Semantic Neighborhood Density and Eye Gaze Time in Human Programmer Attention

This paper studies the relationship between human eye gaze time on words in source code and the Semantic Neighborhood Density (SND) of those words. Human eye gaze time is a popular way to quantify human attention such as the importance of words people read and the cognitive effort people exert. Meanwhile, SND is a measure of how similar a word is in meaning to other words in the same context. SND has a long history in Psychology research where it has been connected to eye gaze time in various domains and helps explain human cognitive factors such as confusion and quality of reading comprehension. But SND carries an unknown and potentially unique meaning in software engineering. In this paper, we compute SND for tokens in source code that people viewed in two previous eye-tracking experiments, one in C and one in Java. We conduct a model-free analysis for statistical relationships between SND and gaze time, and a model-based analysis for predictive power of SND to gaze time. We found that words with high SND tend to have higher gaze time then low SND words, especially for words that are uncommon (i.e., have low frequency). We also found SND and frequency to have a minor predictive power on gaze time, despite high levels of noise common in eye tracking data

• Publication year

  2026

• Venue

  Unknown venue

• Publication date

  2026-03-03

• Fields of study

  Computer Science, Psychology

• Identifiers
  arXiv 2603.03566
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Human Attention During Localization of Memory Bugs in C Programs

  2025cited by this paper
• Enhancing Code LLM Training with Programmer Attention

  2025influential reference
• Unveiling the gaze: deciphering key factors in selecting knowledge workers through eye-tracking analysis

  2025cited by this paper
• On Eye Tracking in Software Engineering

  2024cited by this paper
• Programmer Visual Attention During Context-Aware Code Summarization

  2024cited by this paper
• Enhancing UX Design Through Eye- Tracking and Image Processing: Practical Insights and Applications

  2024cited by this paper
• Assessing Python Style Guides: An Eye-Tracking Study with Novice Developers

  2024cited by this paper
• EyeTrans: Merging Human and Machine Attention for Neural Code Summarization

  2024cited by this paper
• A Language Model of Java Methods with Train/Test Deduplication

  2023cited by this paper
• Identifier Names in Computer Programs: Literature Review

  2023cited by this paper
• Distilled GPT for source code summarization

  2023cited by this paper
• Iconicity ratings for 14,000+ English words

  2023cited by this paper
• Towards Modeling Human Attention from Eye Movements for Neural Source Code Summarization

  2023cited by this paper
• Eye Tracking, Usability, and User Experience: A Systematic Review

  2023cited by this paper
• iTrace-Toolkit: A Pipeline for Analyzing Eye-Tracking Data of Software Engineering Studies

  2023cited by this paper
• Code Llama: Open Foundation Models for Code

  2023cited by this paper
• Age-Related Differences in the Online Processing of Spoken Semantic Context and the Effect of Semantic Competition: Evidence From Eye Gaze.

  2021cited by this paper
• From Novice to Expert: Analysis of Token Level Effects in a Longitudinal Eye Tracking Study

  2021cited by this paper
• The Two Sides of Linguistic Context: Eye-Tracking as a Measure of Semantic Competition in Spoken Word Recognition Among Younger and Older Adults

  2020cited by this paper
• A practical guide on conducting eye tracking studies in software engineering

  2020cited by this paper
• Study of the Possibility of Using Pupillography for Personnel Selection at Hiring

  2020cited by this paper
• Developer Reading Behavior While Summarizing Java Methods: Size and Context Matters

  2019cited by this paper
• A Survey on the Usage of Eye-Tracking in Computer Programming

  2018cited by this paper
• Survey of computer vision algorithms and applications for unmanned aerial vehicles

  2018cited by this paper
• A close call: Interference from semantic neighbourhood density and similarity in language production

  2018cited by this paper
• AudioHighlight: Code Skimming for Blind Programmers

  2018cited by this paper
• Fixing Faults in C and Java Source Code

  2017cited by this paper
• Eye movements in software traceability link recovery

  2017cited by this paper
• Comprehending Studies on Program Comprehension

  2017cited by this paper
• Effects of semantic neighborhood density in abstract and concrete words.

  2017influential reference
• Analysing Java identifier names

  2016cited by this paper
• Semantic Neighborhood Effects for Abstract versus Concrete Words

  2016cited by this paper
• On the Comprehension of Program Comprehension

  2014cited by this paper
• Improving automated source code summarization via an eye-tracking study of programmers

  2014cited by this paper
• GloVe: Global Vectors for Word Representation

  2014cited by this paper
• Empirical studies on the NLP techniques for source code data preprocessing

  2014cited by this paper
• Semantic neighbourhood density effects in word identification during normal reading: evidence from eye movements

  2014influential reference
• The Role of Sleep Spindles and Slow-Wave Activity in Integrating New Information in Semantic Memory

  2013cited by this paper
• Visual attention patterns during program debugging with an IDE

  2012cited by this paper
• Lightweight Transformation and Fact Extraction with the srcML Toolkit

  2011cited by this paper
• On the use of eye tracking in software traceability

  2011cited by this paper
• Eye-tracking based adaptive user interface: implicit human-computer interaction for preference indication

  2011cited by this paper
• Eye movements and processing difficulty in object relative clauses.

  2010cited by this paper
• R. H. Baayen, Analyzing linguistic data: A practical introduction to statistics using R. Cambridge: Cambridge University Press, 2008. Pp. 368. ISBN-13: 978-0-521-70918-7.

  2010cited by this paper
• Exploring lexical co-occurrence space using HiDEx

  2010influential reference
• A survey of cross-validation procedures for model selection

  2009cited by this paper
• Searching and skimming: An exploratory study

  2009cited by this paper
• Applied Regression Analysis and Generalized Linear Models

  2008cited by this paper
• Attractor dynamics and semantic neighborhood density: processing is slowed by near neighbors and speeded by distant neighbors.

  2008influential reference
• Categorical Data Analysis: Away from ANOVAs (transformation or not) and towards Logit Mixed Models.

  2008cited by this paper
• Eye movements in reading words and sentences

  2007cited by this paper
• Investigating effects of selectional restriction violations and plausibility violation severity on eye-movements in reading

  2007cited by this paper
• Permutation, Parametric, and Bootstrap Tests of Hypotheses

  2005cited by this paper
• Length, frequency, and predictability effects of words on eye movements in reading

  2004cited by this paper
• Permutation Methods: A Basis for Exact Inference

  2004cited by this paper
• The effect of plausibility on eye movements in reading.

  2004cited by this paper
• First steps in programming: a rationale for attention investment models

  2002cited by this paper
• Characterizing semantic space: Neighborhood effects in word recognition

  2001influential reference
• Attention!

  1998cited by this paper
• Eye movements in reading and information processing: 20 years of research.

  1998cited by this paper
• From simple associations to the building blocks of language: Modeling meaning in memory with the HAL model

  1998cited by this paper

• No citing papers are available for this paper.