Perceptual fluency and eye movements when viewing urban and natural scenes

Perceptual fluency and eye movements when viewing urban and natural scenes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Perceptual fluency and eye movements when viewing urban and natural scenes Marek Franěk, Jan Petružálek This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5848316/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 8 You are reading this latest preprint version Abstract A number of eye-tracking studies have shown that viewing natural environments is associated with reduced eye movement activity compared with viewing built environments. This has been linked to the cognitive benefits of viewing nature and explained in terms of Kaplan and Kaplan's Attention Restoration Theory. However, the theory has recently been criticized for the lack of empirical evidence supporting its framework. The first aim was to replicate the results of previous eye movement studies using different visual stimuli. In addition, we investigated whether reduced eye movements when viewing natural versus urban images could be explained by greater perceptual fluency and fractal complexity of the images. The participants (N = 66) viewed images of forests with and without foliage and images of urban apartment buildings while their eye movements were recorded. The self-reported perceptual fluency and fractal complexity of the presented images were measured. While eye movement analysis revealed significantly less eye movement activity (longer fixations, shorter fixation durations) when viewing natural images than when viewing urban images, consistent with previous findings, mediation analyses did not reveal significant effects of perceptual fluency or fractal complexity on eye fixation results. There was also no significant difference between natural images with foliage and those without for any of the measured variables. Further research directions are discussed. Research should address the specific spatio-cognitive dimensions of natural images, as well as individual differences that may influence the degree of exploration of specific images. Biological sciences/Psychology Earth and environmental sciences/Environmental social sciences Eye movements Perceptual fluency Natural environment Attention Restoration Theory Cognitive benefit Figures Figure 1 Figure 2 Figure 3 Introduction The cognitive benefits of contact with the natural environment were explained several decades ago by Kaplan and Kaplan's influential Attention Restoration Theory 1 . This theory focuses on how nature can help replenish attention and reduce the fatigue associated with prolonged exposure to the stress that accompanies everyday life in urban environments. Uncluttered natural environments can promote attentional recovery by attracting unfocused attention (soft fascination) and thus help replenish attentional resources. These environments have characteristics that support passive and restorative perception, allowing the brain to rest and recover from the fatigue associated with focused attention. As the theory has recently been criticized for lacking empirical evidence to support its framework 2 , research based on the analysis of gaze behavior using eye-tracking technology can provide deeper insights into the assumption that less effort is required to visually process natural images that to visually process images of the built environment, which may lead to cognitive recovery. Attention Restoration Theory, fascination, and spatio-cognitive dimensions of preference for the environment Attention Restoration Theory 1 argues that living in an information-rich urban environment increases the demands on the mental resources that control attention. As a result, individuals' effortful capacity to direct attention in these information-rich environments is exhausted, leading to mental fatigue. In contrast, natural environments gently capture attention in a bottom-up mode. The theory also suggests that an environment should contain four components to restore attentional capacity: extent , being away , fascination , and compatibility. Importantly, these components are most associated with being in natural environments. According to Attention Restoration Theory, fascination plays a critical role and is a central explanatory principle for the effect of natural environments on cognitive restoration. When viewing scenes with high levels of fascination, people are spontaneously attracted to interesting patterns that do not require attentional effort. Moreover, restoration occurs when people are exposed to stimuli in natural environments that induce “soft” fascination, as opposed to “hard” fascination, which is mostly induced by stimuli in urban environments. According to the theory, softly fascinating environments, as opposed to hardly fascinating environments, effortlessly attract attention in a moderate and pleasant way, allowing the mind to wander and engage in unsolicited thoughts and reflect on important life issues. Specific spatio-cognitive dimensions have been developed in environmental psychology research to describe features of environments and to explain environmental preferences. Kaplan and Kaplan 1 proposed four basic spatio-cognitive dimensions: (1) coherence, or the degree to which elements of environments are related and logically organized: the more coherent, the greater the preference for the environment; (2) legibility, or the degree to which elements allow an observer to understand the environment and its content: the more legible, the greater the preference; (3) complexity, which refers to the number or variety of elements in the environment: the more complex, the greater the preference; and (4) mystery, or the degree to which the environment contains hidden information. These dimensions have been widely used in environmental preference studies (for a review, 41 ). Empirical work based on this theory has confirmed that visual contact with the natural environment has some cognitive benefits. Meta-analyses of the studies conducted have shown that working memory, cognitive flexibility, and, to a less reliable extent, attentional control are improved after exposure to the natural environment, with low to moderate effect sizes (for a review 3 , 4 ). Importantly, these cognitive benefits were observed not only after exposure to the real natural environment but also after exposure to simulations, such as videos or simple presentations of natural images (for a review 5 ). Criticism of Attention Restoration Theory Although the theory has been influential in the field of restoration studies for several decades, its framework has recently been criticized for its important empirical and conceptual shortcomings 2 , 6 . Indeed, the key component of the theory, the concept of fascination, is rather vague, remains underdeveloped, and suffers from a lack of clear operationalization. While there is agreement that the restorative power of natural environments is due to their fascinating qualities, little is known about the physical and visual features that specifically make them fascinating. Second, it has been theorized that restoration occurs particularly with exposure to natural stimuli that induce “soft” fascination 1 , 7 . However, the optimal level of fascination has not been determined. Additionally, the specific features that make a fascinating environment soft rather than hard fascinating have not been explained. Because we do not know how much softness is needed for full restoration, we cannot successfully explain why, for example, natural, vegetation-rich environments are generally more restorative than urban environments are. For example, the wild landscape of high mountains may be more restorative than a quiet, tree-lined city street. The authors 2 , 6 recommend more objective approaches to research, such as the eye-tracking technique, which can provide more direct and objective insight into attentional functions utilized by individuals when perceiving natural environments. Eye movements have been shown to reflect the attentional and cognitive processes occurring during image perception 8 . Eye-tracking measures Eye tracking technology provides several tools for measuring and analyzing eye movements. Research has often used measures of eye fixations while viewing an image, specifically the number of fixations (numerosity measure) and fixation duration (position measure). A high number of fixations on a visual stimulus usually reflects difficulty in analyzing and interpreting information (e.g., 9 ). Longer fixation durations have been interpreted in two different ways. The first interpretation is associated with more effortful cognitive processing and information extraction (e.g., 8 ). However, longer fixations on a visual stimulus could also reflect greater interest and engagement with the stimulus 10 . Another measure that has been used in relevant research is the blink rate, which reflects cognitive load. Blink rates increase as the cognitive load increases (e.g., 11 ). Nature-urban dichotomy and eye movements The seminal study by Berto et al. 12 analyzed eye movements while viewing images of low and high fascination, as measured by the Perceived Restorativeness Scale 13 . As predicted, natural images resulted in high levels of fascination, whereas photographs of the built environment resulted in low levels of fascination. The authors measured the number of eye fixations and eye travel distances. Analyses of eye movements revealed that eye movements associated with low-fascination photographs were characterized by a greater number of fixations and greater exploration (eye travel distances) than those associated with high-fascination photographs. These differences reflect greater eye exploration when urban scenes are processed than when natural scenes are processed. The authors concluded that the differences in eye movements suggest that natural scenes require less effort to process than urban scenes do, which is consistent with Kaplan's description of “soft fascination”. In a subsequent study, Valtchanov and Ellard 14 replicated these findings; specifically, eye movements associated with urban photographs were characterized by a greater number of fixations than those associated with natural scenes. The authors also analyzed fixation durations and blink rates. Fixation durations were shorter for urban scenes than for natural scenes. According to the authors, the combination of shorter fixation durations and a greater number of fixations while viewing urban scenes reflects greater eye exploration when viewing these scenes than when viewing natural scenes. Analysis of blink rates revealed that blink rates increased when viewing urban scenes compared with viewing natural scenes. A higher blink rate reflects effort expended to process an image. The greater number of fixations and shorter fixation durations when viewing urban scenes were also confirmed in subsequent studies by Franěk et al. 15 and Martínez-Soto et al. 16 Interestingly, in the subsequent study by Franěk et al. 17 , image viewing was combined with listening to either slow or fast music. Although the tempo of the music did not affect gaze behavior, the study again replicated previous findings that viewing natural images, in contrast to viewing urban images, was associated with longer fixation durations and fewer fixations. Additionally, Martínez-Soto et al. 16 measured changes in pupil size and reported a greater pupil dilation associated with viewing environments with high restorative potential (natural images) than when viewing those with low restorative potential (built environment). Greater pupil dilation is explained by greater interest in an image. Recently, these findings were also supported by the study of Batool et al. 18 , who used images of high mountains, water bodies, forests, and detailed views of vegetation and flowers, and by Fu and Xue 19 , who used photographs of urban open spaces created by green infrastructure (parks, greenways, green roofs, etc.) as examples of natural images and photographs of urban views (industrial areas, streets, etc.) as examples of the built environment. On the other hand, two more recent studies have reported opposite findings regarding the differences between the length and number of fixations when viewing images of natural and built environments. In the study by Stevenson et al. 20 , participants (children) were asked to walk through either a natural or an urban environment while wearing a mobile eye tracker. In contrast to the aforementioned studies, the authors reported significantly more fixations while walking in the natural environment than when walking in the built environment, but there were no differences in fixation durations in the two environments. However, it is difficult to compare these findings with the data from previous studies because a different device (mobile eye-tracking glasses) was used and participants walked for approximately 10 minutes in a real outdoor environment instead of viewing static photographs. More recently, Marois et al. 21 conducted a study in which participants viewed natural images with different levels of mystery. According to Attention Restoration Theory 1 , the mystery of an environment is one of the features that contributes to the restorativeness of the environment. High-mystery images were rated as significantly more fascinating, as measured by the shortened Perceived Restorativeness Scale 13 , and were liked significantly more by participants than low-mystery images were. The results revealed that high-mystery nature images resulted in a greater number of fixations and shorter fixation durations than low-mystery nature images did. Since high-mystery nature images also score higher on fascination than low-mystery nature images do, this contradicts previous findings that the perception of high-fascination images leads to fewer and longer fixations than low-fascination images do. However, since only natural images were used in this study, it may also be interesting to compare these data with urban images with different levels of mystery. In addition, the mystery of the environment should create curiosity and a desire to explore further, with more information or interesting experiences imminent or hidden from immediate view. Clearly, the characteristics of a high-mystery environment mean that environments with a higher degree of mystery induce more exploration, as manifested by a greater number of fixations when viewing these environments than when viewing low-mystery environments. Perceptual fluency In contrast to studies based on Attention Restoration Theory 1 , Joye and van den Berg 22 provided an alternative explanation for the observed phenomenon of gaze movements while viewing urban and natural images on the basis of perceptual fluency. Perceptual fluency reflects the ease and speed with which certain perceptual and formal features of a stimulus are processed. It refers to how quickly and effortlessly a person can recognize, understand, and