Modestly related memories for when and where an object was seen in a Massive Memory paradigm.
Bibliographic record
Abstract
We know that observers can typically discriminate old images from new ones with over 80% accuracy even if after seeing hundreds of objects for just 2-3 seconds each (“Massive Memory”). What do they know about WHERE and WHEN they saw each object? From previous work, we know that observers can remember the locations of 50-100 out of 300 items (Spatial Massive Memory – SMM). In a different study, observers could mark temporal locations within 10% of the actual time of the item's original appearance (Temporal Massive Memory - TMM). Are SMM and TMM related? In new experiments, 64 observers saw 50 items, each sequentially presented in random locations in a 7x7 grid. They subsequently saw 100 items (50 old). Four sets of instructions were used: (1) Mere Identity instruction asked 16 observers just to remember the items. (2) Spatial instruction asked 16 observers to also remember item locations. (3) Temporal instruction asked 14 observers to remember when items appeared. (4) Full instruction (13 observers) combined Spatial and Temporal instructions. At test, observers in all conditions were told to click on the original location of old items and to indicate when they saw it on a time bar. ~12% of observers appeared to guess on the spatial task and ~50%(!) guessed on the timing task. Interestingly, just 6% guessed on both, exactly as would be predicted if the choice to guess was independent for space and time. Overall, space and time scores were strongly correlated for Full Instructions (r-sq=.64, p=0.001), Temporal (r-sq=.31, p=0.04), and marginally correlated for Spatial (r-sq=.20, p=0.08). The Mere Identity correlation was insignificant (r-sq=.03, p=0.40). Effects of instruction on performance were generally insignificant. Observers can have quite good memory for when and where they saw an object. Those memories seem to be modestly correlated with each other.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".