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Joint models of neural and behaviora...

Turner, Brandon M.

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Joint models of neural and behavioral data

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Joint models of neural and behavioral data/ by Brandon M. Turner, Birte U. Forstmann, Mark Steyvers.
作者:	Turner, Brandon M.
其他作者:	Forstmann, Birte U.
出版者:	Cham :Springer International Publishing : : 2019.,
面頁冊數:	xiii, 109 p. :ill., digital ;24 cm.
內容註:	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
標題:	Cognitive science. -
電子資源:	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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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.
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