interpret sensory input (e.g., 23 ). When stimuli are presented in a clear, organized, and easily understood manner, people are more likely to experience perceptual fluency 24 . Fluent processing is often associated with positive affect, reflecting the fact that fluency indicates efficient stimulus processing 25 . Perceptual fluency has been the subject of much research in the field of visual perception. For example, stimuli with object symmetry 26 , round versus sharp edges 27 , or higher contrast 28 have been found to produce clear preference effects owing to perceptual fluency. Similarly, the visual system is thought to process the visual structure of natural scenes more fluently than that of urban scenes. To support this assumption, Joye et al. 2 conducted a study in which participants performed a cognitive task such as identifying sequences of geometric shapes arranged around an image. They performed the task more accurately when these sequences were arranged around natural scenes, as opposed to scenes with an urban setting. Measuring the perceptual fluency of images can be approached through both subjective and objective methods. Subjective assessments involve individuals rating their own perceptual experiences. A common approach is to use self-report questionnaires in which participants rate the ease of processing images on a predefined scale 38 . Objectively, perceptual fluency can be measured using reaction times or accuracy rates in tasks that require participants to identify or recall images 42 , 43 . Furthermore, electrophysiological methods such as electroencephalography and functional magnetic resonance imaging can capture neural responses associated with perceptual fluency in real time, providing insight into the cognitive processes underlying visual recognition 44 , 45 . Finally, another objective method of measuring perceptual fluency is considered eye-tracking, as mentioned above. For example, less frequent fixations may indicate greater fluency because the observer is processing the image more easily. Fractal patterns and perceptual fluency Several studies have suggested that higher perceptual fluency displayed when viewing natural scenes than when viewing built scenes may be explained by the fractal properties of nature 22 , 29 – 33 . A fractal structure is based on the repetition of similar visual information across multiple levels of scale; the small-scale elements of the structures are copies of the overall structure (e.g., 34 ). Importantly, natural environments often exhibit internally repeated visual information across many levels of scale, which is best described by fractal geometry. For example, fractal patterns are found in the visual structures of trees, other vegetation, and mountains, but these patterns are largely absent in a built environment. Specifically, in a study by Joye et al. 32 , participants viewed computer-generated fractal patterns while performing a cognitive task. Participants performed the cognitive task in which high fractal stimuli were presented more accurately and faster than the task in which low fractal stimuli were presented. Consistent with this, Ho et al. 35 presented images of urban and natural scenes and measured response times to a question about the images. The natural images had higher fractal dimensions than did the urban scenes. The results showed that as the fractal dimension increases, the response time to the image increases because the higher fractal complexity of images reduces the cognitive demands of visual processing. In the subsequent study by van den Berg et al. 33 , participants viewed and rated images of natural and built scenes in their original size and at 400% and 1600% magnification. Unmagnified natural scenes were viewed longer and rated as more restorative than built scenes were and these differences were mediated by the greater perceived complexity of the magnified parts of the natural scenes. This is consistent with the idea that fractal-like, recursive complexity is an important visual cue underlying the restorative potential of natural and built environments. Eye movements while viewing images with different fractal complexities were also investigated in the study by Franěk et al. 36 . Photographs of forests with foliage, forests without foliage, and urban scenes were used. It was predicted that scenes of forests with foliage should have higher fractal complexity than scenes without foliage. As hypothesized by Joye et al. 32 and van den Berg et al. 33 , natural scenes with foliage should have greater fractal complexity than natural scenes without foliage because there is less repetition of similar visual information across multiple levels of scale in trees without foliage. However, this was not confirmed in the study by Franěk et al. 36 . The fractal complexity of urban scenes was significantly lower than that of both types of natural scenes, but there was no significant difference in the fractal complexity of natural scenes with foliage and that of natural scenes without foliage. This may be because the fractal complexity of natural shapes can be fully observed when viewing the details of an object rather than when viewing the whole scene from a certain distance. On the other hand, consistent with previous findings, the analysis of gaze behavior revealed a significantly lower mean number of fixations when viewing both types of natural scenes than when viewing urban scenes. Furthermore, a significantly lower mean number of fixations was found when viewing natural scenes with foliage than when viewing natural scenes without foliage. However, the differences in fixation duration when viewing the two types of natural scenes were not significant. Goals The first aim of the present study was to test the robustness of previous findings showing differences in eye movements when processing images of built and natural environments, specifically whether viewing natural environments is associated with reduced relative eye movement activity, represented by a reduced number of fixations that are longer, in contrast to viewing built environments where the number of fixations is greater but the fixations are shorter. In addition, the number of blinks is also analyzed; according to the study by Valtchanov and Ellard 14 , the number of blinks increases when viewing urban scenes compared with viewing natural scenes. As noted by Valtchanov and Ellard 14 , the results of eye movement analysis may also be influenced by the specific characteristics of the images used in the experiments. Therefore, testing eye movement behavior when viewing different types of photographs, as well as images from different cultural areas and types of landscapes and natural environments would be beneficial. Contemporary cityscapes, in which the visual elements are often not consistently organized, have often been used as examples of the built environment in research on preferred environments. The natural environment is not the only environment that is clear, organized, and easy to understand. Many built environments can also meet these requirements. Therefore, in this study, we chose as an example of a well-understood urban environment an image of the front view of the facades of urban houses built in the early 20th century, which are relatively simple and visually consistent because they are based on the regular repetition of the same structure of windows, cornices, and other architectural elements. Therefore, they should not be difficult to understand and visually process. The natural environment examples used in this study were also more visually consistent scenes with fewer different elements, specifically photographs of views into a dense deciduous forest. The second aim of this study was to test whether the perceptual fluency of a scene is related to eye movement behavior, specifically whether greater perceptual fluency is associated with reduced eye movement activity when viewing a natural environment than when viewing an urban environment. Perceptual fluency is achieved when stimuli are presented in a clear, organized, and easy-to-understand manner 24 . The self-reported perceptual fluency of the presented images was tested and found to be related to eye movement behavior. As perceptual fluency has been linked by some authors 32 , 33 to the fractal composition of the visual stimulus, the next aim is to test the fractal complexity of the presented images and how it relates to eye movements. Images of natural environments with foliage might have greater fractal complexity than those of natural environments with bare trees because there is more repetition of similar visual information across multiple levels of scale. This might also correspond to the greater perceptual fluency of images with foliage compared with images without foliage, which should also be reflected in significant differences in eye fixations. As the previous study by Franěk et al. 36 failed to confirm this assumption, we wanted to test it further in this study. Therefore, we will also investigate the differences between the fractal complexity of natural images with foliage, natural images with bare trees, and images from urban environments and their relationships with eye fixations. Finally, we also follow up on the study by Franěk et al. 36 , which revealed some differences in eye fixation between natural scenes with vegetation and scenes without vegetation. Although the study did not find significant differences between the fractal compositions of scenes with vegetation and scenes with bare trees, it is possible that self-reported perceptual fluency may be perceived differently. In summary, this study addresses three research questions: (RQ1) Will the results of previous eye movement studies be replicated when using the visual stimuli selected in this study? (RQ2) Is self-reported perceptual fluency associated with reduced eye movements, as manifested by longer fixations, fewer fixations, and reduced blink rates, when viewing natural versus urban images? Is there a difference between natural images with foliage and natural images without foliage? (RQ3) Is viewing an image with higher fractal complexity associated with reduced eye movements, as manifested by longer fixations, fewer fixations, and reduced blink rates, when viewing natural versus urban images? Is there a difference between natural images with foliage and natural images without foliage? Methods Design This study utilized a within-subjects design with the independent variable being the type of environment (urban scene, natural scene without foliage, or natural scene with foliage) and the dependent variables representing eye movements, namely, fixation duration, number of fixations, and blink rate. Participants The sample size was calculated with G*Power 37 considering a medium effect size ( f ) of 0.25, an alpha level of .05, a power value of .95, and repeated measures and within-factors. The analysis revealed that the study required at least 36 participants. The data were collected from 66 participants aged between 18 and 25 years (M age = 20.97, SD = 1.11), 42 of whom were female. The participants were undergraduate students of informatics, business, and tourism at the University of [redacted for anonymous review] who were enrolled in various psychology courses. Ethical approval for the experiments was obtained from the Committee for Research Ethics at the University of Hradec Králové, No. 8/2019. All methods were carried out in accordance with relevant guidelines and regulations as approved by the Committee for Research Ethics at the University of Hradec Králové. All the subjects provided written informed consent in accordance with the Declaration of Helsinki. Stimulus Material The images used in the experiment were taken by the authors (Fig. 1 ). They included images of urban and forest scenes. The images were transformed to a resolution of 1920 × 1080 pixels using Adobe Photoshop CS 6 software. All images were balanced for brightness and contrast using the auto levels, auto contrast, and auto colors options in Adobe Photoshop. Three categories of scenes were used. Eight images were taken of urban streets in Prague, Czech Republic. There were no people in the images. Eight natural images of deciduous forests with leaves were taken, mainly from forests near the city of Prague. Another eight natural images of deciduous forests without foliage were taken in the same areas as the previous set of photographs but not exactly in the same places. Thus, twenty-four images were presented in one experimental session. Apparatus The experiment was controlled by a PC computer with a screen resolution of 1920×1200 pixels and a diagonal size of 61 cm. Eye movements were recorded using a Tobii X2-60 eye tracker with a sampling rate of 60 Hz. The eye tracker was mounted under the monitor. The eye-tracking device, stimulus presentation, and data processing were controlled by Tobii Studio Version 3.2 software (Tobii AB, Sweden). Fixations were identified using an identified velocity threshold (I-VT) fixation filter, which classifies eye movements on the basis of an angular velocity threshold. A velocity threshold was applied to differentiate fixations from saccades by merging successive fixations within spatial and temporal proximity. The number of blinks was measured using Tobii's built-in blink detection algorithm, which identifies blinks on the basis of the temporary absence of pupil data. A blink was classified when both eyes were undetected for a duration between 50 mses and 500 msec, distinguishing it from signal loss. Measures Measuring eye movements Three eye movement measures were used: (a) the mean number of fixations when viewing the image, (b) the mean duration of all fixations (in miliseconds) when viewing the image, and (c) the mean number of blinks when viewing the image. Self-reported perceptual fluency The five-item measure of subjective fluency developed by Graf et al. 38 was applied. The measure consists of five semantic differentials: difficult to easy, unclear to clear, disfluent to fluent, effortful to effortless, and incomprehensible to comprehensible . The items were rated on a 7-point scale ranging from 1 (e.g., extremely difficult) to 7 (e.g., extremely easy). Analysis of fractal dimension The fractal structure of the visual stimuli was analyzed via FracLac software for ImageJ, version 2.5 46 , and standard box-counting analysis was performed. The fractal complexity is expressed by the fractal dimension D . Its value lies between 1 and 2. If the number of repeated visual patterns increases, the D value approaches 2. Procedure The participants were tested individually in a laboratory. Upon arrival at the laboratory, the participant signed the informed consent form. They were then informed about the experiment and read the instructions. The instructions given to the participants were as follows: "You are going to take part in a study in which you will look at a series of pictures presented on the computer screen one after the other.. Look at