Trypophobic images gain preferential access to early visual processes

Consciousness and Cognition 67 (2019) 56–68

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Consciousness and Cognition journal homepage: www.elsevier.com/locate/concog

Trypophobic images gain preferential access to early visual processes

T

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Risako Shirai , Hirokazu Ogawa Kwansei Gakuin University, Japan

A R T IC LE I N F O

ABS TRA CT

Keywords: Trypophobia Awareness Continuous flash suppression

Trypophobia is a common but unusual phobia that is induced by viewing many clustered objects. Previous studies suggested that this trypophobia is caused by the specific power spectrum of the images; this idea has not been fully investigated empirically. In the present study, we used breaking continuous flash suppression (b-CFS) to clarify whether the trypophobic images affect access to visual awareness, and what features of trypophobic images contribute to rapid access of awareness. In the b-CFS paradigms, a dynamic masking pattern presented to one eye suppresses the target images shown to the other eye. The participants’ task was to indicate where the target image appeared in a dichoptic display through a mirror stereoscope. The target images consisted of trypophobic, fear-related, clusters or neutral images. The trypophobic images emerged into awareness faster than the other types of images. However, the phase-scrambled versions of the trypophobic images did not show any differences across the image types, suggesting that the trypophobic power spectra themselves did not affect access to awareness. Moreover, the phasescrambled trypophobic images without CFS tended to be detected earlier than the phasescrambled fearful and neutral images. These findings indicate that trypophobic power spectra might affect post-perceptual processing, such as response production.

1. Introduction The fear of specific objects or situations is referred to as “phobia.” In the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-V; American Psychiatric Association, 2013), phobia is defined as “a marked, persistent, and excessive or unreasonable fear when the individual is exposed to a specific object or situation.” Trypophobia involves feelings of anxiety and excessive fear associated with clusters of small holes or bumps, such as lotus seed heads, bubbles, and sea sponges. Although many people report they have trypophobia (Cole & Wilkins, 2013; Can, Zhuoran, & Zheng, 2017; Le, Cole, & Wilkins, 2015; Skaggs, 2014), it is not a specific diagnosis in the DSM-V. The trypophobic images do not involve seemingly dangerous animals or the induction of a frightening event, but they nevertheless can be a source of discomfort for many individuals. Previous studies have attempted to elucidate the causes of trypophobia (Cole & Wilkins, 2013; Can et al., 2017; Yamada & Sasaki, 2017). Several studies have suggested a relationship between the images and discomfort when looking at the Fourier power spectrum of the images (Cole & Wilkins, 2013; Fernandez & Wilkins, 2008; O’Hare & Hibbard, 2011; Wilkins et al., 1984; Sasaki, Yamada, Kuroki, & Miura, 2017). The power spectra of natural images tend to depend as 1/ f2 on the spatial frequency f (Field & Brady, 1997). Thus, when the images are transformed into a sum of sine waves with different frequencies via the Fourier transformation, their log

⁎ Corresponding author at: Department of Integrated Psychological Sciences, Kwansei Gakuin University, 1-155 Uegaharaichiban-cho, Nishinomiya-shi, Hyogo 662-0886 Japan. E-mail address: [email protected] (R. Shirai).

https://doi.org/10.1016/j.concog.2018.11.009 Received 27 July 2018; Received in revised form 16 November 2018; Accepted 25 November 2018 Available online 06 December 2018 1053-8100/ © 2018 Elsevier Inc. All rights reserved.

