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20240329122006.0 |
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200725s2020 xx o ||| 0 eng d |
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|a EBLCP
|b eng
|c EBLCP
|d REDDC
|d OCLCF
|d OCLCQ
|d OCLCO
|d OCLCL
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|a 9781119750468
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|a 1119750466
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|a (OCoLC)1178636438
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|a LB1590.3
|b .N48 2020
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|a 370.1523
|2 23
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|a UAMI
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|a Nguyen-Xuan, Anh.
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|a Cognitive Mechanisms of Learning
|h [electronic resource].
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260 |
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|a Newark :
|b John Wiley & Sons, Incorporated,
|c 2020.
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300 |
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|a 1 online resource (319 p.)
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500 |
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|a Description based upon print version of record.
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|a Cover -- Half-Title Page -- Title Page -- Copyright Page -- Contents -- Foreword -- Acknowledgments -- Introduction -- 1. Useful Concepts and Representation Formalisms -- 1.1. Useful concepts -- 1.1.1. Information -- 1.1.2. Information processing -- 1.1.3. Problem -- 1.1.4. Comprehension -- 1.1.5. Memory -- 1.2. Some formalisms used in cognitive psychology to represent knowledge stored in the LTM -- 1.2.1. Semantic networks: a representation formalism for declarative knowledge -- 1.2.2. A representation formalism for procedural knowledge
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|a 1.2.3. A representation formalism for the comprehension process -- 2. Definition and Historical Overview -- 2.1. Definition -- 2.2. Conceptual frameworks -- 2.3. Principal concepts of problem-solving -- 2.3.1. The "problem space" and "path" concepts -- 2.3.2. The "heuristic" and "search tree" concepts -- 2.4. Formal models -- 2.4.1. Models based on rules of production -- 3. Learning to Solve a Problem -- 3.1. Breaking down a complex problem into sub-problems -- 3.1.1. Lee, J. and Anderson, J.R. (2001) -- 3.2. The four stages of problem-solving -- 3.2.1. Anderson, Pyke and Fincham (2016)
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|a 3.3. The three stages of learning by problem-solving -- 3.3.1. Tenison, Fincham and Anderson (2016) -- 4. Learning a Concept from Examples of Concepts: Induction -- 4.1. Rule-based category learning -- 4.2. The question of "confirmation bias" -- 4.3. The duality between rule-based concept identification and similarity based concept identification -- 4.4. Concluding remarks -- 5. Implicit Learning -- 5.1. Presentation -- 5.2. What have learners learned, and are they aware of the knowledge which they acquire? -- 5.2.1. The princeps research work
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|a 5.2.2. What knowledge does the subject need to acquire? -- 5.3. Fragment status and the question of "abstract" or "concrete" acquired knowledge -- 5.3.1. The status of fragments in artificial grammar learning experiments -- 5.3.2. The nature of acquired knowledge: abstract or concrete? -- 5.4. Conclusion on implicit learning -- 5.4.1. Implicit learning and statistical learning -- 5.4.2. Individual differences -- 5.4.3. Statistical learning mechanisms -- 5.4.4. Applications of statistical learning -- 6. The Role of Prior Knowledge in Constructing a Representation of a Problem
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|a 6.1. Experimental method based on comparing group results -- 6.2. Experimental method based on multiple trials of the same problem with vocal description of actions by the subject: individual protocol and modeling -- 6.3. Experimental method using learning transfer to study the effect of problem presentation in the choice of prior knowledge -- 6.3.1. General hypotheses -- 6.3.2. Material used -- 6.3.3. Experimental hypotheses -- 6.3.4. The experiments -- 6.3.5. Conclusion -- 6.4. Conclusion: the role of prior knowledge in the construction of problem representations
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500 |
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|a 7. Acquiring Knowledge in a Specific Domain
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590 |
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|a ProQuest Ebook Central
|b Ebook Central Academic Complete
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650 |
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|a Cognitive learning theory.
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650 |
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|a Théorie cognitive de l'apprentissage.
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650 |
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|a Cognitive learning theory
|2 fast
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758 |
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|i has work:
|a COGNITIVE MECHANISMS OF LEARNING (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCXXVvTwVFkD44rDpry9Qjd
|4 https://id.oclc.org/worldcat/ontology/hasWork
|
776 |
0 |
8 |
|i Print version:
|a Nguyen-Xuan, Anh
|t Cognitive Mechanisms of Learning
|d Newark : John Wiley & Sons, Incorporated,c2020
|z 9781786305770
|
856 |
4 |
0 |
|u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=6265340
|z Texto completo
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938 |
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|a ProQuest Ebook Central
|b EBLB
|n EBL6265340
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994 |
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|a 92
|b IZTAP
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