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Mobile learning, or m-Learning, has grown in popularity significantly over the last few decades, as evidence of educators and students worldwide using the device as a teaching and learning tool continues to accumulate. The pattern of mobile-learning research from 2001 to 2020 is determined by bibliometric analysis. The study retrieved 3,874 documents for further analysis, based on the keywords associated with mobile learning in the article’s title. The maps depicted the connections between the researchers, countries, all keywords, titles, and abstracts. The title and abstract of this study are used to visualise the co-occurring terms of various phases or concepts associated with mobile learning that were extracted from the Scopus database. The findings indicate strong and direct connections between the concepts in e-learning, implying a significant and direct research connection. China was the leading country in mobile-learning research, and the leading journal was Computers and Education . The top author’s keywords in terms of co-occurrence were "mobile learning", "e-learning", "students", "learning systems", and "m-learning". To conduct a two-decade analysis, this study excludes any publications from the years 1984 and 2021. These critical analyses of prior work are valuable and indispensable resources for mobile-learning scholars and practitioners. It is believed that online-learning applications have increased students’ engagement; and it has eliminated the accessibility gap. Consequently, mobile learning is expected to maintain its popularity over the next few decades. https://doi.org/10.26803/ijlter.20.10.16

Generation of a clear, correct, concise, complete, and coherent linguistic description of the visual patterns in a medical image is a challenging task. Unfortunately, many radiologists fail to satisfactorily perform this task due to various reasons such as workload, scant time, and fatigue. Although AI-based computer-aided detection (CADe) and computer-aided diagnosis (CADx) systems have been developed for observing and interpreting patterns in medical images, they do not generate radiological reports. In recent years, a lot of research has been done to develop automated report generation methods. This paper presents a comprehensive survey of all such methods specifically developed for chest radiographs. It consolidates information about standard chest X-ray datasets, state-of-the-art report generation methods, evaluation metrics, and their results. Deep learning-based techniques for automatically generating chest radiographic reports have been classified and discussed in detail. The encoder-decoder-based techniques have been meticulously categorized for a better understanding of the developments in this area. This paper is also beneficial for the researchers interested in developing automatic report generation systems for imaging modalities other than chest radiographs.

In multistability, perceptual interpretations (“percepts”) of ambiguous stimuli alternate over time. There is considerable debate as to whether similar regularities govern the first percept after stimulus onset and percepts during prolonged presentation. We address this question in a visual pattern-component rivalry paradigm by presenting two overlaid drifting gratings, which participants perceived as individual gratings passing in front of each other (“segregated”) or as a plaid (“integrated”). We varied the enclosed angle (“opening angle”) between the gratings (experiments 1 and 2) and stimulus orientation (experiment 2). The relative number of integrated percepts increased monotonically with opening angle. The point of equality, where half of the percepts were integrated, was at a smaller opening angle at onset than during prolonged viewing. The functional dependence of the relative number of integrated percepts on opening angle showed a steeper curve at onset than during prolonged viewing. Dominance durations of integrated percepts were longer at onset than during prolonged viewing and increased with opening angle. The general pattern persisted when stimuli were rotated (experiment 2), despite some perceptual preference for cardinal motion directions over oblique directions. Analysis of eye movements, specifically the slow phase of the optokinetic nystagmus (OKN), confirmed the veridicality of participants’ reports and provided a temporal characterization of percept formation after stimulus onset. Together, our results show that the first percept after stimulus onset exhibits a different dependence on stimulus parameters than percepts during prolonged viewing. This underlines the distinct role of the first percept in multistability.

We examined the effects of the reading medium (print vs. digital) on readers’ visual patterns, reading performance, and reading attitudes. Two within-subject experiments were conducted with 74 read...

Previous studies have found that surgeons perform better in three-dimensional (3D) surgery than in two-dimensional (2D) surgery. However, no comparative studies have revealed the impact of 3D laparoscopic surgery on the surgeon's vision. To explore the effect of laparoscopic surgeons' depth perception during 3D laparoscopic surgery, 10 participants were recruited and performed 4 sets comparative simulated laparoscopic procedures in a virtual simulator, and eye movement signals were acquired, which were used to characteristics the visual differences. Fixation rate and saccade speed were used to characterise the influence of moderating variables for visual characteristics. The results from the data showed significant differences in eye movement behaviour. Compared with 2D laparoscopic surgery, surgeons have more average fixation rate (p-values = 0.001, 0.000, 0.003 and 0.015, respectively) and faster saccade speed (p-values = 0.037, 0.003, 0.073 and 0.105, respectively) in 3D laparoscopic surgery. The results of this study showed that surgeons had more efficient visual search in 3D laparoscopic surgery. At the same time, the results also indicated that surgeon's mental workload in 3D laparoscopic surgery was low. The relevant conclusions of this paper revealed the advantages of 3D laparoscopic surgery through visual efficiency.

