Theoretical Mechanisms of Paradoxical Choices Involving Information
Bibliographic record
Abstract
Humans and other animals often make decisions in conditions of uncertainty.Choosing an option that provides information and reduces uncertainty can often improve decision making.Yet, in decision-making tasks, animals such as pigeons, starlings, rats, and humans often choose information even when it cannot be used.A review of the behavioral contributions of such noninstrumental information is presented here, starting from the observing response literature, and then focusing on research using the paradoxical (aka suboptimal) choice task.In the paradoxical choice procedure, animals choose between two alternatives that differ in two main aspects: the information presented after each alternative and the overall reinforcement following each alternative.The richer alternative is followed by one or two cues that are followed by food on half of the trials; this is called the noninformative (No-Info) alternative.The leaner alternative is followed by one of two cues-one always followed by food and the other always followed by no food.Thus, both cues are informative about whether reinforcement will be delivered on that trial; this is called the informative alternative.Typically, animals develop a strong preference for the informative (Info) alternative, thereby failing to maximize reward.We review the following factors that influence the strength of this paradoxical choice: response requirement, delay-to-reward; contiguity between choice and information; reinforcement rate, motivational and individual differences, degrees of information, and what is learned about the informative cues.Finally, we review some of the most important models and their theoretical implications for the understanding of the phenomena.
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.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".