The Thirteenth International Workshop on Eye Movements in Programming 2025 will be held on the 26th of May 2025 (9:00 am - 5:00 pm, JST). It is co-located with the 2025 ACM Symposium of Eye Tracking Research & Applications (ETRA) in Tokyo, Japan.

Please check our call for papers!

EMIP 2025 Schedule

More details to be announced soon!

JST (Local) Event Duration
09:00 Welcome, Introducing EMIP 2025 10 minutes
09:10 Opening Keynote by Takatomi Kubo (40 mins + 40 mins Q/A) 80 minutes
10:30 Coffee Break 30 minutes
11:00 Session 1 Visualizing Eye Tracking Data - Working Session  
  Visualizing Novice Programmers’ Attention Distribution using DBSCAN Clustering and Sankey Diagrams, Wudao Yang, Zubair Ahsan, M. A. Rasel, Unaizah Obaidellah 15 minutes
  Group Discussion 30 minutes
11:45 Session 2 Designing Eye Tracking Studies - Working Session  
  Design of An Eye-Tracking Study Towards Assessing the Impact of Generative AI Use on Code Summarization, Suad Mohamed 15 minutes
  Group Discussion 30 minutes
12:30 Lunch Break 60 minutes
13:30 Session 3 Eye Tracking Tools - Working Session  
  Extending Support for Analyzing Eye Tracking Studies on Python Source Code in iTrace, Joshua Behler, Zachary Kozak, Kang-il Park, Bonita Sharif, Jonathan Maletic 15 minutes
  iTrace Demonstration and Group Discussion 30 minutes
14:15 Session 4 Pitfalls and Lessons Learned  
  Where do we go from here? Group Discussion 45 minutes
15:00 Coffee Break 30 minutes
15:30 Group Discussion 60 minutes
16:30 Closing Session and EMIP 2026 30 minutes

Keynote by Takatomi Kubo: Decoding the Codes in the Mind: Toward Elucidating the Computational Mechanisms of Intelligence in the Domain of Programming and Beyond

Abstract: Understanding how programmers perceive, comprehend, and internalize code offers a unique window into the broader mechanisms of human intelligence. In recent years, important progress has been made through empirical methodologies, including eye-tracking and brain imaging. To advance toward a principled understanding of cognitive processes in programming-related tasks, it is pivotal to integrate computational and mathematical perspectives, including AI-based approaches, alongside empirical insights.

In this keynote, I will present a series of studies investigating cognitive processes during code comprehension tasks. First, using brain imaging combined with machine learning techniques, we showed that neural activity patterns during code reading can predict the types of algorithms being considered, and found that programming expertise relates to distributed yet specific brain regions. Second, through eye-tracking analysis, we demonstrated that machine learning models trained on code representations can approximate programmers’ gaze behavior, and that artificial agents imitating human gaze patterns achieve improved performance in code understanding tasks. Third, by applying graph-based neural network models to code, we observed that incorporating hierarchical representations significantly enhances task performance, suggesting that such structures are beneficial for capturing essential aspects of code comprehension. Building upon these foundations, our recent work has begun integrating physiological signals, such as heart rate variability, to capture richer dimensions of cognitive load and emotional dynamics during code comprehension. Inspired by emerging mind captioning techniques — which aim to translate brain activity into natural language descriptions — we explore the possibility of decoding programmers’ internal mental representations during comprehension tasks. Separately, by analyzing the latent structures within code itself, analogous to the hidden syntactic and semantic patterns in natural language, we aim to clarify how these structures relate to the functional behavior of code. Through these interdisciplinary explorations, I argue that studying the cognitive processes underlying code comprehension offers a promising pathway toward elucidating the computational mechanisms of intelligence in the domain of programming and beyond.


Bio: Takatomi Kubo is an Associate Professor in the Division of Information Science at the Nara Institute of Science and Technology (NAIST), Japan. He received his Bachelor of Medicine (B.M.) degree from Osaka University in 2002 and his Ph.D. in Engineering from NAIST in 2012. Prior to his academic career, he practiced as a medical doctor specializing in neurology. Throughout his career, he has maintained a deep interest in human mental functions, including cognition and emotion, and their underlying neural mechanisms. His research interests span biomedical engineering, neuroscience, and machine learning, with a focus on the interdisciplinary integration of medicine and information science. https://takatomi-k.mystrikingly.com/

Accepted Papers

Author(s) Paper Title
Wudao Yang, Zubair Ahsan, M. A. Rasel, Unaizah Obaidellah Visualizing Novice Programmers’ Attention Distribution using DBSCAN Clustering and Sankey Diagrams
Joshua Behler, Zachary Kozak, Kang-il Park, Bonita Sharif, Jonathan Maletic Extending Support for Analyzing Eye Tracking Studies on Python Source Code in iTrace
Suad Mohamed Design of An Eye-Tracking Study Towards Assessing the Impact of Generative AI Use on Code Summarization