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University of Essex

Department of Psychology

Psychology Project (PS300)

“The effect of spatial frequency and waviness of riloids on levels of discomfort and perceived illusory motion”

Tiffany Mitchell

Supervised by Paul Hibbard

1104558

Date: 01/05/14 Word Count: 5,420

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“The effect of spatial frequency and waviness of riloids on levels of discomfort

and perceived illusory motion” Abstract Two experiments were conducted into the effect of spatial frequency and

waviness (mui) of “riloid” stimuli in order to assess the effects of these variables

upon ratings of discomfort and perceived illusory motion. These stimuli were

composed to replicate a painting by Bridget Riley, named “Fall”. Participants were

shown sequences of riloids and asked to rate levels of discomfort and motion in

separate trials. The second experiment involved tracking the eye movements of

participants simultaneously. It was found that discomfort and perceived motion

ratings increased as waviness, or mui (μ) and spatial frequency (λ) increased in

experiment one. However, when mui and frequency peaked, discomfort and motion

ratings decreased. Ratings of discomfort and perceived motion were also highly

correlated. Effects were maximised at mid-range and the correlation between

discomfort and motion suggested that they were measuring the same phenomenon.

Experiment two was conducted to investigate this and to discover if gaze stability

impacted upon these variables. This experiment found that varying μ and λ still had

the same effect upon discomfort and motion ratings. However, mui did not achieve

as high a significance as in experiment 1. Frequency, on the other hand, reached

higher significance for discomfort ratings. Discomfort and motion were still highly

correlated. It was found that while gaze stability demonstrated weak correlations with

both discomfort and motion ratings, these were not statistically significant. This may

have been partially to do with technical faults leading to loss of eye tracking data.

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Introduction Research into the area of optical illusions in psychology is often based around

the work of artists such as Bridget Riley. Perception is a wide topic in the field of

psychology and is core in emphasising the role of individual differences in how each

individual views the world and objects in it. The human eye is a complex mechanism

and researchers are keen to discover in what exact quality and quantity optical

illusions differ from natural images; why we see false movement and distortions in op

art that we do not see when observing natural scenery and control stimuli. Research

into this area often involves analysing neural responses to stimuli and is therefore a

useful tool in bringing psychologists further understanding of the human brain

structure. Optical illusions often affect the clarity of lines by altering patterns (Wade

1978). Such images can induce uncomfortable feeling such as migraines, which

research by Khalil (1991) suggests can be predicted by how susceptible an

individual is to visual discomfort. This research aims to expand on why perceived

motion is experienced and why discomfort often results from viewing optical illusions.

In 2003, Zanker et al conducted a study into gaze stability of observers

viewing optical illusions. This research was based around images with similar

composition to Riley’s painting “Fall” (fig. 1). The algorithms used to generate these

images can be viewed in the methods section of the report. The aim of Zanker’s

study was to determine the role of eye movements in the perception of illusory

motion. Zanker used control images as a comparison marker against the illusory

stimuli and used pinhole viewing to induce alternate focusing, reducing the effects of

accommodation when viewing images and therefore lessening illusory effects. It was

found that observers could indeed keep their gazes fixated within the dedicated

region. However, many saccades (small rapid eye movements) were observed

during the experiment. These did not decrease significantly in frequency when

observing the control images, which suggests that these eye movements are present

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throughout image observations but act to cause the perception of illusory motion only

when optical illusions are viewed; a “motion-detector” network. Experiments by

Ditchburn (1973) have shown that the eyes constantly move involuntarily, even when

participants are instructed to keep their gaze steady.

Zanker, Hermens and Walker conducted research into the strength of motion

illusions in 2010, also using images based on Riley’s “Fall”. To avoid subjective

rating issues, a two-alternative forced choice (2AFC) paradigm was used to offer

participants simultaneous options within a given time limit. Using controls versus

illusory stimuli, Zanker discovered that whilst amplitude and undulation (waviness) of

the lines in the illusions were effective upon illusion strength, other factors such as

stimuli presentation, duration and fixation conditions did not have a significant effect.

Computer predictions of saccades matched data collected in the actual experiment

and this provided support for the role of the individual’s involuntary saccades in the

perception of illusory motion.

