Simplicity in Complexity : Explaining Visual Complexity using Deep Segmentation Models

The complexity of visual stimuli plays an important role in many cognitive phenomena, including attention, engagement, memorability, time perception and aesthetic evaluation. Despite its importance, complexity is poorly understood and ironically, previous models of image complexity have been quite complex. There have been many attempts to find handcrafted features that explain complexity, but these features are usually dataset specific, and hence fail to generalise. On the other hand, more recent work has employed deep neural networks to predict complexity, but these models remain difficult to interpret, and do not guide a theoretical understanding of the problem. Here we propose to model complexity using segment-based representations of images. We use state-of-the-art segmentation models, SAM and FC-CLIP, to quantify the number of segments at multiple granularities, and the number of classes in an image respectively. We find that complexity is well-explained by a simple linear model with these two features across six diverse image-sets of naturalistic scene and art images. This suggests that the complexity of images can be surprisingly simple

Overview of Current Directions in Boredom Research

The concluding chapter of this book represents a collaborative effort between the editors and all contributing authors, resulting in a comprehensive overview of the current directions in boredom research. Summaries of each chapter not only underscore the multitude of perspectives on boredom but also elucidate the diverse approaches employed in its study. Furthermore, the chapter directs attention to both known and unknown aspects of boredom, providing a foundation for future research in the field

What Happens in Your Brain When You Walk Down the Street? Implications of Architectural Proportions, Biophilia, and Fractal Geometry for Urban Science

This article reviews current research in visual urban perception. The temporal sequence of the first few milliseconds of visual stimulus processing sheds light on the historically ambiguous topic of aesthetic experience. Automatic fractal processing triggers initial attraction/avoidance evaluations of an environment’s salubriousness, and its potentially positive or negative impacts upon an individual. As repeated cycles of visual perception occur, the attractiveness of urban form affects the user experience much more than had been previously suspected. These perceptual mechanisms promote walkability and intuitive navigation, and so they support the urban and civic interactions for which we establish communities and cities in the first place. Therefore, the use of multiple fractals needs to reintegrate with biophilic and traditional architecture in urban design for their proven positive effects on health and well-being. Such benefits include striking reductions in observers’ stress and mental fatigue. Due to their costs to individual well-being, urban performance, environmental quality, and climatic adaptation, this paper recommends that nontraditional styles should be hereafter applied judiciously to the built environment

Eine experimentelle Studie zur Vorstellung eines neuen standardisierten Stimulus-Sets zur Erfassung des Erkennens von Hilfsbedürftigkeit bei Kindern (NeoHelp)

It is a general concern in experimental research to ensure that results do not relate to systematic perceptual differences when comparing responses to stimulus categories of different content. Confounding effects can be prevented by using standardized stimuli and carefully controlling their perceptual properties. The study of socioemotional development so far lacks such stimulus sets. Need-of-help recognition is one important aspect of socioemotional development and is a necessary precondition for active helping. In this thesis, I present the NeoHelp stimulus set that has been created to allow standardized testing of need-of-help recognition with clinical and normative populations of different ages. At this point in time the NeoHelp stimulus set consists of 82 black-and-white comic-like drawings of 15 different everyday situations. These pictures can be arranged into 41 pairs depicting the same agent as either in need of help or not. At the same time control picture pairs can be arranged showing a human or a bird in the same situation. Thus experimental paradigms assessing need-of-help recognition can be implemented using the identical stimulus material as used for implementing control paradigms assessing a basic-level human-animal distinction. In this thesis, I provide evidence that the picture pairs of the NeoHelp are very similar regarding low-level picture properties. In addition I present empirical data obtained from a sample of 80 children with a broad age range (3 to 13 years) for three different mutually controlling and complementing paradigms. The results demonstrate that children correctly categorize the pictures’ content regarding both tasks, need-of-help-, as well as humanbird- distinction. I also show that task requirements coherently determine which aspects of the pictures’ content influence response characteristics. Moreover, standard response characteristics (hit rates and response times) for the selection of stimuli and comparison in future studies are provided