Perceptual (Static) Active Inference Approach to the Superior Production Effect of Speaking over Writing: An Experiment and Computational Model Report
Bibliographic record
Abstract
This paper reports a cognitive psychology experiment and a Markov decision process (MDP) model of the production effect—higher memory retrieval that follows speaking aloud or writing/typing words, as opposed to lower memory retrieval when words are read silently. Current models of the production effect draw on the global-matching framework of memory. We identify four limitations of these models and present a MDP model (a perceptual active inference model) to causally explain a superior production effect of speaking over writing. University students performed a word-production task comprising speaking and writing conditions, followed by a memory test. The results showed main effects of condition on accuracy and response times. The MDP model indicated higher sensory precision during memory retrieval in the speaking condition than in the writing condition. Through Bayesian model selection, we evaluated whether the MDP model, as a mechanistic active-inference model, provided higher construct validity than a descriptive linear model (fit via Variational Laplace). The MDP model outperformed the linear model, suggesting that production modalities are hidden states that cause the visual sensory observation of words that had been linguistically produced. Crucially, the MDP model explains both group effects and individual variability, confirming the reliability paradox of statistical models.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".