{"slug": "code-reveal-shapes-programmers-visual-attention", "title": "Code Reveal Shapes Programmers' Visual Attention", "summary": "An eye-tracking study of 53 participants submitted to arXiv on 21 Sep 2026 found that how AI coding interfaces render generated code changes programmers' visual attention, with dynamic rendering producing fewer but longer fixations and more sustained focus, and structured rendering guiding attention toward semantically meaningful units. The authors introduce structured rendering, which reveals code in chunks derived from its syntactic hierarchy to expose high-level structure before low-level details, and compared it against static and character-based rendering. The anonymized dataset and an interactive demo were released to support future research.", "body_md": "# Computer Science > Human-Computer Interaction\n\n  [Submitted on 21 Sep 2026]\n\n# Title:Structure-Aware Rendering: How Code Reveal Shapes Programmers' Visual Attention\n\n[View PDF](https://arxiv.org/pdf/2609.24616)\n\n[HTML (experimental)](https://arxiv.org/html/2609.24616v1)\n\nAbstract:AI coding interfaces present generated code either all at once or token-by-token. These rendering strategies reflect model generation rather than how programmers actually read code: selectively, non-linearly, and guided by the structure. We argue that code rendering is a first-class interaction primitive that shapes how programmers read and understand code. To explore this design space, we introduce structured rendering, a technique that reveals code in semantically meaningful chunks derived from its syntactic hierarchy, exposing high-level structure before low-level details. To isolate rendering effects on visual attention, we conducted an eye-tracking study with 53 participants comparing static, character-based, and structured rendering. Our findings show that rendering alters visual attention and reading behavior: dynamic rendering induces fewer but longer fixations and more sustained focus, while structured rendering further guides attention toward semantically meaningful units and supports high-level understanding. We release the anonymized dataset with an interactive demo ([this https URL](https://codegaze.vercel.app/)) to support future research.\n    \n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer \n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers \n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps \n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations \n\n*(*[What are Smart Citations?](https://www.scite.ai/))\n# Code, Data and Media Associated with this Article\n\nalphaXiv \n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers \n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub \n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub \n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face \n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast \n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))\n# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower \n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender \n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))\n# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [**Learn more about arXivLabs**](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/code-reveal-shapes-programmers-visual-attention", "canonical_source": "https://arxiv.org/abs/2609.24616", "published_at": "2026-09-22 09:58:51+00:00", "updated_at": "2026-09-22 10:24:49.186416+00:00", "lang": "en", "topics": ["ai-research", "ai-tools", "developer-tools", "artificial-intelligence"], "entities": ["arXiv", "codegaze.vercel.app"], "alternates": {"html": "https://wpnews.pro/news/code-reveal-shapes-programmers-visual-attention", "markdown": "https://wpnews.pro/news/code-reveal-shapes-programmers-visual-attention.md", "text": "https://wpnews.pro/news/code-reveal-shapes-programmers-visual-attention.txt", "jsonld": "https://wpnews.pro/news/code-reveal-shapes-programmers-visual-attention.jsonld"}}