each image carefully. Do not try to remember its content or details. Your eye movements will be recorded. Each image is displayed for 15 seconds”. The participants were seated approximately 70 cm from the screen. The pictures were presented in random order. Each trial began with a fixation cross in the center of the screen on a gray background. The participants had to fixate on the fixation cross for 2 s before the image appeared. Each picture was displayed for 15 s. Self-reported perceptual fluency was assessed by a separate group of participants to avoid interference with the visual exploration of the main experiment. The assessment was performed online with visual stimuli presented in random order. Forty-four participants took part in the assessment (M age = 21.80, SD = 1.87; 26 males, 18 females). Results Three dependent variables were analyzed: the number of fixations, the mean fixation duration, and the number of blinks. First, the scores were calculated for each participant and subsequently averaged across images of each category. Next, one-way repeated-measures ANOVAs (number of fixations, fixation durations, and number of blinks) and one-way ANOVAs (analyses of fractal dimensions and self-reported perceptual fluency) were conducted. Statistical analyses were performed using Statistica 12 software. Causal mediation analyses to assess whether self-reported perceptual fluency/fractal complexity mediated the relationship between the environmental type and the number of fixations/fixation durations were conducted using IntellectusStatistics software. The data from the experiment can be found in the Supplementary Information. Number of fixations . A one-way repeated-measures ANOVA revealed that the scene category had a significant effect on the mean number of fixations ( F (1.95, 130.45) = 5.129, p = 0.007, η2 = 0.086), with the following means (Fig. 2 ): urban scenes (M = 43.35, SD = 5.16), nature scenes with foliage (M = 38.36, SD = 6.66), and nature scenes without foliage (M = 39.38, SD = 6.05). Because the assumption of sphericity was violated, a Greenhouse–Geisser correction was applied (ε = 0.973). The post hoc Bonferroni test revealed significant differences in the mean number of fixations between the urban scenes and the natural scenes without foliage ( p = 0.006) and almost significant differences between the urban scenes and the natural scenes with foliage ( p = 0.063). The difference between the natural scenes with foliage and those without foliage was not significant ( p = 0.704). Fixation durations. A one-way repeated-measures ANOVA revealed that the scene category had a significant effect on the mean fixation duration ( F (1.94, 128.20) = 5.397, p = 0.006, η2 = 0.114), with the following means (Fig. 2 ): urban scenes (M = 164.50, SD = 53.21), nature scenes with foliage (M = 187.32, SD = 88.95), and nature scenes without foliage (M = 178.35, SD = 68.49). Because the assumption of sphericity was violated, a Greenhouse–Geisser correction was applied (ε = 0.971). The post hoc Bonferroni test revealed significant differences in the mean number of fixations between the urban scenes and the natural scenes with foliage ( p = 0.001) and between the urban scenes and the natural scenes without foliage ( p = 0.005). The difference between the natural scenes with foliage and those without foliage was not significant ( p = 0.934). Number of blinks. A one-way repeated-measures ANOVA revealed that the scene category had no significant effect on the mean number of blinks ( F (2,124) = 0.926, p = 0.400), with the following means (Fig. 2 ): urban scenes (M = 1.79, SD = 1.36), natural scenes with foliage (M = 1.79, SD = 1.36), and natural scenes without foliage (M = 1.63, SD = 1.20). Self-reported perceptual fluency A one-way ANOVA revealed that the scene category had no significant effect on self-reported perceptual fluency ( F (2,86) = 1.361, p = 0.261), with the following means (Fig. 3 ): urban scenes (M = 4.50, SD = 1.04), natural scenes with foliage (M = 4.35, SD = 1.04), and natural scenes without foliage (M = 4.59, SD = 1.10). Analysis of fractal dimensions A one-way ANOVA revealed that the scene category had a significant effect on the fractal dimensions ( F (2,21) = 8.510, p = 0.002, η2 = 0.002), with the following means (Fig. 3 ): urban scenes (M = 1.79, SD = 0.04), nature scenes with foliage (M = 1.86, SD = 0.01), and nature scenes without foliage (M = 1.83, SD = 0.04). The post hoc Bonferroni test revealed significant differences in fractal dimensions between the urban scenes and the natural scenes with foliage ( p = 0.001). The difference in fractal dimensions between the urban scenes and the natural scenes without foliage was not significant ( p = 0.110), nor was the difference between the natural scenes with foliage and those without foliage ( p = 0.220). Mediation analysis of the effect of self-reported perceptual fluency on eye fixations The average direct effect of the scene category on the number of fixations was significant, B = 1.16, 95.00% CI [0.23, 2.08], p = 0.016. The average indirect effect for the scene category on the number of fixations through self-reported perceptual fluency was not significant, B = -0.11, 95.00% CI [-0.50, 0.30], p = 0.322. The average direct effect of the scene category on fixation duration was significant, B = -11.33, 95.00% CI [-17.14, -5.52], p < 0.001. The average indirect effect of the scene category on fixation duration through self-reported perceptual fluency was not significant, B = 1.15, 95.00% CI [-1.68, 4.03], p = 0.109. Mediation analysis of the effect of the fractal dimension on eye fixations The average direct effect of the scene category on the number of fixations was not significant, B = 1.04, 95.00% CI [-0.19, 2.27], p = 0.094. The average indirect effect for the scene category on the number of fixations through fractal dimensions was not significant, B = 0.007, 95.00% CI [-0.78, 1.07], p = 0.987. The average direct effect of the scene category on fixation duration was significant, B = -11.79, 95.00% CI [-19.77, -3.81], p = 0.006. The average indirect effect or the scene category on fixation duration through fractal dimension was not significant, B = 1.61, 95.00% CI [-3.34, 7.54], p = 0.537. Discussion This study analyzed eye movements while viewing images of the built environment and natural images with and without foliage. The study examined whether lower eye movement activity while viewing natural images compared with images of the built environment might be associated with greater perceptual fluency and the greater fractal complexity of natural images with and without foliage compared with images of the built environment. Viewing natural environments and reduced relative eye movement activity The first research question was whether the results of previous eye movement studies could be replicated using the visual stimuli selected in this study. Previous research has shown that viewing images of natural environments is mostly associated with reduced relative eye movement activity, represented by a reduced number of fixations that are longer, in contrast to viewing built environments where the number of fixations is greater but the fixations are shorter. These findings were replicated in the present study as well. Earlier eye-tracking studies used a fairly wide variety of environments representing natural and built urban environments, but a similar trend in gaze behavior was consistently observed: viewing images of natural environments is associated with reduced relative eye movement activity, represented by a reduced number of fixations that are longer, in contrast to viewing built environments where the number of fixations is greater but the fixations are shorter. In the study by Berto et al. 12 , photographs of the natural environment included lakes, rivers, the sea, hills, woods, forests, and orchards. The photographs of the built environment included industrial areas, residential areas, historic centers, and urban areas. Valtchanov and Ellard 14 used two types of images, urban and natural, taken from around the world. The natural images were either open landscapes or forest scenes, and the urban images were either open views of the city with skyscrapers or street views. In the studies of Franěk et al. 15 , 16 the natural images were photographs of forest scenes, scenes that included a pond, rural landscapes, or photographs of large mountains. The built environment was represented by street scenes from cities in Europe and the United States. The urban scenes varied in character. Some included high-rise buildings, whereas others were streets with typical 19th-century urban buildings or streets with low-rise buildings typical of small towns. As examples of natural environments, Martínez-Soto et al. 16 used images of environments with specific aquatic elements, mountain landscapes with vegetation, urban forests, and rural landscapes as visual stimuli. The built environment was represented by images of house facades without nature, buildings, and housing estates... Batool et al. 18 used detailed views of vegetation and flowers in addition to images of high mountains, water bodies, and forests. Finally, Fu and Xue 19 used images of open urban spaces with parks, greenways, and green roofs as examples of natural images and images of industrial areas and urban streets as examples of the built environment. In the present study, the photographs used were from different geographical locations, and in contrast to previous studies, the natural environments were represented by photographs of a dense deciduous forest in the growing season or when the trees were without foliage. The built environment was represented by images of frontal views of the facades of houses in town. Apart from the work of Stevenson et al. 20 , who used different devices and research techniques, there is only one discrepancy between our results and those of the previous investigation by Valtchanov and Ellard 14 ; the difference in blink rates for the natural and built environments was not significant in the present study. Although blink rates may reflect mental workload, there may be considerable variation in blink rates between participants, within participants, and over time for individual participants 8 , 39 . In addition, participants may show large fluctuations over time. This suggests that the blink rate may not be a reliable measure of mental workload in all situations. Self-reported perceptual fluency and eye movements The second research question concerns the possible relationship between self-reported perceptual fluency and reduced eye movements, as manifested by longer fixations and fewer fixations when viewing urban images and both types of natural images. Although we found significant differences in the number of fixations and fixation durations between urban environments and both types of natural environments, but not between natural environments with and without foliage, differences in self-reported perceptual fluency between urban and natural images, nor within natural images with foliage and natural images without foliage, were not significant. Moreover, the mediation analysis did not reveal a significant effect of self-reported perceptual fluency on eye fixations. By definition, perceptual fluency is achieved when stimuli are presented in a way that is clear, organized, and easy to understand 24 . However, these requirements can also be met by the built environment, such as the preserved parts of historic cities or, of course, the organized facades of urban buildings. Thus, the self-reported perceptual fluency of given images did not explain the described differences in gaze behavior. However, self-reported perceptual fluency is subjective, and the results may depend on the measurement tool used. Notably, Graf et al. 38 argued that there is still a problem of how no consistent way to empirically operationalize the subjective experience of fluency. To date, there is no psychometrically validated and canonical scale for the construct of fluency, i.e., a construct that has both multiple sources and consequences. Therefore, we used the newly developed scale developed by these authors 38 . However, its application in similar types of research has yet to be tested. A more objective approach to the measurement of perceptual fluency might be, for example, the recording of reaction times or accuracy rates in tasks that require participants to identify or recall images. The latter method was used, for example, in the study by Joye et al. 2 . However, as eye movements shift between image viewing and task performance, this method is not suitable for eye-tracking studies based on image viewing and processing. Fractal complexity and eye movements The third research question explored whether viewing images with higher fractal complexity is associated with reduced eye movements, as manifested by longer fixations, fewer fixations, and reduced blink rates when viewing natural versus urban images. Fractal dimension analysis revealed significant differences between urban scenes and natural scenes but only in foliage scenes, suggesting that fractal complexity could be associated with reduced eye movements, as manifested by longer and fewer fixations, but the mediation analysis did not reveal any significant effect of fractal complexity on these fixation variables. Although it was predicted that natural images with foliage would have greater fractal complexity than would natural images without foliage, this was not supported by our data. This finding is also consistent with the previous finding 36 that there were no differences in fractal complexity between nature scenes with and without foliage. Although some studies 32 , 33 suggest that the perceptual fluency of natural images could be mediated by fractal complexity, it seems that the fractal complexity of natural shapes can be fully observed when viewing the details of an object rather than when viewing the whole scene from a certain distance. Therefore, an explanation of perceptual fluency on the basis of easier processing of repetitive fractal patterns may be limited to specific situations where this repetitive structure of visual patterns can be fully observed. Future directions: the analysis of other moderating variables If perceptual fluency or the related fractal scene composition, which may also explain perceptual fluency, is not a sufficient explanation for the repeatedly observed differences in gaze behavior when viewing natural and built scenes. Although the explanation based on perceptual fluency and fractal complexity seems very promising, it is difficult to prove. Next, we discuss several other variables that may influence the observed gaze behavior and that need to be monitored in future research. One of these factors may be the influence of the spatio-cognitive dimensions of complexity and mystery. In the context of Attention Restoration Theory 1 , complexity plays a crucial role in how effectively natural environments restore attention. Complexity