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Author: Stefan Van Der Stigchel
Publisher: MIT Press
ISBN: 0262039265
Category : Psychology
Languages : en
Pages : 148
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Book Description
How we filter out what is irrelevant so we can focus on what we need to know. We are surrounded by a world rich with visual information, but we pay attention to very little of it, filtering out what is irrelevant so we can focus on what we think we need to know. Advertisers, web designers, and other “attention architects” try hard to get our attention, promoting products with videos on huge outdoor screens, adding flashing banners to websites, and developing computer programs with blinking icons that tempt us to click. Often they succeed in distracting us from what we are supposed to be doing. In How Attention Works, Stefan Van der Stigchel explains the process of attention and what the implications are for our everyday lives. The visual attention system is efficient, Van der Stigchel writes, because it doesn't waste energy processing every scrap of visual data it receives; it gathers only relevant information. We focus on one snippet of information and assume that everything else is stable and consistent with past experience; that's why most people miss even the most glaring continuity errors in films. If an object doesn't meet our expectations, chances are we won't see it. Van der Stigchel makes his case with examples from real life, explaining, among other things, the limitations of color perception (and why fire trucks shouldn't be red); the importance of location (security guards and radiologists, for example, have to know where to look); the attention-getting properties of faces and spiders; what we can learn from someone else's eye movements; why we see what we expect to see (magicians take advantage of this); and visual neglect and unattended information.
Author: Stefan Van Der Stigchel
Publisher: MIT Press
ISBN: 0262039265
Category : Psychology
Languages : en
Pages : 148
Get Book Here
Book Description
How we filter out what is irrelevant so we can focus on what we need to know. We are surrounded by a world rich with visual information, but we pay attention to very little of it, filtering out what is irrelevant so we can focus on what we think we need to know. Advertisers, web designers, and other “attention architects” try hard to get our attention, promoting products with videos on huge outdoor screens, adding flashing banners to websites, and developing computer programs with blinking icons that tempt us to click. Often they succeed in distracting us from what we are supposed to be doing. In How Attention Works, Stefan Van der Stigchel explains the process of attention and what the implications are for our everyday lives. The visual attention system is efficient, Van der Stigchel writes, because it doesn't waste energy processing every scrap of visual data it receives; it gathers only relevant information. We focus on one snippet of information and assume that everything else is stable and consistent with past experience; that's why most people miss even the most glaring continuity errors in films. If an object doesn't meet our expectations, chances are we won't see it. Van der Stigchel makes his case with examples from real life, explaining, among other things, the limitations of color perception (and why fire trucks shouldn't be red); the importance of location (security guards and radiologists, for example, have to know where to look); the attention-getting properties of faces and spiders; what we can learn from someone else's eye movements; why we see what we expect to see (magicians take advantage of this); and visual neglect and unattended information.
Author: Karen Nelson-Field
Publisher: Springer Nature
ISBN: 9811515409
Category : Business & Economics
Languages : en
Pages : 161
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Book Description
This book offers a considered voice on the advertising chaos that colours our rapidly changing media environment in a world of fake news, fast facts and seriously depleted attention stamina. Rather than simply herald disruption, Karen Nelson-Field starts an intelligent conversation on what it will take for businesses to win in an attention economy, the advertising myths we need to leave behind and the scientific evidence we can use to navigate a complex advertising and media ecosystem. This book makes sense of viewability standards, coverage and clutter; it talks about the real quality behind a qCPM and takes a deep dive into the relationship between attention and sales. It explains the stark reality of human attention processing in advertising. Readers will learn how to maximise a viewer’s divided attention by leveraging specific media attributes and using attention-grabbing creative triggers. Nelson-Field asks you to pay attention to a disrupted advertising future without panic, but rather with a keen eye on the things that brand owners can learn to control.
