Simon’s scissors and ecological psychology in design for behaviour change

Copyright @ 2012 Social Science Electronic PublishingThis paper introduces a series of working papers (Lockton 2011a,b and 2012a-f) providing a background literature review of different disciplinary perspectives on behaviour, informing the development of the Design with Intent toolkit (Lockton, Harrison and Stanton 2010a,b). In the first section of the paper, Herbert Simon's 'scissors' metaphor, and the relationship between context, cognition and behaviour are discussed, while the remaining sections introduce some ecological psychology perspectives relevant to design for behaviour change, including Barker's behaviour settings, ecological interface design and Gestalt psychology principles

Human-display interactions: Context-specific biases

Recent developments in computer engineering have greatly enhanced the capabilities of display technology. As displays are no longer limited to simple alphanumeric output, they can present a wide variety of graphic information, using either static or dynamic presentation modes. At the same time that interface designers exploit the increased capabilities of these displays, they must be aware of the inherent limitation of these displays. Generally, these limitations can be divided into those that reflect limitations of the medium (e.g., reducing three-dimensional representations onto a two-dimensional projection) and those reflecting the perceptual and conceptual biases of the operator. The advantages and limitations of static and dynamic graphic displays are considered. Rather than enter into the discussion of whether dynamic or static displays are superior, general advantages and limitations are explored which are contextually specific to each type of display

Scene Segmentation and Object Classification for Place Recognition

This dissertation tries to solve the place recognition and loop closing problem in a way similar to human visual system. First, a novel image segmentation algorithm is developed. The image segmentation algorithm is based on a Perceptual Organization model, which allows the image segmentation algorithm to ‘perceive’ the special structural relations among the constituent parts of an unknown object and hence to group them together without object-specific knowledge. Then a new object recognition method is developed. Based on the fairly accurate segmentations generated by the image segmentation algorithm, an informative object description that includes not only the appearance (colors and textures), but also the parts layout and shape information is built. Then a novel feature selection algorithm is developed. The feature selection method can select a subset of features that best describes the characteristics of an object class. Classifiers trained with the selected features can classify objects with high accuracy. In next step, a subset of the salient objects in a scene is selected as landmark objects to label the place. The landmark objects are highly distinctive and widely visible. Each landmark object is represented by a list of SIFT descriptors extracted from the object surface. This object representation allows us to reliably recognize an object under certain viewpoint changes. To achieve efficient scene-matching, an indexing structure is developed. Both texture feature and color feature of objects are used as indexing features. The texture feature and the color feature are viewpoint-invariant and hence can be used to effectively find the candidate objects with similar surface characteristics to a query object. Experimental results show that the object-based place recognition and loop detection method can efficiently recognize a place in a large complex outdoor environment

Unsupervised image saliency detection with Gestalt-laws guided optimization and visual attention based refinement.

Visual attention is a kind of fundamental cognitive capability that allows human beings to focus on the region of interests (ROIs) under complex natural environments. What kind of ROIs that we pay attention to mainly depends on two distinct types of attentional mechanisms. The bottom-up mechanism can guide our detection of the salient objects and regions by externally driven factors, i.e. color and location, whilst the top-down mechanism controls our biasing attention based on prior knowledge and cognitive strategies being provided by visual cortex. However, how to practically use and fuse both attentional mechanisms for salient object detection has not been sufficiently explored. To the end, we propose in this paper an integrated framework consisting of bottom-up and top-down attention mechanisms that enable attention to be computed at the level of salient objects and/or regions. Within our framework, the model of a bottom-up mechanism is guided by the gestalt-laws of perception. We interpreted gestalt-laws of homogeneity, similarity, proximity and figure and ground in link with color, spatial contrast at the level of regions and objects to produce feature contrast map. The model of top-down mechanism aims to use a formal computational model to describe the background connectivity of the attention and produce the priority map. Integrating both mechanisms and applying to salient object detection, our results have demonstrated that the proposed method consistently outperforms a number of existing unsupervised approaches on five challenging and complicated datasets in terms of higher precision and recall rates, AP (average precision) and AUC (area under curve) values

Chapter 12: Perception in Instructional Message Design

This chapter aims to discuss perception from various academic disciplines and its relations and effects on information processing in instructional message design. Improved awareness of this concept assists instructional designers in conveying their message effectively and improves effective instruction in immersive learning environments. In this chapter, Gestalt, neurological, ecological, and computational perspectives and processes on perception are first discussed and followed by applications in instructional message design and instructional design

Visual Recognition Of Graphical User Interface Components Using Deep Learning Technique

Graphical User Interface (GUI) building in software development is a process which ideally need to go through several steps. Those steps in the process start from idea or rough sketch of the GUI, then refined into visual design, implemented in coding or prototype, and finally evaluated for its function and usability to discover design problem and to get feedback from users. Those steps repeated until the GUI considered satisfactory or acceptable by the user. Computer vision technique has been researched and developed to make the process faster and easier; for example generating code for implementation, or automatic GUI testing using component images. But among those techniques, there are still few for usability testing purpose. This preliminary research attempted to make the foundation for usability testing using computer vision technique by built minimalist dataset which has images of various GUI components and used the dataset in deep learning experiment for GUI components visual recognition. The experiment results showed deep learning technique suitable for the intended task, with accuracy of 95% for recognition of two different types of components, and accuracy of 72% for six different types of component

Vision, Action, and Make-Perceive

In this paper, I critically assess the enactive account of visual perception recently defended by Alva Noë (2004). I argue inter alia that the enactive account falsely identifies an object’s apparent shape with its 2D perspectival shape; that it mistakenly assimilates visual shape perception and volumetric object recognition; and that it seriously misrepresents the constitutive role of bodily action in visual awareness. I argue further that noticing an object’s perspectival shape involves a hybrid experience combining both perceptual and imaginative elements – an act of what I call ‘make-perceive.

Perception of Symmetries in Drawings of Graphs

Symmetry is an important factor in human perception in general, as well as in the visualization of graphs in particular. There are three main types of symmetry: reflective, translational, and rotational. We report the results of a human subjects experiment to determine what types of symmetries are more salient in drawings of graphs. We found statistically significant evidence that vertical reflective symmetry is the most dominant (when selecting among vertical reflective, horizontal reflective, and translational). We also found statistically significant evidence that rotational symmetry is affected by the number of radial axes (the more, the better), with a notable exception at four axes.Comment: Appears in the Proceedings of the 26th International Symposium on Graph Drawing and Network Visualization (GD 2018

Spatial grouping determines temporal integration

To make sense out of a continuously changing visual world, people need to integrate features across space and time. Despite more than a century of research, the mechanisms of features integration are still a matter of debate. To examine how temporal and spatial integration interact, the authors measured the amount of temporal fusion (a measure of temporal integration) for different spatial layouts. They found that spatial grouping by proximity and similarity can completely block temporal integration. Computer simulations with a simple neural network capture these findings very well, suggesting that the proposed spatial grouping operations may occur already at an early stage of visual information processing