The influence of exposure duration and context length on word recall: A replication of Tulving et al. (1964)
Bibliographic record
Abstract
pioneered the study of word recognition by investigating the influence of exposure duration and context length on accurate word identification. The present experiment aimed to replicate the original methodology with modern technology and a demographically heterogeneous sample. Male and female participants (n = 58) between 18-69 years with varying levels of education and who identified English or French as their dominant language were randomly assigned to a context length condition containing either 0, 2, 4, or 8 words from specific sentences. Participants were shown 18 target words for 16.67 ms and asked to type the target word after each one was presented. Participants were then instructed to type each target word in a corresponding fragmented sentence which varied in length according to the number of context words presented (0-, 2-, 4-, or 8-word context). The procedure was repeated for 6 subsequent exposure durations (33.33, 50.00, 66.67, 83.33, 100.00, and 116.67 ms). Repeated Measures ANOVA indicated a significant effect of exposure duration and context length on accurate recall (p < .002). These results corroborate those found by Tulving et al. (1964). No significant interaction was observed between these two variables on recall accuracy (p > .05) compared to what was demonstrated by Tulving et al. (1964). This suggests that the robust memory enhancing effects of longer exposure durations during encoding and longer context lengths during retrieval are reliably observed across a diverse participant sample. To improve generalizability, follow-up studies should use a larger participant sample to determine how demographic factors, including age, sex, education, and language, may influence the effects of exposure duration and context length on word recognition in recall tasks.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".