title: Assessment of aversive stimuli dependent attentional binding by the N170 VEP component.
abstract: For social species nonverbal communication by assessment of emotion expression is crucial for building up and maintaining social structures. In humans, body language not only includes gestures but also a variety of facial expressions. Negative associated facial expressions, e.g. disgust, fear, anger call for a higher attentional binding due their evolutionary background, denoting directly personal dangers for the receptive individual. In a number of psychiatric disorders such as schizophrenia or autism spectrum diseases, the assessment of emotions in faces is disturbed, leading to even more pronounced social cuts. In this article we present a new methodology for monitoring the attentional binding to emotion-tinged stimuli in a face recognition task. We were able to demonstrate a significant difference in habituation behavior to neutral and negative associated faces respectively. In future, this methodology might provide a fast and reliable scheme for the detection of psychiatric disorders comprising dysfunction of limbic structures.
authors: Computational Diagnostics and Biocybernetics Unit at Saarland University Hospital and Saarland University of Applied Sciences, Homburg/Saarbruecken, Germany. busse@cdb-unit.de
authors: Michael
authors: M
authors: Busse
authors: Lars
authors: L
authors: Haab
authors: Mai
authors: M
authors: Mariam
authors: Christoph
authors: C
authors: Krick
authors: Tina
authors: T
authors: Weis
authors: Wolfgang
authors: W
authors: Reith
authors: Daniel J
authors: DJ
authors: Strauss
mesh_terms: D000328:Adult; D000465:Algorithms; D000679:Amygdala / Q000503:physiopathology*; D003142:Communication; D004644:Emotions; D005071:Evoked Potentials; D005149:Facial Expression; D005239:Fear; D006185:Habituation, Psychophysiologic; D006801:Humans; D007697:Kinesics; D001523:Mental Disorders / Q000175:diagnosis; D015233:Models, Statistical; D009474:Neurons / Q000473:pathology; D009633:Nonverbal Communication; D010364:Pattern Recognition, Visual; D012815:Signal Processing, Computer-Assisted