Anterograde interference emerges along a gradient as a function of task similarity: A behavioural study
Bibliographic record
Abstract
Anterograde interference emerges when two opposite (B → A) or identical tasks (A → A) are learned in close temporal succession, suggesting that interference cannot be fully accounted for by competing memories. Informed by neurobiological evidence, this work tested the hypothesis that interference depends upon the degree of overlap between the neural networks involved in the learning of two tasks. In a fully within-subject and counterbalanced design, participants (n = 24) took part in two learning sessions where the putative overlap between learning-specific neural networks was behaviourally manipulated across four conditions by modifying reach direction and the effector used during gradual visuomotor adaptation. The results showed that anterograde interference emerged regardless of memory competition-that is, to a similar extent in the B → A and A → A conditions-and along a gradient as a function of the tasks' similarity. Specifically, learning under similar reaching conditions generated more anterograde interference than learning under dissimilar reaching conditions, suggesting that putatively overlapping neural networks are required to generate interference. Overall, these results indicate that competing memories are not the sole contributor to anterograde interference and suggest that overlapping neural networks between two learning sessions are required to trigger interference. One discussed possibility is that initial learning modifies the properties of its neural networks to constrain further plasticity induction and learning capabilities, therefore causing anterograde interference in a network-dependent manner. One implication is that learning-specific neural networks must be maximally dissociated to minimize the interfering influences of previous learning on subsequent learning.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".