{"product_id":"from-human-attention-to-computational-attention-multidisciplinary","title":"From Human Attention to Computational Attention - Multidisciplinary","description":"\u003cp\u003eIn this review of From Human Attention to Computational Attention the bottom line is clear: this book is for researchers and practitioners who need a broad, cross-disciplinary look at attention and its computational modeling. It combines accessible exposition with in-depth coverage and serves as a bridge between psychology, neuroscience, engineering and computer science. The single biggest reason to buy is its multi-disciplinary synthesis, which helps readers translate theoretical ideas about \u003cstrong\u003eattention modeling\u003c\/strong\u003e into practical approaches for saliency, signal detection and real-life applications.\u003c\/p\u003e\u003ch2\u003eKey Features\u003c\/h2\u003e\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eMultidisciplinary scope:\u003c\/strong\u003e Brings together work from psychology, neuroscience, engineering and computer science to give a rounded understanding of attention.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eFocus on attention modeling:\u003c\/strong\u003e Explains computational approaches to saliency and prioritization that can inform both research and applied systems.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eTheory to practice:\u003c\/strong\u003e Addresses real-life applications so readers can see how models of attention are used outside the lab.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eAccessible yet exhaustive:\u003c\/strong\u003e Balances readable explanations with comprehensive coverage, making complex topics approachable for motivated readers.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSignal-focused treatment:\u003c\/strong\u003e Covers signal detection and different signal types to clarify how attention interacts with incoming information.\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch2\u003eWho It's For\u003c\/h2\u003e\u003cp\u003eThis book is best suited to graduate students, researchers and industry engineers who want a rigorous but readable resource on \u003cstrong\u003ecomputational attention\u003c\/strong\u003e. Those working on saliency models, neural systems for attention, or applied perception systems will find the collected perspectives useful for building or improving models.\u003c\/p\u003e\u003cp\u003eIt is less appropriate for casual readers seeking a light introduction to cognition; the volume assumes interest in technical concepts and cross-disciplinary literature and is strongest for readers prepared to engage with scholarly material.\u003c\/p\u003e\u003ch2\u003ePros \u0026amp; Cons\u003c\/h2\u003e\u003cp\u003e\u003cstrong\u003ePros\u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eComprehensive multidisciplinary coverage that connects theory and application.\u003c\/li\u003e\n\u003cli\u003ePractical discussion of saliency models and signal detection useful for implementation.\u003c\/li\u003e\n\u003cli\u003eReadable presentation that still delivers exhaustive material for serious study.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cstrong\u003eCons\u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\u003cli\u003eDense subject matter may be challenging for readers without background in at least one contributing discipline.\u003c\/li\u003e\u003c\/ul\u003e\u003ch2\u003eSpecifications\u003c\/h2\u003e\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003eTitle\u003c\/td\u003e\n\u003ctd\u003eFrom Human Attention to Computational Attention\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSeries\u003c\/td\u003e\n\u003ctd\u003eSpringer Series in Cognitive and Neural Systems\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eEditors \/ Authors\u003c\/td\u003e\n\u003ctd\u003eMatei Mancas, Vincent P. Ferrera, Nicolas Riche, John G. Taylor\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eScope\u003c\/td\u003e\n\u003ctd\u003eMultidisciplinary: psychology, neuroscience, engineering, computer science\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMain topics\u003c\/td\u003e\n\u003ctd\u003eAttention modeling, saliency models, signal detection, real-life applications\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAudience\u003c\/td\u003e\n\u003ctd\u003eResearchers, graduate students, industry practitioners\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\u003ch2\u003eOur Verdict\u003c\/h2\u003e\u003cp\u003eFrom Human Attention to Computational Attention is a strong value for readers who need a broad, integrative resource on attention and its computational treatment. Its multidisciplinary synthesis and emphasis on \u003cstrong\u003eattention modeling\u003c\/strong\u003e make it particularly useful for researchers and engineers seeking to apply theory to practical systems.\u003c\/p\u003e\u003ch2\u003eFrequently Asked Questions\u003c\/h2\u003e\u003cp\u003e\u003cstrong\u003eDoes this book cover practical applications?\u003c\/strong\u003e\u003cbr\u003eYes. The book explicitly addresses real-life applications and discusses how attention models are applied outside academic settings.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWho edited the volume?\u003c\/strong\u003e\u003cbr\u003eThe editors and contributors include Matei Mancas, Vincent P. Ferrera, Nicolas Riche and John G. Taylor.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eIs it suitable for beginners?\u003c\/strong\u003e\u003cbr\u003eIt is accessible but best suited to readers with some background or willingness to engage with cross-disciplinary scholarly material.\u003c\/p\u003e","brand":"Matei Mancas, Vincent P. Ferrera, Nicolas Riche, John G. Taylor","offers":[{"title":"Default Title","offer_id":48610225979611,"sku":"1493980505","price":135.86,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0724\/1043\/1707\/files\/61T-NfCJiBL._SL1254.jpg?v=1778584008","url":"https:\/\/gearmusthave.com\/products\/from-human-attention-to-computational-attention-multidisciplinary","provider":"GearMustHave","version":"1.0","type":"link"}