and the eye tracker, for any of the 12 conditions (all f < 0.07). These results suggest that our participants were able to maintain their gaze on the fixation target, and that the effect of fixation ability on the pattern of coherence thresholds reported later was probably small.

Four-Way ANOVA

The results of the 2 (Speed) × 2 (Axis) × 3 (Visual Field) × 2 (Age-Group) ANOVA are shown in Table 1. Because of the statistically significant four-way interaction between age-group, speed, visual field location, and motion axis, F(2, 140) = 3.90, p = .022, f = 0.056, we proceeded to examine the effects of group, speed, and visual field location as a function of each motion axis.

Table 1.

Results of the 2 (Speed) × 2 (Axis) × 3 (Visual Field) × 2 (Age-Group) ANOVA.

Source	df	MS	F	p	Cohen’s f
AGE	1	0.501	21.20	<.001^a	0.30
Residual	70	0.024
S	1	3.575	167.89	<.001^a	2.40
S × AGE	1	0.163	7.66	.007^a	0.11
Residual	70	0.021
VF	2	0.052	11.52	<.001^a	0.16
VF × AGE	2	0.012	2.73	.069	0.04
Residual	140	0.005
MA	1	0.240	24.88	<.001^a	0.36
MA × AGE	1	0.002	0.17	.68	0.00
Residual	70	0.010
S × VF	2	0.026	4.84	.009^a	0.07
S × VF × AGE	2	0.004	0.74	.48	0.01
Residual	140	0.005
S × MA	1	0.000	0.017	.90	0.00
S × MA × AGE	1	0.00	0.00	.99	0.00
Residual	70	0.005
VF × MA	2	0.003	0.43	.65	0.01
VF × MA × AGE	2	0.018	2.98	.054	0.04
Residual	140	0.006
S × VF × MA	2	0.004	0.80	.45	0.01
S × VF × MA × AGE	2	0.020	3.90	.022^a	0.06
Residual	140	0.005

Note. AGE = age-group; S = speed; VF = visual field; MA = motion axis; MS = mean square.

^aStatistically significant at α = .05.Italics denote residuals used in the denominator of each F-test.

Left/Right Motion Direction Discrimination

The individual and mean coherence thresholds for each age-group as a function of speed and visual field condition for left/right motion discrimination are shown in Figure 1. The ANOVA showed a significant main effect of age-group, F(1, 70) = 14.49, p < .001, f = 0.21, such that children had higher thresholds than adults; a main effect of speed, F(1, 70) = 156.41, p < .001, f = 2.24, such that thresholds were lower for fast (30 deg/s) than for slow (1 deg/s) speeds; and a main effect of visual field location, F(2, 140) = 4.60, p = .012, f = 0.07, such that thresholds were lower in the central (M = 0.20) than the upper (M = 0.22) and lower visual fields (M = 0.22), but similar between upper and lower visual fields. There was no visual field location by age-group interaction, F(2, 140) = 0.67, p = .51, f = 0.01, and no visual field location by speed interaction, F(2, 140) = 0.89, p = .41, f = 0.01. However, the speed by age-group interaction was significant, F(1, 70) = 7.09, p = .010, f = 0.10. These effects were qualified by a significant three-way interaction between age-group, speed, and visual field location, F(2, 140) = 3.31, p = .039, f = 0.05.

Figure 1.

Individual and Mean Coherence Thresholds Plotted as a Function of Visual Field Location for Left/Right Motion Direction Discrimination. Error bars indicate standard error.

A simple main effects analysis was conducted to probe the effects of age-group and speed at each visual field location. In both the upper and lower field locations, there was a main effect of age-group, F(1, 70) = 9.85, p = .002, f = 0.14; and F(1, 70) = 5.61, p = .021, f = 0.08, respectively, such that children had higher thresholds than adults. There was also a main effect of speed, F(1, 70) = 75.08, p < .001, f = 1.07; and F(1, 70) = 77.58, p < .001, f = 1.11, respectively, such that participants had lower coherence thresholds for the fast than the slow condition. Neither location yielded a significant age-group by speed interaction, F(1, 70) = 0.73, p = .40, f = 0.01; and F(1, 70) = 1.11, p = .30, f = 0.02, respectively. In the central visual field, however, the age-group by speed interaction was significant, F(1, 70) = 20.27, p < .001, f = 0.29. Simple main effects analysis indicated that while there was no significant difference between children and adults at fast speeds for the central visual field, F(1, 140) = 0.00, p = .99, d = 0.05, children had significantly higher thresholds than adults at slow speeds, F(1, 140) = 38.40, p < .001, d = 1.14. That is, the children’s performance was speed-tuned in the central visual field.

