Jacobus J Barnard

Abstract:

In theory, the essence of emotion is coordination across experiential, behavioral, and physiological systems in the service of functional responding to environmental demands. However, people often regulate emotions, which could either reduce or enhance cross-system concordance. The present study tested the effects of two forms of emotion regulation (expressive suppression, positive reappraisal) on concordance of subjective experience (positive-negative valence), expressive behavior (positive and negative), and physiology (inter-beat interval, skin conductance, blood pressure) during conversations between unacquainted young women. As predicted, participants asked to suppress showed reduced concordance for both positive and negative emotions. Reappraisal instructions also reduced concordance for negative emotions, but increased concordance for positive ones. Both regulation strategies had contagious interpersonal effects on average levels of responding. Suppression reduced overall expression for both regulating and uninstructed partners, while reappraisal reduced negative experience. Neither strategy influenced the uninstructed partners' concordance. These results suggest that emotion regulation impacts concordance by altering the temporal coupling of phasic subsystem responses, rather than by having divergent effects on subsystem tonic levels. © 2013 Elsevier B.V. All rights reserved.

Abstract:

We present a methodology for modeling the statistics of image features and associated text in large datasets. The models used also serve to cluster the images, as images are modeled as being produced by sampling from a limited number of combinations of mixing components. Furthermore, because our approach models the joint occurrence image features and associated text, it can be used to predict the occurrence of either, based on observations or queries. This supports an attractive approach to image search as well as novel applications such a suggesting illustrations for blocks of text (auto-illustrate) and generating words for images outside the training set (auto-annotate). In this paper we illustrate the approach on 10,000 images of work from the Fine Arts Museum of San Francisco. The images include line drawings, paintings, and pictures of sculpture and ceramics. Many of the images have associated free text whose nature varies greatly, from physical description to interpretation and mood. We incorporate statistical natural language processing in order to deal with free text. We use WordNet to provide semantic grouping information and to help disambiguate word senses, as well as emphasize the hierarchical nature of semantic relationships.

Abstract:

We present a statistical model for organizing image collections which integrates semantic information provided by associated text and visual information provided by image features. The model is very promising for information retrieval tasks such as database browsing and searching for images based on text and/or image features. Furthermore, since the model learns relationships between text and image features, it can be used for novel applications such as associating words with pictures and unsupervised learning for object recognition.