Current research conducted by Clarke, Hare and Hibbard (in preparation) into

optical illusions is looking into the mechanisms behind and ways of measuring

potential discomfort upon viewing the stimuli. This research also looks into the

possibility that excessive neural responses cause optical illusions to occur and cause

discomfort to individuals. “Riloids” used by Zanker in previous research were also

used in this study. Various methods were used to rate participant’s subjective

discomfort judgments, in order to establish a reliable normalised scale. This research

found that spatial frequency had a significant effect upon discomfort, due to contrast

sensitivity. Additionally, mui (“waviness”) of stripes had an effect, although smaller

than that of spatial frequency. Cortical function appeared to have a very important

role in these findings. Spatial frequency has previously had an effect upon

discomfort, as in O’Hare and Hibbard’s research (2011), spatial frequency of 0.375-

1.5 cycles per degree was found to cause more discomfort in observers than higher

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frequencies. Fernandez and Wilkins (2008) additionally suggest that mid-range

spatial frequencies cause higher discomfort levels.

The two experiments we have conducted in this instance uses the same

images that Zanker and other researchers utilised, in the form of Bridget Riley’s

“Fall”. This research builds upon previous studies into apparent motion but also

discomfort, aiming to find out if there is a relationship between these two subjective

ratings instead of only observing and gathering data based on just one, as in the

other experiments. The independent variables to be altered will be spatial frequency

and mui (waviness). Based on the studies discussed, there is already convincing

evidence linking spatial frequency with both discomfort in optical illusions - mui also

appears to be linked, as in Zanker et al (2010). Our aim is to find out if motion and

discomfort are related by conducting a correlation and if illusory movement is indeed

caused by the motion-detector that Zanker suggests. We will test ratings of

discomfort and perceived illusory motion against varying levels of line waviness and

spatial frequency.

It is hypothesised that a) increasing the waviness (mui) of lines will increase

ratings of both subjective discomfort and perceived illusory motion. It is also thought

that b) increasing the grating (spatial frequency) of lines will have an increasing

effect upon the dependent variables. Additionally, we expect in that c) in experiment

2, observers will find it difficult to keep their gaze steady as mui and spatial

frequency increase. Lastly, we expect that d) there will be a positive correlation

between measures of discomfort and motion ratings, as one increases so will the

other.

Experiment two utilises the methods of eye tracking to observe the

contributions of eye movements to perceived motion. This will give an idea of how

stable observers can keep their gaze whilst viewing the optical illusions. The

relationship between discomfort and motion has yet to be tested, so should provide

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interesting insight into the field of optical illusions in psychology and provide ground

for further research, dependent upon any effects observed.

Figure 1. “Fall” by Bridget Riley. Method: Experiment 1 Participants

10 undergraduate students were recruited from the University of Essex, using

opportunity and volunteer sampling. Two of the participants were the experimenters.

Restrictions applied to individuals with migraines or epilepsy, who could not

participate due to the sensitive nature of the images.

Apparatus The experiment was written in Matlab 2013 with the Psychophysics Toolbox

software and presented on a Windows PC. Participants progressed through the

experiment by using the space bar on a QWERTY keyboard. The screen resolution

was 1024x768 and the refresh rate of the monitor was 100Hz.

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Materials and Stimuli

Participants each took part in one block of 42 trials to rate discomfort and one

block of 42 trials to rate perceived motion, totalling 84 trials per participant. Stimuli

were presented in random order and the order that participants completed the two

blocks was alternated to eliminate order effects. Trials were composed of seven

spatial frequency levels (0.375, 0.75, 1.5, 3, 6, 9 and 12 λ) and three levels of mui (1,

150 and 300 μ), totalling 21 trials (Table 1) completed twice per block.

The images used were generated by Zanker, Doyle and Walker (2003). This

algorithm modulated the intensity I of a sinusoidal grating with a period λ along the

horizontal axis x. The level of grey ranged from 0.0 (black) to 1.0 (white). The

formula is shown below:

The sine wave function ϕ was modulated sinusoidally along the y axis with phase

amplitude A, and modulation period μ. The formula is shown below:

These algorithms were used to generate the series of images shown in Table 1. The

amplitude (A) stayed consistently at 32, unless μ=1, as straight lines had an

amplitude of zero (A=0).

The stimuli were presented on a white background. A discomfort scale (Fig.2)

was shown to participants before the discomfort block and a motion animation (Fig.

3) created by the researchers was shown before the motion block. This was to

demonstrate both scales in a relatable form to participants. Some example stimuli

are shown in figure 4.

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Table 1: The 21 experimental trials.

Figure 2: Example discomfort rating scale (Wong et al. 2001). Figure 3: Example animated motion rating scale.

Figure 4: Examples of the stimuli used.

D Design The study followed a partially experimental and partially correlational format.

