The art of planning ahead: When do we prepare for the future and when is it effective?
Bibliographic record
Abstract
Preparing for the future during ongoing activities is an essential skill. Yet it is currently unclear to what extent we can prepare for the future in parallel with another task. In two experiments, we investigated how characteristics of a present task influenced whether and when participants prepared for the future, as well as its usefulness. We focused on the influence of concurrent working memory load, assuming that working memory would interfere most strongly with preparation. In both experiments, participants performed a novel sequential dual-task paradigm, in which they could voluntarily prepare for a second task while performing a first task. We identified task preparation by means of eye tracking, by detecting when participants switched their gaze to information about the second task while performing the first task. The results showed that participants prepared, but also that there were large individual differences in how often they did so. When participants prepared, it was productive, as evidenced by faster RTs on the second task and only a small cost to the present task. The probability of preparation and its productiveness decreased with increases in the difficulty of the first task. In particular, we found that working memory load from the first task interfered with preparation. We conclude from our study that people can productively prepare for the future while performing an ongoing task, and that it is possible to track this preparation process empirically. In addition, we conclude that working memory resources play an important role in task preparation. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".