In Art as Experience, Dewey brings together the deep and enduring connection between art and human experience. Art is understood not as a commodity or solitary object but as an intensely meaningful...

Art as experience

Thank you very much for downloading introduction to robust estimation and hypothesis testing. As you may know, people have search numerous times for their favorite books like this introduction to robust estimation and hypothesis testing, but end up in harmful downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they cope with some harmful bugs inside their laptop.

Introduction To Robust Estimation And Hypothesis Testing

Contents: Foreword. Preface. What Are Statistical Decisions? Non-Parametric Measures of Sensitivity. Gaussian Distributions of Signal and Noise With Equal Variances. Gaussian Distributions of Signal and Noise With Unequal Variances. Conducting a Rating Scale Experiment. Choice Theory Approximations to Signal Detection Theory. Threshold Theory. The Laws of Categorical and Comparative Judgement. Appendices: Answers to Problems. Logarithms. Integration of the Expression for the Logistic Curve. Computer Programmes for Signal Detection Analysis. Tables.

A Primer of Signal Detection Theory; Table of D' and β

Everyday, people flexibly perform different categorizations of common faces, objects and scenes. Intuition and scattered evidence suggest that these categorizations require the use of different visual information from the input. However, there is no unifying method, based on the categorization performance of subjects, that can isolate the information used. To this end, we developed Bubbles, a general technique that can assign the credit of human categorization performance to specific visual information. To illustrate the technique, we applied Bubbles on three categorization tasks (gender, expressive or not and identity) on the same set of faces, with human and ideal observers to compare the features they used.

Bubbles: A Technique to Reveal the Use of Information in Recognition Tasks EOA Report

About the Author. Acknowledgements. Acronyms. 1 Introduction. 1.1 Motivation. 1.2 Outline. 2 Vision. 2.1 Eye. 2.2 Retina. 2.3 Visual Pathways. 2.4 Sensitivity to Light. 2.5 Color Perception. 2.6 Masking and Adaptation. 2.7 Multi-channel Organization. 2.8 Summary. 3 Video Quality. 3.1 Video Coding and Compression. 3.2 Artifacts. 3.3 Visual Quality. 3.4 Quality Metrics. 3.5 Metric Evaluation. 3.6 Summary. 4 Models and Metrics. 4.1 Isotropic Contrast. 4.2 Perceptual Distortion Metric. 4.3 Summary. 5 Metric Evaluation. 5.1 Still Images. 5.2 Video. 5.3 Component Analysis. 5.4 Summary. 6 Metric Extensions. 6.1 Blocking Artifacts. 6.2 Object Segmentation. 6.3 Image Appeal. 6.4 Summary. 7 Closing Remarks. 7.1 Summary. 7.2 Perspectives. Appendix: Color Space Conversions. References. Index.

Digital Video Quality: Vision Models and Metrics

Wikis are widely promoted as collaborative writing tools and are gaining in popularity in educational settings. However, while wikis include features that are designed to facilitate collaboration, it does not necessarily follow that their use will ensure or even encourage collaborative learning behaviour. The few empirical studies that have considered this issue report equivocal findings. We assessed students collaborative behaviour based on their contributions to a wiki-based shared writing task using a variety of text and time based metrics. We found little evidence of collaboration despite adopting a learning design that was intended to support it. While overall participation was high, a relatively small proportion of students did the bulk of the work and many students' contributions were superficial. Students made little use of the wiki's commenting feature - a critical tool for contextualising and coordinating their contributions for and with others, and the majority of contributions were made very late in the task, making the possibility of extensive collaboration unlikely. These findings are discussed in relation to factors that may lead to the more successful integration of innovative, technology based learning activities into broader undergraduate curricula.

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Using wikis for collaborative learning: Assessing collaboration through contribution

The cone inputs to the unique-hue mechanisms.

The human visual system is much better at analysing the motion of luminance (black and white) patterns than it is at analysing the motion of colour patterns, especially if the pattern is presented very briefly or moves rapidly. We report here that observers reliably distinguish the direction of motion of a colour pattern presented for only 17 milliseconds, provided that the contrast is several times the threshold value (the contrast needed to detect the presence of the pattern). A control experiment, in which a static luminance 'mask' is added to the moving colour pattern, proves that discrimination of the direction of motion of these brief stimuli is colour-specific. The mask drastically impairs discrimination of the direction of motion of a luminance pattern, but it has little effect on a colour pattern. We conclude that the human visual system contains colour-specific motion-detection mechanisms that are capable of analysing very brief signals.

Rapid colour-specific detection of motion in human vision

The feeling of insight in problem solving is typically associated with the sudden realization of a solution that appears obviously correct (Kounios et al., 2006). Salvi and colleagues (2016) found that a solution accompanied with sudden insight is more likely to be correct than a problem solved through conscious and incremental steps. However, Metcalfe (1986) indicated that participants would often present an inelegant but plausible (wrong) answer as correct with a high feeling of warmth (a subjective measure of closeness to solution). This discrepancy may be due to the use of different tasks or due to different methods in the measurement of insight (i.e., using a binary vs continuous scale). In three experiments, we investigated both findings, using many different problem tasks (e.g., Compound Remote Associates, so-called classic insight problems, and non-insight problems). Participants rated insight-related affect (feelings of Aha-experience, confidence, surprise, impasse and pleasure) on continuous scales. As expected we found that, for problems designed to elicit insight, correct solutions elicited higher proportions of reported insight in the solution compared to non-insight solutions; further, correct solutions elicited stronger feelings of insight compared to incorrect solutions.

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Simon J. Cropper

Papers

Using wikis for collaborative learning: Assessing collaboration through contribution

The cone inputs to the unique-hue mechanisms.

Rapid colour-specific detection of motion in human vision

Insight Is Not in the Problem: Investigating Insight in Problem Solving across Task Types

Motion of chromatic stimuli: first-order or second-order?