On the strategic learning of signal associations
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
Abstract Signal detection theory (SDT) has been widely used to identify the optimal response of a receiver to a stimulus when it could be generated by more than one signaler type. While SDT assumes that the receiver adopts the optimal response at the outset, in reality, receivers often have to learn how to respond. We, therefore, recast a simple signal detection problem as a multi-armed bandit (MAB) in which inexperienced receivers chose between accepting a signaler (gaining information and an uncertain payoff) and rejecting it (gaining no information but a certain payoff). An exact solution to this exploration–exploitation dilemma can be identified by solving the relevant dynamic programming equation (DPE). However, to evaluate how the problem is solved in practice, we conducted an experiment. Here humans (n = 135) were repeatedly presented with a four readily discriminable signaler types, some of which were on average profitable, and others unprofitable to accept in the long term. We then compared the performance of SDT, DPE, and three candidate exploration–exploitation models (Softmax, Thompson, and Greedy) in explaining the observed sequences of acceptance and rejection. All of the models predicted volunteer behavior well when signalers were clearly profitable or clearly unprofitable to accept. Overall however, the Softmax and Thompson sampling models, which predict the optimal (SDT) response towards signalers with borderline profitability only after extensive learning, explained the responses of volunteers significantly better. By highlighting the relationship between the MAB and SDT models, we encourage others to evaluate how receivers strategically learn about their environments.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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