Using “Wordle” to assess the effects of goal gradients and near-misses
Bibliographic record
Abstract
As individuals near a desired goal, they become more motivated, and happier. When thwarted in their goal pursuit, frustration and negative affect ensue. Slot-machine research indicates that near-misses (being one symbol away from a jackpot) are highly arousing, frustrating, yet motivating outcomes. We used the popular game Wordle to illustrate goal gradient and near-miss effects. In Wordle, an individual tries to guess a five-letter word in six attempts. Wordle gives feedback - letters turn green, orange, or grey depending on how closely guesses match the target word. By analyzing feedback across guesses, players can gauge whether they are approaching or thwarted in their goal pursuit. Wordle also contains near-misses (guesses only one letter away from the target). When feedback indicated players were approaching their goal, positive affect and motivation significantly increased compared to thwarted outcomes which increased frustration. Near-misses were significantly more subjectively arousing than other outcomes. The first appearance of a near-miss (an "approaching" outcome) led to higher motivation and positive affect and less frustration than if the next guess revealed the same four green letters (a "thwarted" near-miss). These findings shed new light on erroneous cognitions among slot-machine players who misinterpret slots games as having goal gradient properties.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".