A within-subjects (repeated measures) design was used to ensure reliability. The

independent variables were spatial frequency (λ) with seven levels (0.375, 0.75, 1.5,

3, 6, 9 and 12) and mui, or waviness (μ), with three levels (1, 150 and 300). This

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made a total of 21 separate stimuli (Table 1). The dependent variables were

subjective discomfort ratings and ratings of perceived illusory motion, on rating

scales of 1-10. These two variables created the two separate blocks completed by

participants; the “discomfort” and “motion” blocks, of 42 (2x21) trials per block. All

conditions were presented twice per block and in random order for counterbalancing

purposes (84 trials per participant).

Procedure Prior to starting “discomfort” trials, participants were shown a discomfort scale

(Fig. 2) as a point of relation to a 1-10 (1 – lowest discomfort and 10 – highest

discomfort) subjective rating scale. Similarly, prior to “motion” trials, observers were

shown a motion animation (Fig. 3) to relate to the same 1-10 scale for motion. These

examples were used to aid observers in relating the scale to actual feelings of

discomfort and real-life motion. In the experiment, a fixation cross was presented for

50ms, followed by the stimuli for 500ms. The participant would then gave a rating of

1-10 for discomfort or motion, which was spoken aloud and then recorded by the

researchers. The space bar was then pressed by the observer to proceed to the next

trial. Participants were instructed to keep their gaze steady throughout the

experiment and were allowed a short interval between the two blocks to rest. There

were no practice trials prior to the experiment.

Method: Experiment 2

Participants 9 undergraduate students were recruited from the University of Essex, using

opportunity and volunteer sampling. Restrictions applied to individuals with migraines

or epilepsy, who could not participate due to the sensitive nature of the images.

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Caution was applied to those who wore glasses as eye tracking can be unsuccessful

in some instances.

Apparatus The experiment was written in Matlab 2013 with the Psychophysics Toolbox software

and presented on a Windows PC. Participants progressed through the experiment by

using the space bar on a QWERTY keyboard. The screen resolution was 800x600

and the refresh rate of the monitor was 100Hz. The eye tracking software also

operated from a Windows PC, which was set up prior to and controlled during the

experiment by the researchers. The eye tracking equipment used was an SR

Research Eyelink eyetracker.

Materials and Stimuli Participants each took part in one block of 21 trials to rate discomfort and one

block of 21 trials to rate perceived motion, totalling 42 trials per participant. Stimuli

were presented in random order and the order that participants completed the two

blocks was alternated to eliminate order effects. Trials were composed of seven

spatial frequency levels (0.375, 0.75, 1.5, 3, 6, 9 and 12 λ) and three levels of mui (1,

150 and 300 μ), totalling 21 trials (Table 1) completed once per block. The stimuli

was presented on a white background. A discomfort scale (Fig.2) was shown to

participants before the discomfort block and a motion animation (Fig. 3) created by

the researchers was shown before the motion block. This was to demonstrate both

scales in a relatable form to participants. Some example stimuli are shown in figure

4.

The images used were generated by Zanker, Doyle and Walker (2003). The

algorithms used to generate the stimuli were identical to those used in experiment 1.

The stimuli were presented on a white background. A discomfort scale (Fig.2) was

shown to participants before the discomfort block and a motion animation (Fig. 3)

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created by the researchers was shown before the motion block. This was to

demonstrate both scales in a relatable form to participants. Example stimuli can be

viewed in figure 4.

Design

The study followed a partially experimental and partially correlational format.

A within-subjects (repeated measures) design was used to ensure reliability. The

independent variables were spatial frequency (λ) with seven levels (0.375, 0.75, 1.5,

3, 6, 9 and 12) and mui, or waviness (μ), with three levels (1, 150 and 300). This

made a total of 21 separate stimuli (Table 1). The dependent variables were

subjective discomfort ratings and ratings of perceived illusory motion, on rating

scales of 1-10. These two variables created the two separate blocks completed by

participants; the “discomfort” and “motion” blocks, of 21 (3x7) trials per block. All

conditions were presented twice per block and in random order for counterbalancing

purposes (42 trials per participant).