refers to the richness and variety of elements within an environment that can attract attention without overwhelming it; according to the theory, moderate complexity gently holds attention, prevents rumination, and provides a sense of mental refreshment. In contrast, high-complexity environments, which can be complicated or chaotic, can require too much cognitive effort, undermining the restorative process. Finally, low-complexity environments are simple and may lack sufficient stimulation, leading to boredom or disengagement. Eye movement activity while viewing a scene indicates the ease/difficulty of visually processing the scene, regardless of whether more or less complex exploration of the elements contained in a scene is required to understand the environment. Obviously, if there are fewer elements in a given natural scene than in a scene with a built environment, understanding a natural scene may require less visual effort than understanding a scene with a built environment. An example of this can be seen in the visual stimuli used in our study, where nature was represented by views of the forest, where there was usually only one type of tree of a similar size, whereas in our images of built environments, there were usually more elements that could attract attention and require further observation—for example, cars on the road, looking for possible pedestrians, observing what might be on balconies, outside windows, and so on. Another spatial-cognitive dimension that may influence observed gaze behavior is mystery, as shown in the study by Marois et al. 21 . This study analyzed eye movements while viewing only natural environments that varied in the degree of mystery. They reported that images with higher levels of mystery required more visual scanning. As a typical example of stimuli with different degrees of mystery, the authors presented an image of a path leading through a forest. The low-mystery image was a straight path through a sparse deciduous forest, whereas the high-mystery image was a forest path with several bends. The latter image, of course, requires more visual exploration, with the observer likely trying to determine what might be behind each bend in the path. Therefore, further research would require observing gaze behavior in a variety of environmental settings that differ, for example, in terms of the complexity of environmental elements, mystery, or other relevant characteristics. This is because previous studies have averaged data from different types of natural scenes. While this allows more general conclusions to be drawn about differences between gaze behavior when viewing natural and built environments, it may ignore the influence of specific environmental layouts. In addition, some individual differences may influence the observation of gaze behavior. For example, the study by Batool et al. 18 . also reported that viewing natural scenes was characterized by a lower number of fixations and longer fixation durations than viewing urban scenes. However, they also noted the importance of the degree of preference for a given scene. They reported that the most preferred urban scenes led to significantly more fixations and saccades. Thus, eye movements reflect not only the ease of visual processing but also the interest in a particular environment. Interestingly, when participants viewed natural scenes, Batool et al. 18 reported no significant differences in gaze behavior between the most and least preferred views when the overall grand mean preference rating derived from the entire group of participants was used. However, when the authors 18 also examined individual differences between participants on the basis of their scores on the Nature Relatedness Scale 40 , they reported that when viewing urban scenes, scores on the Nature Relatedness Scale were inversely related to exploratory eye movements, whereas when viewing natural scenes, scores on the Nature Relatedness Scale were significantly positively correlated with the number of saccades. Thus, participants who explored natural scenes more often had higher scores on the Nature Relatedness Scale. This is a very important finding because previous studies have not taken into account the background of the participants. Could participants who live their daily lives in cities without a deeper emotional and cognitive relationship to nature simply visually scan and explore the urban environment more because it is more interesting to them? Limitations This study has several limitations. We only measured self-reported perceptual fluency using an instrument that has not been adequately tested in this area of research. Unfortunately, we did not have more sophisticated measures at our disposal, such as electrophysiological methods. On the other hand, eye tracking has been reported as a possible method of measuring perceptual fluency. Another limitation may be that perceived perceptual fluency was not measured for the participants directly involved in the eye-tracking study but for another group of participants. It is also necessary to consider that differences in foliage can affect not only the fractal properties, but also the contextual properties of images. Bare branches signaling the end of autumn versus the bright green leaves of spring may change the emotional impact of images. However, an otherwise similar experiment cannot be performed; bare branches compared with branches with leaves always have these contextual characteristics. Finally, because of the enormous diversity of the natural environment, the generalization of results is always limited when specific visual stimuli are used. Declarations Acknowledgements This research was funded by the Faculty of Informatics and Management at the University of Hradec Králové, the Student Specific Research Grants 1/2024. We thank Tomáš Havlíček, Illia Holubka, Tamara Polášková, Radek Pařízek, Josef Srpek, and Downar Yahor for their help in organizing and conducting the experiments. Competing interests The authors declare no competing interests. Data availability The data is contained in the Supplementary Information. Author Contributions: Conceptualization, M.F.; methodology, M.F., J.P.; data curation, J.P.; writing—original draft preparation, M.F.; writing—review and editing, M.F., J.P. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement Ethical approval for the experiments was obtained from the Committee for Research Ethics at the University of Hradec Králové, No. 8/2019. All methods were carried out in accordance with relevant guidelines and regulations as approved by the Committee for Research Ethics at the University of Hradec Králové. All subjects gave written informed consent in accordance with the Declaration of Helsinki. Informed Consent Statement Informed consent was obtained from all subjects involved in this study Supplementary Information Dataset.xlxs References Kaplan, R. & Kaplan, S. The Experience of Nature: A Psychological Perspective (Cambridge University Press, 1989). Joye, Y., Pals, R., Steg, L. & Evans, B. L. 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Electrophysiological correlates associated with contributions of perceptual and conceptual fluency to familiarity. Front. Hum. Neurosci. 9 , 321. 10.3389/fnhum.2015.00321 (2015). Karperien, A. FracLac for ImageJ (Version 2.5). (2012). Available from: http://rsb.info nih. gov/ij/plugins/fraclac/FLHelp/Introduction.htm . Cited 15 March 2025. Additional Declarations No competing interests reported. Supplementary Files Dataset.xlsx Cite Share Download PDF Status: Published Journal Publication published 16 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 09 May, 2025 Reviews received at journal 07 May, 2025 Reviews received at journal 05 May, 2025 Reviewers agreed at journal 26 Apr, 2025 Reviewers agreed at journal 25 Apr, 2025 Reviewers invited by journal 25 Apr, 2025 Submission checks completed at journal 21 Apr, 2025 First submitted to journal 06 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5848316","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":448442262,"identity":"500d8a09-d605-4820-8e18-fa5f0acdb4c2","order_by":0,"name":"Marek Franěk","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYJACxgYGORDNzPCB4YABsVqMwVoYZ5CshZmHGC267WcfPpzBYJDPP7v5sLFt2x1jBvbjD/BqMTuTbmy4gcHAcsadY8nJuW3PzBh4EhLwazmQxib5gOGPAcONHOPDuW2HbRgkGA7g13L+GfvPBwwGBvI38j8ftgRrAfoNr5YbaWyMQIcZGNzIYU5mbDtsxiDBjFcHUMszZskZQB2GN9KMDXvOHTZm40kjoOV8GuPHngoDA7kbyY8lfpQdNuwnFGIQgBwZbESoHwWjYBSMglFAAAAAPSBEPf3fSDkAAAAASUVORK5CYII=","orcid":"","institution":"University of Hradec Králové","correspondingAuthor":true,"prefix":"","firstName":"Marek","middleName":"","lastName":"Franěk","suffix":""},{"id":448442263,"identity":"c0bdf832-260f-49c6-a574-0180164544c7","order_by":1,"name":"Jan Petružálek","email":"","orcid":"","institution":"University of Hradec Králové","correspondingAuthor":false,"prefix":"","firstName":"Jan","middleName":"","lastName":"Petružálek","suffix":""}],"badges":[],"createdAt":"2025-01-17 10:23:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5848316/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5848316/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-07850-5","type":"published","date":"2025-07-16T15:57:02+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82047214,"identity":"a1ad40a1-bd64-4fa7-978f-a0292385047a","added_by":"auto","created_at":"2025-05-06 09:40:49","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":393412,"visible":true,"origin":"","legend":"\u003cp\u003eExamples of stimuli used in the study: (a) urban images, (b) natural images with foliage, and (c) natural images without foliage.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5848316/v1/abde3eb7a064da2c894d4701.jpeg"},{"id":82047216,"identity":"8ad22283-52d7-47df-98d1-306164f39797","added_by":"auto","created_at":"2025-05-06 09:40:49","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":245576,"visible":true,"origin":"","legend":"\u003cp\u003eEye movement measures: (a) mean number of fixations, (b) mean fixation duration (msec), (c) mean number of blinks.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5848316/v1/a69b7ad5ce6fd9aff6e06595.jpeg"},{"id":82047212,"identity":"34de27c3-8c10-4968-a80c-a3ffb9d90a9b","added_by":"auto","created_at":"2025-05-06 09:40:49","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":92181,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Self-reported perceptual fluences. The scale ranged from 1 (e.g., extremely difficult) to 7 (e.g., extremely easy). (b) Fractal dimensions. The value lies between 1 and 2.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5848316/v1/3c3c69dc637345020a2103ac.jpeg"},{"id":87219364,"identity":"19c78f7f-e67d-4b8f-9ac1-dd1bd00a75c4","added_by":"auto","created_at":"2025-07-21 16:04:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1751953,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5848316/v1/19b969a3-9b54-4bff-a5c5-a9fe953e43ae.pdf"},{"id":82047209,"identity":"c7954b52-f088-4159-b56d-a2c8b05e4560","added_by":"auto","created_at":"2025-05-06 09:40:48","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":31515,"visible":true,"origin":"","legend":"","description":"","filename":"Dataset.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5848316/v1/dcbe76d23078d11baa96255c.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Perceptual fluency and eye movements when viewing urban and natural scenes","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe cognitive benefits of contact with the natural environment were explained several decades ago by Kaplan and Kaplan's influential Attention Restoration Theory\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. This theory focuses on how nature can help replenish attention and reduce the fatigue associated with prolonged exposure to the stress that accompanies everyday life in urban environments. Uncluttered natural environments can promote attentional recovery by attracting unfocused attention (soft fascination) and thus help replenish attentional resources. These environments have characteristics that support passive and restorative perception, allowing the brain to rest and recover from the fatigue associated with focused attention. As the theory has recently been criticized for lacking empirical evidence to support its framework\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, research based on the analysis of gaze behavior using eye-tracking technology can provide deeper insights into the assumption that less effort is required to visually process natural images that to visually process images of the built environment, which may lead to cognitive recovery.\u003c/p\u003e\n\u003ch3\u003eAttention Restoration Theory, fascination, and spatio-cognitive dimensions of preference for the environment\u003c/h3\u003e\n\u003cp\u003eAttention Restoration Theory\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e argues that living in an information-rich urban environment increases the demands on the mental resources that control attention. As a result, individuals' effortful capacity to direct attention in these information-rich environments is exhausted, leading to mental fatigue. In contrast, natural environments gently capture attention in a bottom-up mode. The theory also suggests that an environment should contain four components to restore attentional capacity: \u003cem\u003eextent\u003c/em\u003e, \u003cem\u003ebeing away\u003c/em\u003e, \u003cem\u003efascination\u003c/em\u003e, and \u003cem\u003ecompatibility.\u003c/em\u003e Importantly, these components are most associated with being in natural environments. According to Attention Restoration Theory, fascination plays a critical role and is a central explanatory principle for the effect of natural environments on cognitive restoration. When viewing scenes with high levels of fascination, people are spontaneously attracted to interesting patterns that do not require attentional effort. Moreover, restoration occurs when people are exposed to stimuli in natural environments that induce \u0026ldquo;soft\u0026rdquo; fascination, as opposed to \u0026ldquo;hard\u0026rdquo; fascination, which is mostly induced by stimuli in urban environments. According to the theory, softly fascinating environments, as opposed to hardly fascinating environments, effortlessly attract attention in a moderate and pleasant way, allowing the mind to wander and engage in unsolicited thoughts and reflect on important life issues.