Measuring streetscape complexity based on the statistics of local contrast and spatial frequency

Intracranial study in humans: Neural spectral changes during watching comedy movie of Charlie Chaplin

Humor plays a prominent role in our lives. Thus, understanding the cognitive and neural mechanisms of humor is particularly important. Previous studies that investigated neural substrates of humor used functional MRI and to a lesser extent EEG. In the present study, we conducted intracranial recording in human patients, enabling us to obtain the signal with high temporal precision from within specific brain locations. Our analysis focused on the temporal lobe and the surrounding areas, the temporal lobe was most densely covered in our recording. Thirteen patients watched a fragment of a Charlie Chaplin movie. An independent group of healthy participants rated the same movie fragment, helping us to identify the most funny and the least funny frames of the movie. We compared neural activity occurring during the most funny and least funny frames across frequencies in the range of 1-170 Hz. The most funny compared to least funny parts of the movie were associated with activity modulation in the broadband high-gamma (70-170 Hz; mostly activation) and to a lesser extent gamma band (40-69Hz; activation) and low frequencies (1-12 Hz, delta, theta, alpha bands; mostly deactivation). With regard to regional specificity, we found three types of brain areas: (I) temporal pole, middle and inferior temporal gyrus (both anterior and posterior) in which there was both activation in the high-gamma/gamma bands and deactivation in low frequencies; (II) ventral part of the temporal lobe such as the fusiform gyrus, in which there was mostly deactivation the low frequencies; (III) posterior temporal cortex and its environment, such as the middle occipital and the temporo-parietal junction, in which there was activation in the high-gamma/gamma band. Overall, our results suggest that humor appreciation might be achieved by neural activity across the frequency spectrum.

The impact of visual complexity on perceived safety and comfort of the users: A study on urban streetscape of Sri Lanka

Increase in the variety of development in urban context has made it more complicated and complex for the users of public spaces. Absence of sufficient information to read the surrounding causes psychological anxiousness leading to perceived danger or discomfort for the urbanites. Consequently, perceived safety and comfort of the users is distinctively low in urban contexts, creating neglected and underused spaces. Complexity is one of the information processing variables as per Kaplan and Kaplan's informational model which helps users to comprehend the surrounding environment. The streetscape plays a vital role in the daily movement patterns within the urban cities and is the transition boundary between the public and private realms. Visual complexity of these streets is a result of different configurations of elements within the urban areas. This research is conducted to ascertain the relationship between visual complexity levels of the streets with the perceived safety and comfort of the users. Shannon Diversity Index (SDI) and Fractal dimension analysis were conducted with 48 SVIs (Street View Images) selected within 1km radius of Colpetty junction, Colombo Sri Lanka covering all the possible compositions found within the context. The visual index data extraction had identified ten major components within the selected 48 SVIs. 78 subjective ranking responses for perceived safety, comfort (preference) and perceived complexity were collected from snowball sampling. Findings of the study revealed that perceived safety levels and preference scores for the SVIs are related to the Shannon Diversity Index calculation in an inverted 'U' shape where the highest and lowest SDI values are related with low preference scores and low safety levels. The SVIs with medium SDI values are perceived as the safest and most preferred by the users of urban streets of Colombo Sri Lanka. The SDI and fractal dimension values were significantly correlated with the perceived complexity scores of the users. The results of this study can be accommodated in the planning and designing of urban streetscapes of tropical climates for sustainable and friendly urban expansions.

Intelligent Assessment for Visual Quality of Streets: Exploration Based on Machine Learning and Large-Scale Street View Data

At present, the collection and analysis of large amounts of key data for the visual quality assessment of streets are performed manually. The assessment efficiency is not high, and the effective information is not fully explored. This study aims to establish an intelligent method for assessing the visual quality of streets. Taking the Hexi District of Tianjin as an example and using street view images as the assessment medium, an assessment model of objective physical indicators is established based on PaddleSeg, an assessment model of subjective perceptual indicators is established based on neural image assessment, and a visual quality assessment model of streets is established based on a random forest. The above models can intelligently evaluate the visual quality of streets and key indicators affecting visual quality. The influence of each key indicator on the visual quality of streets and the relationship between objective physical indicators and subjective perceptual indicators are analyzed. Through a combination of subjective and objective as well as qualitative and quantitative methods, the results show satisfactory assessment accuracy. In short, this study uses machine-learning techniques to improve the scientific rigor and efficiency of visual quality assessment and expand the scale of visual quality assessment data.

