Inner Speech, Self-regulation and the Modular-with-Feedback-Theory of Free Will
Bibliographic record
Abstract
This paper demonstrates a synergy between the Inner Speech model of free will and the Modular-with-Feedback Theory. The first section examines determinism and causation to argue that free will requires the ability of an agent to make a non-deterministic choice, which could have been decided otherwise. This in spite of physical, hereditary and environmental ad hoc factors which inevitably influence choice. Section two introduces the Modular-with-Feedback Theory which proposes free will to be compatible, not with determinism, but with chance. It provides a model of how free will emerges from oscillating neuronal activity in neural modules. These, representing ideas, oscillate subconsciously, competing for conscious attention. Although the choice between them is partly random the modules are able to maintain a sense of context and consistency, leading to a conscious desire for a sense of character. Learning from experience, we use feedback to rebalance. Conscious decisions, using inner speech, train the subconscious to advance, in the future, options better conforming to our desired will. Section three discusses how consciousness emerges non-deterministically in a manner consistent with a causally interactive dualism that is, at a hidden level, monist. Section four explains how inner speech self-regulates our behavior by talking us through free, usually consistent choices, conferring moral responsibility. Some abnormalities of inner speech diminishing free will are discussed, and further research programs proposed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.007 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".