Plans as Emotion Regulation Tools? Examining the Consequences of Planning on Affect
Bibliographic record
Abstract
Psychologists have studied extensively the consequences of planning for motivation and task performance, but little work has examined whether plan-making serves another function, that of helping us feel better about the yet-to-be completed task. In the present research, we examined whether making plans for completing a future task positively impacts feelings related to that task. In three studies, we tested the possibility that planning decreases negative emotions about the task planned for, and whether some types of planning are more beneficial for this than others. In Studies 1 and 2, participants were asked to nominate an important task they had yet to complete and that they had felt concerned about completing lately, and then instructed to either make a plan to complete the task using one of the specified planning types, or were not asked to make a plan. Participants then rated their feelings about the task on twenty emotions adjectives (PANAS; Watson, Clark, & Tellegen, 1988). In Study 3, participants were prompted to think about an upcoming exam, and then either (1) made a specific plan to prepare for it followed by giving affect ratings (experimental condition), or (2) rated their affect first and then made a specific plan (control condition). The results of Study 1 (N = 144) supported our hypothesis – following planning, mental simulation planners reported lower levels of negative affect than implementation intention planners and no plan controls. No differences were found for positive affect. These results were not replicated in Study 2 (N = 133) or Study 3 (N = 147), where feelings about the task did not differ depending on whether participants planned or not, or planning type. Overall, our findings did not yield consistent evidence that planning for an important future task has immediate affective benefits.
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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 | 0.005 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".