Bibliographic record
Abstract
This paper describes a radial basis memory system that is used to model the performance of human participants in a task of learning to traverse mazes in a virtual environment. The memory model is a multiple-trace system, in which each event is stored as a separate memory trace. In the modeling of the maze traversal task, the events that are stored as memories are the perceptions and decisions taken at the intersections of the maze. As the virtual agent traverses the maze, it makes decisions based upon all of its memories, but those that match best to the current perceptual situation, and which were successful in the past, have the greatest influence. As the agent carries out repeated attempts to traverse the same maze, memories of successful decisions accumulate, and performance gradually improves. The system uses only three free parameters, which most importantly includes adjustments to the standard deviation of the underlying Gaussian used as the radial basis function. It is demonstrated that adjustments of these parameters can easily result in exact modeling of the average human performance in the same task, and that variation of the parameters matches the variation in human performance. We conclude that human memory interaction that does not involve conscious memorization, as in learning navigation routes, may be much more primitive and simply explained than has been previously thought.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".