Distributed metacognition: Increased bias and deficits in metacognitive sensitivity when retrieving information from the internet.
Bibliographic record
Abstract
Our metacognitive ability to monitor and evaluate our cognitive performance is central to efficient and adaptive behaviors. Research investigating this ability has focused largely on tasks that rely exclusively on internal processes (e.g., memory). However, our dayto-day cognitive activities often consist of the mixes of internal and external processes. In the present investigation, we expand research on metacognition to this distributed domain. We examined participants’ ability to accurately monitor their performance in a knowledge retrieval task when they were required to rely on only their internal knowledge and when required to rely on both internal knowledge and utilizing the internet. One hundred and ninety-four participants completed an online study consisting of answering general knowledge questions. Individuals were also randomly assigned to provide accuracy judgments either prospectively or retrospectively. Results revealed metacognitive bias (i.e., overconfidence) increased when using the internet and when making retrospective judgments. Metacognitive sensitivity was also worse when using the internet, especially when individuals made prospective judgments about what their performance would be. Furthermore, metacognitive bias was positively related across the internal knowledge and internet conditions. These results provide the beginnings of an understanding of metacognition and behavior in distributed cognitive contexts involving the internet.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".