Electrophysiological signatures of ongoing thoughts during naturalistic behavior
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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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 | 0.004 |
| 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".