Judgments of learning reveal conscious access to stimulus memorability
Bibliographic record
Abstract
Despite the massive capacity of visual long-term memory, individuals do not successfully encode all visual information they wish to remember. This variability in encoding success has been traditionally ascribed to fluctuations in individuals’ cognitive states (e.g., sustained attention) and differences in memory encoding processes (e.g., depth of encoding). However, recent work has shown that a considerable amount of variability in encoding success stems from intrinsic stimulus properties that determine the ease of encoding across individuals. While researchers have identified several perceptual and semantic properties that contribute to stimulus memorability, much remains unknown, including whether individuals are aware of the memorability of stimuli they encounter. In the present study, we investigated whether individuals have conscious access to the memorability of real-world stimuli while forming self-referential judgments of learning (JOL) during explicit memory encoding (Experiments 1A–B) and when asked about the perceived memorability of a stimulus in the absence of attempted encoding (Experiments 2A–B). We found that JOLs and perceived memorability estimates (PME) were consistent across individuals and predictive of memorability, confirming that individuals can access memorability with or without stimulus encoding. At the same time, access to memorability was not comprehensive. We found that individuals unexpectedly remembered and forgot consistent sets of stimuli as well. When we compared access to memorability between JOLs and PMEs, we found that individuals had more access during JOLs. Thus, our findings demonstrate that individuals have partial access to stimulus memorability and that explicit encoding increases the amount of access that is available.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".