Procedure The eye tracking equipment was set up, calibrated and validated to ensure

the equipment had the correct coverage of the right eye of the observer. Prior to

starting “discomfort” trials, participants were shown a discomfort scale (Fig. 2) as a

point of relation to a 1-10 (1 – lowest discomfort and 10 – highest discomfort)

subjective rating scale. Similarly, prior to “motion” trials, observers were shown a

motion animation (Fig. 3) to relate to the same 1-10 scale for motion. These

examples were used to aid observers in relating the scale to actual feelings of

discomfort and real-life motion. In the experiment, a fixation cross was presented for

50ms, followed by the stimuli for 500ms. The participant would then gave a rating of

1-10 for discomfort or motion, which was spoken aloud and then recorded by the

researchers. The space bar was then pressed by the observer to proceed to the next

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trial. Participants were instructed to keep their gaze steady throughout the

experiment and were allowed a short interval between the two blocks to rest. There

were no practice trials prior to the experiment but the validation of the eye tracking

system ensured that the data collected was consistent in quality.

Results: Experiment 1 Each of the 10 participants obtained a score for all conditions (3 mui x 7

spatial frequency) twice, for both discomfort and perceived motion. The mean and

standard error were calculated for all 21 conditions to give an idea of average score

and variability. Graphs were then created for a) the effect of altering mui and spatial

frequency on discomfort, b) the effect of altering mui and spatial frequency on

perceived illusory motion and c) the relationship (if any) between discomfort and

motion ratings. Two 3x7 within-subjects ANOVAs were carried out to test for any

significant effects of the independent variables upon either perceived discomfort or

illusory motion. A standard significance level of .05 was adopted.

Figure 5: Motion and discomfort ratings given with varying mui.

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1 150 300

Mo

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Mui

Motion and Discomfort

Illusory Motion

Discomfort

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Figure 6: Motion and discomfort ratings given with varying spatial frequency.

Table 2: Mean discomfort and motion ratings given for each condition in experiment one. Standard error of the mean is shown in brackets.

MUI 1

FREQUENCY 0.375 0.75 1.5 3 6 9 12

MOTION 1.5 (0.18) 1.25 (0.13) 1.65 (0.26) 2 (0.45) 2 (0.22) 2.1 (0.43) 1.7 (0.2)

DISCOMFORT 1.45 (0.14) 1.8 (0.25) 2.35 (0.39) 2.3 (0.33) 2.8 (0.5) 2.65 (0.61) 2.3 (0.44)

MUI 150

FREQUENCY 0.375 0.75 1.5 3 6 9 12

MOTION 2.2 (0.37) 2.95 (0.43) 3.25 (0.37) 4.75 (0.63) 5.8 (0.59) 5.65 (0.52) 4.85 (0.7)

DISCOMFORT 3.1 (0.53) 2.55 (0.53) 3.5 (0.68) 4.75 (0.75) 5.4 (0.87) 5.2 (0.82) 4.6 (0.79)

MUI 300

FREQUENCY 0.375 0.75 1.5 3 6 9 12

MOTION 1.7 (0.17) 2.2 (0.27) 3.05 (0.56) 3.75 (0.56) 5.1 (0.61) 4.05 (0.5) 3.35 (0.38)

DISCOMFORT 2.2 (0.28) 2.2 (0.37) 3.4 (0.57) 4.35 (0.64) 4.6 (0.65) 4.15 (0.72) 3.75 (0.67)

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0.375 0.75 1.5 3 6 9 12

Mo

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Spatial Frequency

Motion and Discomfort

Illusory Motion

Discomfort

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Figure 7: The correlation carried out between the dependent variables of discomfort and motion for experiment 1.

The highest mean rating for discomfort recorded in the experiment was 5.8

(150 mui, 6 frequency), whereas the lowest was 1.25 (1 mui, 0.75 frequency). The

condition with the highest level of variability (standard error) was 0.87 (mui 150,

frequency 6 for discomfort). The condition with the lowest variability was 0.13 for mui

1, frequency 0.75 for motion.

As figure 5 shows, ratings of motion and discomfort increased as mui

(waviness) increased. However, the peak of the highest discomfort ratings was at

150 mui. Ratings then dropped as mui reached its highest value of 300. Figure 6

details that as with mui, ratings of motion and discomfort increased as spatial

frequency of gratings increased. Ratings also peaked at around mid-range

(frequency 6) and rapidly declined after this stage.

A 3x7 within-subjects ANOVA was conducted for the dependent variable of

discomfort to test for any significant effects relating to mui and spatial frequency. It

was found that mui achieved statistical significance at F(2,18)=14.016, p<.001,

suggesting that varying mui had a significant effect on discomfort ratings. Frequency

also reached significance, at F(6,54)=7.853, p<.001, supporting the theory that

0

1

2

3

4

5

6

0 1 2 3 4 5 6 7

Dis

com

fort

Motion

Discomfort and Motion Correlation

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altering frequency also affects ratings of discomfort significantly. Mui additionally

demonstrated a significant relationship with spatial frequency, reaching a significant

relationship at F(12,108)=2.969, p=.001. This shows that the alteration of mui and

spatial frequency levels acted in combination to achieve a significant effect upon

discomfort ratings. The largest F statistic in this ANOVA was that of mui, suggesting

that mui is the independent variable with the strongest relationship to ratings of

subjective discomfort.

