| LI ZIQI | M, 2回目発表 | 光メディアインタフェース | 向川 康博☆, | 川西 康友, | 薗頭 元春, | 舩冨 卓哉, | 藤村 友貴, | 北野 和哉 |
| 弥代 虎太朗 | M, 1回目発表 | サイバネティクス・リアリティ工学 | 清川 清, | 加藤 博一, | 内山 英昭, | Perusquia Hernandez Monica, | 平尾 悠太朗 | |
| SIY JOSHUA SAMUEL | M, 2回目発表 | サイバネティクス・リアリティ工学 | 清川 清, | 向川 康博, | 内山 英昭, | Perusquia Hernandez Monica, | 平尾 悠太朗 | |
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title: EMA: Effort Metric Attention for Anatomical Effort-Guided Human Motion Diffusion
abstract: Human motion diffusion models can synthesize action sequences from text, but controlling motion intensity remains challenging. Existing approaches rely on effort-related adverbs, which are ambiguous and fail to capture quantitative aspects such as pacing, often resulting in flat and monotonous dynamics. We propose an intensity-control framework based on Effort Metric Attention (EMA), a cross-attention module that conditions diffusion on numerical effort signals. Inspired by Laban Movement Analysis (LMA), the framework focuses on the Time and Weight effort factors. We approximate these factors using two kinematic metrics: peak joint positional change for pacing and collective joint positional change for motion amount. EMA enables fine-grained, region-wise control without costly post-hoc optimization. We introduce two evaluation tasks, metric-to-motion consistency and body-part-level effort modulation, to assess numerical fidelity and localized control. Experiments and a user study show near-monotonic alignment between specified effort levels, generated motion dynamics, and established LMA descriptors. These results indicate effective and interpretable control of effort dynamics in practice. language of the presentation: English | ||||||||
| 渡邉 奈緒美 | M, 2回目発表 | 脳・行動モデリング | 田中 沙織, | 清川 清, | CAI LIN, | 荻島 大凱 | ||
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title: The Role of Cognitive Maps in Programming
abstract: Engineers exhibit a wide range of skill levels, from novice junior engineers to highly experienced senior engineers. Significant differences can be observed in the speed and accuracy of tasks such as bug fixing and code comprehension. However, these differences cannot be fully explained by years of experience alone and are often collectively described as what is commonly referred to as "technical intuition." This study aims to elucidate the cognitive processes underlying this "technical intuition" from a neuroscientific perspective. In particular, it focuses on the formation and use of cognitive maps for conceptual and structural information, rather than physical space, in the context of programming. Using experimental methods that combine Eye Tracking and EEG, we investigate the neural basis of such cognitive maps. Specifically, this study adopts an exploratory, stepwise approach by integrating a general structural comprehension task conducted prior to programming activities with subsequent code comprehension and exploration tasks. Through this design, we aim to examine the involvement of cognitive maps in programming in a gradual and systematic manner. Ultimately, this research seeks to contribute to engineering education and to the improvement of reasoning and exploration capabilities in AI agents. language of the presentation: Japanese 発表題目: プログラミングにおける認知地図の関与 発表概要: エンジニアには駆け出しのジュニアエンジニアから熟達したシニアエンジニアまで幅広いスキル差が存在する。バグ修正やコード理解の速度・精度において顕著な差が見られるが、この差は単なる経験年数では十分に説明できず、一般に「センス」として捉えられてきた。本研究は、この「センス」の背景にある認知的プロセスを神経科学的手法を用いて明らかにすることを目的とする。特に、物理的空間ではなく、概念的・構造的な情報に対する認知地図の形成と利用に着目し、Eye TrackingおよびEEGを用いた実験によりその神経的基盤を探求する。具体的には、プログラミング行為に先立つ一般的な構造理解課題と、実際のコード理解・探索課題を組み合わせ、認知地図の関与を段階的に検証する探索的アプローチを採用する。本研究を通じて、エンジニア教育やAI Agentの推論・探索能力の向上に寄与することを目指す。 | ||||||||