Color priming facilitates cued location recall in a visuospatial short-term memory partial report paradigm
Bibliographic record
Abstract
Visuospatial short-term memory (VSTM) has a limited capacity for items that can be accurately encoded and later retrieved. This is impacted not just by the number of items but also but the diversity of item features and item locations. Partial report paradigms have demonstrated more items are briefly stored in VSTM than can be encoded to long-term memory (e.g., Sperling, 1960). In the present study, we explored the impact of priming on memory retrieval of cued item locations. Participants studied an array of letters arranged in a grid. Each letter was blue, green, or red. In half of the trials, a color prime indicated the color of the letter that would later be cued. Half of the color primes came before the study array and half came after (retro-prime). A previous letter location was then cued with an outlined square that remained on screen until a participant response. Prime versus retro-prime trials were completed in blocks and counterbalanced between participants to avoid order effects. Prime and no-prime trials were randomized in both blocks, as were prime colors, letter colors, and order of letters within each array. A one-way ANOVA comparing priming conditions found priming aided in retrieval significantly better than retro-priming or not priming. A 2 (Priming) x 3 (Color) repeated-measures ANOVA found no significant difference in priming color; however a significant interaction between these two factors was found. Future research will aim to improve the efficacy of retro-priming through timing and feature manipulation. This study improves our understanding of VSTM and the impact of color priming on this memory system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".