title: Neural architecture underlying classification of face perception paradigms.
abstract: We present a novel strategy for deriving a classification system of functional neuroimaging paradigms that relies on hierarchical clustering of experiments archived in the BrainMap database. The goal of our proof-of-concept application was to examine the underlying neural architecture of the face perception literature from a meta-analytic perspective, as these studies include a wide range of tasks. Task-based results exhibiting similar activation patterns were grouped as similar, while tasks activating different brain networks were classified as functionally distinct. We identified four sub-classes of face tasks: (1) Visuospatial Attention and Visuomotor Coordination to Faces, (2) Perception and Recognition of Faces, (3) Social Processing and Episodic Recall of Faces, and (4) Face Naming and Lexical Retrieval. Interpretation of these sub-classes supports an extension of a well-known model of face perception to include a core system for visual analysis and extended systems for personal information, emotion, and salience processing. Overall, these results demonstrate that a large-scale data mining approach can inform the evolution of theoretical cognitive models by probing the range of behavioral manipulations across experimental tasks.
authors.affiliation: Department of Physics, Florida International University, Miami, FL, USA; Department of Psychology, Florida International University, Miami, FL, USA. Electronic address: alaird@fiu.edu.
authors.forename: Angela R
authors.initials: AR
authors.lastname: Laird
authors.affiliation: Department of Physics, Florida International University, Miami, FL, USA; Research Imaging Institute, University of Texas Health Science Center San Antonio, San Antonio, TX, USA.
authors.forename: Michael C
authors.initials: MC
authors.lastname: Riedel
authors.affiliation: Department of Psychology, Florida International University, Miami, FL, USA.
authors.forename: Matthew T
authors.initials: MT
authors.lastname: Sutherland
authors.affiliation: Institute of Neuroscience and Medicine, Research Center Jülich, Jülich, Germany; Institute for Clinical Neuroscience and Medical Psychology, Heinrich-Heine University, Dusseldorf, Germany.
authors.forename: Simon B
authors.initials: SB
authors.lastname: Eickhoff
authors.affiliation: Research Imaging Institute, University of Texas Health Science Center San Antonio, San Antonio, TX, USA.
authors.forename: Kimberly L
authors.initials: KL
authors.lastname: Ray
authors.affiliation: Research Imaging Institute, University of Texas Health Science Center San Antonio, San Antonio, TX, USA.
authors.forename: Angela M
authors.initials: AM
authors.lastname: Uecker
authors.affiliation: Research Imaging Institute, University of Texas Health Science Center San Antonio, San Antonio, TX, USA.
authors.forename: P Mickle
authors.initials: PM
authors.lastname: Fox
authors.affiliation: Department of Psychology and Neuroscience, Georgia State University, Atlanta, GA, USA.
authors.forename: Jessica A
authors.initials: JA
authors.lastname: Turner
authors.affiliation: Research Imaging Institute, University of Texas Health Science Center San Antonio, San Antonio, TX, USA; Research Service, South Texas Veterans Administration Medical Center, San Antonio, TX, USA; State Key Laboratory for Brain and Cognitive Sciences, University of Hong Kong, Hong Kong.
authors.forename: Peter T
authors.initials: PT
authors.lastname: Fox
mesh_terms: D001288:Attention / Q000502:physiology; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping; D016000:Cluster Analysis; D057225:Data Mining; D004644:Emotions / Q000502:physiology; D005149:Facial Expression; D000066499:Facial Recognition / Q000502:physiology*; D059907:Functional Neuroimaging; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D011939:Mental Recall / Q000502:physiology; D021641:Recognition, Psychology / Q000502:physiology; D012107:Research Design
keywords: Cognitive paradigms; Face perception; Faces; Functional neuroimaging; Meta-analysis; Neuroinformatics