Information Overload in Processing Consumer Reviews: The Role of Argumentation Changes

Information overload theory suggests that consumers can only processa certain amount and complexity of information. In this study, we analyze whetherinformation overload can also occur while processing individual product reviewswith a high rate of argumentation changes. An argumentation change denotes achange from positive to negative arguments, and vice versa.We propose a NeuroISexperiment in which participants are presented a given set of product reviews witha low or high rate of argumentation changes. The participants are asked about theirperceived helpfulness of the product review, their purchase intention for the product,and self-reported information overload. During the experiment, we measurecognitive activity based on electroencephalogram (EEG) and eye-tracking. Weexpect that a higher rate of argumentation changes is linked to greater cognitive activity,and, in particular, lower perceived review helpfulness and purchase intention

evolution, structure and function of metazoan splicing factor PRPF39

In the yeast U1 snRNP the Prp39/Prp42 heterodimer is essential for early steps of spliceosome assembly. In metazoans no Prp42 ortholog exists, raising the question how the heterodimer is functionally substituted. Here we present the crystal structure of murine PRPF39, which forms a homodimer. Structure-guided point mutations disrupt dimer formation and inhibit splicing, manifesting the homodimer as functional unit. PRPF39 expression is controlled by NMD-inducing alternative splicing in mice and human, suggesting a role in adapting splicing efficiency to cell type specific requirements. A phylogenetic analysis reveals coevolution of shortened U1 snRNA and the absence of Prp42, which correlates with overall splicing complexity in different fungi. While current models correlate the diversity of spliceosomal proteins with splicing complexity, our study highlights a contrary case. We find that organisms with higher splicing complexity have substituted the Prp39/Prp42 heterodimer with a PRPF39 homodimer

Beyond Deep Ensembles: A Large-Scale Evaluation of Bayesian Deep Learning under Distribution Shift

Bayesian deep learning (BDL) is a promising approach to achieve well-calibrated predictions on distribution-shifted data. Nevertheless, there exists no large-scale survey that evaluates recent SOTA methods on diverse, realistic, and challenging benchmark tasks in a systematic manner. To provide a clear picture of the current state of BDL research, we evaluate modern BDL algorithms on real-world datasets from the WILDS collection containing challenging classification and regression tasks, with a focus on generalization capability and calibration under distribution shift. We compare the algorithms on a wide range of large, convolutional and transformer-based neural network architectures. In particular, we investigate a signed version of the expected calibration error that reveals whether the methods are over- or under-confident, providing further insight into the behavior of the methods. Further, we provide the first systematic evaluation of BDL for fine-tuning large pre-trained models, where training from scratch is prohibitively expensive. Finally, given the recent success of Deep Ensembles, we extend popular single-mode posterior approximations to multiple modes by the use of ensembles. While we find that ensembling single-mode approximations generally improves the generalization capability and calibration of the models by a significant margin, we also identify a failure mode of ensembles when finetuning large transformer-based language models. In this setting, variational inference based approaches such as last-layer Bayes By Backprop outperform other methods in terms of accuracy by a large margin, while modern approximate inference algorithms such as SWAG achieve the best calibration.Comment: Code at https://github.com/Feuermagier/Beyond_Deep_Ensemble