15,075 research outputs found

    The various forms of civilization arranged in chronological strata: manipulating the HTOED

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    Negative Results in Computer Vision: A Perspective

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    A negative result is when the outcome of an experiment or a model is not what is expected or when a hypothesis does not hold. Despite being often overlooked in the scientific community, negative results are results and they carry value. While this topic has been extensively discussed in other fields such as social sciences and biosciences, less attention has been paid to it in the computer vision community. The unique characteristics of computer vision, particularly its experimental aspect, call for a special treatment of this matter. In this paper, I will address what makes negative results important, how they should be disseminated and incentivized, and what lessons can be learned from cognitive vision research in this regard. Further, I will discuss issues such as computer vision and human vision interaction, experimental design and statistical hypothesis testing, explanatory versus predictive modeling, performance evaluation, model comparison, as well as computer vision research culture

    Causal mapping as a teaching tool for reflecting on causation in human evolution (advance online)

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    Spontaneity and Materiality: What Photography Is in the Photography of James Welling

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    Images are double agents. They receive information from the world, while also projecting visual imagination onto the world. As a result, mind and world tug our thinking about images, or particular kinds of images, in contrary directions. On one common division, world traces itself mechanically in photographs, whereas mind expresses itself through painting.1 Scholars of photography disavow such crude distinctions: much recent writing attends in detail to the materials and processes of photography, the agency of photographic artists, and the social determinants of the production and reception of photographs. As such writing makes plain, photographs cannot be reduced to mechanical traces.2 Yet background conceptions of photography as trace or index persist almost by default, as no framework of comparable explanatory power has yet emerged to replace them. A conception of photography adequate to developments in recent scholarship is long overdue. Rather than constructing such a conception top-down, as philosophers are wont to do, this paper articulates it by examining selected works by James Welling.3 There are several reasons for this: Welling’s practice persistently explores the resources and possibilities of photography, the effect of these explorations is to express a particular metaphysics of the mind’s relation to its world, and appreciating why this metaphysics is aptly expressed by exploring photography requires a revised conception of what photography is. In as much as it provides a framework for a richer interpretation of Welling, the new conception is also capable of underwriting a wide range of critical and historical approaches to photography

    When is an action caused from within? Quantifying the causal chain leading to actions in simulated agents

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    An agent's actions can be influenced by external factors through the inputs it receives from the environment, as well as internal factors, such as memories or intrinsic preferences. The extent to which an agent's actions are "caused from within", as opposed to being externally driven, should depend on its sensor capacity as well as environmental demands for memory and context-dependent behavior. Here, we test this hypothesis using simulated agents ("animats"), equipped with small adaptive Markov Brains (MB) that evolve to solve a perceptual-categorization task under conditions varied with regards to the agents' sensor capacity and task difficulty. Using a novel formalism developed to identify and quantify the actual causes of occurrences ("what caused what?") in complex networks, we evaluate the direct causes of the animats' actions. In addition, we extend this framework to trace the causal chain ("causes of causes") leading to an animat's actions back in time, and compare the obtained spatio-temporal causal history across task conditions. We found that measures quantifying the extent to which an animat's actions are caused by internal factors (as opposed to being driven by the environment through its sensors) varied consistently with defining aspects of the task conditions they evolved to thrive in.Comment: Submitted and accepted to Alife 2019 conference. Revised version: edits include adding more references to relevant work and clarifying minor points in response to reviewer
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