Inhibiting beliefs demands attention
Bibliographic record
Abstract
Research across a variety of domains has found that people fail to evaluate statistical information in an atheoretical manner. Rather, people tend to evaluate statistical information in light of their pre-existing beliefs and experiences. The locus of these biases continues to be hotly debated. In two experiments we evaluate the degree to which reasoning when relevant beliefs are readily accessible (i.e., when reasoning with Belief-Laden content) versus when relevant beliefs are not available (i.e., when reasoning with Non-Belief-Laden content) differentially demands attentional resources. In Experiment 1 we found that reasoning with scenarios that contained Belief-Laden content required fewer attentional resources than reasoning with scenarios that contained Non-Belief-Laden content, as evidenced by smaller costs on a secondary memory load task for the former than the latter. This trend was reversed in Experiment 2 when participants were instructed to ignore their beliefs when reasoning with Belief-Laden and Non-Belief-Laden scenarios. These findings provide evidence that beliefs automatically influence reasoning, and attempting to ignore them comes with an attentional cost.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".