The 3x7 within-subjects ANOVA conducted for the dependent variable of

perceived illusory motion tested for statistically significant effects in relation to the

independent variables of mui and spatial frequency. The ANOVA revealed that mui

reached significance at F(2,18)=36.897, p<.001, suggesting a significant relationship

between mui and ratings of perceived motion. Frequency achieved statistical

significance at F(6,54)=16.578, p<.001. This also reveals a significant relationship

between varying spatial frequency and ratings of illusory motion. Lastly, mui and

frequency reached significance at F(12,108)=6.765, p<.001. This shows that varying

levels of mui and spatial frequency combined to reach significance for the dependent

variable of perceived illusory motion. The largest F statistic in the motion ANOVA

was again the independent variable of mui, suggesting a strong relationship to

perceived motion.

Lastly, a Pearson’s r correlation was carried out to compare the dependent

variables of subjective discomfort and illusory motion (figure 7). It was found that

discomfort and motion ratings achieved a significant, strong positive correlation of r =

.966, p = <.001. This reveals that ratings of discomfort and ratings of motion are very

highly related to one another.

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Results: Experiment 2

Each of the 9 participants obtained a score for all 21 conditions (3 mui x 7

spatial frequency), for both discomfort and perceived motion (42 scores per

observer). The mean and standard error were calculated for all 21 conditions to give

an idea of average score and variability. Graphs were then created for a) the effect

of altering mui and spatial frequency on discomfort and b) the effect of altering mui

and spatial frequency on perceived illusory motion. Two ANOVAs were carried out to

test for any significant effects of the independent variables upon either perceived

discomfort or illusory motion. A standard significance level of .05 was adopted.

Discomfort and motion were again correlated.

Gaze stability was analysed in relation to first mui and then spatial frequency.

It was then correlated against discomfort and then motion. Unfortunately, eye

tracking was not successful for 6 out of 9 participants, due to a technical fault.

Therefore, only eye tracking data from 3 participants is available for analysis.

Figure 8: Motion and discomfort ratings given with varying mui.

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Figure 9: Motion and discomfort ratings given with varying spatial frequency.

Table 3: Mean discomfort and motion ratings given for each condition in experiment two. Standard error of the mean is shown in brackets.

MUI 1

FREQUENCY 0.375 0.75 1.5 3 6 9 12

MOTION 2.67 (0.9) 1.78 (0.43) 3.11 (0.54) 2.33 (0.37) 4.22 (0.91) 4.67 (1.1) 4.67 (0.97)

DISCOMFORT 2.44 (0.53) 2.89 (0.56) 4 (0.91) 4.33 (0.97) 6.44 (0.8) 4.56 (0.99) 6.33 (0.82)

MUI 150

FREQUENCY 0.375 0.75 1.5 3 6 9 12

MOTION 3 (0.82) 4.22 (0.68) 4.78 (0.57) 7 (0.58) 7.89 (0.42) 7.44 (0.41) 7 (0.62)

DISCOMFORT 3.22 (0.87) 4.44 (0.87) 5.22 (0.89) 6 (0.83) 6.89 (0.81) 6.22 (0.94) 7 (0.83)

MUI 300

FREQUENCY 0.375 0.75 1.5 3 6 9 12

MOTION 3 (0.94) 3.56 (0.89) 4.44 (0.58) 5 (0.87) 5.89 (0.63) 6.22 (0.85) 7 (0.76)

DISCOMFORT 2.44 (0.67) 3.44 (0.9) 4.56 (0.88) 5.78 (0.62) 7.33 (0.53) 6 (0.73) 6.44 (0.75)

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0.375 0.75 1.5 3 6 9 12

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Rat

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Spatial Frequency

Motion and Discomfort

Illusory Motion

Discomfort

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Figure 10: The correlation carried out between the dependent variables of discomfort and motion for experiment 2.

Figure 11: Gaze stability in relation to varying mui levels.

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Dis

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Illusory Motion

Discomfort and Motion

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Gaz

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Mui

Gaze Stability

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Figure 12: Gaze stability in relation to varying spatial frequency levels.

Figure 13: Gaze stability when combining varied mui and varied spatial frequency.