Up/Down Motion Direction Discrimination

The individual and mean coherence thresholds for each speed, age-group, and visual field condition for up/down direction discrimination are shown in Figure 2. The ANOVA showed significant main effects of age-group, F(1,70) = 15.64, p < .001, f = 0.22, such that children had higher thresholds than adults; speed, F(1, 70) = 119.54, p < .001, f = 1.71, such that thresholds were lower for fast (30 deg/s) than slow (1 deg/s) speeds; and visual field location, F(2, 140) = 5.58, p = .005, f = 0.08, such that thresholds were lower in the central (M = 0.22) than the lower visual field (M = 0.26). These were qualified by significant two-way interactions: visual field location by age-group, F(2, 140) = 4.30, p = .015, f = 0.06; speed by age-group, F(1, 70) = 5.48, p = .022, f = 0.08; and visual field location by speed, F(2, 140) = 4.90, p = .009, f = 0.07. The three-way interaction between age-group, speed, and visual field location was not significant, F(2, 140) = 1.23, p = .30, f = 0.02.

Figure 2.

Individual and Mean Coherence Thresholds Plotted as a Function of Visual Field Location for Up/Down Motion Direction Discrimination. Error bars indicate standard error.

A simple main effects analysis was conducted to probe the three two-way interactions. In the central visual field, there was a main effect of speed only, F(1, 70) = 48.71, p < .001, f = 0.69, such that thresholds were lower for fast than for slow speeds; with no main effect of age-group, F(1, 70) = 1.63, p = .21, f = 0.02, or age-group by speed interaction, F(1, 70) = 1.04, p = .31, f = 0.02. In the upper and lower visual fields, there was a main effect of age-group, F(1, 70) = 19.96, p < .001, f = 0.29; and F(1, 70) = 10.38, p = .002, f = 0.15 respectively, and a main effect of speed, F(1, 70) = 98.75, p < .001, f = 1.41; and F(1, 70) = 74.70, p < .001, f = 1.07 respectively. In both the upper and lower visual field locations, these effects were qualified by a significant age-group by speed interaction, F(1, 70) = 4.14, p = .046, f = 0.06; and F(1, 70) = 5.32, p = .024, f = 0.08 respectively. Simple main effects for the upper visual field indicated significantly higher thresholds for children than adults for the slow, F(1, 140) = 34.00, p < .001, d = 1.04, and for the fast, F(1, 140) = 7.40, p = .007, d = 0.57, speed. For the lower visual field, children also had significantly higher thresholds than adults for the slow speed, F(1, 140) = 38.60, p < .001, d = 0.80, but not the fast speed, F(1, 140) = 1.60, p = .21, d = 0.23. That is, the children’s performance was speed-tuned in the lower visual field.

Discussion

This study measured coherence thresholds for global motion direction discrimination as a function of age, speed, motion axis, and visual field location. We investigated whether the speed-tuned immaturity observed in the central visual field for left/right discrimination (Meier & Giaschi, 2017) is dependent on visual field location, as suggested by certain frameworks of motion processing.

As summarized earlier, it has been proposed that in lieu of a single motion system, fast and slow motion systems exist (Joshi & Falkenberg, 2015; Manning et al., 2012) and are mediated by the dorsal and ventral streams, respectively (Gilaie-Dotan et al., 2013; Lorteije et al., 2008; Zhuo et al., 2003). Based on this, we might expect a lower visual field advantage for fast speeds and an upper visual field advantage for slow speeds. However, we did not find a visual field advantage for either speed. As such, this study provides no support for the linking of motion speed to visual field location.

We found an overall performance advantage for direction discrimination in the horizontal axis, compared with the vertical axis. This advantage is consistent with some recent findings (using 6 deg/s: Pilz et al., 2017; and 5 deg/s: Pilz & Papadaki, 2019). Eye movements have also shown a horizontal advantage including more stable and accurate performance in a smooth pursuit task when following horizontally moving targets (Ke et al., 2013), and the gain of optokinetic nystagmus for motion stimuli moving horizontally is more accurate with faster stimulus velocities than for stimuli moving vertically (Van den Berg & Collewijn, 1988). Perhaps this horizontal advantage is a result of relevant visual experience. For example, as we navigate our surroundings, we are much more likely to encounter cars, people, and even words (reading) moving horizontally across our retina. In support of this, a study has shown that a larger number of neurons respond preferentially to horizontal than vertical orientations (Li et al., 2003).

