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Neural correlates of magno- and parvocellular contributions to emotional perception and emotional learning

Antragsteller Dr. Christian Steinberg
Fachliche Zuordnung Kognitive und systemische Humanneurowissenschaften
Förderung Förderung von 2013 bis 2017
Projektkennung Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 234848892
 
Human emotional vision has been explained in the context of several pathway models. For instance, the Dual route model (LeDoux, 1994) and the multiple waves model (Pessoa and Adolphs, 2010) propose that crude emotion-related information flows along dual or multiple visual channels. This information can be processed very rapidly to influence brain activity at several processing stages later on. Importantly, both models assume that initial emotional brain responses are mediated by fast signal transmission along magnocellular (M) fibers as opposed to the (slower) parvocellular (P) system. Surprisingly, little is known about the exact contributions of both kinds of visual information to emotional vision. Very recent findings by the applicant and others show that emotional modulations of brain activity can occur in a very early time-window of visual processing, which might relate to this fast transmission of emotional information via magnocellular channels. To reveal the impact of magno- and parvocellular contributions on human emotional vision, the project proposes the use of isoluminant (p-biased) and low-luminance-contrast (m-biased) visual emotional stimuli such as faces and scenes, which are specifically designed to directly bias processing in one of the two streams. Magno- and parvocellular contributions will be examined in the context of emotional perception and emotional learning with stimuli that either have an inherent emotional value (study I, II) or that will gain emotional significance via affective associative learning with a classical (study III) or novel MultiCS conditioning procedure (study IV). Using electroencephalography, the time-course of magno- and parvocellular emotional processing will be tracked with high temporal resolution and neural generators will be identified using inverse source modeling techniques. The results obtained from the proposed studies are of interest not only for basic emotion research but may also be of relevance for future clinical research concerning anxiety disorders.
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