Homei Miyashita

Nobuhito Kasahara, Shota Yamanaka, Homei Miyashita

Typical success-rate prediction models for tapping exclude targets near screen edges. However, design constraints often force such placements, and in scrollable user interfaces, any element can move close to the screen edges. In this work, we model how target-edge distance affects touch pointing accuracy. We propose the Skewed Dual Normal Distribution Model, which assumes the tap-coordinate distribution is skewed by a nearby edge. The results showed that as targets approached the edge, the distribution's peak shifted toward the edge, and its tail extended away. In contrast to prior reports, the success rate improved when the target touched the edge, suggesting a strategy of ``tapping the target together with the edge.'' Our model predicts success rates across a wide range of conditions, including edge-adjacent targets. Through three experiments of horizontal, vertical, and 2D pointing, we demonstrated the generalizability and utility of our proposed model.

Ryuto Tomihari, Taiki Kinoshita, Yosuke Oba et al.

In projectors and head-mounted displays, an out-of-focus image appears blurred. Even when a display itself is in focus, computer operation may be hindered if the display is far from the user or if a user has poor visual acuity, because the user cannot see the screen clearly. In this study, we conducted an experiment in which participants performed a pointing task under blurred display conditions and investigated the relationship between blur strength and user performance. The results showed that movement time and error rate increased as blur became stronger, and that the effect of blur on movement time was larger when targets were smaller. We further showed that movement time can be estimated with high accuracy by a model that improves on Fitts' law. In a follow-up experiment to examine the applicability of this model, we adjusted target size for each participant and showed that the effect of blur level on movement time could be reduced. These findings suggest potential use in tools that adapt user interfaces to users' visual acuity.

Yuki Hou, Haruki Tamoto, Qinghua Zhao et al.

Existing retrieval methods in Large Language Models show degradation in accuracy when handling temporally distributed conversations, primarily due to their reliance on simple similarity-based retrieval. Unlike existing memory retrieval methods that rely solely on semantic similarity, we propose SynapticRAG, which uniquely combines temporal association triggers with biologically-inspired synaptic propagation mechanisms. Our approach uses temporal association triggers and synaptic-like stimulus propagation to identify relevant dialogue histories. A dynamic leaky integrate-and-fire mechanism then selects the most contextually appropriate memories. Experiments on four datasets of English, Chinese and Japanese show that compared to state-of-the-art memory retrieval methods, SynapticRAG achieves consistent improvements across multiple metrics up to 14.66% points. This work bridges the gap between cognitive science and language model development, providing a new framework for memory management in conversational systems.