\u003c/p\u003e \u003cp\u003eSpecific spatio-cognitive dimensions have been developed in environmental psychology research to describe features of environments and to explain environmental preferences. Kaplan and Kaplan\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e proposed four basic spatio-cognitive dimensions: (1) coherence, or the degree to which elements of environments are related and logically organized: the more coherent, the greater the preference for the environment; (2) legibility, or the degree to which elements allow an observer to understand the environment and its content: the more legible, the greater the preference; (3) complexity, which refers to the number or variety of elements in the environment: the more complex, the greater the preference; and (4) mystery, or the degree to which the environment contains hidden information. These dimensions have been widely used in environmental preference studies (for a review,\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003eEmpirical work based on this theory has confirmed that visual contact with the natural environment has some cognitive benefits. Meta-analyses of the studies conducted have shown that working memory, cognitive flexibility, and, to a less reliable extent, attentional control are improved after exposure to the natural environment, with low to moderate effect sizes (for a review\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e). Importantly, these cognitive benefits were observed not only after exposure to the real natural environment but also after exposure to simulations, such as videos or simple presentations of natural images (for a review\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCriticism of Attention Restoration Theory\u003c/h2\u003e \u003cp\u003eAlthough the theory has been influential in the field of restoration studies for several decades, its framework has recently been criticized for its important empirical and conceptual shortcomings\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Indeed, the key component of the theory, the concept of fascination, is rather vague, remains underdeveloped, and suffers from a lack of clear operationalization. While there is agreement that the restorative power of natural environments is due to their fascinating qualities, little is known about the physical and visual features that specifically make them fascinating. Second, it has been theorized that restoration occurs particularly with exposure to natural stimuli that induce \u0026ldquo;soft\u0026rdquo; fascination\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. However, the optimal level of fascination has not been determined. Additionally, the specific features that make a fascinating environment soft rather than hard fascinating have not been explained. Because we do not know how much softness is needed for full restoration, we cannot successfully explain why, for example, natural, vegetation-rich environments are generally more restorative than urban environments are. For example, the wild landscape of high mountains may be more restorative than a quiet, tree-lined city street. The authors\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e recommend more objective approaches to research, such as the eye-tracking technique, which can provide more direct and objective insight into attentional functions utilized by individuals when perceiving natural environments. Eye movements have been shown to reflect the attentional and cognitive processes occurring during image perception\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEye-tracking measures\u003c/h3\u003e\n\u003cp\u003eEye tracking technology provides several tools for measuring and analyzing eye movements. Research has often used measures of eye fixations while viewing an image, specifically the number of fixations (numerosity measure) and fixation duration (position measure). A high number of fixations on a visual stimulus usually reflects difficulty in analyzing and interpreting information (e.g.,\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e). Longer fixation durations have been interpreted in two different ways. The first interpretation is associated with more effortful cognitive processing and information extraction (e.g.,\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e). However, longer fixations on a visual stimulus could also reflect greater interest and engagement with the stimulus\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Another measure that has been used in relevant research is the blink rate, which reflects cognitive load. Blink rates increase as the cognitive load increases (e.g.,\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e).\u003c/p\u003e\n\u003ch3\u003eNature-urban dichotomy and eye movements\u003c/h3\u003e\n\u003cp\u003eThe seminal study by Berto et al.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e analyzed eye movements while viewing images of low and high fascination, as measured by the Perceived Restorativeness Scale\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. As predicted, natural images resulted in high levels of fascination, whereas photographs of the built environment resulted in low levels of fascination. The authors measured the number of eye fixations and eye travel distances. Analyses of eye movements revealed that eye movements associated with low-fascination photographs were characterized by a greater number of fixations and greater exploration (eye travel distances) than those associated with high-fascination photographs. These differences reflect greater eye exploration when urban scenes are processed than when natural scenes are processed. The authors concluded that the differences in eye movements suggest that natural scenes require less effort to process than urban scenes do, which is consistent with Kaplan's description of \u0026ldquo;soft fascination\u0026rdquo;.\u003c/p\u003e \u003cp\u003eIn a subsequent study, Valtchanov and Ellard\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e replicated these findings; specifically, eye movements associated with urban photographs were characterized by a greater number of fixations than those associated with natural scenes. The authors also analyzed fixation durations and blink rates. Fixation durations were shorter for urban scenes than for natural scenes. According to the authors, the combination of shorter fixation durations and a greater number of fixations while viewing urban scenes reflects greater eye exploration when viewing these scenes than when viewing natural scenes. Analysis of blink rates revealed that blink rates increased when viewing urban scenes compared with viewing natural scenes. A higher blink rate reflects effort expended to process an image. The greater number of fixations and shorter fixation durations when viewing urban scenes were also confirmed in subsequent studies by Franěk et al.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e and Mart\u0026iacute;nez-Soto et al.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e Interestingly, in the subsequent study by Franěk et al.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, image viewing was combined with listening to either slow or fast music. Although the tempo of the music did not affect gaze behavior, the study again replicated previous findings that viewing natural images, in contrast to viewing urban images, was associated with longer fixation durations and fewer fixations. Additionally, Mart\u0026iacute;nez-Soto et al.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e measured changes in pupil size and reported a greater pupil dilation associated with viewing environments with high restorative potential (natural images) than when viewing those with low restorative potential (built environment). Greater pupil dilation is explained by greater interest in an image. Recently, these findings were also supported by the study of Batool et al.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, who used images of high mountains, water bodies, forests, and detailed views of vegetation and flowers, and by Fu and Xue\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, who used photographs of urban open spaces created by green infrastructure (parks, greenways, green roofs, etc.) as examples of natural images and photographs of urban views (industrial areas, streets, etc.) as examples of the built environment.\u003c/p\u003e \u003cp\u003eOn the other hand, two more recent studies have reported opposite findings regarding the differences between the length and number of fixations when viewing images of natural and built environments. In the study by Stevenson et al.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, participants (children) were asked to walk through either a natural or an urban environment while wearing a mobile eye tracker. In contrast to the aforementioned studies, the authors reported significantly more fixations while walking in the natural environment than when walking in the built environment, but there were no differences in fixation durations in the two environments. However, it is difficult to compare these findings with the data from previous studies because a different device (mobile eye-tracking glasses) was used and participants walked for approximately 10 minutes in a real outdoor environment instead of viewing static photographs.\u003c/p\u003e \u003cp\u003eMore recently, Marois et al.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e conducted a study in which participants viewed natural images with different levels of mystery. According to Attention Restoration Theory\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, the mystery of an environment is one of the features that contributes to the restorativeness of the environment. High-mystery images were rated as significantly more fascinating, as measured by the shortened Perceived Restorativeness Scale\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, and were liked significantly more by participants than low-mystery images were. The results revealed that high-mystery nature images resulted in a greater number of fixations and shorter fixation durations than low-mystery nature images did. Since high-mystery nature images also score higher on fascination than low-mystery nature images do, this contradicts previous findings that the perception of high-fascination images leads to fewer and longer fixations than low-fascination images do. However, since only natural images were used in this study, it may also be interesting to compare these data with urban images with different levels of mystery. In addition, the mystery of the environment should create curiosity and a desire to explore further, with more information or interesting experiences imminent or hidden from immediate view. Clearly, the characteristics of a high-mystery environment mean that environments with a higher degree of mystery induce more exploration, as manifested by a greater number of fixations when viewing these environments than when viewing low-mystery environments.\u003c/p\u003e\n\u003ch3\u003ePerceptual fluency\u003c/h3\u003e\n\u003cp\u003eIn contrast to studies based on Attention Restoration Theory\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, Joye and van den Berg\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e provided an alternative explanation for the observed phenomenon of gaze movements while viewing urban and natural images on the basis of perceptual fluency. Perceptual fluency reflects the ease and speed with which certain perceptual and formal features of a stimulus are processed. It refers to how quickly and effortlessly a person can recognize, understand, and interpret sensory input (e.g., \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e). When stimuli are presented in a clear, organized, and easily understood manner, people are more likely to experience perceptual fluency\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Fluent processing is often associated with positive affect, reflecting the fact that fluency indicates efficient stimulus processing\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePerceptual fluency has been the subject of much research in the field of visual perception. For example, stimuli with object symmetry\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, round versus sharp edges\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, or higher contrast\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e have been found to produce clear preference effects owing to perceptual fluency. Similarly, the visual system is thought to process the visual structure of natural scenes more fluently than that of urban scenes. To support this assumption, Joye et al.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e conducted a study in which participants performed a cognitive task such as identifying sequences of geometric shapes arranged around an image. They performed the task more accurately when these sequences were arranged around natural scenes, as opposed to scenes with an urban setting.\u003c/p\u003e \u003cp\u003eMeasuring the perceptual fluency of images can be approached through both subjective and objective methods. Subjective assessments involve individuals rating their own perceptual experiences. A common approach is to use self-report questionnaires in which participants rate the ease of processing images on a predefined scale\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Objectively, perceptual fluency can be measured using reaction times or accuracy rates in tasks that require participants to identify or recall images\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Furthermore, electrophysiological methods such as electroencephalography and functional magnetic resonance imaging can capture neural responses associated with perceptual fluency in real time, providing insight into the cognitive processes underlying visual recognition\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Finally, another objective method of measuring perceptual fluency is considered eye-tracking, as mentioned above. For example, less frequent fixations may indicate greater fluency because the observer is processing the image more easily.\u003c/p\u003e\n\u003ch3\u003eFractal patterns and perceptual fluency\u003c/h3\u003e\n\u003cp\u003eSeveral studies have suggested that higher perceptual fluency displayed when viewing natural scenes than when viewing built scenes may be explained by the fractal properties of nature\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan additionalcitationids=\"CR30 CR31 CR32\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. A fractal structure is based on the repetition of similar visual information across multiple levels of scale; the small-scale elements of the structures are copies of the overall structure (e.g.,\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e). Importantly, natural environments often exhibit internally repeated visual information across many levels of scale, which is best described by fractal geometry. For example, fractal patterns are found in the visual structures of trees, other vegetation, and mountains, but these patterns are largely absent in a built environment.\u003c/p\u003e \u003cp\u003eSpecifically, in a study by Joye et al.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, participants viewed computer-generated fractal patterns while performing a cognitive task. Participants performed the cognitive task in which high fractal stimuli were presented more accurately and faster than the task in which low fractal stimuli were presented. Consistent with this, Ho et al.