Comparison of Quantitative Morphology of Layered and Arbitrary Patterns: Contrary to Visual Perception, Binary Arbitrary Patterns Are Layered from a Structural Point of View

Patterns found among both living systems, such as fish scales, bones, and tree rings, and non-living systems, such as terrestrial and extraterrestrial dunes, microstructures of alloys, and geological seismic profiles, are comprised of anisotropic layers of different thicknesses and lengths. These layered patterns form a record of internal and external factors that regulate pattern formation in their various systems, making it potentially possible to recognize events in the formation history of these systems. In our previous work, we developed an empirical model (EM) of anisotropic layered patterns using an N-partite graph, denoted as G(N), and a Boolean function to formalize the layer structure. The concept of isotropic and anisotropic layers was presented and described in terms of the G(N) and Boolean function. The central element of the present work is the justification that arbitrary binary patterns are made up of such layers. It has been shown that within the frame of the proposed model, it is the isotropic and anisotropic layers themselves that are the building blocks of binary layered and arbitrary patterns; pixels play no role. This is why the EM can be used to describe the morphological characteristics of such patterns. We present the parameters disorder of layer structure, disorder of layer size, and pattern complexity to describe the degree of deviation of the structure and size of an arbitrary anisotropic pattern being studied from the structure and size of a layered isotropic analog. Experiments with arbitrary patterns, such as regular geometric figures, convex and concave polygons, contour maps, the shape of island coastlines, river meanders, historic texts, and artistic drawings are presented to illustrate the spectrum of problems that it may be possible to solve by applying the EM. The differences and similarities between the proposed and existing morphological characteristics of patterns has been discussed, as well as the pros and cons of the suggested method.

The Cognitive-Emotional Design and Study of Architectural Space: A Scoping Review of Neuroarchitecture and Its Precursor Approaches

Humans respond cognitively and emotionally to the built environment. The modern possibility of recording the neural activity of subjects during exposure to environmental situations, using neuroscientific techniques and virtual reality, provides a promising framework for future design and studies of the built environment. The discipline derived is termed "neuroarchitecture". Given neuroarchitecture's transdisciplinary nature, it progresses needs to be reviewed in a contextualised way, together with its precursor approaches. The present article presents a scoping review, which maps out the broad areas on which the new discipline is based. The limitations, controversies, benefits, impact on the professional sectors involved, and potential of neuroarchitecture and its precursors' approaches are critically addressed.

Predicting human complexity perception of real-world scenes

Perceptual load is a well-established determinant of attentional engagement in a task. So far, perceptual load has typically been manipulated by increasing either the number of task-relevant items or the perceptual processing demand (e.g. conjunction versus feature tasks). The tasks used often involved rather simple visual displays (e.g. letters or single objects). How can perceptual load be operationalized for richer, real-world images? A promising proxy is the visual complexity of an image. However, current predictive models for visual complexity have limited applicability to diverse real-world images. Here we modelled visual complexity using a deep convolutional neural network (CNN) trained to learn perceived ratings of visual complexity. We presented 53 observers with 4000 images from the PASCAL VOC dataset, obtaining 75 020 2-alternative forced choice paired comparisons across observers. Image visual complexity scores were obtained using the TrueSkill algorithm. A CNN with weights pre-trained on an object recognition task predicted complexity ratings with r = 0.83. By contrast, feature-based models used in the literature, working on image statistics such as entropy, edge density and JPEG compression ratio, only achieved r = 0.70. Thus, our model offers a promising method to quantify the perceptual load of real-world scenes through visual complexity.

Quantifying the complexity of black-and-white images

We propose a complexity measure for black-and-white (B/W) digital images, based on the detection of typical length scales in the depicted motifs. Complexity is associated with diversity in those length scales. In this sense, the proposed measure penalizes images where typical scales are limited to small lengths, of a few pixels –as in an image where gray levels are distributed at random– or to lengths similar to the image size –as when gray levels are ordered into a simple, broad pattern. We introduce a complexity index which captures the structural richness of images with a wide range of typical scales, and compare several images with each other on the basis of this index. Since the index provides an objective quantification of image complexity, it could be used as the counterpart of subjective visual complexity in experimental perception research. As an application of the complexity index, we build a “complexity map” for South-American topography, by analyzing a large B/W image that represents terrain elevation data in the continent. Results show that the complexity index is able to clearly reveal regions with intricate topographical features such as river drainage networks and fjord-like coasts. Although, for the sake of concreteness, our complexity measure is introduced for B/W images, the definition can be straightforwardly extended to any object that admits a mathematical representation as a function of one or more variables. Thus, the quantification of structural richness can be adapted to time signals and distributions of various kinds.

Predicting perceived visual complexity of abstract patterns using computational measures: The influence of mirror symmetry on complexity perception

Visual complexity is relevant for many areas ranging from improving usability of technical displays or websites up to understanding aesthetic experiences. Therefore, many attempts have been made to relate objective properties of images to perceived complexity in artworks and other images. It has been argued that visual complexity is a multidimensional construct mainly consisting of two dimensions: A quantitative dimension that increases complexity through number of elements, and a structural dimension representing order negatively related to complexity. The objective of this work is to study human perception of visual complexity utilizing two large independent sets of abstract patterns. A wide range of computational measures of complexity was calculated, further combined using linear models as well as machine learning (random forests), and compared with data from human evaluations. Our results confirm the adequacy of existing two-factor models of perceived visual complexity consisting of a quantitative and a structural factor (in our case mirror symmetry) for both of our stimulus sets. In addition, a non-linear transformation of mirror symmetry giving more influence to small deviations from symmetry greatly increased explained variance. Thus, we again demonstrate the multidimensional nature of human complexity perception and present comprehensive quantitative models of the visual complexity of abstract patterns, which might be useful for future experiments and applications.

