Pre-Stimulus Spontaneous Brain Activity Predicts the Subjective Assessment of Subject's-Own-Name
Bibliographic record
Abstract
This project analyzed fMRI data from an experiment which sought to identify pre-stimulus spontaneous brain activity that could be used as predictors of the subjective assessment of noise as the subject's own name (SON).Subjects were asked to distinguish SON from other names after being cued by fully masked auditory stimuli.A pre--stimulus contrast successfully identified possible predictors: more activation in the TPJ and R/LSTG when subjects thought they heard SON as compared to other names.A comparable contrast for the evoked--response indicated insula and dmPFC activation.Results from; behavioural data, a contrast based on objective stimuli and an interaction--effect ANOVA all confirmed that subjects could not identify names in the stimuli, thus supporting the assertion that subjects were indeed making subjective assessments.Reaction times were also found to be faster when subjects thought they heard other names as compared to SON.
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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 | 0.002 |
| 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) | 0.002 | 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".