Deep convolutional neural networks (deep CNNs) show a large power for robust recognition of visual patterns. The neocognitron, which was first proposed by Fukushima (1979), is recognized as the origin of deep CNNs. Its architecture was suggested by the neurophysiological findings on the visual systems of mammals. It acquires the ability to recognize visual patterns robustly through learning. Although the neocognitron has a long history, improvements of the network are still continuing. This article discusses the recent neocognitron, focusing on differences from the conventional deep CNNs. Some other functions of the visual system can also be realized by networks extended from the neocognitron, for example, recognition of partly occluded patterns, the mechanism of selective attention, and so on.

The product’s aesthetic characteristics influence the user’s interactions with them, as well as they can change the meanings attributed to the products. When it comes to assistive technologies, these associations can lead to reinforcing the stigma of disability related to its users. Therefore, this research aimed to measure the visual aesthetic experience with three different models of hearing aids, using the eye-tracking method. Thus, identifying how aesthetic principles behave in hearing aids, according to the visual patterns identified. In addition to eye tracking, a questionnaire was used to obtain data about the subjects. As main results, greater preference was expressed for the model with a higher degree of novelty, as well as it was possible to verify greater concentration of the eye gaze in the models of hearing aids referred by the subjects. In addition, he results corroborate with the iterature identifying relationships etween duration of fixations and roportion, as well as number of fixations and pureness. Therefore, when measuring aesthetic experience in an objective way, one can understand cognitive and affective aspects of the interaction with hearing aids.

Keno is a renowned game of chance offered by both lotteries and casinos throughout the world. The premise of the game is simple: a set of numbers is drawn at random and players are paid for selecting all or most of those numbers in advance. The mathematical foundation governing the odds and payouts for these basic wagers is elementary. More sophisticated bets, however, have associated probabilities which cannot be easily calculated by applying a manageable series of formulas. As a result, keno’s wagering menu has remained stagnant since the game’s widespread inception. Namely, proposition bets, now commonplace in virtually every other form of gaming, have never been developed for keno to any large degree. This is both unfortunate and surprising because keno is ideally suited for such an expansion. Unlike most other lottery-style games, keno highlights all drawn numbers on a rectangular grid, creating random visual patterns at the conclusion of each drawing. Creative new wagers based on these patterns are rigorously derived herein. These new additions should greatly enhance the game for both gamblers and enterprising gaming operators alike.

The investigation of self‐prioritization via a simple matching paradigm represents a new way of enhancing our knowledge about the processing of self‐relevant content and also increases our understanding of the self‐concept itself. By associating formerly neutral material with the self, and assessing the resulting prioritization of these newly formed self‐associations, conclusions can be drawn concerning the effects of self‐relevance without the burden of highly overlearned materials such as one’s own name. This approach was used to gain further insights into the structure and complexity of self‐associations: a tactile pattern was associated with the self and thereafter, the prioritization of the exact same visual pattern was assessed – enabling the investigation of crossmodal self‐associations. The results demonstrate a prioritization of self‐associated material that rapidly extends beyond the borders of a sensory modality in which it was first established.

Predictive models trained on sequencing profiles often fail to achieve expected performance when externally validated on unseen profiles. While many factors such as batch effects, small data sets, and technical errors contribute to the gap between source and unseen data distributions, it is a challenging problem to generalize the predictive models across studies without any prior knowledge of the unseen data distribution. Here, this study proposes DeepBioGen, a sequencing profile augmentation procedure that characterizes visual patterns of sequencing profiles, generates realistic profiles based on a deep generative model capturing the patterns, and generalizes the subsequent classifiers. DeepBioGen outperforms other methods in terms of enhancing the generalizability of the prediction models on unseen data. The generalized classifiers surpass the state-of-the-art method, evaluated on RNA sequencing tumor expression profiles for anti-PD1 therapy response prediction and WGS human gut microbiome profiles for type 2 diabetes diagnosis. VT's OASF Published version This work is partially supported by VT's OASF support.

Research on consumer reading (i.e., visual patterns) of Asian restaurant menus is new territory for academic research. This study is the first attempt to scrutinize consumers’ visual patterns towar...

