PLOS Science Wednesday: Hi reddit, my name is Michael and my research challenges the notion that self-control is a finite resource that diminishes with use over time – Ask Me Anything!
Bibliographic record
Abstract
Hi reddit, My name is Michael Inzlicht and I am a professor at The University of Toronto. My research focuses on the topic of self-control and the related concepts of cognitive control and executive function. I recently published a study titled, “A pre-registered naturalistic observation of within domain mental fatigue and domain-general depletion of self-control” in PLOS ONE. In this paper, we monitored over 16,000 students as they engaged in voluntary learning on an online program to examine the impact of time-of-day and within-task fatigue on participation and performance. Contrary to models of self-control that suggest that self-control is domain general and runs out, we did not find that task engagement decreased at the end of the day. These findings join others (e.g., http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0147770) that cast doubt on the notion that self-control is based on some finite resource that diminishes with use. I will be answering your questions at 1pm ET. Ask me Anything! Don’t forget to follow me on Twitter @minzlicht!
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.070 | 0.054 |
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".