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Computational models of conditioning [electronic resource]

Computational models of conditioning [electronic resource]

자료유형
E-Book(소장)
개인저자
Schmajuk, Nestor A.
서명 / 저자사항
Computational models of conditioning [electronic resource] / edited by Nestor Schmajuk.
발행사항
Cambridge ;   New York :   Cambridge University Press,   c2010.  
형태사항
1 online resource (viii, 275 p.) : ill.
ISBN
9780511860416 (electronic bk.) 0511860412 (electronic bk.) 9780511857805 (electronic bk.) 0511857802 (electronic bk.) 9780521113649 0521113644
요약
"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 in the field provide the latest advancements and present detailed descriptions of how the models simulate conditioned behavior and its physiological bases, offering advanced students and researchers examples of how the models are used to analyze existing experimental results and to 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 behavior"--
일반주기
Title from e-Book title page.  
내용주기
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 ;;; [and others] -- 7. Computer simulation of the cerebellum / Michael D. Mauk -- 8. The operant/respondent distinction: a computational neural-network analysis / Jose; E. Burgos.
서지주기
Includes bibliographical references and index.
이용가능한 다른형태자료
Issued also as a book.  
일반주제명
Paired-association learning --Congresses. Cognition --Congresses. Eyelid conditioning --Congresses. Conditioning (Psychology). Association Learning. Models, Neurological.
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245 0 0 ▼a Computational models of conditioning ▼h [electronic resource] / ▼c edited by Nestor Schmajuk.
260 ▼a Cambridge ; ▼a New York : ▼b Cambridge University Press, ▼c c2010.
300 ▼a 1 online resource (viii, 275 p.) : ▼b ill.
500 ▼a Title from e-Book title page.
504 ▼a Includes bibliographical references and index.
505 0 0 ▼g 1. ▼t Evolution of attention in learning / ▼r John K. Kruschke and Richard A. Hullinger -- ▼g 2. ▼t The arguments of associations / ▼r Justin A. Harris -- ▼g 3. ▼t The hybrid modeling approach to conditioning / ▼r Michael E. Le Pelley -- ▼g 4. ▼t Within-compound associations: models and data / ▼r James E. Witnauer and Ralph R. Miller -- ▼g 5. ▼t Associative modulation of US processing: implications for understanding of habituation / ▼r Allan R. Wagner and Edgar H. Vogel -- ▼g 6. ▼t Attention, associations, and configurations in conditioning / ▼r Nestor A. Schmajuk ;;; [and others] -- ▼g 7. ▼t Computer simulation of the cerebellum / ▼r Michael D. Mauk -- 8. ▼t The operant/respondent distinction: a computational neural-network analysis / ▼r Jose; E. Burgos.
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 in the field provide the latest advancements and present detailed descriptions of how the models simulate conditioned behavior and its physiological bases, offering advanced students and researchers examples of how the models are used to analyze existing experimental results and to 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 behavior"-- ▼c Provided by publisher.
530 ▼a Issued also as a book.
538 ▼a Mode of access: World Wide Web.
650 0 ▼a Paired-association learning ▼v Congresses.
650 0 ▼a Cognition ▼v Congresses.
650 0 ▼a Eyelid conditioning ▼v Congresses.
650 2 ▼a Conditioning (Psychology).
650 2 ▼a Association Learning.
650 2 ▼a Models, Neurological.
700 1 ▼a Schmajuk, Nestor A.
856 4 0 ▼u https://oca.korea.ac.kr/link.n2s?url=http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=347868
945 ▼a KLPA
991 ▼a E-Book(소장)

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 중앙도서관/e-Book 컬렉션/ 청구기호 CR 153.1 등록번호 E14007218 도서상태 대출불가(열람가능) 반납예정일 예약 서비스 M