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Gaze Stability

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Gaze Stability

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Figure 14: Gaze stability and discomfort correlation.

Figure 15: Gaze stability and motion correlation.

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Discomfort

Gaze Stability and Discomfort

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Gaze Stability and Motion

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Table 4: Gaze stability means and standard errors for all 21 conditions in experiment 2.

Gaze Stability 1 150 300 Mean SE

0.375 22.08 30.06 57.14 36.42 10.61

0.75 34.23 51.93 6 30.72 13.37

1.5 12.97 10.01 16.3 13.09 1.81

3 11.26 15.56 40.26 22.36 9.03

6 38.7 32.84 23.68 31.74 4.37

9 11.39 7.47 15.34 11.4 2.27

12 27.59 9.16 10.42 15.72 5.94

Mean 22.6 22.43 24.16 SE 4.27 6.25 6.91

The highest mean rating for discomfort recorded in the experiment was 7.33

(300 mui, 6 frequency for discomfort), whereas the lowest was 2.44 (1 mui, 0.375

frequency and 300 mui, 0.375 frequency – also both for discomfort). The mui level of

1 consistently achieves the lowest values for both motion and discomfort ratings. The

condition with the highest level of variability is mui 1, frequency 9 at 1.1 for motion,

whereas the condition with the lowest variability is mui 1, frequency 3 at 0.37 for

motion.

Figure 8 shows that ratings of motion and discomfort increased as mui

(waviness) increased. The peak of the highest discomfort and motion ratings was at

150 mui before ratings dropped as mui ascended to 300. Figure 9 shows that as with

mui, ratings of motion and discomfort gradually increased as spatial frequency of

gratings increased. Ratings peaked at around 6 spatial frequency for discomfort and

then dipped at 9λ and rose again at 12 λ. Motion ratings, however, peak and stay

fairly level at 6 λ, a clear indicator that spatial frequency impacted upon the two

dependent variables in a unique manner.

A 3x7 within-subjects ANOVA was conducted for the dependent variable of

discomfort to test for any significant effects relating to mui and spatial frequency. Mui

achieved significance at F(2,16)=10.627, p=.001, suggesting that variance in mui still

had a statistically significant effect upon ratings of discomfort, although the F statistic

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is not as strongly supportive as that of experiment one. Frequency reached statistical

significance at F(6,48)=13.671, p<.001, showing that there is an even stronger

relationship between spatial frequency level and discomfort than in the first

experiment. Mui and spatial frequency F(12,96)=.711, p=.737 did not achieve

significance in this instance against discomfort, which implies that when mui and

frequency interact together, there is no significant effect upon ratings of discomfort.

The largest F statistic calculated in this ANOVA was that of frequency’s effect upon

discomfort, showing that this was the independent variable with the largest effect on

subjective discomfort ratings.

Another 3x7 within-subjects ANOVA was carried out for the dependent

variable of motion to test for significant effects of mui and spatial frequency. Mui

achieved significance at F(2,16)=13.001, p=<.001, suggesting that variance in mui

still has a significant effect upon ratings of motion, although not as strong as the

effect observed in experiment one. Frequency reached statistical significance at

F(6,48)=15.046, p<.001, showing that there is a slightly weaker relationship between

spatial frequency level and discomfort than in the first experiment. Mui and spatial

frequency reached significance at F(12,96)=2.068, p=.026. This means that the

interaction of changing mui and spatial frequency had a significant effect upon

motion level ratings. The largest F statistic in this ANOVA was the effect of frequency

upon ratings of perceived motion, suggesting that frequency had the largest effect on

motion ratings.

A Pearson’s r correlation was undertaken to compare the dependent variables

of subjective discomfort and illusory motion (figure 10). It was found that discomfort

and motion ratings achieved a significant, strong positive correlation of r = .842, p =

<.001. This reveals that ratings of discomfort and ratings of motion are very highly

related to one another, as in experiment 1.

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It is crucial to remember that the gaze stability data discussed here is a limited

representation, as a technical fault lead to data loss. The data analysed here is the

data of 3 participants only (3 discomfort rating data and 3 motion rating data). The

condition with the highest mean eye movement was 300 mui/0.375 frequency

(57.14), whereas the lowest was 300 mui/0.75 frequency (6). The mui with the

highest gaze stability variation is 300 (6.91) and the lowest mui 1 (4.27). The

frequency level with the highest variation was 0.75 (13.37) and the lowest was 1.5

(1.81).

