Attention and Visual Memory in Visualization and Computer Graphics Christopher G
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Compare and Contrast Two Models Or Theories of One Cognitive Process with Reference to Research Studies
! The following sample is for the learning objective: Compare and contrast two models or theories of one cognitive process with reference to research studies. What is the question asking for? * A clear outline of two models of one cognitive process. The cognitive process may be memory, perception, decision-making, language or thinking. * Research is used to support the models as described. The research does not need to be outlined in a lot of detail, but underatanding of the role of research in supporting the models should be apparent.. * Both similarities and differences of the two models should be clearly outlined. Sample response The theory of memory is studied scientifically and several models have been developed to help The cognitive process describe and potentially explain how memory works. Two models that attempt to describe how (memory) and two models are memory works are the Multi-Store Model of Memory, developed by Atkinson & Shiffrin (1968), clearly identified. and the Working Memory Model of Memory, developed by Baddeley & Hitch (1974). The Multi-store model model explains that all memory is taken in through our senses; this is called sensory input. This information is enters our sensory memory, where if it is attended to, it will pass to short-term memory. If not attention is paid to it, it is displaced. Short-term memory Research. is limited in duration and capacity. According to Miller, STM can hold only 7 plus or minus 2 pieces of information. Short-term memory memory lasts for six to twelve seconds. When information in the short-term memory is rehearsed, it enters the long-term memory store in a process called “encoding.” When we recall information, it is retrieved from LTM and moved A satisfactory description of back into STM. -
Brain Disorders and Therapy the Composition of Visual Memory and the Impact of Emotions: the Color of Memory
Brain Disorders and Therapy Extended Abstract The composition of visual memory and the impact of emotions: the color of memory Nydia J Gutierrez ABSTRACT between DE and VR are disparate. It is important to remember that in many visual memory tasks the guess factor in DE is Art therapy creates an implicit memory that helps to reduce smaller than that observed (e.g., 50 percent guess rate for Faces stress and anticipates emotional cognitive changes and in WMS – III). behavior. It creates a regular function between peripheral nervous system, autonomic nervous system, and the somatic nervous system. This model is represented as a neural Long-term memory is a central component of human learning algorithm that defines brain functions and models. The that allows for a range of abilities and behaviors that are also model represents a brain relation, behavior, and correlation to essential to survival. While this core competency for human thought and the human mind. In the peripheral procedural and declarative memory has been widely nervous system we find dividend between the autonomic established, it remains uncertain if specific mechanisms nervous system and the somatic nervous system. subserve basic sensory or perceptual processes. Here we use a visual learning model to demonstrate how people are prepared The model design and created in this research presents daily to look for Different visual features in the environment functioning cognitive variables that serve as a unity to reduce contribute to consistent performance changes over consecutive stress and negative emotional experiences. There is an days but, oddly, the subsequent ability to learn similar visual increase of variation and stimulation that carries effects and features is removed. -
Computer Graphics and Visualization
European Research Consortium for Informatics and Mathematics Number 44 January 2001 www.ercim.org Special Theme: Computer Graphics and Visualization Next Issue: April 2001 Next Special Theme: Metacomputing and Grid Technologies CONTENTS KEYNOTE 36 Physical Deforming Agents for Virtual Neurosurgery by Michele Marini, Ovidio Salvetti, Sergio Di Bona 3 by Elly Plooij-van Gorsel and Ludovico Lutzemberger 37 Visualization of Complex Dynamical Systems JOINT ERCIM ACTIONS in Theoretical Physics 4 Philippe Baptiste Winner of the 2000 Cor Baayen Award by Anatoly Fomenko, Stanislav Klimenko and Igor Nikitin 38 Simulation and Visualization of Processes 5 Strategic Workshops – Shaping future EU-NSF collaborations in in Moving Granular Bed Gas Cleanup Filter Information Technologies by Pavel Slavík, František Hrdliãka and Ondfiej Kubelka THE EUROPEAN SCENE 39 Watching Chromosomes during Cell Division by Robert van Liere 5 INRIA is growing at an Unprecedented Pace and is starting a Recruiting Drive on a European Scale 41 The blue-c Project by Markus Gross and Oliver Staadt SPECIAL THEME 42 Augmenting the Common Working Environment by Virtual Objects by Wolfgang Broll 6 Graphics and Visualization: Breaking new Frontiers by Carol O’Sullivan and Roberto Scopigno 43 Levels of Detail in Physically-based Real-time Animation by John Dingliana and Carol O’Sullivan 8 3D Scanning for Computer Graphics by Holly Rushmeier 44 Static Solution for Real Time Deformable Objects With Fluid Inside by Ivan F. Costa and Remis Balaniuk 9 Subdivision Surfaces in Geometric -
Cognitive Science 1
