Judaism and Emotion - Texts, Performance, Experience

Judaism and Emotion breaks with stereotypes that, until recently, branded Judaism as a rigid religion of laws and prohibitions. Instead, authors from different fields of research discuss the subject of Judaism and emotion from various scholarly perspectives; they present an understanding of Judaism that does not exclude spirituality and emotions from Jewish thought. In doing so, the contributions account for the relation between the representation of emotion and the actual emotions that living and breathing human beings feel in their everyday lives. While scholars of rabbinic studies and theology take a historical-critical and socio-historical approach to the subject, musicologists and scholars of religious studies focus on the overall research question of how the literary representations of emotion in Judaism are related to ritual and musical performances within Jewish worship. They describe in a more holistic fashion how Judaism serves to integrate various aspects of social life. In doing so, they examine the dynamic interrelationship between Judaism, cognition, and culture.

Analyzing spatio-temporal patterns of genuine cross-correlations

In multivariate time series analysis, the equal-time cross-correlation is a classic and computationally efficient measure for quantifying linear interrelations between data channels. When the cross-correlation coefficient is estimated using a finite amount of data points, its non-random part may be strongly contaminated by a sizable random contribution, such that no reliable conclusion can be drawn about genuine mutual interdependencies. The random correlations are determined by the signals' frequency content and the amount of data points used. Here, we introduce adjusted correlation matrices that can be employed to disentangle random from non-random contributions to each matrix element independently of the signal frequencies. Extending our previous work these matrices allow analyzing spatial patterns of genuine cross-correlation in multivariate data regardless of confounding influences. The performance is illustrated by example of model systems with known interdependence patterns. Finally, we apply the methods to electroencephalographic (EEG) data with epileptic seizure activity.