H. E. Yang

Contemporary Western thought might be somehow characterized by the interaction of theology and humanities, and their refusal to the one-dimension subject. The reason why Slavoj Žižek tries to fight for “the Christian legacy” and “the perverse core of Christianity”— with “structure,” “narrative,” “symbolic order” and other conceptual tools normally used in literary studies—is believed to be a rediscovery of the role of Christianity and theology as the archetype for rendering truth and value system instead of a religious experience merely. And so, the argument of identity and meaning-generation might be rephrased in a different framework. This paper intends to trace Žižek’s works theologically and reinterpret its potentiality in Chinese context.

In various guises (usually referred to as the “basic emotion” or “discrete emotion” approach), scientists and philosophers have long argued that certain categories of emotion are natural kinds. In a recent paper, Colombetti (2009) proposed yet another natural kind account, and in so doing, characterized and critiqued psychological constructionist approaches to emotion, including our own Conceptual Act Model. In this commentary, we briefly address three topics raised by Columbetti. First, we correct several common misperceptions about the discrete emotion approach to emotion. Second, we discuss misconceptions of our Conceptual Act Model. Finally, we briefly comment on Columbetti's Dynamical Discrete Emotion model

Judging similarities among objects, events, and experiences is one of the most basic cognitive abilities, allowing us to make predictions and generalizations. The main assumption in similarity judgment is that people selectively attend to salient features of stimuli and judge their similarities on the basis of the common and distinct features of the stimuli. However, it is unclear how people select features from stimuli and how they weigh features. Here, we present a computational method that helps address these questions. Our procedure combines image-processing techniques with a machine-learning algorithm and assesses feature weights that can account for both similarity and categorization judgment data. Our analysis suggests that a small number of local features are particularly important to explain our behavioral data