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e presented images of urban and natural scenes and measured response times to a question about the images. The natural images had higher fractal dimensions than did the urban scenes. The results showed that as the fractal dimension increases, the response time to the image increases because the higher fractal complexity of images reduces the cognitive demands of visual processing. In the subsequent study by van den Berg et al.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, participants viewed and rated images of natural and built scenes in their original size and at 400% and 1600% magnification. Unmagnified natural scenes were viewed longer and rated as more restorative than built scenes were and these differences were mediated by the greater perceived complexity of the magnified parts of the natural scenes. This is consistent with the idea that fractal-like, recursive complexity is an important visual cue underlying the restorative potential of natural and built environments.\u003c/p\u003e \u003cp\u003eEye movements while viewing images with different fractal complexities were also investigated in the study by Franěk et al.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Photographs of forests with foliage, forests without foliage, and urban scenes were used. It was predicted that scenes of forests with foliage should have higher fractal complexity than scenes without foliage. As hypothesized by Joye et al.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e and van den Berg et al.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, natural scenes with foliage should have greater fractal complexity than natural scenes without foliage because there is less repetition of similar visual information across multiple levels of scale in trees without foliage. However, this was not confirmed in the study by Franěk et al.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. The fractal complexity of urban scenes was significantly lower than that of both types of natural scenes, but there was no significant difference in the fractal complexity of natural scenes with foliage and that of natural scenes without foliage. This may be because the fractal complexity of natural shapes can be fully observed when viewing the details of an object rather than when viewing the whole scene from a certain distance. On the other hand, consistent with previous findings, the analysis of gaze behavior revealed a significantly lower mean number of fixations when viewing both types of natural scenes than when viewing urban scenes. Furthermore, a significantly lower mean number of fixations was found when viewing natural scenes with foliage than when viewing natural scenes without foliage. However, the differences in fixation duration when viewing the two types of natural scenes were not significant.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGoals\u003c/h2\u003e \u003cp\u003eThe first aim of the present study was to test the robustness of previous findings showing differences in eye movements when processing images of built and natural environments, specifically whether viewing natural environments is associated with reduced relative eye movement activity, represented by a reduced number of fixations that are longer, in contrast to viewing built environments where the number of fixations is greater but the fixations are shorter. In addition, the number of blinks is also analyzed; according to the study by Valtchanov and Ellard\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, the number of blinks increases when viewing urban scenes compared with viewing natural scenes. As noted by Valtchanov and Ellard\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, the results of eye movement analysis may also be influenced by the specific characteristics of the images used in the experiments. Therefore, testing eye movement behavior when viewing different types of photographs, as well as images from different cultural areas and types of landscapes and natural environments would be beneficial.\u003c/p\u003e \u003cp\u003eContemporary cityscapes, in which the visual elements are often not consistently organized, have often been used as examples of the built environment in research on preferred environments. The natural environment is not the only environment that is clear, organized, and easy to understand. Many built environments can also meet these requirements. Therefore, in this study, we chose as an example of a well-understood urban environment an image of the front view of the facades of urban houses built in the early 20th century, which are relatively simple and visually consistent because they are based on the regular repetition of the same structure of windows, cornices, and other architectural elements. Therefore, they should not be difficult to understand and visually process. The natural environment examples used in this study were also more visually consistent scenes with fewer different elements, specifically photographs of views into a dense deciduous forest.\u003c/p\u003e \u003cp\u003eThe second aim of this study was to test whether the perceptual fluency of a scene is related to eye movement behavior, specifically whether greater perceptual fluency is associated with reduced eye movement activity when viewing a natural environment than when viewing an urban environment. Perceptual fluency is achieved when stimuli are presented in a clear, organized, and easy-to-understand manner\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. The self-reported perceptual fluency of the presented images was tested and found to be related to eye movement behavior.\u003c/p\u003e \u003cp\u003eAs perceptual fluency has been linked by some authors\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e to the fractal composition of the visual stimulus, the next aim is to test the fractal complexity of the presented images and how it relates to eye movements. Images of natural environments with foliage might have greater fractal complexity than those of natural environments with bare trees because there is more repetition of similar visual information across multiple levels of scale. This might also correspond to the greater perceptual fluency of images with foliage compared with images without foliage, which should also be reflected in significant differences in eye fixations. As the previous study by Franěk et al.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e failed to confirm this assumption, we wanted to test it further in this study. Therefore, we will also investigate the differences between the fractal complexity of natural images with foliage, natural images with bare trees, and images from urban environments and their relationships with eye fixations.\u003c/p\u003e \u003cp\u003eFinally, we also follow up on the study by Franěk et al.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, which revealed some differences in eye fixation between natural scenes with vegetation and scenes without vegetation. Although the study did not find significant differences between the fractal compositions of scenes with vegetation and scenes with bare trees, it is possible that self-reported perceptual fluency may be perceived differently.\u003c/p\u003e \u003cp\u003eIn summary, this study addresses three research questions:\u003c/p\u003e \u003cp\u003e(RQ1) Will the results of previous eye movement studies be replicated when using the visual stimuli selected in this study?\u003c/p\u003e \u003cp\u003e(RQ2) Is self-reported perceptual fluency associated with reduced eye movements, as manifested by longer fixations, fewer fixations, and reduced blink rates, when viewing natural versus urban images? Is there a difference between natural images with foliage and natural images without foliage?\u003c/p\u003e \u003cp\u003e(RQ3) Is viewing an image with higher fractal complexity associated with reduced eye movements, as manifested by longer fixations, fewer fixations, and reduced blink rates, when viewing natural versus urban images? Is there a difference between natural images with foliage and natural images without foliage?\u003c/p\u003e \u003c/div\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDesign\u003c/h2\u003e \u003cp\u003eThis study utilized a within-subjects design with the independent variable being the type of environment (urban scene, natural scene without foliage, or natural scene with foliage) and the dependent variables representing eye movements, namely, fixation duration, number of fixations, and blink rate.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThe sample size was calculated with G*Power\u003csup\u003e37\u003c/sup\u003e considering a medium effect size (\u003cem\u003ef\u003c/em\u003e) of 0.25, an alpha level of .05, a power value of .95, and repeated measures and within-factors. The analysis revealed that the study required at least 36 participants. The data were collected from 66 participants aged between 18 and 25 years (M\u003csub\u003eage\u003c/sub\u003e = 20.97, SD\u0026thinsp;=\u0026thinsp;1.11), 42 of whom were female. The participants were undergraduate students of informatics, business, and tourism at the University of [redacted for anonymous review] who were enrolled in various psychology courses. Ethical approval for the experiments was obtained from the Committee for Research Ethics at the University of Hradec Kr\u0026aacute;lov\u0026eacute;, No. 8/2019. All methods were carried out in accordance with relevant guidelines and regulations as approved by the Committee for Research Ethics at the University of Hradec Kr\u0026aacute;lov\u0026eacute;. All the subjects provided written informed consent in accordance with the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStimulus Material\u003c/h2\u003e \u003cp\u003eThe images used in the experiment were taken by the authors (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). They included images of urban and forest scenes. The images were transformed to a resolution of 1920 \u0026times; 1080 pixels using Adobe Photoshop CS 6 software. All images were balanced for brightness and contrast using the auto levels, auto contrast, and auto colors options in Adobe Photoshop. Three categories of scenes were used. Eight images were taken of urban streets in Prague, Czech Republic. There were no people in the images. Eight natural images of deciduous forests with leaves were taken, mainly from forests near the city of Prague. Another eight natural images of deciduous forests without foliage were taken in the same areas as the previous set of photographs but not exactly in the same places. Thus, twenty-four images were presented in one experimental session.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eApparatus\u003c/h2\u003e \u003cp\u003eThe experiment was controlled by a PC computer with a screen resolution of 1920\u0026times;1200 pixels and a diagonal size of 61 cm. Eye movements were recorded using a Tobii X2-60 eye tracker with a sampling rate of 60 Hz. The eye tracker was mounted under the monitor. The eye-tracking device, stimulus presentation, and data processing were controlled by Tobii Studio Version 3.2 software (Tobii AB, Sweden). Fixations were identified using an identified velocity threshold (I-VT) fixation filter, which classifies eye movements on the basis of an angular velocity threshold. A velocity threshold was applied to differentiate fixations from saccades by merging successive fixations within spatial and temporal proximity. The number of blinks was measured using Tobii's built-in blink detection algorithm, which identifies blinks on the basis of the temporary absence of pupil data. A blink was classified when both eyes were undetected for a duration between 50 mses and 500 msec, distinguishing it from signal loss.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMeasures\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003eMeasuring eye movements\u003c/h2\u003e \u003cp\u003eThree eye movement measures were used: (a) the mean number of fixations when viewing the image, (b) the mean duration of all fixations (in miliseconds) when viewing the image, and (c) the mean number of blinks when viewing the image.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSelf-reported perceptual fluency\u003c/h2\u003e \u003cp\u003eThe five-item measure of subjective fluency developed by Graf et al.\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e was applied. The measure consists of five semantic differentials: \u003cem\u003edifficult\u003c/em\u003e to \u003cem\u003eeasy, unclear\u003c/em\u003e to \u003cem\u003eclear, disfluent\u003c/em\u003e to \u003cem\u003efluent, effortful\u003c/em\u003e to \u003cem\u003eeffortless, and incomprehensible\u003c/em\u003e to \u003cem\u003ecomprehensible\u003c/em\u003e. The items were rated on a 7-point scale ranging from 1 (e.g., extremely difficult) to 7 (e.g., extremely easy).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of fractal dimension\u003c/h2\u003e \u003cp\u003eThe fractal structure of the visual stimuli was analyzed via FracLac software for ImageJ, version 2.5\u003csup\u003e46\u003c/sup\u003e, and standard box-counting analysis was performed. The fractal complexity is expressed by the fractal dimension \u003cem\u003eD\u003c/em\u003e. Its value lies between 1 and 2. If the number of repeated visual patterns increases, the \u003cem\u003eD\u003c/em\u003e value approaches 2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eProcedure\u003c/h2\u003e \u003cp\u003eThe participants were tested individually in a laboratory. Upon arrival at the laboratory, the participant signed the informed consent form. They were then informed about the experiment and read the instructions. The instructions given to the participants were as follows: \"You are going to take part in a study in which you will look at a series of pictures presented on the computer screen one after the other.. Look at each image carefully. Do not try to remember its content or details. Your eye movements will be recorded. Each image is displayed for 15 seconds\u0026rdquo;. The participants were seated approximately 70 cm from the screen. The pictures were presented in random order. Each trial began with a fixation cross in the center of the screen on a gray background. The participants had to fixate on the fixation cross for 2 s before the image appeared. Each picture was displayed for 15 s.\u003c/p\u003e \u003cp\u003e Self-reported perceptual fluency was assessed by a separate group of participants to avoid interference with the visual exploration of the main experiment. The assessment was performed online with visual stimuli presented in random order. Forty-four participants took part in the assessment (M\u003csub\u003eage\u003c/sub\u003e = 21.80, SD\u0026thinsp;=\u0026thinsp;1.87; 26 males, 18 females).