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contrast energy is proportional to their log spatial frequency (Field & Brady, 1997). However, the trypophobic images do not possess visual properties that are typically associated with natural images. Indeed, trypophobic images show relatively high-contrast energy at mid-range spatial frequencies compared to the images of clusters that do not cause trypophobia (Cole & Wilkins, 2013; Can et al., 2017; Le et al., 2015; Skaggs, 2014). There are at least two distinct frameworks for understanding why the deviation from a 1/f structure induces discomfort. Some studies have suggested that since the visual system has evolved to efficiently process scenes in the environment, images with atypical statistics induce discomfort, leading to aversion (e.g., Fernandez & Wilkins, 2008). However, Cole and Wilkins (2013) presented a slightly different framework, showing that images of poisonous animals (e.g., blue-ringed octopus, box jellyfish or deathstalker scorpion) possess power spectral characteristics similar to those of trypophobic images, and therefore suggested that trypophobia arises because the inducing stimuli share a core spectral feature that does not reach conscious awareness in the case of dangerous animals. However, power spectra of the images alone cannot completely account for the phenomenon. Indeed, Le et al. (2015) suggested that humans also feel discomfort in response to the trypophobic images even when the images are filtered to have a 1/f amplitude spectrum, such that the high-contrast energy at mid-range spatial frequencies is removed. These findings indicate that we might not be susceptible to only the power spectral features when we look at trypophobic images. Recently, trypophobia has also been explained by evoking the concept of cognitive appraisal (e.g., disgust). Some studies suggested that the appearance of trypophobic images is associated with potential contagious diseases (e.g., skin disease; Imaizumi, Furuno, Hibino, & Koyama, 2016; Kupfer & Le, 2017; Vlok-Barnard & Stein, 2017; Furuno, Imaizumi, Maeda, Hibino, & Koyama, 2017, Yamada & Sasaki, 2017; Ayzenberg, Hickey, & Lourenco, 2018). For example, Ayzenberg et al. (2018) demonstrated that when looking at the trypophobic images, the pupil responses showed a similar pattern to when looking at disgusting stimuli. Thus, such an association with contagious disease might lead to aversive responding toward harmless objects such as lotus seed heads or bubbles. Thus, although there are several accounts of factors that could explain trypophobia, trypophobia has yet to be fully investigated empirically. To date, experimental studies focusing on the visual-perceptual processing of trypophobic stimuli are lacking. Because the capacity of our sensory systems is limited, not all visual inputs from our environment access conscious percepts. Therefore, sensory systems are thought to selectively process the most significant visual inputs. It is widely known that threat signals are privileged in human visual perception, including detection (Devue, Belopolsky, & Theeuwes, 2011; Fox, Russo, Bowles, & Dutton, 2001; LoBue & DeLoache, 2008; Nummenmaa, Hyönä, & Calvo, 2006; Öhman, Flykt, & Esteves, 2001; but see Bar-Haim, Lamy, Pergamin, Bakermans-Kranenburg, & van IJzendoorn, 2007) and access to conscious awareness (Yang, Zald, & Blake, 2007; Morris, Öhman, & Dolan, 1998; Whalen et al., 1998; Morris, DeGelder, Weiskrantz, & Dolan, 2001). Our sensory systems might be particularly sensitive to the trypophobic images and such images therefore might affect visual experiences. The purpose of the present study was to reveal whether trypophobic images gain preferential access to awareness and, if so, what image-related factors facilitate this speed of access to awareness. We used a breaking continuous flash suppression (b-CFS) paradigm. The CFS is a technique for suppressing the conscious percept of visual stimuli (Tsuchiya & Koch, 2005; Stein, Hebart, & Sterzer, 2011; Jiang, Costello, & He, 2007). In the b-CFS paradigm, the dynamic masking pattern is presented to one eye, which can suppress the awareness for a target image presented to another eye until the target image breaks the suppression (for a review, see Gayet, Van der Stigchel, & Paffen, 2014). By measuring target image detection times, researchers have investigated the time it takes for a target images to reach conscious awareness. For example, Gayet, Paffen, Belopolsky, Theeuwes, and Van der Stigchel (2016) associated one of two colored annuli with aversive stimulation (i.e., electric shock), and then asked their participants to discriminate the orientation of the colored annuli appearing on a dichoptic display through a mirror stereoscope. They showed that the colored annuli that were paired with an electric shock were discriminated more rapidly than the colored annuli that were not paired with an electric shock, suggesting that the threat signals affected the speed of access to awareness. As noted above, b-CFS paradigms have assumed that response time differences could be attributed to differential unconscious processing during suppression. However, the response times in the b-CFS paradigm are susceptible to the influence of conscious detection or response criteria (Jiang et al., 2007; Stein et al., 2011; Gayet et al., 2014). For example, under normal viewing, the threshold of conscious detection can be determined by the speed of visual processing, and this speed decreases under difficult viewing conditions (e.g., cluttered scenes, Stein et al., 2011), which could also happen under CFS. In addition, it is possible that certain properties of the images affect response times in the partial awareness state around the threshold. Therefore, it is necessary to dissociate the contributions of unconscious processing and “post-perceptual” processing in detection performance. We used a nonsuppression condition that was designed to perceptually resemble the suppression condition based on Jiang et al. (2007), which was assumed to involve “post-perceptual” factors equivalent to those operating in the suppression condition. If RT differences in the nonsuppression condition were comparable to those in the suppression condition, it would imply that both response times reflect “postperceptual” differences. Moreover, if RT differences in the suppression condition were larger than those in the non-suppression condition, we could conclude that RT differences in the suppression condition reflects both the effects of unconscious processing as well as any “post-perceptual” processing. Therefore, the interpretation of b-CFS results would crucially depend on the comparison of non-suppression and suppression conditions (Stein et al., 2011). We presented target images to the non-dominant eye through a mirror stereoscope in two experiments, such that their contrast increased linearly while dynamic masking patterns were presented to the dominant eye. Participants were asked to press a left or right key to discriminate where the target image appeared on the dichoptic display. We measured the response times until the key was pressed as a measure of the time for accessing the conscious percepts of visual stimuli. We compared response times for trypophobic images with that for fear-related, cluster, and neutral images. As discussed above, if the trypophobic images have ecological value, we would expect preferential access to awareness for trypophobic images during CFS compared to neutral images. There are two possible mechanisms through which trypophobic images might gain prioritized access to awareness. First, it is possible that the valence or arousal for trypophobic images is evaluated prior to reaching visual awareness, and 57

