Learning about environmental geometry: A flaw in Miller and Shettleworth's (2007) operant model.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Many studies have examined how humans and other animals reestablish a sense of direction following disorientation in enclosed environments. Results showing that geometric shape of an enclosure is typically encoded, sometimes to the exclusion of featural cues, have led to suggestions that geometry might be encoded in a dedicated geometric module. Recently, Miller and Shettleworth (2007) proposed that the reorientation task be viewed as an operant task and they presented an associative operant model that appears to account for many empirical findings from reorientation studies. In this paper we show that, although Miller and Shettleworth's insights into the operant nature of the reorientation task may be sound, their mathematical model has a serious flaw. We present simulations to illustrate the implications of the flaw. We also propose that the output of a simple neural network, the perceptron, can be used to conduct operant learning within the reorientation task and can solve the problem in Miller and Shettleworth's model.
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.
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) | 0.001 | 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 | 0.001 |
| 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 it