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThree dependent variables were analyzed: the number of fixations, the mean fixation duration, and the number of blinks. First, the scores were calculated for each participant and subsequently averaged across images of each category. Next, one-way repeated-measures ANOVAs (number of fixations, fixation durations, and number of blinks) and one-way ANOVAs (analyses of fractal dimensions and self-reported perceptual fluency) were conducted. Statistical analyses were performed using \u003cem\u003eStatistica 12\u003c/em\u003e software. Causal mediation analyses to assess whether self-reported perceptual fluency/fractal complexity mediated the relationship between the environmental type and the number of fixations/fixation durations were conducted using \u003cem\u003eIntellectusStatistics\u003c/em\u003e software. The data from the experiment can be found in the Supplementary Information.\u003c/p\u003e \u003cp\u003e \u003cem\u003eNumber of fixations\u003c/em\u003e. A one-way repeated-measures ANOVA revealed that the scene category had a significant effect on the mean number of fixations (\u003cem\u003eF\u003c/em\u003e(1.95, 130.45)\u0026thinsp;=\u0026thinsp;5.129, \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.007, η2\u0026thinsp;=\u0026thinsp;0.086), with the following means (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e): urban scenes (M\u0026thinsp;=\u0026thinsp;43.35, SD\u0026thinsp;=\u0026thinsp;5.16), nature scenes with foliage (M\u0026thinsp;=\u0026thinsp;38.36, SD\u0026thinsp;=\u0026thinsp;6.66), and nature scenes without foliage (M\u0026thinsp;=\u0026thinsp;39.38, SD\u0026thinsp;=\u0026thinsp;6.05). Because the assumption of sphericity was violated, a Greenhouse\u0026ndash;Geisser correction was applied (ε\u0026thinsp;=\u0026thinsp;0.973). The post hoc Bonferroni test revealed significant differences in the mean number of fixations between the urban scenes and the natural scenes without foliage (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) and almost significant differences between the urban scenes and the natural scenes with foliage (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.063). The difference between the natural scenes with foliage and those without foliage was not significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.704).\u003c/p\u003e \u003cp\u003e \u003cem\u003eFixation durations.\u003c/em\u003e A one-way repeated-measures ANOVA revealed that the scene category had a significant effect on the mean fixation duration (\u003cem\u003eF\u003c/em\u003e(1.94, 128.20)\u0026thinsp;=\u0026thinsp;5.397, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006, η2\u0026thinsp;=\u0026thinsp;0.114), with the following means (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e): urban scenes (M\u0026thinsp;=\u0026thinsp;164.50, SD\u0026thinsp;=\u0026thinsp;53.21), nature scenes with foliage (M\u0026thinsp;=\u0026thinsp;187.32, SD\u0026thinsp;=\u0026thinsp;88.95), and nature scenes without foliage (M\u0026thinsp;=\u0026thinsp;178.35, SD\u0026thinsp;=\u0026thinsp;68.49). Because the assumption of sphericity was violated, a Greenhouse\u0026ndash;Geisser correction was applied (ε\u0026thinsp;=\u0026thinsp;0.971). The post hoc Bonferroni test revealed significant differences in the mean number of fixations between the urban scenes and the natural scenes with foliage (\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.001) and between the urban scenes and the natural scenes without foliage (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005). The difference between the natural scenes with foliage and those without foliage was not significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.934).\u003c/p\u003e \u003cp\u003e \u003cem\u003eNumber of blinks.\u003c/em\u003e A one-way repeated-measures ANOVA revealed that the scene category had no significant effect on the mean number of blinks (\u003cem\u003eF\u003c/em\u003e(2,124)\u0026thinsp;=\u0026thinsp;0.926, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.400), with the following means (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e): urban scenes (M\u0026thinsp;=\u0026thinsp;1.79, SD\u0026thinsp;=\u0026thinsp;1.36), natural scenes with foliage (M\u0026thinsp;=\u0026thinsp;1.79, SD\u0026thinsp;=\u0026thinsp;1.36), and natural scenes without foliage (M\u0026thinsp;=\u0026thinsp;1.63, SD\u0026thinsp;=\u0026thinsp;1.20).\u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eSelf-reported perceptual fluency\u003c/h2\u003e \u003cp\u003eA one-way ANOVA revealed that the scene category had no significant effect on self-reported perceptual fluency (\u003cem\u003eF\u003c/em\u003e(2,86)\u0026thinsp;=\u0026thinsp;1.361, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.261), with the following means (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e): urban scenes (M\u0026thinsp;=\u0026thinsp;4.50, SD\u0026thinsp;=\u0026thinsp;1.04), natural scenes with foliage (M\u0026thinsp;=\u0026thinsp;4.35, SD\u0026thinsp;=\u0026thinsp;1.04), and natural scenes without foliage (M\u0026thinsp;=\u0026thinsp;4.59, SD\u0026thinsp;=\u0026thinsp;1.10).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of fractal dimensions\u003c/h2\u003e \u003cp\u003eA one-way ANOVA revealed that the scene category had a significant effect on the fractal dimensions (\u003cem\u003eF\u003c/em\u003e(2,21)\u0026thinsp;=\u0026thinsp;8.510, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002, η2\u0026thinsp;=\u0026thinsp;0.002), with the following means (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e): urban scenes (M\u0026thinsp;=\u0026thinsp;1.79, SD\u0026thinsp;=\u0026thinsp;0.04), nature scenes with foliage (M\u0026thinsp;=\u0026thinsp;1.86, SD\u0026thinsp;=\u0026thinsp;0.01), and nature scenes without foliage (M\u0026thinsp;=\u0026thinsp;1.83, SD\u0026thinsp;=\u0026thinsp;0.04). The post hoc Bonferroni test revealed significant differences in fractal dimensions between the urban scenes and the natural scenes with foliage (\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.001). The difference in fractal dimensions between the urban scenes and the natural scenes without foliage was not significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.110), nor was the difference between the natural scenes with foliage and those without foliage (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.220).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eMediation analysis of the effect of self-reported perceptual fluency on eye fixations\u003c/h2\u003e \u003cp\u003eThe average direct effect of the scene category on the number of fixations was significant, \u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.16, 95.00% CI [0.23, 2.08], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016. The average indirect effect for the scene category on the number of fixations through self-reported perceptual fluency was not significant, \u003cem\u003eB\u003c/em\u003e = -0.11, 95.00% CI [-0.50, 0.30], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.322. The average direct effect of the scene category on fixation duration was significant, \u003cem\u003eB\u003c/em\u003e = -11.33, 95.00% CI [-17.14, -5.52], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001. The average indirect effect of the scene category on fixation duration through self-reported perceptual fluency was not significant, \u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.15, 95.00% CI [-1.68, 4.03], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.109.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eMediation analysis of the effect of the fractal dimension on eye fixations\u003c/h2\u003e \u003cp\u003eThe average direct effect of the scene category on the number of fixations was not significant, \u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.04, 95.00% CI [-0.19, 2.27], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.094. The average indirect effect for the scene category on the number of fixations through fractal dimensions was not significant, \u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007, 95.00% CI [-0.78, 1.07], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.987. The average direct effect of the scene category on fixation duration was significant, \u003cem\u003eB\u003c/em\u003e = -11.79, 95.00% CI [-19.77, -3.81], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006. The average indirect effect or the scene category on fixation duration through fractal dimension was not significant, \u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.61, 95.00% CI [-3.34, 7.54], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.537.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study analyzed eye movements while viewing images of the built environment and natural images with and without foliage. The study examined whether lower eye movement activity while viewing natural images compared with images of the built environment might be associated with greater perceptual fluency and the greater fractal complexity of natural images with and without foliage compared with images of the built environment.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003eViewing natural environments and reduced relative eye movement activity\u003c/h2\u003e \u003cp\u003eThe first research question was whether the results of previous eye movement studies could be replicated using the visual stimuli selected in this study. Previous research has shown that viewing images of natural environments is mostly associated with reduced relative eye movement activity, represented by a reduced number of fixations that are longer, in contrast to viewing built environments where the number of fixations is greater but the fixations are shorter. These findings were replicated in the present study as well.\u003c/p\u003e \u003cp\u003eEarlier eye-tracking studies used a fairly wide variety of environments representing natural and built urban environments, but a similar trend in gaze behavior was consistently observed: viewing images of natural environments is associated with reduced relative eye movement activity, represented by a reduced number of fixations that are longer, in contrast to viewing built environments where the number of fixations is greater but the fixations are shorter.\u003c/p\u003e \u003cp\u003eIn the study by Berto et al.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, photographs of the natural environment included lakes, rivers, the sea, hills, woods, forests, and orchards. The photographs of the built environment included industrial areas, residential areas, historic centers, and urban areas. Valtchanov and Ellard\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e used two types of images, urban and natural, taken from around the world. The natural images were either open landscapes or forest scenes, and the urban images were either open views of the city with skyscrapers or street views. In the studies of Franěk et al.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e the natural images were photographs of forest scenes, scenes that included a pond, rural landscapes, or photographs of large mountains. The built environment was represented by street scenes from cities in Europe and the United States. The urban scenes varied in character. Some included high-rise buildings, whereas others were streets with typical 19th-century urban buildings or streets with low-rise buildings typical of small towns. As examples of natural environments, Mart\u0026iacute;nez-Soto et al.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e used images of environments with specific aquatic elements, mountain landscapes with vegetation, urban forests, and rural landscapes as visual stimuli. The built environment was represented by images of house facades without nature, buildings, and housing estates... Batool et al.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e used detailed views of vegetation and flowers in addition to images of high mountains, water bodies, and forests. Finally, Fu and Xue\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e used images of open urban spaces with parks, greenways, and green roofs as examples of natural images and images of industrial areas and urban streets as examples of the built environment. In the present study, the photographs used were from different geographical locations, and in contrast to previous studies, the natural environments were represented by photographs of a dense deciduous forest in the growing season or when the trees were without foliage. The built environment was represented by images of frontal views of the facades of houses in town.\u003c/p\u003e \u003cp\u003eApart from the work of Stevenson et al.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, who used different devices and research techniques, there is only one discrepancy between our results and those of the previous investigation by Valtchanov and Ellard\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e; the difference in blink rates for the natural and built environments was not significant in the present study. Although blink rates may reflect mental workload, there may be considerable variation in blink rates between participants, within participants, and over time for individual participants\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. In addition, participants may show large fluctuations over time. This suggests that the blink rate may not be a reliable measure of mental workload in all situations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003eSelf-reported perceptual fluency and eye movements\u003c/h2\u003e \u003cp\u003eThe second research question concerns the possible relationship between self-reported perceptual fluency and reduced eye movements, as manifested by longer fixations and fewer fixations when viewing urban images and both types of natural images. Although we found significant differences in the number of fixations and fixation durations between urban environments and both types of natural environments, but not between natural environments with and without foliage, differences in self-reported perceptual fluency between urban and natural images, nor within natural images with foliage and natural images without foliage, were not significant. Moreover, the mediation analysis did not reveal a significant effect of self-reported perceptual fluency on eye fixations. By definition, perceptual fluency is achieved when stimuli are presented in a way that is clear, organized, and easy to understand\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. However, these requirements can also be met by the built environment, such as the preserved parts of historic cities or, of course, the organized facades of urban buildings. Thus, the self-reported perceptual fluency of given images did not explain the described differences in gaze behavior. However, self-reported perceptual fluency is subjective, and the results may depend on the measurement tool used. Notably, Graf et al.