컨텐츠정보

목차

Cover -- Half-title -- Title -- Copyright -- Contents -- Contributors -- Introduction -- The models -- Beyond parsimony: redundancy and reliability -- Evaluation of the models -- Evaluation of the data -- Future challenges -- Society for Computational Modeling of Associative Learning -- References -- 1 Evolution of attention in learning -- Abstract -- Fast learning favors selective attention -- Fast learning of what favors selective attention to what? -- Attention to what? The representation -- Learning of what? The environment -- Definition of context -- An environment with context-dependent relevance -- Designing fast learners -- Assessing selective attention: exhibiting highlighting -- Other signatures of attentional learning? -- Design space and functional desiderata -- Results: optimal learners exhibit highlighting -- Evolution: genetic algorithms discover fast learners -- Overview -- Agents -- Environment and fitness -- Reproduction -- Assessing selective attention by highlighting -- Simulation parameters -- Results: evolved learners exhibit highlighting -- Quantifying the improvement in fitness -- The dynamics of context duration -- Discussion -- Environments that encourage attentional learning -- Linearly separable, four outcomes, with contextual dependency -- Linearly separable with no contextual dependency -- Summary: environments that encourage attentional learning -- Costs and benefits of selective attention -- Author note -- References -- 2 The arguments of associations -- Abstract -- Configural solutions to non-linear discriminations -- Elemental solutions to non-linear discriminations -- Acknowledgments -- References -- 3 The hybrid modeling approach to conditioning -- Abstract -- CS processing and US processing -- US processing -- Summed error -- Individual error -- CS processing -- Evidence for the Mackintosh model: positive transfer -- Evidence for the Pearce–Hall model: negative transfer -- A reconciliation: two CS-processing mechanisms -- A hybrid model -- Component 1: individual error term (US-processing) -- Component 2: summed-error term (US-processing) -- Component 3: “attentional associability,” a (CS-processing) -- Component 4: “salience associability,” s (CS-processing) -- Simulation 1: Baxter, et al. (1999, Experiment 2) -- Simulation 2: Dopson, et al. (2010, Experiment 1) -- Simulation 3: Haselgrove, Esber, Pearce, and Jones (in press) -- CS-processing mechanisms in human contingency learning -- Falsifiability and parsimony -- Conclusion -- References -- 4 Within-compound associations: models and data -- Abstract -- Within-compound associations: models and data -- Simulation of models and general modeling methods -- The sometimes competing retrieval model (SOCR) -- The US-processing (USP) model -- Scaling -- Hill climbing -- Simulation 1: counteraction between two blocking stimuli -- Results and discussion -- Simulation 2: within-compound associations in second-order conditioning -- Results and discussion -- Simulation 3: the role of CS–context associations in extinction -- Results and discussion -- Simulation 4: the effect of CS duration on the CS’s and context’s response potential -- Results and discussion -- Simulation 5: counteraction between latent inhibition and overshadowing -- Results and discussion -- Simulation 6: CS preexposure attenuates conditioned inhibition -- Results and discussion -- Simulation 7: an inhibitory within-compound association attenuates overshadowing -- Results and discussion -- Conclusions -- Author note -- References -- 5 Associative modulation of US processing: implications for understanding of habituation -- Abstract -- Diminished processing of expected USs -- CER potentiation of defensive responses -- Separating specific diminution and generalized potentiation effects -- Simulations via the quantitative assumptions of SOP and AESOP -- Observations and conclusions -- Acknowledgments -- References -- 6 Attention, associations, and configurations in conditioning -- Abstract -- Schmajuk, Lam, and Gray’s (1996) attentional–associative model -- Some emergent properties of the model -- Perception and imagination -- Context-dependent and context-independent latent inhibition -- Awareness -- Excitatory conditioning tends to be context independent -- Inhibitory contextual conditioning tends to be stronger than excitatory contextual conditioning -- Limiting term for VCS,US associations -- Mediated acquisition and extinction -- Mediated changes in attention -- Learning (storage) and performance (retrieval) -- Cognitive mapping -- Attentional and associative representations in the brain -- Attention and error-correcting rules -- Attention and conditioned inhibition -- Latent inhibition -- Interactions between latent inhibition and overshadowing -- Extinction -- Summation tests following extinction -- Spontaneous recovery -- Schmajuk and Di Carlo’s (1992) associational–configural model -- Occasion setting -- Schmajuk and Kutlu (2010) attentional–configural version of the SLG model -- Causal learning -- Additivity training preceding blocking -- Additivity training following blocking -- Conclusion -- Complex models are needed -- Number of parameters will be large -- Evaluation of the models -- References -- 7 Computer simulation of the cerebellum -- Abstract -- Eyelid conditioning and the cerebellum -- What the cerebellum computes -- A brief summary of previous findings -- Computer simulation of cerebellar learning -- Sites and rules for synaptic plasticity -- Successes and failures -- Rescorla–Wagner in the cerebellum -- Back to conditioned response timing -- Open questions and future directions -- References -- 8 The operant/respondent distinction: a computational neural-network analysis -- The behavioral framework -- The computational neural framework -- Neurocomputational submodel -- Network submodel -- Taking stock -- The analysis -- Concluding remarks -- Author note -- References -- Index -- .

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