Poster cog sci11 42x24

1. Tibor Bosse 1 , Jessica Brenninckmeyer 2 , Raffael Kalisch 2 , Christian Paret 2 , Matthijs Pontier 1, 3 1 VU University, Department of Artificial Intelligence 2 University Medical Center Hamburg-Eppendorf (UKE), Institute for Systems Neuroscience 3 www.few.vu.nl/~mpr210, [email_address] Matching Skin Conductance Data to a Computational Model of Reappraisal Abstract In the present paper we show that an existing computational model of emotion regulation can, if reduced to its reappraisal-specific components, fit skin conductance data obtained from an empirical study of reappraisal. By applying parameter tuning techniques, optimal fits of the model have been found against the (averaged) patterns of the skin conductance data. The errors that were found turned out to be relatively low. Moreover, they have been compared with the errors produced by a baseline variant of the model where the adaptive cycle has been removed, and were found substantially lower. CoMERG: The reappraisal model Results Exp.1 adaptation. Exp.1 no adaptation. Exp.2 adaptation. Exp.2 no adaptation Discussion The reappraisal specific parts of CoMERG turned out to fit the data quite well. Moreover, the fit of a baseline variant of the model without the adaptation layer was substantially worse. Future Research -Fit CoMERG to fMRI data, and refine the model if necessary. The model might then also be useful for prediction brain activation time courses. -Apply CoMERG to Embodied Conversational Agents and Robots to make them perform human-like emotion regulation behavior. Combined with other emotion models, this should make the ECAs and robots exhibit emotional intelligence Background In previous research, CoMERG, a Cognitive Model for Emotion Regulation, produced simulations that match Gross’ theory. However, it was never validated against empirical data. Therefore, here we fit the model to skin conductance data that resulted from two empirical studies of reappraisal.  n c n  n v n ERL d v n-norm w n  c n Adaptation layer