\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e argued that there is still a problem of how no consistent way to empirically operationalize the subjective experience of fluency. To date, there is no psychometrically validated and canonical scale for the construct of fluency, i.e., a construct that has both multiple sources and consequences. Therefore, we used the newly developed scale developed by these authors\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. However, its application in similar types of research has yet to be tested. A more objective approach to the measurement of perceptual fluency might be, for example, the recording of reaction times or accuracy rates in tasks that require participants to identify or recall images. The latter method was used, for example, in the study by Joye et al.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. However, as eye movements shift between image viewing and task performance, this method is not suitable for eye-tracking studies based on image viewing and processing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003eFractal complexity and eye movements\u003c/h2\u003e \u003cp\u003eThe third research question explored whether viewing images with higher fractal complexity is associated with reduced eye movements, as manifested by longer fixations, fewer fixations, and reduced blink rates when viewing natural versus urban images. Fractal dimension analysis revealed significant differences between urban scenes and natural scenes but only in foliage scenes, suggesting that fractal complexity could be associated with reduced eye movements, as manifested by longer and fewer fixations, but the mediation analysis did not reveal any significant effect of fractal complexity on these fixation variables. Although it was predicted that natural images with foliage would have greater fractal complexity than would natural images without foliage, this was not supported by our data. This finding is also consistent with the previous finding\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e that there were no differences in fractal complexity between nature scenes with and without foliage. Although some studies\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e suggest that the perceptual fluency of natural images could be mediated by fractal complexity, it seems that the fractal complexity of natural shapes can be fully observed when viewing the details of an object rather than when viewing the whole scene from a certain distance. Therefore, an explanation of perceptual fluency on the basis of easier processing of repetitive fractal patterns may be limited to specific situations where this repetitive structure of visual patterns can be fully observed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eFuture directions: the analysis of other moderating variables\u003c/h2\u003e \u003cp\u003eIf perceptual fluency or the related fractal scene composition, which may also explain perceptual fluency, is not a sufficient explanation for the repeatedly observed differences in gaze behavior when viewing natural and built scenes. Although the explanation based on perceptual fluency and fractal complexity seems very promising, it is difficult to prove. Next, we discuss several other variables that may influence the observed gaze behavior and that need to be monitored in future research. One of these factors may be the influence of the spatio-cognitive dimensions of complexity and mystery.\u003c/p\u003e \u003cp\u003eIn the context of Attention Restoration Theory\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, complexity plays a crucial role in how effectively natural environments restore attention. Complexity refers to the richness and variety of elements within an environment that can attract attention without overwhelming it; according to the theory, moderate complexity gently holds attention, prevents rumination, and provides a sense of mental refreshment. In contrast, high-complexity environments, which can be complicated or chaotic, can require too much cognitive effort, undermining the restorative process. Finally, low-complexity environments are simple and may lack sufficient stimulation, leading to boredom or disengagement.\u003c/p\u003e \u003cp\u003eEye movement activity while viewing a scene indicates the ease/difficulty of visually processing the scene, regardless of whether more or less complex exploration of the elements contained in a scene is required to understand the environment. Obviously, if there are fewer elements in a given natural scene than in a scene with a built environment, understanding a natural scene may require less visual effort than understanding a scene with a built environment. An example of this can be seen in the visual stimuli used in our study, where nature was represented by views of the forest, where there was usually only one type of tree of a similar size, whereas in our images of built environments, there were usually more elements that could attract attention and require further observation\u0026mdash;for example, cars on the road, looking for possible pedestrians, observing what might be on balconies, outside windows, and so on.\u003c/p\u003e \u003cp\u003eAnother spatial-cognitive dimension that may influence observed gaze behavior is mystery, as shown in the study by Marois et al.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. This study analyzed eye movements while viewing only natural environments that varied in the degree of mystery. They reported that images with higher levels of mystery required more visual scanning. As a typical example of stimuli with different degrees of mystery, the authors presented an image of a path leading through a forest. The low-mystery image was a straight path through a sparse deciduous forest, whereas the high-mystery image was a forest path with several bends. The latter image, of course, requires more visual exploration, with the observer likely trying to determine what might be behind each bend in the path. Therefore, further research would require observing gaze behavior in a variety of environmental settings that differ, for example, in terms of the complexity of environmental elements, mystery, or other relevant characteristics. This is because previous studies have averaged data from different types of natural scenes. While this allows more general conclusions to be drawn about differences between gaze behavior when viewing natural and built environments, it may ignore the influence of specific environmental layouts.\u003c/p\u003e \u003cp\u003eIn addition, some individual differences may influence the observation of gaze behavior. For example, the study by Batool et al.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. also reported that viewing natural scenes was characterized by a lower number of fixations and longer fixation durations than viewing urban scenes. However, they also noted the importance of the degree of preference for a given scene. They reported that the most preferred urban scenes led to significantly more fixations and saccades. Thus, eye movements reflect not only the ease of visual processing but also the interest in a particular environment. Interestingly, when participants viewed natural scenes, Batool et al.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e reported no significant differences in gaze behavior between the most and least preferred views when the overall grand mean preference rating derived from the entire group of participants was used.\u003c/p\u003e \u003cp\u003eHowever, when the authors\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e also examined individual differences between participants on the basis of their scores on the Nature Relatedness Scale\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, they reported that when viewing urban scenes, scores on the Nature Relatedness Scale were inversely related to exploratory eye movements, whereas when viewing natural scenes, scores on the Nature Relatedness Scale were significantly positively correlated with the number of saccades. Thus, participants who explored natural scenes more often had higher scores on the Nature Relatedness Scale. This is a very important finding because previous studies have not taken into account the background of the participants. Could participants who live their daily lives in cities without a deeper emotional and cognitive relationship to nature simply visually scan and explore the urban environment more because it is more interesting to them?\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations. We only measured self-reported perceptual fluency using an instrument that has not been adequately tested in this area of research. Unfortunately, we did not have more sophisticated measures at our disposal, such as electrophysiological methods. On the other hand, eye tracking has been reported as a possible method of measuring perceptual fluency. Another limitation may be that perceived perceptual fluency was not measured for the participants directly involved in the eye-tracking study but for another group of participants. It is also necessary to consider that differences in foliage can affect not only the fractal properties, but also the contextual properties of images. Bare branches signaling the end of autumn versus the bright green leaves of spring may change the emotional impact of images. However, an otherwise similar experiment cannot be performed; bare branches compared with branches with leaves always have these contextual characteristics. Finally, because of the enormous diversity of the natural environment, the generalization of results is always limited when specific visual stimuli are used.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the Faculty of Informatics and Management at the University of Hradec Králové, the Student Specific Research Grants 1/2024. We thank Tomáš Havlíček, Illia Holubka, Tamara Polášková, Radek Pařízek, Josef Srpek, and Downar Yahor for their help in organizing and conducting the experiments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data is contained in the Supplementary Information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, M.F.; methodology, M.F., J.P.; data curation, J.P.; writing—original draft preparation, M.F.; writing—review and editing, M.F., J.P. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for the experiments was obtained from the Committee for Research Ethics at the University of Hradec Králové, No. 8/2019. All methods were carried out in accordance with relevant guidelines and regulations as approved by the Committee for Research Ethics at the University of Hradec Králové. All subjects gave written informed consent in accordance with the Declaration of Helsinki.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all subjects involved in this study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDataset.xlxs\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKaplan, R. \u0026amp; Kaplan, S. \u003cem\u003eThe Experience of Nature: A Psychological Perspective\u003c/em\u003e (Cambridge University Press, 1989).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJoye, Y., Pals, R., Steg, L. \u0026amp; Evans, B. L. 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Cited 15 March 2025.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Eye movements, Perceptual fluency, Natural environment, Attention Restoration Theory, Cognitive benefit","lastPublishedDoi":"10.21203/rs.3.rs-5848316/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5848316/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eA number of eye-tracking studies have shown that viewing natural environments is associated with reduced eye movement activity compared with viewing built environments. This has been linked to the cognitive benefits of viewing nature and explained in terms of Kaplan and Kaplan's Attention Restoration Theory. However, the theory has recently been criticized for the lack of empirical evidence supporting its framework. The first aim was to replicate the results of previous eye movement studies using different visual stimuli. In addition, we investigated whether reduced eye movements when viewing natural versus urban images could be explained by greater perceptual fluency and fractal complexity of the images. The participants (N\u0026thinsp;=\u0026thinsp;66) viewed images of forests with and without foliage and images of urban apartment buildings while their eye movements were recorded. The self-reported perceptual fluency and fractal complexity of the presented images were measured. While eye movement analysis revealed significantly less eye movement activity (longer fixations, shorter fixation durations) when viewing natural images than when viewing urban images, consistent with previous findings, mediation analyses did not reveal significant effects of perceptual fluency or fractal complexity on eye fixation results. There was also no significant difference between natural images with foliage and those without for any of the measured variables. Further research directions are discussed. Research should address the specific spatio-cognitive dimensions of natural images, as well as individual differences that may influence the degree of exploration of specific images.\u003c/p\u003e","manuscriptTitle":"Perceptual fluency and eye movements when viewing urban and natural scenes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-06 09:40:44","doi":"10.21203/rs.3.rs-5848316/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-09T04:59:08+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-07T17:25:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-05T20:39:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"331222785127551065864321252760825944093","date":"2025-04-26T18:16:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"157213801520038602406100718335085139400","date":"2025-04-25T15:07:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-25T11:47:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-21T08:57:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-04-06T11:22:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c39a3916-682f-4da6-b333-bd0cd3641fa3","owner":[],"postedDate":"May 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":47727207,"name":"Biological sciences/Psychology"},{"id":47727208,"name":"Earth and environmental sciences/Environmental social sciences"}],"tags":[],"updatedAt":"2025-07-21T16:01:40+00:00","versionOfRecord":{"articleIdentity":"rs-5848316","link":"https://doi.org/10.1038/s41598-025-07850-5","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-07-16 15:57:02","publishedOnDateReadable":"July 16th, 2025"},"versionCreatedAt":"2025-05-06 09:40:44","video":"","vorDoi":"10.1038/s41598-025-07850-5","vorDoiUrl":"https://doi.org/10.1038/s41598-025-07850-5","workflowStages":[]},"version":"v1","identity":"rs-5848316","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5848316","identity":"rs-5848316","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}