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such emotional evaluation of trypophobic images might affect the speed of access to awareness. Second, it is possible that low-level features, such as the high contrast physical signals attributed to clusters, might induce rapid access to awareness for the trypophobic images. In the former case, subjective valence plays a critical role in facilitating access to awareness for trypophobic images. Therefore, we would expect that trypophobic and fear-related images would similarly facilitate speed of access to awareness. In the latter case in which the appearance of clusters would explain the fast detection of trypophobic images, we would expect a similar advantage for trypophobic and cluster images featuring clusters of holes or bumps. Importantly, we predict that trypophobic images would be detected more rapidly than fear-related or cluster images, if the appearance of clusters and cognitive appraisal in trypophobic images synergistically affected the privileged access to consciousness. 2. Experiment 1 2.1. Methods 2.1.1. Participants We conducted a power analysis (G*power; Faul, Erdfelder, Lang, & Buchner, 2007; Faul, Erdfelder, Buchner, & Lang, 2009) using the effect size (f) in previous studies (e.g., Yang et al., 2007) as references. Based on the effect size (f) of 0.40, which was proposed as a large effect size by Cohen (1992), it was decided that a minimum of 17 participants was needed to achieve a power level of 0.90. Thus, 22 graduate and undergraduate students from the Kwansei Gakuin University (5 male and 17 females, mean age = 19.33 years) participated in Experiment 1. All participants reported having normal vision or corrected-to-normal vision and provided their informed consent. The participants were seated 60 cm from a computer display and viewed a pair of dichoptic displays through a mirror stereoscope. Ocular dominance of each participant was determined by the Dolman method (similar to Jiang et al., 2007) at the beginning of the experiments. 2.1.2. Stimuli 2.1.2.1. Target images. In Experiment 1, the target images consisted of 20 trypophobic, 20 fear-related, 20 clusters, and 20 neutral images. The trypophobic images consisted of clustered objects such as a lotus seed head (Fig. 1A) and were selected from a Web site (www.trypophobia.com) that exhibits images frequently associated with trypophobia. The cluster images depicted clustered configurations of various stimuli such as perforated metal, and were selected from a Google image search but not published on the Web site (www.trypophobia.com). The fear-related images consisted of potentially fear-inducing animals, such as bears or sharks. The neutral images were those of emotionally neutral objects (e.g., kitchen tools). The fear-related and neutral images (see Appendix A) were selected from the International Affective Picture System (IAPS; Lang, Bradley, & Cuthbert, 2005). All target images were converted to gray scale, resized to 3.2° × 3.2°, and adjusted in mean intensity and root mean square contrast (RMS contrast) using the SHINE toolbox (Willenbockel et al., 2010) in MATLAB R2015a (Mathworks, Natick, MA). Prior to the experiments, we confirmed the power spectrum of the images in the same way as Cole and Wilkins (2013). We applied a Hanning window to the images to remove edge effects, and the fast-Fourier-transform algorithm was applied to all images. Spatial frequencies were divided into bins, and the power spectrum of images summed in bins of spatial frequencies. We calculated the percentage variance of the power spectrum of each image type to confirm whether the power spectra of the images deviated from the 1/f slope. If the variance of the power spectrum is a low percentage, this means that the power spectra of the images deviate from the 1/f slope and are unusual natural images. A one-way ANOVA with the factor of target image (trypophobic, fear-related, clusters, or neutral) conducted on percentage variance revealed a main effect of target image, F(3, 76) = 10.07, p < .001, ηp2 = 0.28. The percentage variance of trypophobic images (63.98%) was lower than of fear-related (81.38%), t(76) = 3.45, p < .01, d = 1.45, and neutral images (85.28%), t(76) = 4.22, p < .001, d = 1.88. The percentage variance did not differ between trypophobic and clusters images (63.81%), t(76) = 0.03, p = .97, d = 0.008. Importantly, the trypophobic images had an excess of a characteristic power spectrum at 16.00–32.00 cycles per image (cpi) compared to the other images. The clusters images have more contrast energy at 22.63–32.00 cpi compared to neutral images, but this energy is smaller than that of the trypophobic images. These findings suggest

Fig. 1. The target images in Experiment 1 are illustrated. Image of a lotus seed head as an example of a trypophobic image (A), a spider as an example of a fearful image (B), perforated metal as an example of a cluster image (C), waterfall as an example of a neutral image (D). 58

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Fig. 2. The target images in Experiment 2 are illustrated. All images are phase-scrambled versions of the images in Fig. 1(A–D).

that the percentage variance of the trypophobic images (63.81%) reflected the greater high contrast energy at 16.00–32.00 cpi, which is consistent with Cole and Wilkins’s findings that the trypophobic images tend to have high contrast energy at midrange spatial frequencies compared to clusters images that do not cause trypophobia. Thus, the trypophobic images have unusual characteristic properties compared to those of other natural images. 2.1.2.2. Mask image. The pair of dichoptic displays consisted of high-contrast, colored dynamic masking patterns that changed at a rate of 10 Hz to suppress awareness for the target image. The mask image is a Mondrian-like pattern and similar to the masks used by Lapate et al. (2016). The mask consisted of 100 colored patches that are converted from RGB color space. The shapes of each patch were rectangular and were presented at a random height (20–100 pixels) and width (20–100 pixels). 2.1.3. Procedure In Experiment 1, the participants engaged in a b-CFS session and then an evaluation session. The b-CFS session consisted of suppression and non-suppression conditions (Fig. 2). 2.1.3.1. CFS session. In the suppression condition, each trial began with a fixation-cross presented to both eyes (Fig. 2A). The target image was presented to the participant’s non-dominant eye and the dynamic masking pattern was presented to the other eye. The location of the target image was at the left or right side of the fixation cross, at 1.9° from fixation, measured from the center of the images (see Fig. 3). The target image contrast linearly increased from 0% to 100% over a period of 4000 ms. After the contrast of the target image reached 100%, that of the masking pattern gradually decreased from 100% to 0% over 5000 ms. Each trial ended when the participants responded or when 9000 ms elapsed from the trial onset. The participants were instructed to press a left or right key to indicate the location of the target image. To confirm the possibility that performance differences between image types might reflect processes that are irrelevant to CFS, such as the conscious detection of the target or response initiation to the target (Jiang et al., 2007; Stein et al., 2011), we included a non-suppression condition as described above. The procedure of the non-suppression condition was identical to that of the suppression condition, with the exception that the target image and the masking pattern were presented to both eyes (Fig. 3B).

