Learning by Imitation, Reinforcement and Verbal Rules in Problem Solving Tasks
Bibliographic record
Abstract
Learning by imitation is a powerful process for acquiring new knowledge, but there has been little research exploring imitation’s potential in the problem solving domain. Classical problem solving techniques tend to center around reinforcement learning, which requires significant trial-and-error learning to reach successful goals and problem solutions. Heuristics, hints, and reasoning by analogy have been favored as improvements over reinforcement learning, whereas imitation learning has been regarded as rote memorizing. However, research on imitation learning in animals and infants suggests that what is being learned is the overall arrangement of actions (sequencing and planning). Applied to problem solving, this suggests that imitation learning might enable a problem solver to infer a complex hierarchical problem representation from observation alone. We compared three types of learning in problem solving tasks: imitation learning (a group that viewed successful problem solving demonstrations), reinforcement learning (a group that got feedback indicating whether their answer was correct or not) and explicit rule learning (a group that was presented specific instructions to solve the problem). The task required participants to find, with three uses of a scale, the one ball which was either heavier or lighter than the rest of a set of 12 balls. We found that subjects in the imitation learning and explicit learning groups outperformed those in the reinforcement learning group. We conclude that learning by imitation in problem solving tasks is worthwhile, efficient and even superior to explicit learning because of the minimal time and energy investment required from the mentor.
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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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".