Author: Addie Johnson
Publisher: SAGE
ISBN: 0761927611
Category : Psychology
Languages : en
Pages : 489
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Book Description
Attention: Theory and Practice provides a balance between a readable overview of attention and an emphasis on how theories and paradigms for the study of attention have developed. The book highlights the important issues and major findings while giving sufficient details of experimental studies, models, and theories so that results and conclusions are easy to follow and evaluate. Rather than brushing over tricky technical details, the authors explain them clearly, giving readers the benefit of understanding the motivation for and techniques of the experiments in order to allow readers to think through results, models, and theories for themselves. Attention is an accessible text for advanced undergraduate and graduate students in psychology, as well as an important resource for researchers and practitioners interested in gaining an overview of the field of attention.
Author: Brian Bruya
Publisher: MIT Press
ISBN: 0262013843
Category : Medical
Languages : en
Pages : 459
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Book Description
The phenomena of effortless attention and action and the challenges they pose to current cognitive models of attention and action.
Author: Harold Pashler
Publisher: MIT Press
ISBN: 9780262661560
Category : Psychology
Languages : en
Pages : 516
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Book Description
In the past two decades, attention has been one of the most investigated areas of research in perception and cognition. However, the literature on the field contains a bewildering array of findings, and empirical progress has not been matched by consensus on major theoretical issues. The Psychology of Attention presents a systematic review of the main lines of research on attention; the topics range from perception of threshold stimuli to memory storage and decision making. The book develops empirical generalizations about the major issues and suggests possible underlying theoretical principles. Pashler argues that widely assumed notions of processing resources and automaticity are of limited value in understanding human information processing. He proposes a central bottleneck for decision making and memory retrieval, and describes evidence that distinguishes this limitation from perceptual limitations and limited-capacity short-term memory.
Author: Chris Bailey
Publisher: Vintage Canada
ISBN: 0735273693
Category : Self-Help
Languages : en
Pages : 0
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Book Description
Canada's productivity expert returns with a totally fresh angle on how to do more with less. Throughout his experiments and research, Chris Bailey came across many little-known insights into how we focus (a key element of productivity), including the surprising idea that focus isn't so much a state of heightened awareness (as we'd assume), but a balance between two frames of mind. The most recent neuroscientific research on attention reveals that our brain has two powerful modes that can be unlocked when we use our attention well: a focused mode (hyperfocus), which is the foundation for being highly productive, and a creative mode (scatterfocus), which enables us to connect ideas in novel ways. Hyperfocus helps readers unlock both, so they can concentrate more deeply, think more clearly, and work and live more deliberately. Diving deep into the science and theories about how and why we bring our attention to bear on life's big goals and everyday tasks, Chris Bailey takes his unique approach to productivity to the next level in Hyperfocus, while retaining the approachable voice and perspective that made him a fast favourite.
Author: Maura Thomas
Publisher: Sourcebooks, Inc.
ISBN: 1728217482
Category : Business & Economics
Languages : en
Pages : 70
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Book Description
Are you tired of feeling overwhelmed and scattered? Do you wish you could maximize your productivity and achieve success effortlessly? In Attention Management, productivity expert Maura Thomas unveils the ultimate guide to increasing your focus and harnessing your attention for peak performance. In this game-changing book, Thomas shares her practical mindfulness techniques to help you regain control over your attention and optimize your productivity. With a clear and systematic approach, she empowers you to effectively prioritize tasks, eliminate distractions, and enhance your ability to concentrate on what truly matters. Whether you're a student, professional, or entrepreneur, this book will equip you with the tools and knowledge to: Conquer information overload and regain mental clarity Cultivate laser-like focus amidst digital distractions Overcome procrastination and stay motivated Reduce stress and increase overall well-being Cultivate a healthy work-life balance Boost creativity and unlock your full potential A must-read business book for anyone seeking to transform their productivity and achieve lasting success!
