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Joint models of neural and behaviora...

Turner, Brandon M.

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Joint models of neural and behavioral data

Record Type:	Electronic resources : Monograph/item
Title/Author:	Joint models of neural and behavioral data/ by Brandon M. Turner, Birte U. Forstmann, Mark Steyvers.
Author:	Turner, Brandon M.
other author:	Forstmann, Birte U.
Published:	Cham :Springer International Publishing : : 2019.,
Description:	xiii, 109 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1. Motivation -- Chapter 2. A Tutorial on Joint Modeling -- Chapter 3. Assessing Model Performance with Generalization Tests -- Chapter 4. Applications -- Chapter 5. Future Directions -- Chapter 6. Other Approaches.
Contained By:	Springer eBooks
Subject:	Cognitive science. -
Online resource:	https://doi.org/10.1007/978-3-030-03688-1
ISBN:	9783030036881

Joint models of neural and behavioral data
Turner, Brandon M.

Joint models of neural and behavioral data[electronic resource] /by Brandon M. Turner, Birte U. Forstmann, Mark Steyvers. - Cham :Springer International Publishing :2019. - xiii, 109 p. :ill., digital ;24 cm. - Computational approaches to cognition and perception,2510-1889. - Computational approaches to cognition and perception..

Chapter 1. Motivation -- Chapter 2. A Tutorial on Joint Modeling -- Chapter 3. Assessing Model Performance with Generalization Tests -- Chapter 4. Applications -- Chapter 5. Future Directions -- Chapter 6. Other Approaches.

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.

ISBN: 9783030036881

Standard No.: 10.1007/978-3-030-03688-1doiSubjects--Topical Terms:

524990
Cognitive science.

LC Class. No.: BF311 / .T876 2019

Dewey Class. No.: 153

Joint models of neural and behavioral data
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100 1 $a Turner, Brandon M. $3 3382086
245 1 0 $a Joint models of neural and behavioral data $h [electronic resource] / $c by Brandon M. Turner, Birte U. Forstmann, Mark Steyvers.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a xiii, 109 p. : $b ill., digital ; $c 24 cm.
490 1 $a Computational approaches to cognition and perception, $x 2510-1889
505 0 $a Chapter 1. Motivation -- Chapter 2. A Tutorial on Joint Modeling -- Chapter 3. Assessing Model Performance with Generalization Tests -- Chapter 4. Applications -- Chapter 5. Future Directions -- Chapter 6. Other Approaches.
520 $a 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.
650 0 $a Cognitive science. $3 524990
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650 2 4 $a Psychological Methods/Evaluation. $3 899288
650 2 4 $a Experimental Psychology. $3 2181949
650 2 4 $a Psychometrics. $3 520603
700 1 $a Forstmann, Birte U. $3 2145088
700 1 $a Steyvers, Mark. $3 3382087
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