The Moderating Effect of Performance Feedback and the Mediating Effect of Self‐Set Goals on the Primed Goal‐Performance Relationship
Bibliographic record
Abstract
The effect of feedback and a self‐set goal on the relationship between a goal primed in the subconscious and performance were examined in three laboratory experiments and one field experiment ( n = 241, 465, 201, 74 respectively), using normative (bogus) and absolute feedback manipulations, and different performance tasks that were coded for both performance quality (i.e. creativity) and quantity. The hypothesis that providing feedback, a moderator in goal setting theory, amplifies the causal effect of a primed goal on performance was supported. Specifically, in experiment 1, participants were randomly assigned to a 2 (prime of effective vs. ineffective performance) × 3 (positive, negative, no feedback) factorial design. The primed goal for effective performance led to higher performance than the negative primed goal. In addition, feedback, regardless of its sign, increased both task and creative performance when a primed goal for effective performance was presented, but did not do so when the goal primed ineffective performance. This effect was replicated in two subsequent laboratory experiments which employed three primed goal conditions (effective/neutral/ineffective). In experiments 2 and 3, a consciously set goal, with no prompting by an experimenter, mediated the relationship between a primed goal and performance when feedback was provided. Experiment 4 provided a conceptual replication in a work setting, involving employees in a customer service department of a large communication company. Finally, a meta‐analysis of these four experiments indicated an average effect size of d = 0.36, 95 per cent CI [0.23, 0.49] with no evidence of heterogeneity across the four experiments. These findings suggest that not only are subconscious goals a foundation for the difficulty level of consciously set goals, but in addition subconscious goals and conscious goals work together in affecting performance.
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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 | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".