Do emotions influence safe browsing? Toward an electroencephalography marker of affective responses to cybersecurity notifications
Bibliographic record
Abstract
Cybersecurity notifications play an important role in encouraging users to use computers safely. Emotional reactions to such notifications are known to positively influence users' adherence to these notifications, though it is challenging for researchers to identify and quantify users' emotional reactions. In this study, we explored electroencephalography (EEG) signals that were elicited by the presentation of various emotionally charged image stimuli provided by the International Affective Picture System (IAPS) and compared signals to those elicited by images of cybersecurity notifications and other computer-related stimuli. Participants provided behavioral assessments of valence and arousal elicited by the images which were used to cross-reference the results. We found that EEG amplitudes corresponding to the late positive potential (LPP) were elevated in reaction to images of cybersecurity notifications as well as IAPS images known to elicit strong positive and negative valence, when compared to neutral valence or other computer-related stimuli. These findings suggest that the LPP may account for emotional deliberation about cybersecurity notifications, which could be a useful measure when conducting future studies into the role such emotional reactions play in encouraging safe computer behavior.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".