Do truthfulness notifications influence perceptions of AI-generated political images? A cognitive investigation with EEG
Bibliographic record
Abstract
Political misinformation is a growing problem for democracies, partly due to the rise of widely accessible artificial intelligence-generated content (AIGC). In response, social media platforms are increasingly considering explicit AI content labeling, though the evidence to support the effectiveness of this approach has been mixed. In this paper, we discuss two studies which shed light on antecedent cognitive processes that help explain why and how AIGC labeling impacts user evaluations in the specific context of AI-generated political images. In the first study, we conducted a neurophysiological experiment with 26 participants using EEG event-related potentials (ERPs) and self-report measures to gain deeper insights into the brain processes associated with the evaluations of artificially generated political images and AIGC labels. In the second study, we embedded some of the stimuli from the EEG study into replica YouTube recommendations and administered them to 276 participants online. The results from the two studies suggest that AI-generated political images are associated with heightened attentional and emotional processing. These responses are linked to perceptions of humanness and trustworthiness. Importantly, trustworthiness perceptions can be impacted by effective AIGC labels. We found effects traceable to the brain’s late-stage executive network activity, as reflected by patterns of the P300 and late positive potential (LPP) components. Our findings suggest that AIGC labeling can be an effective approach for addressing online misinformation when the design is carefully considered. Future research could extend these results by pairing more photorealistic stimuli with ecologically valid social-media tasks and multimodal observation techniques to refine label design and personalize interventions across demographic segments.
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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 | 0.001 | 0.021 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".