Emotional states influence a wide range of cognitive processes, such as attention, memory, and decision-making, and play an increasingly recognized role in human-computer interaction (HCI). Although most prior research has focused on high-level effects of emotion, the impact of incidental emotional states on fine-grained motor behaviors such as pointing and selection remains understudied. Addressing this gap, the present study examines how affective priming with validated emotional stimuli modulates user performance in a Fitts’ Law-based pointing paradigm. By systematically varying task difficulty, we quantitatively assess the effects of emotion on core performance measures: movement time, reaction time, error rate, and throughput. Our findings demonstrate that emotion can modulate low-level motor interactions in digital environments, with important implications for the design of adaptive, emotion-aware interfaces. These results advance the theoretical understanding of the interplay between emotion, cognition, and motor control and offer actionable insights to develop more robust and personalized interactive systems.

Managing multiple activities in virtual reality (VR) is often hindered by fragmented workflows and disruptive application switching. We present JourneyVR, a metaphorical interaction model designed for spatial and sequential workflows that reframes tasks as continuous journeys rather than isolated sessions. Users construct a Journey Map as a layout of islands (tasks) and bridges (transitions), which expands into an immersive world where activities unfold as a coherent, embodied narrative. Through a formative study, a controlled comparison, and an expert evaluation, JourneyVR was shown to enhance experiential continuity, intention to use, and overall satisfaction compared to using a conventional app launcher. Participants highlighted how the metaphor fosters motivation and achievement, while we identify boundaries regarding task type, scalability, and flexibility. Our findings demonstrate that framing sequential activities as navigable journeys can transform fragmented tasks into meaningful narratives, offering concrete guidelines for sustained engagement and more flexible workflows in immersive environments.

As car-sharing services evolve, there is a growing effort to analyze users’ safe driving behaviors and effectively manage shared vehicles. Unlike previous researches that focus on simple situations like sudden acceleration and lane departure using cameras with additional sensors, we introduce a new approach that detects more complex traffic rule violation, especially red-light violation, using only the monocular dashcam videos. The proposed framework employs the attention mechanism of Transformer, and effectively encodes the traffic signal objects and contextual information within the video. It utilizes a novel method, POISE (Positional Object Information by Spatial Encoding), to handle the positional information of traffic signal objects. Our quantitative and qualitative evaluations demonstrate the effectiveness of our proposed framework in detecting red-light violations compared to existing methods.

We propose an intuitive image-to-music retrieval (IMR) framework to improve the user experience on these platforms. The proposed method extracts mood and theme tags by searching for images from a pre-built database that are similar to a query image and then retrieves music with matching tag information. We investigated the system’s effectiveness by comparing participants’ satisfaction, intention to use, and valence between those who interacted with the system and those who did not. We also examined whether using mood or theme attributes affected the user-perceived suitability of the retrieved music. Results showed that all three variables of the interaction group were significantly higher than that of the non-interaction group and that there was no difference in the perceived suitability of music between the mood and theme attributes. Our study concludes that image attributes are effective in successful music retrieval and that interaction is a crucial factor in designing IMR systems.