Humans can perceive randomness in visual dot patterns. The present study examined whether there are individual differences in pattern randomness perception, focusing on the effects of age and gender. The experiment was conducted via crowdsourcing. Observers (n = 1871) were presented with 14 patterns containing various levels of randomness and luminance contrast, and were asked to estimate the degree of perceived randomness and contrast using a 100-point scale. As a result, observers aged 60 and above showed significantly lower estimates of randomness than observers aged 40 and below. Moreover, females showed significantly higher estimates of randomness and contrast than males. Estimated randomness and contrast were significantly correlated. From these results, age-related decline in visual filtering processes was considered as a possible cause of the decline in pattern randomness perception.
Keywords vision, pattern recognition, aging, individual differences
Faculty of Arts and Science, Kyushu University, Fukuoka, Japan
RecievedOct 23 2014 Accepted Dec 26 2014 Published Jan 14 2015
Citation Yamada Y (2015) Gender and age differences in visual perception of pattern randomness. Science Postprint 1(2): e00041. doi:10.14340/spp.2015.01A0002.
Copyright ©2014 The Authors. Science Postprint published by General Healthcare Inc. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs 2.1 Japan (CC BY-NC-ND 2.1 JP) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
Funding This research was supported by a Grant-in-Aid for Research Activity Start-up (#24830054), a Grant-in-Aid for Challenging Exploratory Research (#26560325), and Kyushu University Interdisciplinary Programs in Education and Projects in Research Development (#26806).
Competing interest The author has no competing interests.
Ethics statement The experiment was conducted according to the principles laid down in the Helsinki Declaration. The ethics committees of Kyushu University approved the protocol.
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Corresponding author Yuki Yamada
Address Faculty of Arts and Science, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka, 819-0395, Japan.
We can visually discriminate between regular patterns and random patterns without much effort: a grid pattern of dots clearly looks like a regular array, while a random-dot pattern clearly looks like a random array. From this, it is likely that the human visual system can process information about the randomness of a pattern. However, it is not fully clear what visual mechanism is specifically engaged in pattern randomness perception. Previous studies have proposed that some kind of visual information regarding the positional variation of dots can be used to compute pattern randomness (e.g., distance to the nearest neighbor 1, autocorrelation function of patterns 2, natural scene statistics 3, and algorithmic complexity 4). As yet, these previous studies have not identified the visual processes that correspond to such positional variation signals. Two psychophysical studies simultaneously showed that the visual system can adapt to both pattern regularity and randomness using negative aftereffects as a perceptual manifestation 5, 6. That is, a prolonged observation of regularly (randomly) arranged dot patterns made the appearance of new patterns more random (regular). Rigorous experiments on the pattern regularity/randomness aftereffects for various stimuli (e.g., patterns with first- and second-order components, different orientations, opposite luminance polarity, and spatial jitters) suggested that visual filtering plays a crucial role in processing pattern randomness.
Yamada et al. 6 demonstrated that pattern-randomness perception is produced by a filter-rectify-filter (FRF) process thought to explain how the visual system detects textures e.g., 7–10. The hypothesized FRF process for pattern randomness perception consists of three processing stages 5, 6. In the first stage, linear filters detect the orientation and spatial frequency of each dot. The second stage rectifies the outputs from the first-order filters for visual filtering at the next stage. The third stage sums the outputs from the second stage by orientation-selective second-order filters. When vertically (or horizontally) oriented second-order filters respond with high intensity at the spatial frequency corresponding to the periodicity of a dot pattern, the pattern is perceived as regular, and vice versa.
Visual perception changes during aging. For example, visual acuity 11, contrast sensitivity for first-order stimuli 12-14, contrast sensitivity for second-order stimuli 12, 14, motion perception 15, 16, and binocular depth perception 17 tend to decline with age. Considering the age-related decline in contrast sensitivity 13, 14, it is possible that pattern randomness perception also declines during aging because the final output of the FRF process is likely based on a peak of the stimulus power spectra 6, which is related to stimulus contrast.
