|
" Joint models of neural and behavioral data / "
Brandon M. Turner, Birte U. Forstmann, Mark Steyvers.
| Document Type
|
:
|
BL
|
| Record Number
|
:
|
860132
|
| Main Entry
|
:
|
Turner, Brandon M.,1985-
|
| Title & Author
|
:
|
Joint models of neural and behavioral data /\ Brandon M. Turner, Birte U. Forstmann, Mark Steyvers.
|
| Publication Statement
|
:
|
Cham, Switzerland :: Springer Nature :: Springer,, [2019]
|
| Series Statement
|
:
|
Computational approaches to cognition and perception
|
| Page. NO
|
:
|
1 online resource
|
| ISBN
|
:
|
303003688X
|
|
|
:
|
: 9783030036881
|
|
|
:
|
3030036871
|
|
|
:
|
9783030036874
|
| Bibliographies/Indexes
|
:
|
Includes bibliographical references and index.
|
| Contents
|
:
|
Intro; Foreword; Acknowledgments; Contents; About the Authors; 1 Motivation; 1.1 Neural Data Can Inform Cognitive Theory; 1.2 Statistical Reciprocity Through Joint Models; 1.2.1 Integrative Approach; 1.2.2 Directed Approach; 1.2.3 Covariance Approach; 1.3 Organization; 2 A Tutorial on Joint Modeling; 2.1 The Generative Model; 2.1.1 Neural Submodel; 2.1.2 Behavioral Submodel; 2.1.3 Linking the Submodels; 2.1.4 Simulating Data; 2.2 Inferring the Model; 2.2.1 Equations for the Neural Submodel; 2.2.2 Equations for the Behavioral Submodel; 2.2.3 Equations for the Linking Structure
|
|
|
:
|
3.3.3 Assessing Predictive Accuracy3.3.4 Calculating Likelihoods for Missing Data; 3.4 Conclusions; 4 Applications; 4.1 Structural Relationships to Decision Making; 4.1.1 Greater Constraint on Model Evaluation; 4.2 Single-Trial Linking to Decision Processes; 4.2.1 Generative Analysis; 4.2.2 Predictive Analysis; 4.3 Multi-modal Integration: Combining Behavior, EEG, and fMRI; 4.4 Incorporating Single Cell Neural Data into Joint Models; 5 Future Directions; 5.1 Revisiting the Linking Assumption: Structural Equation Models; 5.1.1 Complexity; 5.1.2 Real-World Application
|
|
|
:
|
5.2 Linking Structure to Function5.2.1 Mapping Structure and Function on Different Levels; 6 Other Approaches; 6.1 Neural Data Constrain Behavioral Model; 6.1.1 Theoretical Approach; 6.1.2 Two-Stage Behavioral Approach; 6.1.3 Direct Input Approach; 6.2 Behavioral Model Predicts Neural Data; 6.2.1 Latent Input Approach; 6.2.2 Two-Stage Neural Approach; 6.3 Conclusions; 7 Conclusions; References; Index
|
| Abstract
|
:
|
This book presents a flexible Bayesian framework for combining neural and cognitive models. Traditionally, studies in cognition and cognitive sciences have been done by either observing behavior (e.g., response times, percentage correct, etc.) or by observing neural activity (e.g., the BOLD response). These two types of observations have traditionally supported two separate lines of study, which are led by two different cognitive modelers. Joining neuroimaging and computational modeling in a single hierarchical framework allows the neural data to influence the parameters of the cognitive model and allows behavioral data to constrain the neural model. This Bayesian approach can be used to reveal interactions between behavioral and neural parameters, and ultimately, between neural activity and cognitive mechanisms. Chapters demonstrate the utility of this Bayesian model with a variety of applications, and feature a tutorial chapter where the methods can be applied to an example problem. The book also discusses other joint modeling approaches and future directions. Joint Models of Neural and Behavioral Data will be of interest to advanced graduate students and postdoctoral candidates in an academic setting as well as researchers in the fields of cognitive psychology and neuroscience.
|
| Subject
|
:
|
Cognition.
|
| Subject
|
:
|
Cognitive science.
|
| |