Fig. 3. Each trial of the breaking continuous flash suppression session (A: suppression condition, B: non-suppression condition). (A) The target image gradually emerged into the non-dominant eye visual field to compete with a masking pattern such as a Mondrian presented to the dominant eye. The contrast of the target image was linearly ramped up from 0% to 100% within 4000 ms of the beginning of the trial; the contrast of the masking pattern decreased from 100% to 0% for 5000 ms. (B), the target image and the masking pattern were presented to both eyes. 59

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Fig. 4. Response times for target images in the suppression condition (left panel) and non-suppression condition (right panel). Error bars represent the standard error of the mean (SEM). *** p < .001, ** p < .01 * p < .05.

Both the suppression and non-suppression conditions included 80 trials. The order of the conditions was counterbalanced across participants. Target image types were intermixed, with the same number of trials for each type. 2.1.3.2. Evaluation session. Subsequently, we conducted an evaluation session where the participants were asked to evaluate the valence and arousal of the images that were presented in the b-CFS session by using the affect grid method (Russell, Weiss, & Mendelsohn, 1989). On each trial, an image (11° × 11°) appeared at the center of the display for 5000 ms, followed by a presentation of the affect grid. The participants evaluated the valence and arousal of the images by mouse-clicking one of the cells of the grid. The horizontal axis of the grid indicated level of valence (from 1 = unpleasant to 9 = pleasant), and the vertical axis indicated level of arousal (from 1 = sleepy to 9 = high arousal). The participants completed 80 trials (20 trypophobic, 20 fear-related, 20 clusters, and 20 neutral images). 2.2. Results 2.2.1. b-CFS session The experimental trials on which the participants made incorrect responses were excluded from the analysis. The error rates were

Fig. 5. CPD (cumulative probability distribution) of response times in the suppression condition (left panel) and non-suppression condition (right panel). 60

