Tracing the Emergence of the Memorability Benefit
Bibliographic record
Abstract
Some visual stimuli are consistently better remembered than others across individuals, due to variations in memorability, or stimulus-intrinsic properties that determine ease of access into visual long-term memory (VLTM). However, it remains unclear what cognitive processes give rise to this mnemonic benefit. One possibility is that this benefit is imbued within the capacity-limited bottleneck of VLTM encoding, namely visual working memory (VWM). More precisely, memorable stimuli may be preferentially encoded into VLTM because fewer cognitive resources are required to represent them efficiently in VWM (efficiency hypothesis). Alternatively, memorable stimuli may be more competitive in obtaining cognitive resources than forgettable stimuli, leading to more successful maintenance in VWM (competitiveness hypothesis). Additionally, the memorability benefit might emerge post-VWM, specifically, if memorable stimuli are less prone to be forgotten (i.e., are “stickier”) than forgettable stimuli after they pass through the encoding bottleneck (stickiness hypothesis). In this study, we conducted two experiments to examine how memorability benefits emerged for VLTM of random human faces (Experiment 1A) and real-world objects (Experiment 1B). Here we found that although memorable stimuli enjoy more efficient and competitive VWM maintenance, only the maintenance efficiency produced reliable benefits in VLTM performance. Additionally, memorable stimuli were also “stickier” after they passed through the encoding bottleneck imposed by VWM. Thus, our results demonstrate that the memorability benefit emerges as a result of interactions between multiple cognitive processes.
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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 | 0.005 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".