{"product_id":"cognitive-mechanisms-of-learning-hardback-9781786305770","title":"Cognitive Mechanisms of Learning (Hardback) 9781786305770","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eCognitive Mechanisms of Learning\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eAnh Nguyen-Xuan (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781786305770, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 31 July 2020\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e314 pages\u003cbr\u003e23.6 x 16.3 x 2.5 cm, 0.635 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eCognitive Mechanisms of Learning presents experimental research works on the issue of knowledge acquisition in Cognitive Psychology. These research works – initiated by groups of researchers with academic backgrounds in Philosophy, Psychology, Linguistics and Artificial Intelligence – explore learning mechanisms by viewing humans as information processing systems.  Although the book is centered on research studies conducted in a laboratory, one chapter is dedicated to applied research studies, derived directly from the fundamental research works. Computer modeling of learning mechanisms is presented, based on the concept of �cognitive architecture�. Three important issues – �the methodology�, �the achievements� and �the evolution� – in the field of learning research are also examined.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eForeword ix\u003c\/p\u003e \u003cp\u003eAcknowledgments xiii\u003c\/p\u003e \u003cp\u003eIntroduction xv\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1. Useful Concepts and Representation Formalisms \u003c\/b\u003e\u003cb\u003e1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1. Useful concepts 1\u003c\/p\u003e \u003cp\u003e1.1.1. Information 1\u003c\/p\u003e \u003cp\u003e1.1.2. Information processing 2\u003c\/p\u003e \u003cp\u003e1.1.3. Problem 2\u003c\/p\u003e \u003cp\u003e1.1.4. Comprehension 4\u003c\/p\u003e \u003cp\u003e1.1.5. Memory 6\u003c\/p\u003e \u003cp\u003e1.2. Some formalisms used in cognitive psychology to represent knowledge stored in the LTM 10\u003c\/p\u003e \u003cp\u003e1.2.1. Semantic networks: a representation formalism for declarative knowledge 11\u003c\/p\u003e \u003cp\u003e1.2.2. A representation formalism for procedural knowledge 13\u003c\/p\u003e \u003cp\u003e1.2.3. A representation formalism for the comprehension process 16\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2. Definition and Historical Overview \u003c\/b\u003e\u003cb\u003e23\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1. Definition 23\u003c\/p\u003e \u003cp\u003e2.2. Conceptual frameworks 26\u003c\/p\u003e \u003cp\u003e2.3. Principal concepts of problem-solving 28\u003c\/p\u003e \u003cp\u003e2.3.1. The “problem space” and “path” concepts 29\u003c\/p\u003e \u003cp\u003e2.3.2. The “heuristic” and “search tree” concepts 32\u003c\/p\u003e \u003cp\u003e2.4. Formal models 35\u003c\/p\u003e \u003cp\u003e2.4.1. Models based on rules of production 36\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3. Learning to Solve a Problem \u003c\/b\u003e\u003cb\u003e43\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1. Breaking down a complex problem into sub-problems 44\u003c\/p\u003e \u003cp\u003e3.1.1. Lee, J. and Anderson, J.R. (2001) 44\u003c\/p\u003e \u003cp\u003e3.2. The four stages of problem-solving 52\u003c\/p\u003e \u003cp\u003e3.2.1. Anderson, Pyke and Fincham (2016) 52\u003c\/p\u003e \u003cp\u003e3.3. The three stages of learning by problem-solving 56\u003c\/p\u003e \u003cp\u003e3.3.1. Tenison, Fincham and Anderson (2016) 56\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4. Learning a Concept from Examples of Concepts: Induction \u003c\/b\u003e\u003cb\u003e63\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1. Rule-based category learning 67\u003c\/p\u003e \u003cp\u003e4.2. The question of “confirmation bias” 72\u003c\/p\u003e \u003cp\u003e4.3. The duality between rule-based concept identification and similarity-based concept identification 75\u003c\/p\u003e \u003cp\u003e4.4. Concluding remarks 85\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5. Implicit Learning \u003c\/b\u003e\u003cb\u003e89\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1. Presentation 89\u003c\/p\u003e \u003cp\u003e5.2. What have learners learned, and are they aware of the knowledge which they acquire? 92\u003c\/p\u003e \u003cp\u003e5.2.1. The princeps research work 92\u003c\/p\u003e \u003cp\u003e5.2.2. What knowledge does the subject need to acquire? 99\u003c\/p\u003e \u003cp\u003e5.3. Fragment status and the question of “abstract” or “concrete” acquired knowledge 102\u003c\/p\u003e \u003cp\u003e5.3.1. The status of fragments in artificial grammar learning experiments 102\u003c\/p\u003e \u003cp\u003e5.3.2. The nature of acquired knowledge: abstract or concrete? 