On the Influence of the Baseline in Neuroimaging Experiments on Program Comprehension
Background : Neuroimaging methods have been proved insightful in program-comprehension research. A key problem is that different baselines have been used in different experiments. A baseline is a task during which the “normal” brain activation is captured as a reference compared to the task of interest. Unfortunately, the influence of the choice of the baseline is still unclear. Aims : We investigate whether and to what extent the selected baseline influences the results of neuroimaging experiments on program comprehension. This helps to understand the tradeoffs in baseline selection with the ultimate goal of making the baseline selection informed and transparent. Method : We have conducted a pre-registered program-comprehension study with 20 participants using multiple baselines (i.e., reading, calculations, problem solving, and cross-fixation). We monitored brain activation with a 64-channel electroencephalography (EEG) device. We compared how the different baselines affect the results regarding brain activation of program comprehension. Results and Implications : We found significant differences in mental load across baselines suggesting that selecting a suitable baseline is critical. Our results show that a standard problem-solving task, operationalized by the Raven-Progressive Matrices, is a well-suited default baseline for program-comprehension studies. Our results highlight the need for carefully designing and selecting a baseline in program-comprehension studies.
Authors
- Annabelle Bergum (ORCID: https://orcid.org/0000-0002-9953-4904)
- Norman Peitek (ORCID: https://orcid.org/0000-0001-7828-4558)
- Janet Siegmund
- Sven Apel (ORCID: https://orcid.org/0000-0003-3687-2233)
- Maurice Rekrut (ORCID: https://orcid.org/0000-0002-5829-5409)
Institutions
- Chemnitz University of Technology (DE)
- German Research Centre for Artificial Intelligence (DE)
- Saarland University (DE)
Publication Details
- Journal
- ACM Transactions on Software Engineering and Methodology
- Published
- 2025-06-17
- DOI
- https://doi.org/10.1145/3744739
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 0.00