Complexity As Key to Designing Cognitive-Friendly Environments for Older People

The lived environment is the arena where our cognitive skills, preferences, and attitudes come together to determine our ability to interact with the world. The mechanisms through which lived environments can benefit cognitive health in older age are yet to be fully understood. The existing literature suggests that environments which are perceived as stimulating, usable and aesthetically appealing can improve or facilitate cognitive performance both in young and older age. Importantly, optimal stimulation for cognition seems to depend on experiencing sufficiently stimulating environments while not too challenging. Environmental complexity is an important contributor to determining whether an environment provides such an optimal stimulation. The present paper reviews a selection of studies which have explored complexity in relation to perceptual load, environmental preference and perceived usability to propose a framework which explores direct and indirect environmental influences on cognition, and to understand these influences in relation to aging processes. We identify ways to define complexity at different environmental scales, going from micro low-level perceptual features of scenes, to design qualities of proximal environments (e.g., streets, neighborhoods), to broad geographical areas (i.e., natural vs. urban environments). We propose that studying complexity at these different scales will provide new insight into the design of cognitive-friendly environments.

Predicting Complexity Perception of Real World Images

The aim of this work is to predict the complexity perception of real world images. We propose a new complexity measure where different image features, based on spatial, frequency and color properties are linearly combined. In order to find the optimal set of weighting coefficients we have applied a Particle Swarm Optimization. The optimal linear combination is the one that best fits the subjective data obtained in an experiment where observers evaluate the complexity of real world scenes on a web-based interface. To test the proposed complexity measure we have performed a second experiment on a different database of real world scenes, where the linear combination previously obtained is correlated with the new subjective data. Our complexity measure outperforms not only each single visual feature but also two visual clutter measures frequently used in the literature to predict image complexity. To analyze the usefulness of our proposal, we have also considered two different sets of stimuli composed of real texture images. Tuning the parameters of our measure for this kind of stimuli, we have obtained a linear combination that still outperforms the single measures. In conclusion our measure, properly tuned, can predict complexity perception of different kind of images.

Effects of presentation duration on measures of complexity in affective environmental scenes and representational paintings

Complexity constitutes an integral part of humans' environment and is inherent to information processing. However, little is known about the dynamics of visual complexity perception of affective environmental scenes (IAPS pictures) and artworks, such as affective representational paintings. In three experiments, we studied the time course of visual complexity perception by varying presentation duration and comparing subjective ratings with objective measures of complexity. In Experiment 1, 60 females rated 96 IAPS pictures, presented either for 1, 5, or 25s, for familiarity, complexity, pleasantness and arousal. In Experiment 2, another 60 females rated 96 representational paintings. Mean ratings of complexity and pleasantness changed according to presentation duration in a similar vein in both experiments, suggesting an inverted U-shape. No common pattern of results was observed for arousal and familiarity ratings across the two picture sets. The correlations between subjective and objective measures of complexity increased with longer exposure durations for IAPS pictures, but results were more ambiguous for paintings. Experiment 3 explored the time course of the multidimensionality of visual complexity perception. Another 109 females rated the number of objects, their disorganization and the differentiation between a figure-ground vs. complex scene composition of pictures presented for 1 and 5s. The multidimensionality of visual complexity only clearly emerged in the 5-s condition. In both picture sets, the strength of the correlations with objective measures depended on the type of subdimension of complexity and was less affected by presentation duration than correlations with general complexity in Experiments 1 and 2. These results have clear implications for perceptual and cognitive theories, especially for those of esthetic experiences, in which the dynamical changes of complexity perception need to be integrated.

Visual Complexity Analysis Using Taxonomic Diagrams of Figures and Backgrounds in Japanese Residential Streetscapes

This study was conducted with the objective of finding the perceivable effect of figures and backgrounds on residential streetscapes and their connections in visual complexity. The visual complexity depends on the extent of information a viewer can observe from a visible area. The information includes a number of visual elements along the streetscapes and their diversity and interconnections. The aim of this research was to analyze the structural hierarchical visual complexity of the streetscapes, caused by varied spatial arrangement and numerous spatial connections of the perceivable visual elements. The visual elements of 60 residential streetscapes were classified into figures and backgrounds using human perception of 20 subjects. The identified figures and backgrounds were arranged in a taxonomic diagram representing their connections. These taxonomic diagrams reflect the structural hierarchical visual complexity. Finally, taxonomic entropy was applied to statistically analyze the structural hierarchical visual complexity. When the taxonomic diagram is vertically and horizontally lengthy and the arrangement of the elements of taxonomic diagram is irregular, the complexity increases depicting a large number of figures whose spatial connections impart a high visual complexity to the streetscapes.