Cognitive Science 1 • Linguistics, Minor (https://e-catalogue.jhu.edu/arts-sciences/full- COGNITIVE SCIENCE time-residential-programs/degree-programs/cognitive-science/ linguistics-minor/) http://www.cogsci.jhu.edu For current course information and registration go to https://sis.jhu.edu/ Cognitive science is the study of the human mind and brain, focusing on classes/ how the mind represents and manipulates knowledge and how mental representations and processes are realized in the brain. Conceiving of the mind as an abstract computing device instantiated in the brain, Courses cognitive scientists endeavor to understand the mental computations AS.050.102. Language and Mind. 3 Credits. underlying cognitive functioning and how these computations are Introductory course dealing with theory, methods, and current research implemented by neural tissue. Cognitive science has emerged at the topics in the study of language as a component of the mind. What it is interface of several disciplines. Central among these are cognitive to "know" a language: components of linguistic knowledge (phonetics, psychology, linguistics, and portions of computer science and artificial phonology, morphology, syntax, semantics) and the course of language intelligence; other important components derive from work in the acquisition. How linguistic knowledge is put to use: language and the neurosciences, philosophy, and anthropology. This diverse ancestry brain and linguistic processing in various domains. has brought into cognitive science several different perspectives and Area: Natural Sciences, Social and Behavioral Sciences methodologies. Cognitive scientists endeavor to unite such varieties AS.050.105. Introduction to Cognitive Neuropsychology. 3 Credits. of perspectives around the central goal of characterizing the structure When the brain is damaged or fails to develop normally, even the most of human intellectual functioning. -
Using Typography and Iconography to Express Emotion
Using typography and iconography to express emotion (or meaning) in motion graphics as a learning tool for ESL (English as a second language) in a multi-device platform. A thesis submitted to the School of Visual Communication Design, College of Communication and Information of Kent State University in partial fulfillment of the requirements for the degree of Master of Fine Arts by Anthony J. Ezzo May, 2016 Thesis written by Anthony J. Ezzo B.F.A. University of Akron, 1998 M.F.A., Kent State University, 2016 Approved by Gretchen Caldwell Rinnert, M.G.D., Advisor Jaime Kennedy, M.F.A., Director, School of Visual Communication Design Amy Reynolds, Ph.D., Dean, College of Communication and Information TABLE OF CONTENTS TABLE OF CONTENTS .................................................................................... iii LIST OF FIGURES ............................................................................................ v LIST OF TABLES .............................................................................................. v ACKNOWLEDGEMENTS ................................................................................ vi CHAPTER 1. THE PROBLEM .......................................................................................... 1 Thesis ..................................................................................................... 6 2. BACKGROUND AND CONTEXT ............................................................. 7 Understanding The Ell Process .............................................................. -
3D Graphics Fundamentals
11BegGameDev.qxd 9/20/04 5:20 PM Page 211 chapter 11 3D Graphics Fundamentals his chapter covers the basics of 3D graphics. You will learn the basic concepts so that you are at least aware of the key points in 3D programming. However, this Tchapter will not go into great detail on 3D mathematics or graphics theory, which are far too advanced for this book. What you will learn instead is the practical implemen- tation of 3D in order to write simple 3D games. You will get just exactly what you need to 211 11BegGameDev.qxd 9/20/04 5:20 PM Page 212 212 Chapter 11 ■ 3D Graphics Fundamentals write a simple 3D game without getting bogged down in theory. If you have questions about how matrix math works and about how 3D rendering is done, you might want to use this chapter as a starting point and then go on and read a book such as Beginning Direct3D Game Programming,by Wolfgang Engel (Course PTR). The goal of this chapter is to provide you with a set of reusable functions that can be used to develop 3D games. Here is what you will learn in this chapter: ■ How to create and use vertices. ■ How to manipulate polygons. ■ How to create a textured polygon. ■ How to create a cube and rotate it. Introduction to 3D Programming It’s a foregone conclusion today that everyone has a 3D accelerated video card. Even the low-end budget video cards are equipped with a 3D graphics processing unit (GPU) that would be impressive were it not for all the competition in this market pushing out more and more polygons and new features every year. -
Internal Attention to Features in Visual Short-Term Memory Guides Object Learning ⇑ ⇑ Judith E