Author: Sebastian Raschka
Publisher: Packt Publishing Ltd
ISBN: 1801816387
Category : Computers
Languages : en
Pages : 775
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Book Description
This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch s simple to code framework. Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Learn applied machine learning with a solid foundation in theory Clear, intuitive explanations take you deep into the theory and practice of Python machine learning Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices Book DescriptionMachine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself. Why PyTorch? PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP). This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.What you will learn Explore frameworks, models, and techniques for machines to learn from data Use scikit-learn for machine learning and PyTorch for deep learning Train machine learning classifiers on images, text, and more Build and train neural networks, transformers, and boosting algorithms Discover best practices for evaluating and tuning models Predict continuous target outcomes using regression analysis Dig deeper into textual and social media data using sentiment analysis Who this book is for If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch. Before you get started with this book, you’ll need a good understanding of calculus, as well as linear algebra.
Author: Jim F. Kukral
Publisher: John Wiley & Sons
ISBN: 0470880686
Category : Business & Economics
Languages : en
Pages : 306
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Book Description
Drive Web traffic and take your business into the future In todays social Web marketplace, attention equals revenue. When you direct more attention online to your brand or business, you drive more long-term revenue. Regardless of who you are or how small your business is, you can have a huge impact using free Internet tools...provided you understand and correctly apply the latest techniques. Attention! gives you an educational and motivational guide to using social media to market your brand or business online. In three parts, you'll discover everything you need to know to get off the ground and thrive in the social mediasphere, including The tools, techniques and tricks to get attention online and turn that attention into profit The theory behind the importance of making your mark on the Internet How other businesses and individuals made money from online marketing Whether you're just starting your business, just moving it online, or already established and looking to take your business to the next level, Attention! is the key to success.
Author: Ivan Vasilev
Publisher: Packt Publishing Ltd
ISBN: 1789952719
Category : Computers
Languages : en
Pages : 456
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Book Description
Gain expertise in advanced deep learning domains such as neural networks, meta-learning, graph neural networks, and memory augmented neural networks using the Python ecosystem Key FeaturesGet to grips with building faster and more robust deep learning architecturesInvestigate and train convolutional neural network (CNN) models with GPU-accelerated libraries such as TensorFlow and PyTorchApply deep neural networks (DNNs) to computer vision problems, NLP, and GANsBook Description In order to build robust deep learning systems, you’ll need to understand everything from how neural networks work to training CNN models. In this book, you’ll discover newly developed deep learning models, methodologies used in the domain, and their implementation based on areas of application. You’ll start by understanding the building blocks and the math behind neural networks, and then move on to CNNs and their advanced applications in computer vision. You'll also learn to apply the most popular CNN architectures in object detection and image segmentation. Further on, you’ll focus on variational autoencoders and GANs. You’ll then use neural networks to extract sophisticated vector representations of words, before going on to cover various types of recurrent networks, such as LSTM and GRU. You’ll even explore the attention mechanism to process sequential data without the help of recurrent neural networks (RNNs). Later, you’ll use graph neural networks for processing structured data, along with covering meta-learning, which allows you to train neural networks with fewer training samples. Finally, you’ll understand how to apply deep learning to autonomous vehicles. By the end of this book, you’ll have mastered key deep learning concepts and the different applications of deep learning models in the real world. What you will learnCover advanced and state-of-the-art neural network architecturesUnderstand the theory and math behind neural networksTrain DNNs and apply them to modern deep learning problemsUse CNNs for object detection and image segmentationImplement generative adversarial networks (GANs) and variational autoencoders to generate new imagesSolve natural language processing (NLP) tasks, such as machine translation, using sequence-to-sequence modelsUnderstand DL techniques, such as meta-learning and graph neural networksWho this book is for This book is for data scientists, deep learning engineers and researchers, and AI developers who want to further their knowledge of deep learning and build innovative and unique deep learning projects. Anyone looking to get to grips with advanced use cases and methodologies adopted in the deep learning domain using real-world examples will also find this book useful. Basic understanding of deep learning concepts and working knowledge of the Python programming language is assumed.
