Animals prefer reinforcement that follows greater effort: Justification of effort or within-trial contrast?
Bibliographic record
Abstract
Justification of effort by humans is a form of reducing cognitive dissonance by enhancing the value of rewards when they are more difficult to obtain.Presumably, assigning greater value to rewards provides justification for the greater effort needed to obtain them.We have found such effects in adult humans and children with a highly controlled laboratory task.More importantly, under various conditions we have found similar effects in pigeons, animals not typically thought to need to justify their behavior to themselves or others.To account for these results, we have proposed a mechanism based on within-trial contrast between the end of the effort and the reinforcement (or the signal for reinforcement) that follows.This model predicts that any relatively aversive event can serve to enhance the value of the reward that follows it, simply through the contrast between those two events.In support of this general model, we have found this effect in pigeons when the prior event consists of: (a) more rather than less effort (pecking), (b) a long rather than a short delay, and (c) the absence of food rather than food.We also show that within-trial contrast can occur in the absence of relative delay reduction theory.Contrast of this kind may also play a role in other social psychological phenomena that have been interpreted in terms of cognitive dissonance.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".