Figure 11 details the change of gaze stability based on the level of mui. Gaze

stability remained almost level at around 23-25, suggesting that mui had little effect

on participant’s ability to hold their gaze. Figure 12 shows how gaze stability altered

based on the level of spatial frequency in stimuli. Eye movement peaks (around 35)

at the lowest frequency of 0.375 and then declines until 1.5, where it rises to around

32 at frequency 6. It then declines again to around 13-15 (9 λ) and rises again at 12 λ

to around 15. Figure 13 displays the gaze stability data in combination with the three

mui levels and seven levels of spatial frequency. There is a general tendency for the

highest eye level movements to appear in mui levels 150 and 300. There is most

variability in eye movement between frequency levels 0.375 and 0.75, whereas level

1 of mui peaks at frequency level 6. The level of 300 mui rises for the second time at

around 3 λ, before falling again.

Correlations were carried out between gaze stability/discomfort and gaze

stability/motion. Figure 14 displays the scatter plot for gaze stability vs discomfort. It

was found that r=-.228, p=.321. This means that as discomfort levels decreased,

more eye movement was observed. Although this demonstrates a negative

correlation, this was not significant. Figure 15 shows the correlation plot for gaze

stability vs reported motion levels. In this case, r=-.284, p=.212. As illusory motion

24

levels decreased, more eye movement occurred. This weak negative correlation was

also not significant.

Discussion: The key findings in experiment 1 were firstly that mui had a significant

relationship with both discomfort levels and perceived illusory motion reports. This

means that the level of mui was predictive of both of these measures. Spatial

frequency also had a significant relationship with both dependent variables and

predicted their outcomes in the same manner. It was also discovered that discomfort

and motion rating levels were very highly correlated.

In experiment 2, mui was found to have a significant relationship with

discomfort and motion ratings still, but to a lesser extent than in the first experiment.

Frequency still achieved statistical significance for discomfort and motion, more so

for discomfort than in the previous experiment. The correlation between discomfort

and motion was still very high. Gaze stability data was incomplete due to a technical

issue with the eye tracker, but eye tracking data for three participants still remained.

Gaze stability demonstrated very weak negative correlations with both subjective

discomfort and perceived illusory motion ratings, but was not statistically significant

in correlation with either dependent variable.

Hypothesis A predicted that increasing mui (waviness) would increase ratings

of both discomfort and perceived motion. This was found to be true until mui reached

the midpoint of 150 for both dependent variables and then declined at 300 mui. This

was the case for both experiments. We can infer from this that the effects of mui

upon both dependent variables is stronger at mid-range; not too low or too high a

waviness.

Hypothesis B stated that by increasing spatial frequency of the grating,

discomfort and motion ratings would also increase. Until mid-range, frequency also

25

had an increasing effect upon the variables and then suddenly dropped. In

experiment 2, however, discomfort ratings peaked again at the highest frequency

level. This also suggests that altering the frequency may be most effective in

initiating feelings of discomfort and creating illusory motion at mid-range (around 6),

where it was not too low nor too high to cause significant illusion or discomfort.

In experiment 2, hypothesis C predicted that observers would find it harder to

keep a steady gaze when mui and spatial frequency increased. Mui seemed to have

little effect upon eye movement, as it remained constant. However, movement in

relation to changing spatial frequency peaked and dropped on a few separate

occasions, most interestingly around mid-range to high frequency. The combination

of changing both mui and spatial frequency levels appears to have levelled out the

previous erratic effects of frequency levels alone. However, there is a general

tendency for eye movements to occur less at the higher end of the scale, particularly

for 150 and 300 μ.

Hypothesis D was that discomfort and motion ratings would positively

correlate and increase respectively to one another. This was found to be true in each

experiment, as discomfort and motion ratings appeared to have a very strong,

positive correlation to one another. However, this created the concern that perhaps

the discomfort and motion concepts used were in fact measuring the same

phenomenon.

When considering gaze stability, the fact that there was very little data is most

likely the reason for the large amount of variability in eye movements. Therefore, it is

not possible to necessarily tell whether participants were able to keep their gazes

within the fixation area. We can, however, tell that there were indeed many saccades

during the experiment, although the exact variability from the fixation point may have

been less if more data was collected. The eye tracking data collected in this

experiment was not supportive of Zanker’s motion-detector network theory, as there

26

was a weak negative correlation between gaze stability and motion – more motion

was observed by participants when the eyes moved less. This could have been due

to the small amount of usable data but could also be explained by another aspect of

Zanker’s 2003 study. The fact that saccades did not lessen in frequency when

observers were presented with control images in Zanker’s research shows that the

eyes are in a constant state of motion, as Ditchburn asserted. Due to individual

differences, some may be less susceptible to motion illusions than others. One

possible theory is that individuals find it harder to keep their gaze stable when

specifically instructed to do so. This still does not support the concept of a motion-

detector network; it is more likely that some of us experience cortical hyper-excitation

as Clarke et al suggest and that dependent on the type of stimuli, illusory motion can

be experienced.

