When is a choice not a choice? Pigeons fail to inhibit incorrect responses on a go/no-go midsession reversal task.
Bibliographic record
Abstract
In a 2-stimulus visual discrimination choice task with a reversal in reward contingencies midway through each session, pigeons produce a surprising number of both anticipatory errors (i.e., responding to the second-correct stimulus before the reversal) and perseverative errors (i.e., responding to the first-correct stimulus after the reversal). Here, we used a go/no-go version of the task to examine the degree to which these errors can be attributed to failure to inhibit incorrect responses near the reversal. We presented pigeons with either a green or red stimulus (randomized across trials), with pecks to 1 reinforced with food and pecks to the other stimulus leading to a 10-s time-out; the reward versus time-out contingencies reversed after 40 trials. Pigeons rarely withheld responses when reward was provided for pecking, but produced many incorrect pecks near the reversal. Subsequent experiments examined these errors with longer sessions and multiple reversals, as well as on choice tasks. Our results suggest that pigeons' errors may be due to an inability to inhibit incorrect responses rather than a deliberate choice of the incorrect stimulus on simultaneous discrimination midsession reversal procedures. Results suggest that pigeons learned independent rules about the 2 stimuli, and that training with multiple reversals changed the rules that governed pigeons' responding. (PsycINFO Database Record
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".