Computational models of conditioning:
Since first described, multiple properties of classical conditioning have been discovered, establishing the need for mathematical models to help explain the defining features. The mathematical complexity of the models puts our understanding of their workings beyond the ability of our intuitive think...
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| Format: | Elektronisch E-Book |
| Sprache: | English |
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Cambridge
Cambridge University Press
2010
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| Online-Zugang: | BSB01 FHN01 FUBA1 Volltext |
| Zusammenfassung: | Since first described, multiple properties of classical conditioning have been discovered, establishing the need for mathematical models to help explain the defining features. The mathematical complexity of the models puts our understanding of their workings beyond the ability of our intuitive thinking and makes computer simulations irreplaceable. The complexity of the models frequently results in function redundancy, a natural property of biologically evolved systems that is much desired in technologically designed products. Experts provide the latest advancements in the field and present detailed descriptions of how the models simulate conditioned behaviour and its physiological bases. It offers advanced students and researchers examples of how the models are used to analyse existing experimental results and design future experiments. This volume is of great interest to psychologists and neuroscientists, as well as computer scientists and engineers searching for ideas applicable to the design of robots that mimic animal behaviour |
| Beschreibung: | Title from publisher's bibliographic system (viewed on 05 Oct 2015) |
| Beschreibung: | 1 Online-Ressource (vii, 275 Seiten) |
| ISBN: | 9780511760402 |
| DOI: | 10.1017/CBO9780511760402 |
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| 505 | 8 | 0 | |t <<1.>> Evolution of attention in learning |r John K. Kruschke and Richard A. Hullinger |t <<2. The>> arguments of associations |r Justin A. Harris |t <<3. The>> hybrid modeling approach to conditioning |r Michael E. Le Pelley |t <<4.>> Within-compound associations: models and data |r James E. Witnauer and Ralph R. Miller |t <<5.>> Associative modulation of US processing: implications for understanding of habituation |r Allan R. Wagner and Edgar H. Vogel |t <<6.>> Attention, associations, and configurations in conditioning |r Nestor A. Schmajuk ;;; [et al.] |t <<7.>> Computer simulation of the cerebellum |r Michael D. Mauk -- 8. The operant/respondent distinction: a computational neural-network analysis |
| 520 | |a Since first described, multiple properties of classical conditioning have been discovered, establishing the need for mathematical models to help explain the defining features. The mathematical complexity of the models puts our understanding of their workings beyond the ability of our intuitive thinking and makes computer simulations irreplaceable. The complexity of the models frequently results in function redundancy, a natural property of biologically evolved systems that is much desired in technologically designed products. Experts provide the latest advancements in the field and present detailed descriptions of how the models simulate conditioned behaviour and its physiological bases. It offers advanced students and researchers examples of how the models are used to analyse existing experimental results and design future experiments. This volume is of great interest to psychologists and neuroscientists, as well as computer scientists and engineers searching for ideas applicable to the design of robots that mimic animal behaviour | ||
| 650 | 4 | |a Paired-association learning / Congresses | |
| 650 | 4 | |a Cognition / Congresses | |
| 650 | 4 | |a Eyelid conditioning / Congresses | |
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Datensatz im Suchindex
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| author2 | Schmajuk, Nestor A. |
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| author_additional | John K. Kruschke and Richard A. Hullinger Justin A. Harris Michael E. Le Pelley James E. Witnauer and Ralph R. Miller Allan R. Wagner and Edgar H. Vogel Nestor A. Schmajuk ;;; [et al.] Michael D. Mauk -- 8. The operant/respondent distinction: a computational neural-network analysis |
| author_facet | Schmajuk, Nestor A. |
| building | Verbundindex |
| bvnumber | BV043944673 |
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| contents | <<1.>> Evolution of attention in learning <<2. The>> arguments of associations <<3. The>> hybrid modeling approach to conditioning <<4.>> Within-compound associations: models and data <<5.>> Associative modulation of US processing: implications for understanding of habituation <<6.>> Attention, associations, and configurations in conditioning <<7.>> Computer simulation of the cerebellum |