We found that immaturities in global motion perception depend on the motion axis. For the vertical motion axis, children had similar coherence thresholds to adults for both slow and fast speeds. This was not the pattern of results for the horizontal motion axis, where children had similar coherence thresholds to adults only for fast speeds, replicating our previous work (Meier & Giaschi, 2014, 2017). This difference seems to be driven by the fact that at slow speeds, adults have higher coherence thresholds for the vertical motion axis (0.26) than they do for the horizontal motion axis (0.20). This is consistent with Raymond’s (1994) observation that adults have better sensitivity for left/right motion detection than for up/down motion detection at the fovea. On the other hand, children’s slow speed thresholds for the central visual field were approximately the same for the horizontal and vertical motion axes (0.31 and 0.29, respectively). Previous studies of motion discrimination using a vertical axis (Ellemberg et al., 2002; Parrish et al., 2005) have noted mature performance at a much younger age (<4 years) than studies using a horizontal axis (Meier & Giaschi, 2014, 2017; Narasimhan & Giaschi, 2012), The current findings may help resolve this apparent conflict, suggesting that our sensitivity to motion in the left/right axis increases with age. This may have implications for studies of developmental disorder: Because motion discrimination along the vertical axis is adult-like at an earlier age than discrimination along the horizontal axis, children with developmental disorders that implicate the motion processing system may have intact vertical motion discrimination and show selective deficits for horizontal motion discrimination.

We found no upper or lower visual field advantage in children and adults’ performance for either vertical or horizontal motion axes, regardless of motion speed. This finding is consistent with previous direction discrimination studies using fast (Bosworth & Dobkins, 2002; Wojciechowski et al., 1995) and slow (Giaschi et al., 2007) motion and extends it to typically-developing school-age children. Our results do not support the suggestion that the lower visual field represents a pathway specialized for motion processing (Gilaie-Dotan, 2016). Future studies comparing global motion to global form tasks might provide a better test of this idea. Recent evidence suggests that parietal and frontal brain areas may be involved in determining global motion sensitivity (Braddick et al., 2016, 2017), and a lower field advantage may not exist for this extended system.

We also investigated the effect of visual field location on speed-tuned immaturities. Consistent with other studies (see Hadad et al., 2015, for a discussion), the children in this age-group (7–10 years) showed minimal differences from adults for centrally presented up/down direction discrimination. For left/right direction discrimination, we replicated our previous finding which demonstrated that at the central visual field, children performed worse than adults for slow- but not fast-speed global motion stimuli (Meier & Giaschi, 2017). However, we found children performed worse than adults for both speeds in the upper and lower visual fields—that is, we found the immaturities for left/right discrimination in the periphery were not speed-tuned. It is possible that attending to briefly presented (600 milliseconds) extrafoveal stimuli is more demanding for children than for adults, leading to immature performance in peripheral locations. However, children did not show immaturities across all peripheral visual field conditions: Performance in the lower visual field for up/down direction discrimination (Figure 2) was adult-like for fast speeds. This suggests that the effect of age is unlikely to be completely explained by task demands or general attentional differences. Overall, the immature performance found in some peripheral conditions reported in this study suggests a longer period of development for motion perception in the periphery, particularly for the horizontal motion axis. We predict that visual developmental disorders that impact motion perception may be more disruptive in the periphery, assuming that aspects of perception that typically mature later are more susceptible to disruption by disorder.

Conclusion

This study measured coherence thresholds for global motion direction discrimination as a function of age, speed, motion axis, and visual field location. We found that children had immature performance for at least one speed in every visual field location except the central visual field in the vertical axis condition. The results are consistent with a longer period of development for motion perception in the periphery for both upper and lower visual fields, particularly for motion along the horizontal axis. This work advances our understanding of normal mechanisms of human motion perception and has implications for understanding the disruption of global motion perception in developmental disorders.

Footnotes

Acknowledgements

The authors would like to thank Violet Chu, Kevin Chang, Laveniya Kugathasan, Ingrid Yuen, Farnaz Javadian, and Arijit Chakraborty for assistance with recruitment and data collection.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by Natural Sciences and Engineering Research Council of Canada Grant 194526 to D. Giaschi. Y. Shahin was supported by a Natural Sciences and Engineering Research Council of Canada Undergraduate Student Research Award and the University of British Columbia Faculty of Medicine Summer Student Research Program.

ORCID iDs

Yousef M. Shahin

Deborah Giaschi

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