It is desirable for machine learning classifiers to provide human-understandable explanations that justify decisions (“explainable artificial intelligence”, XAI). However, state-of-the-art neural network-based models including those used by large language models (LLMs) are intrinsically black-box approaches. To remedy this in the context of a classification model for news bias detection and sub-categorization, we present and evaluate a method for natural language explanation generation. We conduct a study in English with human subjects who were asked to rate machine-generated explanations for sentence-level bias classification decisions for news articles, where our method and a range of alternatives were compared. Our findings suggest that (1) explanations generated by LLMs significantly enhance the comprehensibility of classification decisions and contribute to a greater understanding of how news bias manifests in reporting and (2) one of our methods substantially outperforms all other methods, although we noticed two distinct preferential sub-groups. To the best of our knowledge, ours is the first study to evaluate LLM-generated explanations for news bias classification decisions by directly comparing zero-shot, fine-tuned, and traditional Natural Language Processing (NLP) approaches using feedback from regular users rather than trained experts or synthetic metrics.

The Ebbinghaus illusion, also known as Titchner Circles, is a well- known perceptual illusion affecting the perceived size of a disc enclosed by an annulus of either larger or smaller discs. Though many have found highly consistent results with regard to the effect of the illusion on size perception, there have been mixed results when studying its effect on action-based tasks. In this paper, we present a study utilizing a head-worn virtual environment to examine the effect of the Ebbinghaus illusion on depth judgments as measured using a blind-reaching task. We found that participants’ size judgments were symmetrically affected by the classic "large annulus" and "small annulus" configurations, but their distance judgments were asymmetrically affected. Large annulus configurations had no significant effect on distance judgments while small annulus configurations resulted in significant underestimation of target distances. Despite this asymmetry, both configurations resulted in response times of similar magnitude that were significantly longer than those of the non-illusory control condition.

In the context of immersive VR Head-Mounted Displays, physical keyboards have been proven to be an efficient typing interface. However, text entry using physical keyboards typically requires external camera-based tracking systems. Touch-sensitive physical keyboards allow for on-surface interaction, with sensing integrated into the keyboard itself, but have not been utilized for VR. We propose to utilize touch-sensitive physical keyboards for text entry as an alternative sensing mechanism for tracking user's fingertips and present a first prototype for VR.

Text entry is a challenge for Virtual Reality (VR) applications. In the context of immersive VR Head-Mounted Displays, text entry has been investigated for standard physical keyboards as well as for various hand representations. Specifically, prior work has indicated that minimalistic fingertip visualizations are an efficient hand representation. However, they typically require external tracking systems. Touch-sensitive physical keyboards allow for on-surface interaction, with sensing integrated into the keyboard itself. However, they have not been thoroughly investigated within VR. Our work brings together the domains of VR text entry and touch-sensitive physical keyboards. Specifically, we propose to utilize touch-sensitive physical keyboards for text entry as an alternative sensing mechanism for tracking user's fingertips and study its performance in a preliminary user study.