Thus, the goal of the present study was to examine individual differences in pattern randomness perception from the point of view of the observer’s age. Random-dot patterns were presented at seven levels of stimulus randomness to observers. Perceived randomness was predicted to decrease in older observers. Moreover, random-dot patterns were presented at seven levels of luminance contrast to observers to confirm age-related decline in perceived contrast 12-14. Additionally, previous studies have reported conflicting findings regarding the effect of gender on contrast sensitivity 13, 14, 18, 19. It has also been reported that moving random-dot patterns produced gender effects in coherence threshold 16, 20, 21 and speed discrimination 21. Thus, gender effects on pattern randomness perception were also tested.
One thousand eight hundred and seventy-one Japanese participants were recruited online through a crowd-sourcing service. Data from 178 participants were discarded because they provided neither their age nor gender; thus, data from the remaining 1693 participants were submitted to analysis (mean age ± SD: 38.1 ± 9.8 years, 1061 males and 632 females). The purpose of the study was not revealed to the participants. The experiment was conducted according to the principles laid down in the Helsinki Declaration. The ethics committees of Kyushu University approved the protocol.
Stimuli consisted of 14 random-dot patterns (Figure 1). The patterns consisted of 256 (16 x 16) black dots with a radius of 2 pixels and a height and width of 272 pixels. The position of each dot was determined based on a continuous uniform probability density function with mean μ and range ω in the x and y dimensions
where X represents the position of each dot, μ represents a possible dot position when the dots were completely aligned on a grid, and ω determined the physical randomness of the pattern. A larger ω denotes a more physically random pattern and was varied at seven levels within a range of 2 to 14 pixels. The generation process of the random-dot pattern was identical to that in Yamada et al. 6.
The stimuli in the randomness condition consisted of seven patterns, in which luminance contrast was fixed at maximum (100% Michelson contrast) but varied in randomness (ω = 2, 4, 6, 8, 10, 12, and 14 pixels). The stimuli in the contrast condition consisted of seven patterns, in which randomness was fixed at a mid-level (ω = 8 pixels) but varied in luminance contrast (14, 20, 26, 33, 45, 60, and 100% Michelson contrast).
The experiment was conducted over the Internet. The stimuli were presented to participants on a computer screen. Participants in the randomness condition were asked to view the patterns that were presented one by one and to use a 100-point scale to estimate the perceived randomness (1: the most regular; 100: the most random) of each pattern without any time limit. In the contrast condition, participants were asked to estimate the perceived contrast of each pattern (1: the lowest contrast; 100: the highest contrast). The randomness and contrast conditions were separated as independent experimental blocks. The orders of the blocks and the patterns were randomized across participants.
Figure 2 shows the results of the experiment. First, the effect of age on the pattern randomness perception was analyzed. Data from 1274 participants who reported their age were divided into five groups based on their age (<30, 30 to 39, 40 to 49, 50 to 59, and ≥60 years). A two-way mixed between-within participants analysis of variance (ANOVA) was performed on “estimated randomness” with the age as a between-participants factor and the stimulus randomness (ω = 2, 4, 6, 8, 10, 12, and 14 pixels) as a within-participant factor (Figure 2a). The ANOVA revealed significant main effects of age, F(4, 1269) = 3.52, MSE = 2656.43, p <0.008, and stimulus randomness, F(6, 7614) = 80.39, MSE = 438.32, p <0.0001, and a significant interaction, F(24, 7614) = 2.57, MSE = 438.32, p <0.0001. Multiple-comparisons using a Tukey’s HSD post-hoc test revealed that estimated randomness in the ≥60-year group was significantly lower than that in the <30-year (α <0.01, Cohen’s d = 0.43) and 30 to 39-year groups (α <0.05, Cohen’s d = 0.37). The simple main effect of age was significant at five levels of stimulus randomness, ω = 2: F(4, 8883) = 4.37, MSE = 755.19, p <0.002; ω = 4: F(4, 8883) = 3.57, MSE = 755.19, p <0.007; ω = 6: F(4, 8883) = 3.26, MSE = 755.19, p <0.02; ω = 8: F(4, 8883) = 3.30, MSE = 755.19, p <0.02; ω = 14: F(4, 8883) = 4.21, MSE = 755.19, p <0.003. Moreover, one-sample t-tests revealed that the estimated randomness in four groups was significantly larger than 50, <30-year: t(234) = 5.44, p <0.0001, Cohen’s d = 0.50; 30 to 39-year: t(518) = 6.63, p <0.0001, Cohen’s d = 0.41; 40 to 49-year: t(358) = 4.03, p <0.0001, Cohen’s d = 0.30; 50 to 59-year: t(119) = 3.31, p <0.002, Cohen’s d = 0.43, but not in the ≥60-year group, t(40) = 0.51, p =0.61, Cohen’s d = 0.11.
