+41 22 379 0979
Institution : University of Geneva
Thesis topic : Appraisal-driven patterning of emotional expression and experience
NCCR position : PhD Student
NCCR PI : Klaus Scherer
NCCR most relevant publications
SCHERER, Klaus R., MEULEMAN, Ben. Human Emotion Experiences Can Be Predicted on Theoretical Grounds: Evidence from Verbal Labeling. In: PLOS ONE, 2013, vol. 8, n° 3, p. e58166.
MEULEMAN, Ben, SCHERER, Klaus R. Nonlinear Appraisal Modeling: An Application of Machine Learning to the Study of Emotion Production. In: IEEE Transactions on Affective Computing, 2013, vol. 4, n° 4, p. 398-411.
MORTILLARO, Marcello, MEULEMAN, Ben, SCHERER, Klaus R. Advocating a Componential Appraisal Model to Guide Emotion Recognition. In: International Journal of Synthetic Emotions, 2012, vol. 3, n° 1, p. 18-32.
Between 2003 and 2009, I studied psychology at the University of Ghent, obtaining my master's degree in the field of 'theoretical and experimental psychology'. My master's thesis was written under the supervision of Prof. Dr. Agnes Moors and investigated the automatic processing of coping potential using timed reaction tasks.
Between 2009 and 2010, I followed an additional master's program in statistical data analysis at the University of Ghent. My master's thesis in data analysis was written under supervision of Prof. Dr. Rene Boel and investigated the use of Bayesian methods in feedforward neural networks.
Since February 2011, I have been working as Ph.d. student at the Swiss Centre for Affective Sciences. My project is about computational modeling of appraisal theory of emotion and is jointly supervised by Prof. Dr. Klaus Scherer (University of Geneva) and Prof. Dr. Agnes Moors (University of Ghent).
At present, my research focuses on computational modeling of appraisal theory of emotion. In particular, I investigate the nature of the algorithms that map appraisals (the assumed causes of emotion) onto different response domains associated with emotion such as facial expression, behavioral tendencies, or subjective feeling. The aim of this research is to improve understanding of the appraisal process, validate assumptions of appraisal theory, and aid the development of computational models of emotion production. To accomplish these goals, I make use of advanced models from the field of statistical machine learning.