5,520 research outputs found

    Developing and Evaluating Visual Analogies to Support Insight and Creative Problem Solving

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    The primary aim of this thesis is to gain a richer understanding of visual analogies for insight problem solving, and, in particular, how they can be better developed to ensure their effectiveness as hints. While much work has explored the role of visual analogies in problem solving and their facilitative role, only a few studies have analysed how they could be designed. This thesis employs a mixed method consisting of a practice-led approach for studying how visual analogies can be designed and developed and an experimental research approach for testing their effectiveness as hints for solving visual insight problems

    Designing as Construction of Representations: A Dynamic Viewpoint in Cognitive Design Research

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    This article presents a cognitively oriented viewpoint on design. It focuses on cognitive, dynamic aspects of real design, i.e., the actual cognitive activity implemented by designers during their work on professional design projects. Rather than conceiving de-signing as problem solving - Simon's symbolic information processing (SIP) approach - or as a reflective practice or some other form of situated activity - the situativity (SIT) approach - we consider that, from a cognitive viewpoint, designing is most appropriately characterised as a construction of representations. After a critical discussion of the SIP and SIT approaches to design, we present our view-point. This presentation concerns the evolving nature of representations regarding levels of abstraction and degrees of precision, the function of external representations, and specific qualities of representation in collective design. Designing is described at three levels: the organisation of the activity, its strategies, and its design-representation construction activities (different ways to generate, trans-form, and evaluate representations). Even if we adopt a "generic design" stance, we claim that design can take different forms depending on the nature of the artefact, and we propose some candidates for dimensions that allow a distinction to be made between these forms of design. We discuss the potential specificity of HCI design, and the lack of cognitive design research occupied with the quality of design. We close our discussion of representational structures and activities by an outline of some directions regarding their functional linkages

    Neural Analogical Matching

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    Analogy is core to human cognition. It allows us to solve problems based on prior experience, it governs the way we conceptualize new information, and it even influences our visual perception. The importance of analogy to humans has made it an active area of research in the broader field of artificial intelligence, resulting in data-efficient models that learn and reason in human-like ways. While cognitive perspectives of analogy and deep learning have generally been studied independently of one another, the integration of the two lines of research is a promising step towards more robust and efficient learning techniques. As part of a growing body of research on such an integration, we introduce the Analogical Matching Network: a neural architecture that learns to produce analogies between structured, symbolic representations that are largely consistent with the principles of Structure-Mapping Theory.Comment: AAAI versio

    A protocol study of novice interaction design behaviour in Botswana: solution-driven interaction design

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    Think aloud studies and protocol analysis are well-known in the field of HCI, but most often these studies focus on usability evaluations, or on the use of technology. Rarely are they used to investigate the behaviour of interaction designers. In this paper, we report on a protocol study with novice interaction designers in Botswana. Participants had just completed the design section of an undergraduate module on Interaction Design that actively promotes a problem-driven approach to the design of interactive products, yet the participants behaved in a way that is closer to a solution-driven approach. The module emphasizes user-centred design, prototyping methods to support design development, and evaluating design detail. Yet participants suggest solutions before exploring the context of use, use prototyping methods to capture, rather than to develop, designs, and do not produce detailed designs. In a problem-solving context, some of these behaviours are typical of novices, but in a design context they are also seen in experienced designers. The results presented here reveal the detail of the approach adopted by these students, and contribute to the wider debate concerning the internationalization of HCI education

    What working memory is for

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    Learning to See Analogies: A Connectionist Exploration

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    The goal of this dissertation is to integrate learning and analogy-making. Although learning and analogy-making both have long histories as active areas of research in cognitive science, not enough attention has been given to the ways in which they may interact. To that end, this project focuses on developing a computer program, called Analogator, that learns to make analogies by seeing examples of many different analogy problems and their solutions. That is, it learns to make analogies by analogy. This approach stands in contrast to most existing computational models of analogy in which particular analogical mechanisms are assumed a priori to exist. Rather than assuming certain principles about analogy-making mechanisms, the goal of the Analogator project is to learn what it means to make an analogy. This unique notion is the focus of this dissertation

    Learning to See Analogies: A Connectionist Exploration

    Get PDF
    The goal of this dissertation is to integrate learning and analogy-making. Although learning and analogy-making both have long histories as active areas of research in cognitive science, not enough attention has been given to the ways in which they may interact. To that end, this project focuses on developing a computer program, called Analogator, that learns to make analogies by seeing examples of many different analogy problems and their solutions. That is, it learns to make analogies by analogy. This approach stands in contrast to most existing computational models of analogy in which particular analogical mechanisms are assumed a priori to exist. Rather than assuming certain principles about analogy-making mechanisms, the goal of the Analogator project is to learn what it means to make an analogy. This unique notion is the focus of this dissertation

    VASR: Visual Analogies of Situation Recognition

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    A core process in human cognition is analogical mapping: the ability to identify a similar relational structure between different situations. We introduce a novel task, Visual Analogies of Situation Recognition, adapting the classical word-analogy task into the visual domain. Given a triplet of images, the task is to select an image candidate B' that completes the analogy (A to A' is like B to what?). Unlike previous work on visual analogy that focused on simple image transformations, we tackle complex analogies requiring understanding of scenes. We leverage situation recognition annotations and the CLIP model to generate a large set of 500k candidate analogies. Crowdsourced annotations for a sample of the data indicate that humans agree with the dataset label ~80% of the time (chance level 25%). Furthermore, we use human annotations to create a gold-standard dataset of 3,820 validated analogies. Our experiments demonstrate that state-of-the-art models do well when distractors are chosen randomly (~86%), but struggle with carefully chosen distractors (~53%, compared to 90% human accuracy). We hope our dataset will encourage the development of new analogy-making models. Website: https://vasr-dataset.github.io/Comment: Accepted to AAAI 2023. Website: https://vasr-dataset.github.io
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