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low (< 2.22%) both in the suppression and non-suppression conditions. Fig. 4 (left panel) shows the response times for each target image type in the suppression condition (see also Fig. 5 left panel). The response times were subjected to a one-way ANOVA with a factor of image type (trypophobic, fear-related, clusters, or neutral). The main effect of image type was significant, F(1, 21) = 23.80, p < .001, ηp2 = 0.53. Multiple comparisons using the Holm method and a Bayesian paired-sample t test (JASP Team, 2018) showed that the fear-related (M = 2943.33 ms, SD = 1379.35) and cluster images (M = 3071.94 ms, SD = 1629.44) emerged from suppression significantly faster than neutral images (M = 3485.66 ms, SD = 1553.49), t(21) = 5.87, p < .001, d = 0.37, BF10 = 2720.19, t (21) = 2.68, p < .05, d = 0.26, BF10 = 3.72. Importantly, the trypophobic images (M = 2471.77 ms, SD = 1332.67) emerged from suppression significantly faster than the fear-related, t (21) = 4.48, p < .001, d = 0.09, BF10 = 144.01, the cluster, t(21) = 6.24, p < .001, d = 0.70, BF10 = 5829.13, and the neutral images, t(21) = 6.96, p < .001, d = 0.35, BF10 = 24691.86. These findings demonstrated that the trypophobic images modulated the speed of access to awareness. Fig. 4 (right panel) shows the response times for each image type in the non-suppression condition (see also Fig. 5 right panel). The response times were subjected to a one-way ANOVA with a factor of image type (trypophobic, fear-related, clusters, or neutral). The main effect of image type was significant, F(1, 21) = 24.16, p < .001, ηp2 = 0.54. The main effect of image types was examined with a paired-sample t test using the Holm method and a Bayesian paired-sample t test (JASP Team, 2018). Further analyses showed that the fear-related (M = 1358.95 ms, SD = 442.42) and clusters image (M = 1367.87 ms, SD = 405.10) were detected faster than neutral images (M = 1479.18 ms, SD = 461.59), t(21) = 3.80, p < .01, d = 0.27, BF10 = 34.07, t(21) = 4.18, p < .01 d = 0.26, BF10 = 76.90. Moreover, the trypophobic images (M = 1215.86 ms, SD = 338.27) were detected faster than the fear-related, t (21) = 4.65, p < .001, d = 0.27, BF10 = 208.54, the cluster, t(21) = 6.15, p < .001, d = 0.65, BF10 = 4830.47, and the neutral images, t(21) = 7.24, p < .001, d = 0.36, BF10 = 42045.05. The trypophobic images still affected response times even when the image types were not suppressed by the masking pattern; however, this effect was smaller than that observed under suppression. We conducted an additional analysis to directly compare the advantages of images on response times between the suppression and non-suppression conditions. First, we normalized mean RTs to trypophobic, fear-related, and cluster images by using the mean RTs to neutral images. Then, we compared the suppression and non-suppression conditions using a 2 (suppression and non-suppression) × 3 (trypophobic, fear-related, and clusters) within-subjects ANOVA with the Holm method. The results indicated a main effect of suppression type, F(1, 21) = 17.16, p < .001, ηp2 = 0.45, with the mean RTs in the suppression condition larger than that in the non-suppression condition. Moreover, there was a main effect of image type, F(1, 21) = 26.65, p < .001, ηp2 = 0.56. A pairedsample t test using the Holm method and a Bayesian paired-sample t test (JASP Team, 2018) revealed that the mean RTs to trypophobic images was longer than that to fear-related and cluster images t(21) = 5.81, p < .001, d = 0.59, BF10 = 3059.61, t (21) = 7.23, p < .001, d = 0.65, BF10 = 107702.710. No significant statistical differences were found between the mean RTs to fearrelated and cluster images, t(21) = 1.16, p = .26, d = 0.15, BF10 = 0.29. Importantly, we found a significant interaction between suppression type and image type, F(1, 21) = 8.52, p < .01, ηp2 = 0.29. Especially, the mean RTs to trypophobic images in the suppression condition was larger than that in the non-suppression condition, F(1, 21) = 22.31, p < .001, ηp2 = 0.52. In addition, the mean RTs to fear-related and cluster images in the non-suppression condition was smaller than that in the suppression condition, F(1, 21) = 26.03, p < .001, ηp2 = 0.55, F(1, 21) = 3.62, p = .07, ηp2 = 0.15. Our results indicated that the advantage of each image type (trypophobic, fear-related, and cluster images) on response times during the suppression condition was greater than the advantage of each image during the non-suppression condition. Moreover, the results showed a main effect of image type in the suppression, F(1, 21) = 17.49, p < .001, ηp2 = 0.45, and nonsuppression conditions, F(1, 21) = 15.86, p < .001, ηp2 = 0.43. In the suppression condition, the mean RTs to trypophobic images were significantly larger than that to fear-related and cluster images, t(21) = 4.48, p < .001, d = 0.82, BF10 = 144.01, t(21) = 6.24, p < .001, d = 0.85, BF10 = 5829.13. In non-suppression condition, the mean RTs to trypophobic images were also significantly larger than that to fear-related and cluster images, t(21) = 4.65, p < .001, d = 0.89, BF10 = 208.54, t(21) = 6.15, p < .001, d = 1.02, BF10 = 4830.47. Furthermore there were no significant statistical differences between the mean RTs to fear-related and cluster images, ts(21) < 0.26, ps < 0.80, ds < 0.22, BF10 > 0.38 in either condition. The rapid detection of trypophobic images were repeatedly observed in both suppression and non-suppression conditions. 2.2.2. Evaluation session Fig. 6 (left panel) shows mean valence scores for each image type. A one-way within-subjects ANOVA on valence scores (trypophobic, fear-related, clusters and neutral) revealed a main effect of image type, F(1, 21) = 57.95, p < .001, ηp2 = 0.73. Post-hoc tests of this main effect using the Holm method and Bayesian paired-sample t test (JASP Team, 2018) showed that the valence scores of neutral images (M = 6.05, SD = 1.15) were significantly higher than those of trypophobic (M = 2.95, SD = 1.17), t(21) = 10.66, p < .001, d = 2.67, BF10 = 43.93, fear-related (M = 3.25, SD = 1.09), t(21) = 9.31, p < .001, d = 2.51, BF10 = 60.39, and cluster images (M = 4.10, SD = 1.17), t(21) = 7.70, p < .001, d = 1.69, BF10 = 38.66. Moreover, valence scores of cluster images were significantly higher than those of trypophobic, t(21) = 8.03, p < .001, d = 0.98, BF10 = 4.09, and fear-related images, t(21) = 3.14, p < .01, d = 0.75, BF10 = 0.40. These results suggested that the trypophobic images caused negative emotion in line with the claim of Cole and Wilkins (2013), who argued that the trypophobic images induce discomfort because they have excess power energy at spatial frequency mid-ranges. Further, the valence scores did not differ between fear-related and trypophobic images, t(21) = 1.12, p = .27, d = 0.26, BF10 = 2.13. Thus, we confirmed that the fear-related images (including the scary animals) were evaluated negatively, even though the fear-related images did not share core spectral features with the trypophobic images. Fig. 6 (right panel) shows mean arousal scores for each image type. We conducted a one-way within-subjects ANOVA on arousal scores (trypophobic, fear-related, clusters and neutral), and revealed a main effect for image types, F(1, 21) = 32.71, p < .001, 61

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Fig. 6. Evaluation task results showing valence score (left panel) and arousal score (right panel) for the target images in Experiment 1. Error bars represent the standard error of the mean (SEM). *** p < .001, ** p < .01.

ηp2 = 0.61. We performed a post hoc comparisons using the Holm method and Bayesian paired-sample t test (JASP Team, 2018), which showed that the arousal scores for neutral images (M = 2.94, SD = 1.29) were significantly lower than those of trypophobic (M = 4.96, SD = 1.57), t(21) = 5.90, p < .001, d = 1.40, BF10 = 0.60, fear-related (M = 5.31, SD = 1.31), t(21) = 8.82, p < .001, d = 1.82, BF10 = 0.73, and cluster images (M = 4.32, SD = 1.26), t(21) = 5.93p < .001, d = 1.08, BF10 = 3.71. Moreover, arousal scores for clusters images were significantly lower than those of trypophobic, t(21) = 3.31, p < .01, d = 0.45, BF10 = 0.29, and fearrelated images, t(21) = 4.66, p < .001, d = 0.77, BF10 = 0.47. These results suggested that the trypophobic images were evaluated with more arousal than neutral or cluster images. Furthermore, arousal scores did not differ between fear-related and trypophobic images, t(21) = 1.30, p = .21, d = 0.25, BF10 = 0.26, demonstrating that participants evaluated both trypophobic and fear-related images as high arousal images.

