Effects of expected task difficulty on metacognitive confidence and multitasking
Bibliographic record
Abstract
In daily life, repeated experiences with a task (e.g. driving) will generally result in the development of a belief about one’s ability (“I am a good driver”). Here we ask how such beliefs, termed self-efficacy, interact with metacognitive confidence judgements. Across three pre-registered experiments, participants performed a perceptual discrimination task and reported their decision confidence. We induced contextual beliefs about performance (our operationalisation of self-efficacy) by manipulating the prior probability of an easy or hard trial occurring in each block. In Experiment 1 easy and hard trials generated the same levels of performance (a “subjective difficulty” manipulation), whereas in Experiments 2 and 3 performance differed across difficulty conditions (an “objective difficulty” manipulation). Results showed that context (self-efficacy) and difficulty interacted multiplicatively, consistent with the notion that confidence judgements combine decision evidence with a prior (contextual) belief on being correct. This occurred despite context having no corresponding effect on performance. We reasoned that performing tasks in easy contexts may reduce cognitive “load”, and tested this, in Experiment 3, by instructing participants to perform two tasks concurrently. Consistent with a reduction in load, the effects of context transferred from influencing confidence on our primary task to improving performance on the secondary task. Taken together, these studies reveal that contextual beliefs about performance facilitate multitasking, potentially by reducing the load of tasks believed to be easy, and they extend psychophysical investigations of perceptual decision-making by incorporating ‘higher-order’ beliefs about difficulty context, corresponding to intuitive notions of self-efficacy.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".