Is there an impact of low-level visual properties on long-term memory interferences?
Bibliographic record
Abstract
Several studies have shown that low-level perceptual similarity does not predict interferences in memory. These studies all used protocols that promote declarative learning by the medial temporal lobe. However, we know that low-level brain regions demonstrate neuronal plasticity resulting also from rewards conveyed by the striatum, which—unlike the medial temporal lobe—receives and sends information almost everywhere in the brain, including V1. Thus, the purpose of this study was to test whether low-level visual properties (spatial frequencies and orientations) influence interferences in memory by using a task that promotes response-stimuli association by the striatum. On day 1, two subject groups (N=45) learned to discriminate two sets of 12 target faces from 20 different non-target faces (with auditory feedbacks). The two target face sets were filtered by the same log-polar checkerboards in the Fourier domain in subject group 1 while they were filtered by different, non-overlapping log-polar checkerboards in subject group 2. To promote associations between low-level properties and response, the non-target faces were also filtered by another non-overlapping log-polar checkerboard; thus making low-level properties useful for solving the task. On day 2, subjects had to discriminate between three alternatives: target face set 1, target face set 2 and novel non-target faces. We compared interferences — confusions between target face set 1 and 2 — in subject group 1 and group 2. H0 predicts no differences between subject groups, whereas H1 predicts a greater number of interferences in subject group 1 than group 2 because the group 1 target face sets produce more similar activations in V1. Bayesian analyses indicate substantial evidence (Bf01 = 3.4) in favor of H0, thereby supporting the idea that low-level visual properties do not impact interferences in memory, even when learning is based on a response-stimuli association.
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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".