The majority of angiosperms have flowers with conical epidermal cells, which are assumed to have various functions, such as enhancing the visual signal to pollinators, but detailed optical studies on how conical epidermal cells determine the flower's visual appearance are scarce. Here we report that conical epidermal cells of Mandevilla sanderi flowers effectively reduce surface gloss and create a velvety appearance. Owing to the reduction in surface gloss, the flower further makes more efficient use of floral pigments and light scattering structures inside the flower. The interior backscattering yields a cosine angular dependence of reflected light, meaning that the flowers approximate near-perfect (Lambertian) diffusers, creating a visual signal that is visible across a wide angular space. Together with the large flowers and the tilted corolla tips, this generates a distinct visual pattern, which may enhance the visibility to pollinators.

Although the understanding of cognitive disciplines has progressed, we know relatively little about how the human brain perceives art. Thanks to the growing interest in visual perception, eye-tracking technology has been increasingly used for studying the interaction between individuals and artworks. In this study, eye-tracking was used to provide insights into non-expert visitors’ visual behaviour as they move freely in the historical room of the “Studiolo del Duca” of the Ducal Palace in Urbino, Italy. Visitors looked for an average of almost two minutes. This study revealed which parts of the artefact captured visitors’ attention and also gives interesting information about the main patterns of fruition.

The purpose of this paper is to investigate is there a relation between age and the way that people are using digital media content. By developing information and communication technology in the field of media communications, media con-tent users are able to use more visual information of the same content, affecting the success of decoding the message and users can also manipulate part of the visual presentation by adapting the user interface to their visual needs. This puts the user in the position of the designer of the visual content. The information user today partially takes the role of the creator of the visual aspect of the information, so it comes to repositioning in relation the graphic designer - the recipient of the message. The paper examines whether the age of the user has an impact on the visual pattern used in the digital content utilization. The research approach is qualitative and exploratory in nature, as the aim is to develop a model of information consumption for elderlies in digital environment in which the information quality is challenged by the way of user’s visual approach and visual pattern. Through the empirical part of the research, user behavior was investigated in the use of media web content. Although, there are some differences between two users groups divided by age, we can not claim that these differences are connected with age. The research has shown that the primary reason for certain visual pattern is interest for specific theme. Based on conducted theoretical analysis and empirical research it can be concluded that visual communication in a new, converging media environment is experiencing redefinition and enables rapid transition from status information per se to status per nos, but the interest for content is still dominant regulator of specific users navigation trought the content., Svrha ovog rada je istražiti odnos između dobi i načina na koji ljudi koriste digitalne medijske sadržaje. Razvojem informacijsko-komunikacijske tehnologije u području medijskih komunikacija korisnici medija sadržaja mogu koristiti više vizualnih informacija istog sadržaja, što utječe na uspjeh dekodiranja poruke, a korisnici također mogu manipulirati dijelom vizualne prezentacije prilagodbom korisničkog sučelja njihovim vizualnim potrebama. Time se korisnik postavlja u poziciju dizajnera vizualnog sadržaja. Korisnik informacija danas dijelom preuzima ulogu tvorca vizualnog aspekta informacija, pa dolazi do repozicioniranja u odnosu grafički dizajner - primatelj poruke. U radu se ispituje ima li dob korisnika utjecaj na vizualni obrazac koji se koristi u korištenju digitalnog sadržaja. Istraživački pristup je kvalitativnog i istraživačkog karaktera, a cilj je razviti model konzumiranja informacija za starije osobe u digitalnom okruženju u kojem se kvalitetu podataka dovodi u pitanje korisnikovim vizualnim pristupom i vizualnim obrascem. Empirijskim dijelom istraživanja istraženo je ponašanje korisnika pri korištenju medijskih web sadržaja. Iako postoje neke razlike između dvije korisničke skupine podijeljene prema dobi, ne možemo tvrditi da su te razlike povezane s dobi. Istraživanje je pokazalo da je glavni razlog određenog vizualnog uzorka zanimanje za određenu temu. Na temelju provedene teorijske analize i empirijskog istraživanja može se zaključiti da vizualna komunikacija u novom, konvergiranom medijskom okruženju doživljava redefiniranje i omogućava brzi prijelaz iz statusa per se u status per nos, no zanimanje za sadržaj još uvijek je dominantan regulator navigacije za određene korisnike sadržaja.

