Suboptimal choice: A review and quantification of the signal for good news (SiGN) model.
Bibliographic record
Abstract
) sometimes choose options that provide less food over options that provide more food. This behavior has been variously referred to as suboptimal, maladaptive, or paradoxical because it lowers overall food intake. A great deal of research has been directed at understanding the conditions under which animals and people make suboptimal choices and the mechanisms that drive this behavior. Here, we review the literature on suboptimal choice and the variables that play a role in this phenomenon. Suboptimal choice is most likely to occur when the outcomes following a choice are uncertain, when the outcomes are delayed after the choice, and when the outcomes are signaled only on the option that provides food less often. We propose a mathematical formalization of the signal for good news (SiGN) model which assumes that a signal for a reduction in delay to food reinforces choice. We generate predictions from the model about the effect of parameters that characterize suboptimal choice and we show that, even in the absence of free parameters, the SiGN model provides a very good fit to the choice proportions of birds from a large set of conditions across studies from numerous researchers. R code for SiGN predictions and the data set are available on the Open Science Framework (https://osf.io/39qtj). We discuss limitations of the model, propose directions for future research, and discuss the general applicability of this research to understanding how rewards and signals for reward may combine to reinforce behavior. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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How this classification was reachedexpand
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) | 0.003 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".