Übersicht			Auf mobile öffnen

Lieferstatus:	Auf Bestellung (Lieferzeit unbekannt)
Veröffentlichung:	Januar 2019
Genre:	Psychologie / Pädagogik
	B / Behavioral Science and Psychology / Biopsychologie, Physiologische Psychologie, Neuropsychologie / cognitive psychology / Experimental Psychology / Experimentelle Psychologie / neuropsychology / Physiological & neuro-psychology, biopsychology / Psychological measurement / Psychological methodology / Psychological Methods / Psychological Methods/Evaluation / Psychological testing & measurement / Psychologische Methodenlehre / Psychologische Tests, Messtechniken / Psychology, Experimental / Psychology—Methodology / Psychometrics mehr...
ISBN:	9783030036874
EAN-Code:	9783030036874
Verlag:	Springer EN
Einband:	Gebunden
Sprache:	English
Serie:	Computational Approaches to Cognition and Perception
Dimensionen:	H 235 mm / B 155 mm / D
Gewicht:	387 gr
Seiten:	109
Illustration:	XIII, 109 p. 29 illus., 25 illus. in color., schwarz-weiss Illustrationen, farbige Illustrationen
Zus. Info:	EUDR exemption - product or manufacturing materials placed on the market prior to 31.12.2025.
Bewertung:	Titel bewerten / Meinung schreiben

Inhalt:

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.