Low‐beta repetitive transcranial magnetic stimulation to human dorsolateral prefrontal cortex during object recognition memory sample presentation, at a task‐related frequency observed in local field potentials in homologous macaque cortex, impairs subsequent recollection but not familiarity
Bibliographic record
Abstract
According to dual-process signal-detection (DPSD) theories, short- and long-term recognition memory draws upon both familiarity and recollection. It remains unclear how primate prefrontal cortex (PFC) contributes to these processes, but frequency-specific neuronal activities are considered to play a key role. In Experiment 1, nonhuman primate (NHP) local field potential (LFP) electrophysiological recordings in macaque left dorsolateral PFC (dlPFC) revealed performance-related differences in a low-beta frequency range during the sample presentation phase of a visual object recognition memory task. Experiment 2 employed a similar task in humans and targeted left dlPFC (and vertex as a control) with repetitive transcranial magnetic stimulation (rTMS) at 12.5 Hz during occasional sample presentations. This low-beta frequency rTMS to dlPFC decreased DPSD derived indices of recollection, but not familiarity, in subsequent memory tests of the targeted samples after short delays. The same number of rTMS pulses over the same total duration albeit at a random frequency had no effect on either recollection or familiarity. Neither stimulation protocols had any causal effect upon behaviour when targeted to the control site (vertex). In this study, our hypotheses for our human TMS study were derived from our observations in NHPs; this approach might inspire further translational research through investigation of homologous brain regions and tasks across species using similar neuroscientific methodologies to advance the neural mechanism of recognition memory in primates.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".