Inner speech does not represent an epiphenomenon: Commentary on Verhaeghen & Mirabito (2021)
Bibliographic record
Abstract
Using correlations and hierarchical regression analysis, Verhaeghen and Mirabito (2021) found that while self-awareness was associated with self-regulation, inner speech was not, suggesting that the latter does not play a causal role in either self-awareness nor self-regulation. This motivated the authors to claim that “inner speech is easiest understood as an epiphenomenon” (p. 8). In this Commentary, I suggest that the authors conceptualized and measured inner speech, self-regulation, and self-awareness in inappropriate ways. The two measures chosen to assess inner speech either do not relate to self-regulation (VISQ) or self-awareness (SVQ). Self-awareness was measured using composites of various scales assessing mindfulness (which represents a related, yet different construct) which contains multiple items not representative of a typical self-awareness process. The self-regulation measure was also produced using various subscales assessing self-preoccupation and self-compassion—two self-processes very loosely associated with the target construct. Different results would have been obtained if the authors had used established measures. Their results contradict what has been consistently reported in the literature and do not cast doubt on the recognized fact that inner speech plays a significant, and often causal, role in self-awareness and self-regulation.
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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 | 0.012 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.008 | 0.003 |
| Research integrity | 0.038 | 0.069 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".