Cognition 129 (2013) 292–308 Contents lists available at ScienceDirect Cognition journal homepage: www.elsevier.com/locate/COGNIT Internal attention to features in visual short-term memory guides object learning ⇑ ⇑ Judith E. Fan , Nicholas B. Turk-Browne Green Hall, Department of Psychology, Princeton University, Princeton, NJ 08540, United States article info abstract Article history: Attending to objects in the world affects how we perceive and remember them. What are Received 11 October 2012 the consequences of attending to an object in mind? In particular, how does reporting the Revised 1 April 2013 features of a recently seen object guide visual learning? In three experiments, observers Accepted 20 June 2013 were presented with abstract shapes in a particular color, orientation, and location. After viewing each object, observers were cued to report one feature from visual short-term memory (VSTM). In a subsequent test, observers were cued to report features of the same Keywords: objects from visual long-term memory (VLTM). We tested whether reporting a feature Features and objects from VSTM: (1) enhances VLTM for just that feature (practice-benefit hypothesis), (2) Memory encoding Memory retrieval enhances VLTM for all features (object-based hypothesis), or (3) simultaneously enhances Selective attention VLTM for that feature and suppresses VLTM for unreported features (feature-competition Visual short-term memory hypothesis). The results provided support for the feature-competition hypothesis, whereby Visual working memory the representation of an object in VLTM was biased towards features reported from VSTM and away from unreported features (Experiment 1). This bias could not be explained by the amount of sensory exposure or response learning (Experiment 2) and was amplified by the reporting of multiple features (Experiment 3). -
Reliability in Cognitive Neuroscience: a Meta-Meta-Analysis Books Written by William R
Reliability in Cognitive Neuroscience: A Meta-Meta-Analysis Books Written by William R. Uttal Real Time Computers: Techniques and Applications in the Psychological Sciences Generative Computer Assisted Instruction (with Miriam Rogers, Ramelle Hieronymus, and Timothy Pasich) Sensory Coding: Selected Readings (Editor) The Psychobiology of Sensory Coding Cellular Neurophysiology and Integration: An Interpretive Introduction. An Autocorrelation Theory of Form Detection The Psychobiology of Mind A Taxonomy of Visual Processes Visual Form Detection in 3-Dimensional Space Foundations of Psychobiology (with Daniel N. Robinson) The Detection of Nonplanar Surfaces in Visual Space The Perception of Dotted Forms On Seeing Forms The Swimmer: An Integrated Computational Model of a Perceptual-Motor System (with Gary Bradshaw, Sriram Dayanand, Robb Lovell, Thomas Shepherd, Ramakrishna Kakarala, Kurt Skifsted, and Greg Tupper) Toward A New Behaviorism: The Case against Perceptual Reductionism Computational Modeling of Vision: The Role of Combination (with Ramakrishna Kakarala, Sriram Dayanand, Thomas Shepherd, Jaggi Kalki, Charles Lunskis Jr., and Ning Liu) The War between Mentalism and Behaviorism: On the Accessibility of Mental Processes The New Phrenology: On the Localization of Cognitive Processes in the Brain A Behaviorist Looks at Form Recognition Psychomyths: Sources of Artifacts and Misrepresentations in Scientifi c Cognitive neuroscience Dualism: The Original Sin of Cognitivism Neural Theories of Mind: Why the Mind-Brain Problem May Never Be Solved Human Factors in the Courtroom: Mythology versus Science The Immeasurable Mind: The Real Science of Psychology Time, Space, and Number in Physics and Psychology Distributed Neural Systems: Beyond the New Phrenology Neuroscience in the Courtroom: What Every Lawyer Should Know about the Mind and the Brain Mind and Brain: A Critical Appraisal of Cognitive Neuroscience Reliability in Cognitive Neuroscience: A Meta-Meta-Analysis Reliability in Cognitive Neuroscience: A Meta-Meta-Analysis William R. -
Math 253: Mathematical Methods for Data Visualization – Course Introduction and Overview (Spring 2020)
Math 253: Mathematical Methods for Data Visualization – Course introduction and overview (Spring 2020) Dr. Guangliang Chen Department of Math & Statistics San José State University Math 253 course introduction and overview What is this course about? Context: Modern data sets often have hundreds, thousands, or even millions of features (or attributes). ←− large dimension Dr. Guangliang Chen | Mathematics & Statistics, San José State University2/30 Math 253 course introduction and overview This course focuses on the statistical/machine learning task of dimension reduction, also called dimensionality reduction, which is the process of reducing the number of input variables of a data set under consideration, for the following benefits: • It reduces the running time and storage space. • Removal of multi-collinearity improves the interpretation of the parameters of the machine learning model. • It can also clean up the data by reducing the noise. • It becomes easier to visualize the data when reduced to very low dimensions such as 2D or 3D. Dr. Guangliang Chen | Mathematics & Statistics, San José State University3/30 Math 253 course introduction and overview There are two different kinds of dimension reduction approaches: • Feature selection approaches try to find a subset of the original features variables. Examples: subset selection, stepwise selection, Ridge and Lasso regression. ←− Already covered in Math 261A • Feature extraction transforms the data in the high-dimensional space to a space of fewer dimensions. ←− Focus of this course Examples: principal component analysis (PCA), ISOmap, and linear discriminant analysis (LDA). Dr. Guangliang Chen | Mathematics & Statistics, San José State University4/30 Math 253 course introduction and overview Dimension reduction methods to be covered in this course: • Linear projection methods: – PCA (for unlabled data), – LDA (for labled data) • Nonlinear embedding methods: – Multidimensional scaling (MDS), ISOmap – Locally linear embedding (LLE) – Laplacian eigenmaps Dr. -
