Task evoked EEG reveals neural processing differences in aphantasia
Bibliographic record
Abstract
Aphantasia, the inability to generate voluntary visual images, affects an estimated 3-4% of the population and provides a valuable model for examining how the brain supports cognition without imagery. Functional MRI studies have reported reduced coordination between visual and higher-order association areas involved in imagery control. However, the temporal characteristics of these neural differences remain unclear, as electroencephalographic (EEG) evidence limited to single-case studies. Here we present the first group-level EEG investigation of aphantasia, comparing 62 individuals with the condition to 59 controls during rest and tasks probing attention and working memory. In the oddball task, participants with aphantasia demonstrated smaller P300 amplitudes, an EEG index of attentional allocation and the updating of task-relevant information into working memory. Given the absence of behavioural differences between groups, we propose this result reflects reduced engagement of imagery-related processes during this task rather than diminished cognitive function. Under high working-memory load in the n-back task, individuals with aphantasia showed lower delta power-brain activity linked to regulating sensory input and maintaining internal representations. Notably, delta power was also associated with imagery vividness (VVIQ), potentially indicating greater engagement of these sensory-regulating processes in individuals with stronger imagery. Together, these findings suggest that individuals with aphantasia may rely more on non-visual strategies for information maintenance, thereby reducing sensory interference and demands on inhibitory control. This study provides the first EEG evidence that people with aphantasia have distinct-but effective-neural dynamics that support cognition without visual imagery.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".