Many attempts have been made towards combining RGB and 3D poses for the recognition of Activities of Daily Living (ADL). ADL may look very similar and often necessitate to model fine-grained details to distinguish them. Because the recent 3D ConvNets are too rigid to capture the subtle visual patterns across an action, this research direction is dominated by methods combining RGB and 3D Poses. But the cost of computing 3D poses from RGB stream is high in the absence of appropriate sensors. This limits the usage of aforementioned approaches in real-world applications requiring low latency. Then, how to best take advantage of 3D Poses for recognizing ADL? To this end, we propose an extension of a pose driven attention mechanism: Video-Pose Network (VPN), exploring two distinct directions. One is to transfer the Pose knowledge into RGB through a feature-level distillation and the other towards mimicking pose driven attention through an attention-level distillation. Finally, these two approaches are integrated into a single model, we call VPN++. We show that VPN++ is not only effective but also provides a high speed up and high resilience to noisy Poses. VPN++, with or without 3D Poses, outperforms the representative baselines on 4 public datasets. Code is available at https://github.com/srijandas07/vpnplusplus., Comment: submitted to a journal

The technologies of Industry 4.0 provide an opportunity to improve the effectiveness of Visual Management in manufacturing. The opportunity of improvement is twofold. From one side, Visual Management theory and practice can inspire the design of new software tools suitable for Industry 4.0, on the other side, the technology of Industry 4.0 can be used to increase the effectiveness of visual software tools. The paper first explores how the theoretical result on Visual Management can be used as a guideline to improve human-computer interaction, then a methodology is proposed for the design of visual patterns for manufacturing. Four visual patterns are presented that contribute to the solution of problems frequently encountered in discrete manufacturing industries, these patterns help to solve planning and control problems thus providing support to various management functions. Positive implications of this research concern people engagement and empowerment as well as improved problem solving, decision-making and management of manufacturing processes.

Despite recent progress in the research of people’s emotional response to the environment, the built—rather than natural—environment’s emotional effects have not yet been thoroughly examined. In response to this knowledge gap, we recruited 26 participants and scrutinized their emotional response to various urban street scenes through an immersive exposure experiment using virtual reality. We utilized new physiological monitoring technologies that enable synchronized observation of the participants’ electroencephalography, electrodermal activity, and heart rate, as well as their subjective indicators. With the newly introduced measurement for the global visual patterns of the built environment, we built statistical models to examine people’s emotional response to the physical element configuration and color composition of street scenes. We found that more diverse and less fragmented scenes inspired positive emotional feelings. We also found (in)consistency among the physiological and subjective indicators, indicating a potentially interesting neural−physiological interpretation for the classic form−function dichotomy in architecture. Besides the practical implications on promoting physical environment design, this study combined objective physiology-monitoring technology and questionnaire-based research techniques to demonstrate a better approach to quantify environment−emotion relationships.

The reasonable definition of semantic interpretability presents the core challenge in explainable AI. This paper proposes a method to modify a traditional convolutional neural network (CNN) into an interpretable compositional CNN, in order to learn filters that encode meaningful visual patterns in intermediate convolutional layers. In a compositional CNN, each filter is supposed to consistently represent a specific compositional object part or image region with a clear meaning. The compositional CNN learns from image labels for classification without any annotations of parts or regions for supervision. Our method can be broadly applied to different types of CNNs. Experiments have demonstrated the effectiveness of our method., Comment: IJCAI2021

Visual cognitive strategies in construction hazard recognition (CHR) signifies prominent value for the development of CHR computer vision techniques and safety training. Nonetheless, most studies are based on either sparse fixations or cross-sectional (accumulative) statistics, which lack consideration of temporality and yielding limited visual pattern information. This research aims to investigate the temporal visual search patterns for CHR and the cognitive strategies they imply. An experimental study was designed to simulate CHR and document participants’ visual behavior. Temporal qualitative comparative analysis (TQCA) was applied to analyze the CHR visual sequences. The results were triangulated based on post-event interviews and show that: (1) In the potential electrical contact hazards, the intersection of the energy-releasing source and wire that reflected their interaction is the cognitively driven visual area that participants tend to prioritize, (2) in the PPE-related hazards, two different visual strategies, i.e., “scene-related” and “norm-guided”, can usually be generalized according to the participants’ visual cognitive logic, corresponding to the bottom-up (experience oriented) and top-down (safety knowledge oriented) cognitive models. This paper extended recognition-by-components (RBC) model and gestalt model as well as providing feasible practical guide for safety trainings and theoretical foundations of computer vision techniques for CHR.

