A curved honulo improves your short-term and long-term memory.
Bibliographic record
Abstract
During his distinguished career, Bill Hockley contributed to memory research in many ways, with work characterized by rigorous and innovative experimental designs. One of the areas he has explored is that of memory for associative information. We echo this interest here and attempt to emulate his careful experimental attitude. We report four experiments which examined how previously established links can support the development of new episodic associations. More specifically, we tested the idea that sound-symbolism links can support learning of new associations. Sound-symbolism links are relationships between phonemes and object characteristics that participants find natural-even if they have never encountered the items before. For instance, the nonword "honulo" is more readily seen to refer to a shape with curved contours than to a shape that has sharp angles. In Experiment 1, 70 participants studied three pairs and their memory for the associations between the members of each pair was tested in a paired-recognition task. Results demonstrate that sound-symbolism associations support the learning of new associations. Experiment 2 confirmed that the effect is replicated in a between-participants design. In Experiment 3, we replicated the findings with a 30-s filled interval between presentation and test, and in Experiment 4, we extended the delay to 2 min, establishing that the pattern is also found with a paradigm more typical of episodic memory. The results are discussed in terms of the importance of associative memory, while referring to some of the ideas Bill Hockley championed in his own work. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.005 |
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".