Word Imageability Enhances Association-memory by Increasing Hippocampal Engagement
Bibliographic record
Abstract
The hippocampus is thought to support association-memory, particularly when tested with cued recall. One of the most well-known and studied factors that influences accuracy of verbal association-memory is imageability; participants remember pairs of high-imageability words better than pairs of low-imageability words. High-imageability words are also remembered better in tests of item-memory. However, we previously found that item-memory effects could not explain the enhancement in cued recall, suggesting that imageability enhances association-memory strength. Here we report an fMRI study designed to ask, what is the role of the hippocampus in the memory advantage for associations due to imageability? We tested two alternative hypotheses: (1) Recruitment Hypothesis: High-imageability pairs are remembered better because they recruit the underlying hippocampal association-memory function more effectively. Alternatively, (2) Bypassing Hypothesis: Imageability functions by making the association-forming process easier, enhancing memory in a way that bypasses the hippocampus, as has been found, for example, with explicit unitization imagery strategies. Results found, first, hippocampal BOLD signal was greater during study and recall of high- than low-imageability word pairs. Second, the difference in activity between recalled and forgotten pairs showed a main effect, but no significant interaction with imageability, challenging the bypassing hypothesis, but consistent with the predictions derived from the recruitment hypothesis. Our findings suggest that certain stimulus properties, like imageability, may leverage, rather than avoid, the associative function of the hippocampus to support superior association-memory.
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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) | 0.004 | 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".