The Impact of Reward on Task Performance in Individuals of High and Low Need for Achievement
Bibliographic record
Abstract
The purpose of the study was to determine whether individuals with high need for achievement would outperform those with low need for achievement in the absence of extrinsic reward, and whether both groups would perform at the same level when provided with extrinsic motivation. Forty-two students at Huron University College completed a measure of achievement motivation, and were given 3 minutes to complete an anagram task. Half of the participants were rewarded for each correct answer on the task, and half were not. The results were not significant. There was no main effect for Need for Achievement, E"(l, 36) = .04, p > .05, and no main effect for Reward, F(1, 36) = .22, p > .05. There was also no significant interaction effect, F (1, 36) = .27, p > .05. Possible reasons for the lack of significant reasons, including small sample size and low desirability of reward are discussed.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".