On the other hand, in support of Zanker et al (2010), varying mui was found to

be effective on perceived motion. Furthermore, these experiments provided further

evidence that as in Clarke et al, mui had an effect upon discomfort. Interestingly, mui

demonstrated a very strong relationship with discomfort as well as motion. It can be

inferred from this that as well as spatial frequency, mui is a key factor in the

perception of optical illusions.

O’Hare and Hibbard (2011) detailed that the effect of spatial frequency on

discomfort ratings peaked at around 0.375-1.5 cycles per degree. However, this

research concluded that the peak of both discomfort and motion ratings for spatial

frequency was at 6 cycles per degree. It is a noteworthy comparison but a quite

separate finding from other research. This could have been caused by the

methodological differences between studies. For example, O’Hare and Hibbard used

a viewing distance of around 45cm, whereas specific viewing distance was not

monitored within this research

27

Fernandez and Wilkins (2008) commented that mid-range frequencies would

have a larger effect upon discomfort levels and this has indeed proved to be the

case. However, it has also proved to be the case that mid-range spatial frequency

was most effective at creating illusory motion and also that mid-range mui has the

largest effect on both discomfort and motion ratings. When both mui and spatial

frequency are at mid-range, this effect is also present. This presents an interesting

conclusion for further investigations, as mui and spatial frequency have proven to be

excellent predictors of both subjective ratings in this instance and provide a strong

predictor still when combined.

In relation to the strong positive correlation between discomfort and motion, it is

quite possible that individuals found it hard to differentiate between the two

measures. It is plausible that had a different scale (perhaps a 1-5 instead of 1-10

scale for both measures) been used, participants may have found it easier to rate

stimuli. The fact that both discomfort and motion ratings had a (non-significant) weak

negative correlation with gaze stability still implies that they may measure the same

thing. However, it is totally possible that discomfort and motion are separate

measures that both coincidentally have a very strong relationship to the chosen

variables.

In summary, the interesting findings from these experiments are that discomfort

and motion are highly correlated and that mui and spatial frequency both have strong

effects. The fact that mid-range seems to be the optimum level for causing

discomfort and illusory motion may be due to individual contrast sensitivity combined

with the low clarity of lines that can occur when the two independent variables

interact. Further study into gaze stability is needed to verify these conclusions, as the

small sample size in experiment 2 led to data distortions and more patterns may

have emerged if this issue was rectified. One large area of study that is largely

lacking in this area of study is that of actual brain activity measures. Clearer

28

conclusions could be drawn if there was an accurate picture of cortical activity in

relation to both eye tracking data and subjective measures. This could then highlight

the role in which individual differences contribute to such phenomena and provide

useful insight into the study of visual perception in psychology.

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References: Clarke A. D. F., O’Hare L., Hibbard P. B. (in preparation). Rating Riloids: Measurement and mechanisms of visual discomfort in Op Art-like images.

Ditchburn R. W. (1973). Eye-movements and visual perception. Oxford, England: Clarendon. (1973). xv 421 pp. Fernandez, D., & Wilkins, A. J. (2008). Uncomfortable images in art and nature. Perception, 37(7), 1098-1013. Khalil, N. M. (1991). Investigations of visual function in migraine using visual evoked potentials and visual psychophysical tests. PhD thesis. University of London. O’Hare, L., Hibbard, P. B. (2011). Spatial frequency and visual discomfort. Vision Research, 51(15), 1767-1777.

Riley B, 1999 The Eye's Mind: Bridget Riley. Collected Writings 1965-1999 (London: Thomas & Hudson)

Wade, N. (1978). Op Art and visual perception. Perception, 7(1), 21-46.

Wong D.L., Hockenberry-Eaton M., Wilson D., Winkelstein M.L., Schwartz P.: Wong’s Essentials of Pediatric Nursing, ed. 6, St. Louis, 2001, p. 1301. Zanker J.M., Doyle M., Walker R. (2003). Gaze stability of observers watching Op

Art Pictures. Perception, 32, 1037- 1049.

Zanker J.M., Hermens F., Walker R. (2010). Quantifying and modeling the strength

of motion illusions perceived in static patterns. Journal of Vision, 10(2):13, 1-

14.