Bibliographic record
Abstract
We show how uncertainty and insight can be modeled using Reflexively Autocatalytic Foodset-generated (RAF) networks. RAF networks have been used to model the self-organization of adaptive networks associated with the origin and early evolution both biological life, and the kind of cognitive structure necessary for cultural evolution. The RAF approach is applicable in these seemingly disparate cases because it provides a theoretical framework for formally describing systems composed of elements that interact to form new elements, and for studying under what conditions these (initial + new) elements collectively become integrated wholes of various types. Here, the elements are mental representations, and the whole is a conceptual network. The initial components—referred to as foodset items—are mental representations that are innate, or were acquired through social learning or individual learning (of pre-existing information). The new elements—referred to as foodset-derived items—are mental representations that result from creative thought (resulting in new information). The demarcation into foodset versus foodset-derived elements provides a natural means of (i) grounding abstract concepts in direct experiences (foodset-derived elements emerge through ‘reactions’ that can be traced back to foodset items), and (ii) precisely describing and tracking how new ideas emerge from earlier ones. Thus, RAFs can model how endogenous conceptual restructuring results in new conduits by which uncertainties can be resolved and needs can be met. A source of uncertainty is modeled as an element that resists integration into the conceptual network, which is described as a maxRAF containing the bulk of the individual’s mental representations. This disconnect produces arousal, which catalyzes one or more interactions amongst mental representations. We illustrate the approach using the historical example of Kekulé’s realization that benzene is ring-shaped through a reverie of a snake biting its tail. We show how a single conceptual change can precipitate a cascade of reiterated cognitive ‘reactions’ (self-organized criticality) that affect the network’s global structure, and discuss why this may help explain why cognitive restructuring can be therapeutic. Finally, we discuss educational implications of the RAF approach.
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How this classification was reachedexpand
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 | 0.000 |
| 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) | 0.006 | 0.001 |
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; both teacher heads agree on what is shown here.
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".