Individual differences in visual imagery determine how event information is remembered
Bibliographic record
Abstract
Individuals differ in how they mentally imagine past events. When reminiscing about a past experience, some individuals remember the event accompanied by rich visual images, while others will remember it with few of these images. In spite of the implications that these differences in the use of imagery have to the understanding of human memory, few studies have taken them into consideration. We examined how imagery interference affecting event memory retrieval was differently modulated by spatial and object imagery ability. We presented participants with a series of video-clips depicting complex events. Participants subsequently answered true/false questions related to event, spatial, or feature details contained in the videos, while simultaneously viewing stimuli that interfered with visual imagery processes (dynamic visual noise; DVN) or a control grey screen. The impact of DVN on memory accuracy was related to individual differences in spatial imagery ability. Individuals high in spatial imagery were less accurate at recalling details from the videos when simultaneously viewing the DVN stimuli compared to those low in spatial imagery ability. This finding held for questions related to the event and spatial details but not feature details. This study advocates for the inclusion of individual differences when studying memory processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| 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) | 0.002 | 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".