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Related Experiment Video

Updated: Jun 3, 2026

Dissociation of the Confounding Influences of Expectancy and Integrative Difficulty Residing in Anomalous Sentences in Event-related Potential Studies
05:22

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Fixation-related potentials reveal that confusing program code elicits a late frontal positivity.

Annabelle Bergum1, Anna-Maria Maurer2, Norman Peitek1

  • 1Computer Science, Saarland University, Saarbrücken, Germany.

Scientific Reports
|June 1, 2026
PubMed
Summary
This summary is machine-generated.

This study used fixation-related potentials (FRPs) to examine how programmers process confusing code. Confusing code patterns trigger brain responses similar to those seen with unexpected words in language, suggesting shared neurocognitive mechanisms.

Keywords:
Atoms of confusionEEGFixation-related potentialsLate frontal positivityNatural language comprehensionProgram comprehension

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Area of Science:

  • Cognitive Science
  • Software Engineering
  • Psycholinguistics

Background:

  • Software complexity necessitates efficient code comprehension and maintenance.
  • Understanding the neurocognitive basis of code processing is crucial for programmer support.

Purpose of the Study:

  • To investigate the neurocognitive mechanisms underlying the processing of confusing code patterns (atoms of confusion) in programmers.
  • To explore similarities between program code and natural language processing in the brain.

Main Methods:

  • Analysis of fixation-related potentials (FRPs) during ecologically valid program code comprehension.
  • Comparison of brain responses to confusing code versus clean code.

Main Results:

  • Confusing code patterns elicited a late frontal positivity (400-700 ms) compared to clean code.
  • This neural signature resembles event-related potential (ERP) components observed during natural language processing.

Conclusions:

  • The brain utilizes similar neurocognitive mechanisms for processing unexpected, informative inputs in both program code and natural language.
  • These findings suggest that programmers update their situation models similarly for code and language, impacting information extraction.