Simulating stimulus- and TMS-induced interference in short-term memory using a model of prefrontal cortex
Bibliographic record
Abstract
Scalar short-term memory (STM) tasks are those in which the to-be-remembered property of a stimulus can be represented as a scalar quantity – for example, the frequency of a tactile vibration or an auditory pure tone, or the duration or amplitude of a stimulus. Scalar STM tasks have been studied extensively using single-cell methods [1-3], and have proven to be useful model systems for examining behavioural aspects of short-term memory [4-6] and developing computational models [7-10]. In two studies [11,12], we applied a model of prefrontal cortex [9] to experimental datasets [5,13]. In Study 1, we simulated the effects of presenting an irrelevant (distractor) stimulus to experimental subjects during the maintenance period of a vibrotactile scalar STM task by assuming the stimulus was encoded into memory, intruding into the PFC memory store. We were able to replicate previous experimental results [5], and our results also suggested that distractors were only encoded into memory on approximately 50% of trials, consistent with experimental indications that activity in sensory cortex may be inhibited during memory maintenance in order to protect the contents of memory against interference [6,14]. In Study 2, we simulated a previous vibrotactile scalar STM study in which TMS was applied to somatosensory cortex during the maintenance period of the memory task, resulting in decreased performance [13]. We were able to replicate experimental results by assuming that TMS produced increased, noisy neural activity in sensory cortex, which then degraded the contents of the PFC memory store through feedforward interference.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".