| ctrlnum | (ZDB-20-CBO)CR9780511760402 (OCoLC)967604159 (DE-599)BVBBV043944673 |
| dewey-full | 153.1 |
| dewey-hundreds | 100 - Philosophy & psychology |
| dewey-ones | 153 - Conscious mental processes & intelligence |
| dewey-raw | 153.1 |
| dewey-search | 153.1 |
| dewey-sort | 3153.1 |
| dewey-tens | 150 - Psychology |
| discipline | Psychologie |
| doi_str_mv | 10.1017/CBO9780511760402 |
| format | Electronic eBook |
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| isbn | 9780511760402 |
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| spelling | Computational models of conditioning edited by Nestor Schmajuk, Duke University Cambridge Cambridge University Press 2010 1 Online-Ressource (vii, 275 Seiten) txt rdacontent c rdamedia cr rdacarrier Title from publisher's bibliographic system (viewed on 05 Oct 2015) <<1.>> Evolution of attention in learning John K. Kruschke and Richard A. Hullinger <<2. The>> arguments of associations Justin A. Harris <<3. The>> hybrid modeling approach to conditioning Michael E. Le Pelley <<4.>> Within-compound associations: models and data James E. Witnauer and Ralph R. Miller <<5.>> Associative modulation of US processing: implications for understanding of habituation Allan R. Wagner and Edgar H. Vogel <<6.>> Attention, associations, and configurations in conditioning Nestor A. Schmajuk ;;; [et al.] <<7.>> Computer simulation of the cerebellum Michael D. Mauk -- 8. The operant/respondent distinction: a computational neural-network analysis Since first described, multiple properties of classical conditioning have been discovered, establishing the need for mathematical models to help explain the defining features. The mathematical complexity of the models puts our understanding of their workings beyond the ability of our intuitive thinking and makes computer simulations irreplaceable. The complexity of the models frequently results in function redundancy, a natural property of biologically evolved systems that is much desired in technologically designed products. Experts provide the latest advancements in the field and present detailed descriptions of how the models simulate conditioned behaviour and its physiological bases. It offers advanced students and researchers examples of how the models are used to analyse existing experimental results and design future experiments. This volume is of great interest to psychologists and neuroscientists, as well as computer scientists and engineers searching for ideas applicable to the design of robots that mimic animal behaviour Paired-association learning / Congresses Cognition / Congresses Eyelid conditioning / Congresses (DE-588)1071861417 Konferenzschrift gnd-content Schmajuk, Nestor A. edt Erscheint auch als Druck-Ausgabe 978-0-521-11364-9 https://doi.org/10.1017/CBO9780511760402 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Computational models of conditioning <<1.>> Evolution of attention in learning <<2. The>> arguments of associations <<3. The>> hybrid modeling approach to conditioning <<4.>> Within-compound associations: models and data <<5.>> Associative modulation of US processing: implications for understanding of habituation <<6.>> Attention, associations, and configurations in conditioning <<7.>> Computer simulation of the cerebellum Paired-association learning / Congresses Cognition / Congresses Eyelid conditioning / Congresses |
| subject_GND | (DE-588)1071861417 |
| title | Computational models of conditioning |
| title_alt | <<1.>> Evolution of attention in learning <<2. The>> arguments of associations <<3. The>> hybrid modeling approach to conditioning <<4.>> Within-compound associations: models and data <<5.>> Associative modulation of US processing: implications for understanding of habituation <<6.>> Attention, associations, and configurations in conditioning <<7.>> Computer simulation of the cerebellum |
| title_auth | Computational models of conditioning |
| title_exact_search | Computational models of conditioning |
| title_full | Computational models of conditioning edited by Nestor Schmajuk, Duke University |
| title_fullStr | Computational models of conditioning edited by Nestor Schmajuk, Duke University |
| title_full_unstemmed | Computational models of conditioning edited by Nestor Schmajuk, Duke University |
| title_short | Computational models of conditioning |
| title_sort | computational models of conditioning |
| topic | Paired-association learning / Congresses Cognition / Congresses Eyelid conditioning / Congresses |
| topic_facet | Paired-association learning / Congresses Cognition / Congresses Eyelid conditioning / Congresses Konferenzschrift |
| url | https://doi.org/10.1017/CBO9780511760402 |
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