Exploring semantic memory organization using a proactive interference paradigm
Bibliographic record
Abstract
Several decades of research into semantic memory have yielded two main perspectives as to how semantic memory may be organized. One hypothesis is that information is stored according to taxonomical categories (e.g., animals, objects); the other hypothesis suggests that information is stored according to featural attributes (e.g., functional and perceptual properties). Using a proactive interference (PI) paradigm, this study aimed to investigate these two hypotheses by contrasting the impact of categorical and featural cues on patterns of PI effects (i.e., buildup and release). Using the same stimuli and task, while examining recall performance and intrusion errors when featural and categorical information were opposed, allowed for a direct measure of the contribution of these two types of information for semantic organization. To explore semantic organization across the lifespan, 20 healthy younger and 20 healthy older participants were tested. Given that semantic memory deficits frequently occur in Alzheimer's disease (AD), the performance of the healthy older participants was also compared to 16 participants with AD to examine differences in semantic organization of featural and categorical information in individuals for whom there is a potential breakdown of semantic memory. All groups showed expected PI effects when stimuli were categorically cued. Participants also showed a release from PI when the featural cue changed (but the category remained the same). An unexpected release from PI effect was found in the featural PI continued condition in which the featural cue remained the same (e.g., USED FOR TRANSPORTATION) but there was an implicit switch in category (e.g., from OBJECTS to ANIMALS). The results are discussed in terms of the implications for the categorical and featural hypotheses of semantic memory organization.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".