A review of plasticity induced by auditory and visual tetanic stimulation in humans
Bibliographic record
Abstract
Long-term potentiation is a form of synaptic plasticity thought to play an important role in learning and memory. Recently noninvasive methods have been developed to induce and measure activity similar to long-term potentiation in humans. Sensory tetani (trains of quickly repeating auditory or visual stimuli) alter the electroencephalogram in a manner similar to electrical stimulation that results in long-term potentiation. This review briefly covers the development of long-term potentiation research before focusing on in vivo human studies that produce long-term potentiation-like effects using auditory and visual stimulation. Similarities and differences between traditional (animal and brain tissue) long-term potentiation studies and human sensory tetanization studies will be discussed, as well as implications for perceptual learning. Although evidence for functional consequences of sensory tetanization remains scarce, studies involving clinical populations indicate that sensory induced plasticity paradigms may be developed into diagnostic and research tools in clinical settings. Individual differences in the effects of sensory tetanization are not well-understood and provide an interesting avenue for future research. Differences in effects found between research groups that have emerged as the field has progressed are also yet to be resolved.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".