2.3. Discussion Under CFS, the trypophobic images emerged into awareness more rapidly than the other types of images. Importantly, our data indicated that the trypophobic images were detected more rapidly than the fear-related images. Since the arousal and valence scores did not differ between the fear-related and the trypophobic images, it is plausible that negative emotion alone cannot explain the preferential access to awareness for the trypophobic images. Moreover, the trypophobic images were detected more rapidly than the cluster images, suggesting that cluster visibility alone cannot explain the preferential access to awareness of the trypophobic images. Our data showed that the fear-related images accessed awareness faster than the neutral images. These findings are consistent with Gomes, Soares, Silva, and Silva (2017), who showed that threatening animals (e.g., snakes) accessed awareness faster than nonthreatening animals (e.g., birds). In addition, the cluster images were detected more rapidly than the neutral images. Because the clusters were evaluated more negatively than the neutral images, such negative emotion might influence whether a stimulus enters conscious awareness. Moreover, a possible contributor to this finding might have been the repetitive spatial patterns. Conlon, Lovegrove, Chekaluk, and Pattison (1999) suggested that our visual system is sensitive to repetitive spatial patterns such as square waves. Given that the cluster images included such repetitive spatial patterns, such cluster patterns might facilitate access to awareness under CFS. In the present experiment, the reaction time difference among the image types was observed not only in the suppression condition but also the non-suppression condition. One may consider that these differences were not due to rapid access to awareness, but instead due to changes in post-perceptual factors, such as a decision or response criterion. However, the effects of the image type were much smaller in the non-suppression condition than those in the suppression condition. Thus, it is difficult to assume that the RT difference reflects only such “post-perceptual” differences. Rather, as noted in the Introduction (see also Stein et al., 2011), although image type may affect “post-perceptual” processing, it should play an important role in changing the speed of access to awareness. Still, since the RT patterns between the suppression and non-suppression conditions would look highly similar, one might argue that 62

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the larger RT differences in the suppression condition are simply due to the longer overall RTs (i.e., a condition with longer RTs tends to yield larger RT differences). This possibility will be discussed in the Discussion section for Experiment 2. Experiment 1 suggested that the trypophobic images enjoy a privilege of reaching consciousness earlier. Several studies have suggested that the power spectra of the trypophobic images are a key predictor for inducing trypophobia (e.g., Cole & Wilkins, 2013; Van Strien & Van der Peijl, 2018). Thus, it is likely that the power spectra of the trypophobic images are responsible for such privileged processing. However, we have not examined whether the power spectra of the trypophobic images themselves are sufficient to gain preferential access to awareness. 3. Experiment 2 In Experiment 2, we investigated whether the unique power spectra of trypophobic images were sufficient to affect access to awareness. To accomplish this purpose, we manipulated the phase spectra utilizing the phase-scramble procedure, which changes the phase structures of an image but preserves the power spectra. A Fourier transform converts an image to power and phase spectral features. In particular, the phase spectra determine visual structures of images, such as edges and curves (Banno & Saiki, 2015; Kovesi, 2000). Therefore, the phase scrambling was expected to affect the identification of an image content. If the trypophobic power spectra were a key predictor of the speed of access to awareness, the trypophobic nature of images were expected to affect detection times, even if the phase information were scrambled. 3.1. Method 3.1.1. Participants Based on the power analysis (G*power; Faul et al., 2007; Faul et al., 2009) identical to Experiment 1, twenty-one undergraduate students (3 males and 18 females, mean age = 19.57 years) participated in Experiment 2. One male participant was excluded from the following analysis because he did not complete the entire experiment. All participants reported having normal vision or corrected-to-normal vision and provided their informed consent. 3.1.2. Stimuli In Experiment 2, we applied the phase-scramble technique to the images used in Experiment 1 (Fig. 1B). The randomized phase structures and the original power spectra were synthesized, and then phase-scrambled images were created by the inverse-Fouriertransform-algorithm. Through this procedure, the phase spectra were replaced with a random phase value, whereas power spectra were preserved. We created 20 phase-scrambled trypophobic images, 20 phase-scrambled fear-related images, 20 phase-scrambled cluster images, and 20 phase-scrambled neutral images. 3.1.3. Procedure The procedure was identical to that of Experiment 1, with the exception that all image types were converted to phase-scrambled images. 3.2. Results 3.2.1. b-CFS session The error trials were excluded from the analysis. The error rates were very low (< 1.69%) for both the suppression and nonsuppression conditions. Fig. 7 (left panel) shows response times for each image type in the suppression condition (see also Fig. 8 left panel). Response times were subjected to a one-way ANOVA with a factor of image type (trypophobic, fear-related, clusters, or neutral). The main effect of image type was not significant, F(1, 19) = 3.18, p = .09, ηp2 = 0.14. These results imply that image type did not affect the response times in the suppression condition. The power spectra of the images themselves might not actually affect access to awareness. Fig. 7 (right panel) shows response times for each image type in the non-suppression condition (see also Fig. 8 right panel). Response times were again subjected to a one-way ANOVA with a factor of image type (trypophobic, fear-related, clusters, or neutral). This time the main effect of image type was significant, F(1, 19) = 14.48, p < .01, ηp2 = 0.43. The main effect of image type was further examined with a paired-sample t test using the Holm method and Bayesian paired-sample t test (JASP Team, 2018). Analyses showed that the neutral images (M = 1391.27 ms, SD = 420.94) were detected more slowly than the trypophobic images (M = 1173.48 ms, SD = 286.76), fear-related images (M = 1249.01 ms, SD = 353.12), and cluster images (M = 1235.22 ms, SD = 340.19), ts(19) > 3.99, ps < 0.01, BF10 > 45.20. Moreover, the trypophobic images tended to be detected earlier than the fear-related images. The response times did not differ between fear-related images and cluster images, or between trypophobic images and cluster images. These results suggest the possibility that image power spectra do affect post-perceptual factors. 3.2.2. Evaluation session Fig. 9 (left panel) shows mean valence scores for each image type. A one-way within-subjects ANOVA on valence scores (trypophobic, fear-related, clusters, or neutral) revealed a significant main effect of image type, F(1, 19) = 13.17, p < .01, ηp2 = 0.41. Paired-sample t tests (Holm method) and Bayesian paired-sample t test (JASP Team, 2018) showed that the valence scores of neutral 63