Volume Rendering
Volume Rendering 1.1. Introduction Rapid advances in hardware have been transforming revolutionary approaches in computer graphics into reality. One typical example is the raster graphics that took place in the seventies, when hardware innovations enabled the transition from vector graphics to raster graphics. Another example which has a similar potential is currently shaping up in the field of volume graphics. This trend is rooted in the extensive research and development effort in scientific visualization in general and in volume visualization in particular. Visualization is the usage of computer-supported, interactive, visual representations of data to amplify cognition. Scientific visualization is the visualization of physically based data. Volume visualization is a method of extracting meaningful information from volumetric datasets through the use of interactive graphics and imaging, and is concerned with the representation, manipulation, and rendering of volumetric datasets. Its objective is to provide mechanisms for peering inside volumetric datasets and to enhance the visual understanding. Traditional 3D graphics is based on surface representation. Most common form is polygon-based surfaces for which affordable special-purpose rendering hardware have been developed in the recent years. Volume graphics has the potential to greatly advance the field of 3D graphics by offering a comprehensive alternative to conventional surface representation methods. The object of this thesis is to examine the existing methods for volume visualization and to find a way of efficiently rendering scientific data with commercially available hardware, like PC’s, without requiring dedicated systems. 1.2. Volume Rendering Our display screens are composed of a two-dimensional array of pixels each representing a unit area. -
Flood Insurance Rate Map (FIRM) Graphics Guidance
Guidance for Flood Risk Analysis and Mapping Flood Insurance Rate Map (FIRM) Graphics November 2019 Requirements for the Federal Emergency Management Agency (FEMA) Risk Mapping, Assessment, and Planning (Risk MAP) Program are specified separately by statute, regulation, or FEMA policy (primarily the Standards for Flood Risk Analysis and Mapping). This document provides guidance to support the requirements and recommends approaches for effective and efficient implementation. The guidance, context, and other information in this document is not required unless it is codified separately in the aforementioned statute, regulation, or policy. Alternate approaches that comply with all requirements are acceptable. For more information, please visit the FEMA Guidelines and Standards for Flood Risk Analysis and Mapping webpage (www.fema.gov/guidelines-and-standards-flood-risk-analysis-and- mapping). Copies of the Standards for Flood Risk Analysis and Mapping policy, related guidance, technical references, and other information about the guidelines and standards development process are all available here. You can also search directly by document title at www.fema.gov/library. FIRM Graphics November 2019 Guidance Document 6 Page i Document History Affected Section or Date Description Subsection This guidance has been revised to align various November descriptions and specifications to standard operating Sections 4.3 and 5.2 2019 procedures, including labeling of structures and application of floodway and special floodway notes. FIRM Graphics November -
Scaling up Visual Attention and Visual Working Memory to the Real World
CHAPTER TWO Scaling up visual attention and visual working memory to the real world Timothy F. Brady*, Viola S. Stormer,€ Anna Shafer-Skelton, Jamal R. Williams, Angus F. Chapman, Hayden M. Schill Department of Psychology, University of California, San Diego, La Jolla, CA, United States *Corresponding author: e-mail address: [email protected] Contents 1. Introduction 30 2. Visual attention 31 2.1 Introduction 31 2.2 From simple features to multi-feature objects: Principles of attentional selection 32 2.3 Attentional selection at the level of real-world objects and semantic categories 35 2.4 Attention to, and processing of, “ensembles” and groups of objects 39 2.5 Cross-modal influences on perception and attention: Real-world objects make sounds 42 2.6 Visual attention conclusion 46 3. Visual working memory 47 3.1 Introduction 47 3.2 Visual working memory stores not just individual items, but ensemble information 48 3.3 The world has more than objects: Scenes and surfaces in visual memory 52 3.4 We have knowledge about the objects we wish to remember: Learning and knowledge in visual working memory 54 3.5 Expertise and visual working memory 58 4. Conclusion 59 References 60 Abstract Both visual attention and visual working memory tend to be studied either with very simple stimuli and low-level paradigms, which are designed to allow us to understand the representations and processes in detail, or with fully realistic stimuli that make such precise understanding difficult but are more representative of the real world. In this chapter we argue for an intermediate approach in which visual attention and visual # Psychology of Learning and Motivation, Volume 70 2019 Elsevier Inc.