Counting ability is one of the many aspects of animal cognition and has enjoyed great interest over the last couple of decades. The impetus for studying counting ability in nonhuman animals has likely come from more than a general interest in animal cognition, as the analysis of animal abilities amplifies our understanding of human cognition. In addition, a model animal with the ability to count could be used to replace human subjects in related studies. Here we designed a behavioral paradigm to train rhesus monkeys to count 1-to-6 visual patterns presented sequentially with long and irregular interpattern intervals on a touch screen. The monkeys were required to make a response to the sixth pattern exclusively, inhibiting response to any patterns appearing at other ordinal positions. All stimulus patterns were of the same size, color, location, and shape to prevent monkeys making the right choice due to non-number physical cues. In the long delay period, the monkey had to enumerate how many patterns had been presented sequentially and had to remember in which ordinal position the current pattern was located. Otherwise, it was impossible for them to know which pattern was the target one. The results show that all three monkeys learned to correctly choose the sixth pattern within 3 months. This study provides convincing behavioral evidence that rhesus monkeys may have the capacity to count.

The objective of this study was to identify cognitive apprehensions used by fifth- and sixth-grade students (10–12-year-olds) when answering far generalization questions in two problems of ...

This article contributes to research on advertising effectiveness by investigating the combined influence of ad headlines and visual patterns in the ad on consumer product evaluations. Headlines can convey motion (e.g., “move,” “quick”); when the associated ad features a regular visual pattern, it evokes stronger product evaluations than if it depicts an irregular visual pattern. Thus, the way the advertised products are aligned visually represents critical decisions for ad designers. As Study 1 reveals, if the regular visual pattern of an advertisement combines with verbal information conveying motion, stronger product evaluations result compared with the use of an irregular visual pattern. Study 2 extends these findings by demonstrating that a regular pattern creates mental simulation, such that consumers imagine themselves experiencing the product, which mediates the relationship between visual patterns and product evaluations. Study 3 uses text mining and image annotation analyses to provide ecological validity for the findings, corroborating them in the context of brand messages on Twitter.

Beginning with the classic work of Shepard, Hovland, & Jenkins (1961), Type II visual patterns (e.g., exemplars are large white squares OR small black triangles) have held a special place i...

Applied ergonomics research examines not only the fit, form and function of military uniforms, but also their ability to effectively camouflage personnel as they perform job-related tasks. Many of these job-related tasks involve moving through environments, but existing literature examining camouflage effectiveness often assumes that movement effectively "breaks" even the best camouflage patterns, rendering them of limited utility for reducing the visual signature of a moving target. However, recent research demonstrates that animals equipped with adaptive camouflage change their patterning in predictable ways during movement and this adaptation decreases detectability, suggesting that uniform patterning may still hold value for reducing conspicuity during movement. The present experiment examined whether three visual pattern characteristics, local contrast, orientation, and spatial frequency, would influence the detectability of a moving human target. Participants attempted to detect and localize a simulated human target moving across a background scene, and a factorial design varied target movement speed, and the local contrast, spatial frequency, and orientation of its camouflage patterning. Results showed that target detectability was strongly influenced by target movement rate, pattern local contrast, and pattern spatial frequency, and these effects persisted even under conditions of very fast movement. Importantly, we found that the effect of local contrast was most robust under conditions of movement, suggesting its importance for reducing detectability of moving personnel. We conclude that movement is not always sufficient to break the concealment offered by a pattern with low contrast and a spatial frequency match with its background. Results are discussed in the context of visual processing theories and the application of these findings to the design and development of static and adaptive camouflage patterns for military personnel.

The CA3 and dentate gyrus (DG) regions of the hippocampus are considered key for disambiguating sensory inputs from similar experiences in memory, a process termed pattern separation. The neural mechanisms underlying pattern separation, however, have been difficult to compare across species: rodents offer robust recording methods with less human-centric tasks while humans provide complex behavior with less recording potential. To overcome these limitations, we trained monkeys to perform a visual pattern separation task similar to those used in humans while recording activity from single CA3/DG neurons. We find that when animals discriminate recently seen novel images from similar (lure) images, behavior indicative of pattern separation, CA3/DG neurons respond to lure images more like novel than repeat images. Using a population of these neurons, we are able to classify novel, lure, and repeat images from each other using this pattern of firing rates. Notably, one subpopulation of these neurons is more responsible for distinguishing lures and repeats—the key discrimination indicative of pattern separation.