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Fig. 7. Response times for phase-scrambled versions of the target images in the suppression condition (left panel) and non-suppression condition (right panel). Error bars represent the standard error of the mean (SEM). ** p < .01.

Fig. 8. CPD (cumulative probability distribution) of response times in the suppression condition (left panel) and non-suppression condition (right panel).

images (M = 4.31, SD = 1.27) were significantly larger than those for trypophobic images (M = 3.08, SD = 0.89), t(19) = 3.98, p < .01, d = 0.39, BF10 = 43.93, fear-related images (M = 3.53, SD = 0.95), t(19) = 4.13, p < .01, d = 0.14, BF10 = 60.39, and cluster images (M = 3.41, SD = 0.82), t(19) = 3.91, p < .01, d = 0.84, BF10 = 38.66. These results show that the neutral images were evaluated as more pleasant than the other types of images. Moreover, the valence scores of trypophobic images tended to be smaller than those for clusters, t(19) = 2.74, p < .05, d = 1.13, BF10 = 4.09, and fear-related images, t(19) = 2.36, p = .06, d = 0.49, BF10 = 2.13. However, the valence scores did not differ between fear-related and clusters images, t(19) = 1.12, p = .28, d = 0.70, BF10 = 0.40. These findings indicated that the participants rated the trypophobic images as more unpleasant than the other image types. Moreover, the fear-related and cluster images were evaluated as more unpleasant than the neutral images. Since the phase-scrambled images were no longer identifiable as meaningful emotional scenes, this unpleasantness would not be due to the appearance of the images. Fig. 9 (right panel) shows mean arousal scores for each image type. The arousal scores were subjected to a one-way ANOVA with a factor of image type (trypophobic, fear-related, clusters, or neutral). The main effect of image type was not significant, F(1, 19) = 2.10, p = .16, ηp2 = 0.10. These results show that the trypophobic power spectra themselves do not affect arousal scores.

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Fig. 9. Evaluation task results showing valence score (left panel) and arousal score (right panel) of the phase-scrambled target images in Experiment 2. Error bars represent the standard error of the mean (SEM). ** p < . 001,* p < .05.

3.3. Discussion The four types of phase-scrambled image did not show any response time differences in the b-CFS task, suggesting that the power spectra of the images themselves did not affect the speed of access to awareness. Unlike Experiment 1, we observed the RT differences between the image types only in the non-suppression condition, but not in the suppression condition. If the RT differences between the image types in the suppression condition of Experiment 1 can be simply explained by longer overall response times relative to the non-suppression condition, we should have observed the similar differences in the suppression condition of Experiment 2. Thus, the results support the notion that the detection performances in the suppression condition reflect modulation of the speed of access to awareness. The phase-scrambled trypophobic images were evaluated more negativity than other types of image. This finding was consistent with Cole and Wilkins (2013) and indicated that the images with specific power spectra cause discomfort. Moreover, this negative emotion seemed to modulate the response times for trypophobic images in both suppression and non-suppression conditions. One may wonder why the trypophobic phase-scrambled images were evaluated as more negative than other types of images, although they had comparable arousal ratings. This could be because phase-scrambling made image content highly ambiguous. A previous study has demonstrated that ambiguity of images differently affected arousal and valence scores (De Cesarei & Codispoti, 2008). Participants in this study evaluated blurred images as less pleasant and less arousing than intact images, and, more importantly, the neutralization of affective evaluation for blurred images was more evident in arousal ratings. Thus, it is plausible that the phase-scrambling procedure adopted in the present experiment might have particularly neutralized the arousal rating of images, such that differences between image types were observed only in the valence ratings.