A similar ANOVA was performed on “estimated luminance contrast” with age as a between-participants factor and stimulus luminance contrast (14, 20, 26, 33, 45, 60, and 100% Michelson contrast) as a within-participant factor (Figure 2b). The ANOVA revealed a significant main effect of stimulus luminance contrast, F(6, 7614) = 344.69, MSE = 174.46, p <0.0001, and a significant interaction, F(24, 7614) = 5.51, MSE = 174.46, p <0.0001, but the main effect of age was not significant, F(4, 1269) = 0.84, MSE = 2334.83, p = 0.50. The simple main effect of age was significant at three levels of stimulus luminance contrast, 14%: F(4, 8883) = 3.83, MSE = 483.09, p <0.005; 20%: F(4, 8883) = 4.37, MSE = 483.09, p <0.002; 26%: F(4, 8883) = 2.42, MSE = 483.09, p <0.05 and was marginally significant at two levels of stimulus luminance contrast, 33%: F(4, 8883) = 2.02, MSE = 483.09, p = 0.09; 100%: F(4, 8883) = 1.98, MSE = 483.09, p = 0.10. Moreover, one-sample t-tests revealed that the estimated luminance contrast in three groups was significantly smaller than 50, <30-year: t(234) = 4.57, p <0.0001, Cohen’s d = 0.42; 30 to 39-year: t(518) = 4.29, p <0.0001, Cohen’s d = 0.27; 40 to 49-year: t(358) = 4.64, p <0.0001, Cohen’s d = 0.35, but not in the 50 to 59-year group, t(119) = 1.56, p = 0.12, Cohen’s d = 0.20, and the ≥ 60-year group, t(40) = 0.38, p = 0.71, Cohen’s d = 0.08.
Additionally, the effect of gender on pattern randomness and luminance contrast perception was tested (Figure 3). All the data from 1693 participants were used. A mixed ANOVA was performed on “estimated randomness” with gender as a between-participants factor and stimulus randomness as a within-participants factor (Figure 3a). The ANOVA revealed significant main effects of gender, F(1, 1691) = 10.17, MSE = 2635.63, p <0.002, and stimulus randomness, F(6, 10149) = 248.14, MSE = 433.76, p <0.0001, but the interaction was not significant, F(6, 10149) = 1.56, MSE = 433.76, p = 0.15. A similar ANOVA was performed on “estimated luminance contrast” (Figure 3b). The ANOVA revealed significant main effects of gender, F(1, 1691) = 4.30, MSE = 2323.25, p <0.04, and stimulus randomness, F(6, 10149) = 1091.95, MSE = 172.71, p <0.0001, and a significant interaction, F(6, 10149) = 15.26, MSE = 172.71, p <0.0001. The simple main effect of gender was significant at three levels of stimulus luminance contrast, 45%: F(1, 11837) = 5.25, MSE = 479.93, p <0.03; 60%: F(1, 11837) = 16.99, MSE = 479.93, p <0.0001; 100%: F(1, 11837) = 28.96, MSE = 479.93, p <0.0001.
Finally, the correlation between “estimated randomness” and “estimated luminance contrast” was analyzed (Figure 4). The results showed that these responses were significantly correlated with one another (r = 0.52, p <0.0001).
