Staff Page - Sonja Schmer-Galunder

Mrs. Schmer-Galunder started working at Smart Information Flow Technology in the spring of 2009. She has a bachelor’s degree in Psychology/Neuroscience and a master’s degree in Cultural Anthropology from Lund University, Sweden. Prior to working for SIFT, she was employed as research scientist at the Social, Cognitive and Affective Neuroscience Lab of Dr. Ed Smith, Dr. Tor Wager and Dr. Kevin Ochsner at Columbia University, where she worked on several projects investigating the interaction between the neural basis of emotion perception and bodily expressions. Her work has involved the psychology of emotional behavior and brain activity (measured with functional magnetic resonance imaging) and its relation to peripheral physiology, including measures of autonomic and endocrine activity, in particular under perceived psychosocial stress. She worked on the development of novel experimental paradigms that integrate methodologies and knowledge from cognitive neuroscience and behavioral endocrinology in order to study complex cognitive, emotional and somatic neurocircuitry. Mrs. Schmer-Galunder worked on research of stress resilience to post traumatic stress disorde(PTSD) among 9-11 terror attack survivors, a study investigating why psychological and neural mechanisms render some individuals resilient in the face of trauma. Other projects involved the influence of social context information on emotional processing and the influence of placebo effects on cognition. In 2006, she worked at the Davachi Memory Lab at New York University on the experimental development with the goal to understand the role of the hippocampus in transitive inference judgments for social dominance relations.

During her time at the Lunds University, she gained experience working with Latent Semantic Analysis (LSA) and has authored two papers and presented posters on the use of LSA as a subjective and automatic measure of gender valence in huge media text corpora. She has contributed to developing a new software using LSA for the generation of semantic spaces, featuring an integrated set of tools for studying, crating, collecting and visualizing semantic spaces.