Deep neural network decodes aspects of stimulus-intrinsic memorability inaccessible to humans.
Bibliographic record
Abstract
Some stimuli are more memorable than others. Humans have demonstrated partial access to the properties that make a given stimulus more or less memorable. Recently, a deep neural network named ResMem was shown to successfully decode the memorability of visual stimuli as well. However, it remains unknown whether ResMem's predictions of memorability reflect the influence of stimulus-intrinsic properties or other stimulus-extrinsic factors that are known to induce interindividual consistency in memory performance (e.g., interstimulus similarity). Additionally, it is not clear whether ResMem and humans share access to overlapping properties of memorability. Here, in three experiments, we show that ResMem predicts stimulus-intrinsic memorability independent of stimulus-extrinsic factors, and that it captures aspects of memorability that are inaccessible to human observers. Taken together, our results confirm the multifaceted nature of memorability and establish a method for isolating its aspects that are largely inaccessible to humans. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".