104\u003c\/p\u003e \u003cp\u003e5.4. Conclusion on implicit learning 107\u003c\/p\u003e \u003cp\u003e5.4.1. Implicit learning and statistical learning 107\u003c\/p\u003e \u003cp\u003e5.4.2. Individual differences 109\u003c\/p\u003e \u003cp\u003e5.4.3. Statistical learning mechanisms 110\u003c\/p\u003e \u003cp\u003e5.4.4. Applications of statistical learning 111\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6. The Role of Prior Knowledge in Constructing a Representation of a Problem \u003c\/b\u003e\u003cb\u003e113\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1. Experimental method based on comparing group results 114\u003c\/p\u003e \u003cp\u003e6.2. Experimental method based on multiple trials of the same problem with vocal description of actions by the subject: individual protocol and modeling 123\u003c\/p\u003e \u003cp\u003e6.3. Experimental method using learning transfer to study the effect of problem presentation in the choice of prior knowledge 126\u003c\/p\u003e \u003cp\u003e6.3.1. General hypotheses 127\u003c\/p\u003e \u003cp\u003e6.3.2. Material used 128\u003c\/p\u003e \u003cp\u003e6.3.3. Experimental hypotheses 131\u003c\/p\u003e \u003cp\u003e6.3.4. The experiments 132\u003c\/p\u003e \u003cp\u003e6.3.5. Conclusion 139\u003c\/p\u003e \u003cp\u003e6.4. Conclusion: the role of prior knowledge in the construction of problem representations 139\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7. Acquiring Knowledge in a Specific Domain \u003c\/b\u003e\u003cb\u003e143\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1. Learning through (self-)explanation 143\u003c\/p\u003e \u003cp\u003e7.1.1. Learning to solve problems by studying examples of solutions 144\u003c\/p\u003e \u003cp\u003e7.1.2. Acquisition of declarative knowledge concerning the human circulatory system 147\u003c\/p\u003e \u003cp\u003e7.1.3. Knowledge acquisition in physics 154\u003c\/p\u003e \u003cp\u003e7.1.4. Brief conclusion 160\u003c\/p\u003e \u003cp\u003e7.2. Problem-based learning 161\u003c\/p\u003e \u003cp\u003e7.2.1. Results 164\u003c\/p\u003e \u003cp\u003e7.3. Appendix: some notes on cognitive load theory 165\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8. Causal Learning \u003c\/b\u003e\u003cb\u003e169\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1. Historical overview 170\u003c\/p\u003e \u003cp\u003e8.2. Conceptual framework 172\u003c\/p\u003e \u003cp\u003e8.2.1. Temporal and spatial contiguity 172\u003c\/p\u003e \u003cp\u003e8.2.2. Temporal priority: cause before effect 173\u003c\/p\u003e \u003cp\u003e8.2.3. Contingency 174\u003c\/p\u003e \u003cp\u003e8.2.4. Prior experience 175\u003c\/p\u003e \u003cp\u003e8.3. Formalization and experimental research on adults 176\u003c\/p\u003e \u003cp\u003e8.3.1. Probabilistic models of causal learning 177\u003c\/p\u003e \u003cp\u003e8.3.2. Two examples of research on adults 180\u003c\/p\u003e \u003cp\u003e8.3.3. Causal learning in adults: conclusion 193\u003c\/p\u003e \u003cp\u003e8.4. Experimental research on children 194\u003c\/p\u003e \u003cp\u003e8.4.1. The above\/below relation 196\u003c\/p\u003e \u003cp\u003e8.4.2. The “same\/different” relation 198\u003c\/p\u003e \u003cp\u003e8.4.3. Knowledge of the domain in which a problem situation is represented 200\u003c\/p\u003e \u003cp\u003e8.4.4. Self-directed learning in children 203\u003c\/p\u003e \u003cp\u003e8.4.5. Conclusion: causal learning in children 204\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9. Symbolic Processing System Models in Cognitive Psychology \u003c\/b\u003e\u003cb\u003e213\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1. Why formalize? 213\u003c\/p\u003e \u003cp\u003e9.2. Modeling complex skill acquisition using ACT-R 214\u003c\/p\u003e \u003cp\u003e9.3. Modeling a two-player game 219\u003c\/p\u003e \u003cp\u003e9.4. A model of learning through multiple analogies 229\u003c\/p\u003e \u003cp\u003e9.4.1. Knowledge acquisition: first type 230\u003c\/p\u003e \u003cp\u003e9.4.2. Knowledge acquisition: second type 232\u003c\/p\u003e \u003cp\u003e9.5. Robert Siegler’s two models for learning arithmetic calculation 239\u003c\/p\u003e \u003cp\u003e9.6. Links between SPS models in cognitive psychology and learning models in AI 247\u003c\/p\u003e \u003cp\u003eConclusion 251\u003c\/p\u003e \u003cp\u003eReferences 261\u003c\/p\u003e \u003cp\u003eIndex 285\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Psychology [\u003ca title=\"See our other books on Psychology\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Psychology%20%5BJM%5D%22\"\u003eJM\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-ISTE","offers":[{"title":"Brand New","offer_id":52446749360408,"sku":"9781786305770","price":118.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781786305770.jpg?v=1785112760","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/cognitive-mechanisms-of-learning-hardback-9781786305770","provider":"Freshly Printed Books","version":"1.0","type":"link"}