The present study examined the individual difference in pattern randomness perception in terms of participants’ age. The results showed that age significantly affected the perception of random-dot patterns. That is, the estimated randomness of the stimulus patterns was significantly lower for older observers. However, it is important to note that this difference might not suggest that the patterns looked more regular for the older observers. The results of the one-sample t-tests suggest that only the responses of observers aged 60 and above were not significantly different from 50, which means the most ambiguous decision. Therefore, the results indicated that the older observers were uncertain when they estimated the randomness. Moreover, the older observers were also uncertain when they estimated the luminance contrast of the patterns. These results are consistent with the prediction. A previous study showed that contrast sensitivity decreases with increasing observer age 12-14. Other previous studies have suggested that visual processing with linear filters is crucial for pattern randomness perception 5, 6. The present study found that pattern randomness perception deteriorates during aging, and this may be related to the age-related decline in contrast sensitivity. The significant correlation between the responses for pattern randomness and luminance contrast supports this view.
An unexpected gender effect was also found. That is, females estimated both pattern randomness and luminance contrast more highly than males. This is a novel finding in pattern randomness perception. On contrast sensitivity, Owsley et al. 13 and Tang and Zhou 14 reported no gender difference in contrast sensitivity for first- and second-order stimuli. However, Dobkins et al. 18 showed that luminance contrast sensitivity was higher in 2-month-old boys than in girls, while Peterzell et al. 19 found that luminance contrast sensitivity was higher in 6-month-old girls than in boys. Although the present finding is consistent with the last case and other motion perception studies 16, 20, 21, further investigation is needed for understanding gender differences in visual perception.
What is the neural underpinning of the aging effect on pattern randomness perception? In a previous study, the primary visual area (V1) and the lateral occipital complex (LOC) were suggested as the neural correlates of pattern randomness perception 6. V1 is susceptible to the effects of aging 22-25, and previous studies have reported that the LOC is also affected by aging 26, 27. Thus, these areas are possibly involved in the age-related decline of pattern randomness perception. Neurophysiological evidence of an age-linked relationship between activity in V1 and LOC and perceived randomness should be clarified. Moreover, it may also be worth examining a gender-linked relationship between them.
The present study is preliminary. One of the reasons for this is that the experimental environment was not controlled, because the present study used online sampling. That is, displays (e.g., resolution and gamma), visual distance, and the lighting intensity of rooms were not uniform across participants, but these conditions should be controlled in the laboratory for rigorous measurements of visual perception of pattern randomness and luminance contrast. Furthermore, the discrepancy between online-measured and laboratory-measured perceptual characteristics should be indeed compared directly. Previous studies have suggested that the data quality in online experiments is as reliable as that in traditional laboratory experiments 28, 29, but only when participants correctly understood the experimenter’s instructions 30. These studies confirmed the validity of online experiments for cognitive psychology, such as reaction time tasks (e.g., flanker, inhibition of return, priming, Simon, spatial-cueing, Stroop, and stop-signal tasks), accuracy tasks (e.g., attentional blink and spatial working memory tasks), and learning tasks. However, the validity of perceptual tasks has not yet been confirmed. Moreover, while the present study used a rating method, this method did not provide information about threshold and bias, and hence it calls for the introduction of more rigorous psychophysical methods such as a staircase method 5, 6. For excluding the internal noise of an observer from scaling, a good candidate is maximum likelihood difference scaling 31. This is a robust and rigorous method to estimate perceptual differences among test images along various psychological continua 31-36. Future investigations require replicating the present study in the laboratory, or ameliorating the present study by taking advantage of the very large sample size in the online experiment to boost the reliability of the present findings.
The present study illustrated the individual difference in estimated randomness of a two-dimensional dot pattern observed in terms of observer’s gender and age: The older or male observers tended to underestimate randomness of the patterns. Results suggested that age-related decline in contrast sensitivity may account for age-related decline in pattern randomness perception. Future investigations using psychophysical and neurophysiological techniques are needed, but this study is an important step toward a better understanding of the underlying mechanism of pattern randomness perception. Moreover, the present study indicated the possibility that visual perception can be tested with crowdsourcing. Further research in combination with rigorous laboratory experiments is required for establishment of online experiments that ensure reliable data.