This research examines the effect of social exclusion on consumers’ preferences for visual density. Based on seven experimental studies, we reveal that consumers who perceive themselves as socially excluded evaluate products with dense visual patterns more positively than their nonexcluded peers. This effect occurs because social exclusion triggers a feeling of psychological emptiness and dense patterns can provide a sense of being “filled,” which helps to alleviate this feeling of emptiness. This effect is attenuated when consumers physically fill something or experience a feeling of “temporal density” (i.e., imagining a busy schedule with many tasks packed into a short time). These results shed light on consumers’ socially grounded product aesthetic preferences and offer practical implications for marketers, designers, and policy makers.

Bumblebees Bombus terrestris are good at learning to distinguish between patterned flowers. They can differentiate between flowers that differ only in their patterning of scent, surface texture, temperature, or electrostatic charge, in addition to visual patterns. As recently shown, bumblebees trained to discriminate between nonvisual scent patterns can transfer this learning to visually patterned flowers that show similar spatial patterning to the learnt scent patterns. Bumblebees can, therefore, transfer learnt patterns between different sensory modalities, without needing to relearn them. We used differential conditioning techniques to explore whether cross-modal transfer of learnt patterns also occurred between visual and temperature patterns. Bumblebees that successfully learnt to distinguish rewarding and unrewarding temperature patterns did not show any preferences for the corresponding unlearnt visual pattern. Similarly, bumblebees that learnt visual patterns did not transfer these to temperature patterns, suggesting that they are unable to transfer learning of temperature and visual patterns. We discuss how cross-modality pattern learning may be limited to modalities that have potentially strong neurological links, such as the previously demonstrated transfer between scent and visual patterns. Electronic supplementary material The online version of this article (10.1007/s00359-019-01320-w) contains supplementary material, which is available to authorized users.

Content fingerprinting has been widely used for protecting the copyright of on-line digital media. By aggregating the perceptual attributes of digital media into an invariant digest, content fingerprinting enables user-generated-contents (UGC) networks to identify the unauthorized distribution of copyrighted contents. In this paper, we propose an image fingerprinting algorithm based on invariant generative model. The proposed work formulates fingerprinting algorithm as a hierarchy of parametric models. For better generalization performance, we first train the models to learn generic visual patterns from local image structures, which is accomplished by fitting the statistical distribution of local patches. The learned models are then fine-tuned to address the robustness and discriminability requirements of content fingerprinting. Moreover, our training scheme also regularizes the norm of gradients to force the models to learn visual features that are insensitive to distortion. The learned models are cascaded with a pooling operation to form the building block of fingerprinting algorithm. Considering the security requirement of copyright protection, we also develop a key-dependent scheme to randomize fingerprint computation. Experimental results validate that the proposed work can withstand a wide variety of distortions and achieve a higher content identification accuracy than competing algorithms.

The visual patterns of fractal stimuli on a computer screen and the brightness patterns of light projected onto room surfaces have independently been shown to influence human perceptual responses. It is not clear, however, what effect would result if the same fractal patterns were projected as light patterns on room surfaces. This paper reports on the results of three studies investigating visual interest, visual preference, and mood responses elicited by varying complexities of fractal light patterns projected on walls and floors of an interior space. The results suggest that fractal light patterns of medium to medium-high complexity (quantified by the fractal dimension D = 1.5–1.7) were significantly more visually interesting than other patterns. Crucially, viewing distance did not significantly influence visual interest or visual preference. Based on these studies, the use of medium to medium-high complexity fractal light patterns in interior spaces may be useful for enhancing occupants’ visual interest and mood.

The visual patterns in recurrence plots of time-series data can be quantified using recurrence quantification analysis (RQA), a phase-space-based method. The ability to quantitate recurrence plots affords the possibility of using them to solve central biomedical problems, for example detecting the presence of neurological diseases. Our goal was to assess this application by statistically comparing the values of plot-based quantifiers of electroencephalograms (EEGs) from patients having multiple sclerosis (MS) with values from the EEGs of control subjects. First, employing a model system consisting of the addition of known deterministic signals to the EEG of normal subjects, we empirically identified the embedding conditions that facilitated detection of the effect of the addition of the signals. Second, we used the conditions thus identified to compare EEGs from 10 patients with MS and 10 age- and gender-matched control subjects, using seven standard recurrence-plot quantifiers. We identify embedding dimension of 5 points and time delay of 5 points as conditions that maximize the ability of RQA to detect the presence of deterministic activity in EEGs time series sampled at 500 Hz. The values of the RQA quantifiers computed from the EEGs of the MS patients were significantly greater than the corresponding values from the controls, indicating that the presence of the disease was associated with detectable changes in the EEG (family-wise error