A Wandering Mind is a Pondering Mind: Lapses in Sustained Attention Can Be Beneficial
Bibliographic record
Abstract
Attention is a foundational set of processes that dictate what information undergoes additional cognitive processing, and what information is ignored. Nonetheless, attention cannot be sustained indefinitely—given sufficient time, lapses in sustained attention will occur. So what are the consequences for lapses in attention for learning? The conventional understanding is that lapses impair learning, however, my dissertation indicates a more nuanced relationship, which enhances learning. In chapter two I leveraged recent improvements in tracking sustained attention during a correlated flanker task. I found that participants with more lapses in attention were able to learn more about seemingly irrelevant distractor information, and critically, this finding was driven by greater learning during the moments when attention lapsed. These findings suggest that lapses in attention can enhance learning for seemingly-irrelevant information. In chapter three I tested children aged four to eleven years with the same correlated flanker task. I again found that lapses in attention were correlated with greater learning (with a marginally larger benefit during momentary lapses). This finding is an important complement to individual-differences research in developmental psychology; they provide clear support for the role of sustained attention specifically, without influence from extraneous variables that may differ between age groups. Finally, in chapter four I used the same reaction time based approach to track attention during a category learning paradigm. Here I found tentative evidence that lapses in attention boosted learning for category-level information, and that this learning is driven by the induction of abstract rules rather than memory for specific stimuli. These findings are consistent with the hypothesis that lapses in attention allow for forgetting of non-diagnostic information. Overall, I contend that lapses are not necessarily “suboptimal” for learning—an overly simplistic description—but rather, provide an opportunity to take advantage of alternative learning opportunities. Clearly outlining the unexpected learning benefits from lapses in attention is critical for attaining a more balanced understanding of sustained attention. Lapses in sustained attention ultimately have a nuanced impact on human cognition, and this should be reflected in the laboratory and the real world.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".