Bibliographic record
Abstract
Presented is an implementation of a time sychronous middleware for Python ACT-R and the open-source robotics simulator, MORSE (Echeverria et al., 2012;Echeverria, Lassabe, Degroote, & Lemaignan, 2011), a novel vision system, and novel motor system, which I collectively call ACT-R 3D.A new 3D camera and a crude body-model robot was added to the MORSE system to facilitate modeling of afforance-based research on aperture passage (walking through apertures and rotating shoulders as needed).Presented as a proof of concept are three affordance models based in ACT-R.The models tests a novel theory, the Theory of Geometric Affordances, that proposes that humans make geometric comparisons between apertures (depth, width, height) and stored representations of body postures (body schema).Both models are individually qualitatively compared against human performance for overall shoulder rotation while walking through apertures of various widths (Warren & Whang, 1987;Higuchi, Seya, & Imanaka, 2012) and overall safety margin while passing through apertures (Higuchi et al., 2012).The second model (Model 2) shows the best performance, with the same model exhibiting rotation similar to human performance across both experiments.Model 2 supports the conclusion that an abstract geometric comparison mechanism is sufficient to support aperture passage judgment without the use of semantically-laden labels.This is the first known affordance model, modeled in a computational cognitive architecture, to match preliminary human performance data.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".