Electrophysiological signatures of ongoing thoughts during naturalistic behavior
Why this work is in the frame
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Bibliographic record
Abstract
Humans engage in a continuous stream of ongoing mental experience. Recent work examining the neural correlates of several dimensions of thoughts has revealed their functional connectivity patterns using fMRI during constrained experimental tasks. Less is known about the electrophysiological basis of various thoughts dimensions in more naturalistic settings. To address this, we first examined the electrophysiological signatures of ongoing thoughts during naturalistic tasks in seven participants across seven recording sessions. We then combined deep learning algorithms with electrophysiological data to determine the utility of these signals in predicting thought dimensions. Based on a total of 49 data sets, our results revealed distinct oscillatory markers of 7 dimensions of ongoing thought as participants completed any computer-based activities they wished to perform. In addition to identifying electrophysiological markers consistent with those observed in experimental settings for internally oriented thoughts and freely moving thoughts, we found novel patterns not previously reported for off-task thoughts, goal-oriented thoughts, and sticky thoughts, primarily characterized by spectral activity in canonical theta, alpha, and beta bands. Importantly, applying deep learning algorithms on electrophysiological data reliably detected all seven thought dimensions at above chance levels for both within-participant (MCC = 0.22-0.43) and across-participant (MCC = 0.14-0.31) approaches. Together, these results established the electrophysiological signatures of seven dimensions of ongoing thought, assembling a comprehensive set of brain-to-experience mapping of the phenomenological features of thoughts. Our findings provide an important step toward predicting thought patterns in the real world with clinical implications for establishing biomarkers of typical and atypical thought patterns.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it