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020 $a9783031452710$q(electronic bk.)

020 $a3031452712$q(electronic bk.)

020 $z9783031452703

020 $z3031452704

024 7 $a10.1007/978-3-031-45271-0$2doi

035 $aebs3866606

040 $aNhCcYBP$cNhCcYBP

041 $aeng

050 4$aQP360.5$b.I58 2024

072 7$aPSAN$2bicssc

072 7$aSCI089000$2bisacsh

072 7$aPSAN$2thema

082 04$a612.8/233$223/eng/20240405

245 03$aAn introduction to model-based cognitive neuroscience /$cBirte U. Forstmann, Brandon M. Turner, editors.

250 $aSecond edition.

264 1$aCham :$bSpringer,$c2024.

300 $a1 online resource (vi, 388 pages) :$billustrations (some color).

336 $atext$btxt$2rdacontent

337 $acomputer$bc$2rdamedia

338 $aonline resource$bcr$2rdacarrier

500 $aIncludes index.

505 0 $aIntro -- Contents -- General Introduction to Model-Based Cognitive Neuroscience -- 1 Introduction -- 1.1 What Is Model-Based Cognitive Neuroscience? -- 1.2 Neural Data Constrain the Behavioral Model -- 1.3 Behavioral Model Predicts Neural Data -- 1.4 Simultaneous Modeling -- 2 Prominent Models and Measures in the Field of Model-Based Cognitive Neuroscience -- 2.1 Types of Behavioral Measures -- 2.2 Types of Neural Measures -- 2.3 Types of Cognitive Models -- 3 Applications in the Field of Model-Based Cognitive Neuroscience -- 4 Future Directions -- 5 Open Challenges -- References

505 8 $aLinking Models with Brain Measures -- 1 Introduction -- 2 Some Functions of Models in Science -- 3 Levels of Analysis -- 4 Other Types of Models Useful in Analysing Brain Data -- 5 General Comparison of Model and Brain Data -- 6 Cognitive Model as Integral Part of the Data Analysis -- 7 Individual Differences -- 8 Models Can Uncover Useful Latent States -- 9 Comparing Model and Brain Representations -- 10 Multiple Levels of Representation -- 11 Conclusions -- Questions for Consideration -- Further Reading -- References -- Reinforcement Learning -- 1 Introduction -- 2 Reinforcement Learning

505 8 $a2.1 Pavlovian Conditioning -- 2.1.1 Temporal-Difference Learning -- 2.2 Instrumental Conditioning -- 2.2.1 Actor-Critic Model -- 3 Model-Based fMRI -- 3.1 Univariate Approach -- 3.2 Multivariate Analyses -- 4 Considerations When Linking RL and fMRI Models -- 4.1 Evaluating Model Quality -- 4.2 Addressing Model Considerations -- 5 Bridging Levels of Analyses -- 5.1 Neural Correlates of Computational Processes -- 5.2 Leveraging fMRI to Adjudicate Between Models -- 5.3 Future Directions -- 6 Exercises -- 7 Further Reading -- References -- An Introduction to the Diffusion Model of Decision-Making

505 8 $a1 Historical Origins -- 2 Diffusion Processes and Random Walks -- 3 The Standard Diffusion Model -- 4 Components of Processing -- 5 Bias and Speed-Accuracy Tradeoff Effects -- 6 Mathematical Methods for Diffusion Models -- 7 The Representation of Empirical Data -- 8 Fitting the Model to Experimental Data -- 9 Diffusion Models of Continuous Outcome Decisions -- 10 Conclusion -- 11 Suggestions for Further Reading -- 12 Exercises -- References -- Discovering Cognitive Stages in M/EEG Data to Inform CognitiveModels -- 1 Introduction -- 2 Part 1: The Discovery of Processing Stages in M/EEG Data

505 8 $a2.1 The HsMM-MVPA Method -- 2.2 Discovering Cognitive Processing Stages in Associative Recognition -- 3 Part 2: A Symbolic Process Model -- 3.1 The Cognitive Architecture ACT-R -- 3.2 A Model of Associative Recognition -- 4 General Discussion -- Exercises -- Answers -- Further Reading -- References -- Spiking, Salience, and Saccades: Using Cognitive Models to Bridge the Gap Between ``How'' and ``Why'' -- 1 Introduction -- 1.1 Dimensions of Constraint -- 2 A Case Study: SCRI -- 2.1 Phenomena to Be Explained -- 2.2 The Model -- 2.2.1 Motivating Principles -- 2.2.2 Conceptual Outline

533 $aElectronic reproduction.$bIpswich, MA$nAvailable via World Wide Web.

588 0 $aOnline resource; title from PDF title page (SpringerLink, viewed April 5, 2024).

650 0$aCognitive neuroscience$xMathematical models.$3116307

650 0$aHuman behavior models.$3116308

650 0$aNeuropsychology.$324310

700 1 $aForstmann, Birte U.,$eeditor.$3116309

700 1 $aTurner, Brandon M.,$d1985-$eeditor.$3116310

710 2 $aEBSCOhost$387894

776 08$cOriginal$z3031452704$z9783031452703

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