4. General discussion The purpose of the present study was to reveal whether and how trypophobic images gain faster access to awareness. We found that trypophobic images emerged into awareness more rapidly than the other types of images tested, which is in line with the idea of a threat-advantage effect on access to visual awareness (Gayet et al., 2016; Tsuchiya, Moradi, Felsen, Yamazaki, & Adolphs, 2009; Yang et al., 2007). However, this advantage diminished when the images were phase-scrambled, suggesting that the trypophobic power spectra themselves are not sufficient to facilitate emergence into awareness. It is possible that the trypophobic power spectra might affect unconscious processing by interacting with the image phase structures. However, the present study did not examine whether the phase spectra of the trypophobic images themselves affect access to awareness. Further studies should examine this possibility. Such experiments would lead to improved understanding of the visual processing that is specific to trypophobic images. As expected, intact trypophobic images preferentially accessed awareness as compared to fear-related and cluster images. What could explain this privileged process for trypophobic images? A possible contributor to this finding might have been the implicit 65

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affective processing of the trypophobic images. For rapid defensive responses to potential sources of the pathogen, it is believed that the perceptual system has evolved to be sensitive to perceptual cues indicating the presence of contagious disease (e.g., Schaller & Park, 2011). Thus, it is plausible that the trypophobic patterns might stimulate such perceptual biases without conscious awareness and therefore alter the suppression durations. Another possible explanation concerns a combined effect of negative emotion and cluster patterns. In the present study, the fear-related and cluster images were detected more rapidly than the neutral images, suggesting that negative emotion associated with the fear-related images and the visual patterns of the cluster images might each convey an advantage for accessing awareness. Since the trypophobic images enjoy both factors, it is likely that these factors interact and then facilitate the speed of emergence from CFS suppression. Note that these explanations are not mutually exclusive. Future research should further examine the mechanisms underlying privileged access to awareness for the trypophobic images. We suggest that trypophobic power spectra themselves might affect “post-perceptual” processing. The power spectra affected the response times in the non-suppression condition, but not in the suppression condition. Several studies that are in line with our findings have suggested that spatial frequencies influence post-perceptual processing (Seijdel, Jahfari, Groen, & Scholte, 2018; Jahfari, Ridderinkhof, & Scholte, 2013). For example, Jahfari et al. (2013) suggested that when participants were instructed to categorize the gender of face images that were filtered to include mostly low, high, or broad spatial frequencies, the types of available spatial frequencies differentially influenced the speed of voluntarily controlled processes such as response initiation or inhibition. Similarly, it is likely that the spectral composition of trypophobic images might influence the processing of response selection after gaining access to awareness. If the power spectra of images really affected post-perceptual processing, these post-perceptual processes should be observed as RT differences not only in the non-suppression condition but in the suppression condition of Experiment 2 as well. That is, while the non-suppression condition has been developed to selectively capture non-suppression processes, the response times in the suppression condition include the pre-perceptual as well as post-perceptual differences. However, we did not observe such differences in the suppression condition of Experiment 2. We speculate that the power spectra of images might have affected post-perceptual processes (and thereby RTs) in the suppression condition to a similar extent as in the non-suppression condition, but that the small effect sizes elicited by these processes could not be reliably detected within the response time variance in the suppression condition of Experiment 2. Of course, suppression and non-suppression conditions also differ with regard to a number of other factors such as local contrast between stimuli and background (Stein et al., 2011). Therefore, it is suggested that future studies should clarify the effect of visual images’ power spectra on post-perceptual processing. Several limitations of this study need to be described. The first is that this study did not assess emotional responses when presenting images under CFS. Physiological methods might be useful for clarifying the issue of emotional responses. For example, previous studies have reported that the presence of threat images induced the skin conductance responses (SCR) under CFS (Raio, Carmel, Carrasco, & Phelps, 2012; Lapate, Rokers, Li, & Davidson, 2014; but see Hedger, Adams, & Garner, 2015). Moreover, fMRI studies have indicated that differential amygdala activation to threat and neutral images were also observed under binocular rivalry and CFS (e.g., Jiang & He, 2006; Pasley, Mayes, & Schultz, 2004; Vizueta, Patrick, Jiang, Thomas, & He, 2012). Measuring responses in central and peripheral nervous systems during CFS would further advance our understanding of the early visual processing of trypophobic stimuli. The second limitation concerns the possibility that our results might be specific to the image stimuli used in the current experiments. We can further strengthen the contention that processing trypophobic images is prioritized in early visual processing by using other images in future studies. Furthermore, certain previous studies have suggested that response times under CFS were partially determined by interactions between masking patterns and suppressed stimuli in low-level features (e.g., Yang & Blake, 2012; Han & Alais, 2018). Thus, utilizing other types of masking patterns in future works is expected to increase the generalizability of our findings. In summary, the present study demonstrated that trypophobic images enjoy a privilege of rapid access to visual awareness. Moreover, it appears that neither negative emotion nor cluster patterns alone can account for this advantage. Furthermore, our findings indicate that the trypophobic power spectra themselves are not sufficient to facilitate emergence into awareness. We propose the possibility that the combined effect of the negative emotion and cluster patterns or the implicit affective processing of the trypophobic images is responsible for the rapid access to awareness. Author note Risako Shirai, Department of Integrated Psychological Sciences, Kwansei Gakuin University, Japan; Hirokazu Ogawa, Department of Integrated Psychological Sciences, Kwansei Gakuin University, Japan. Acknowledgments This work was supported by JSPS KAKENHI Grant Number JP18J12574. We would thank Hayaki Banno for his insightful advice on the image analysis. We are also grateful to Gayet Surya and an anonymous reviewer for their comments on earlier version of this paper. Appendix A. IAPS numbers of used images Neutral: 7000, 7034, 7035, 7040, 7042, 7050, 7052, 7053, 7060, 7080, 7090, 7004, 7175, 7006, 7009, 7010, 7020, 7030, 7031, 7025; Fear-related: 1090, 1230, 1220, 1205, 1201, 1110, 1301, 1525, 1310, 1101, 1111, 1321, 1114, 1120, 1726, 1040, 1026, 1022, 1200, 1051. 66

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