A framework for understanding reasoning errors: From fake news to climate change and beyond
Bibliographic record
Abstract
Humans have the capacity, but perhaps not always the willingness, for great intelligence. From global warming to the spread of misinformation and beyond, our species is facing several major challenges that are the result of the limits of our own reasoning and decision-making. So, why are we so prone to errors during reasoning? In this chapter, I will outline a framework for understanding reasoning errors that is based on a three-stage dual-process model of analytic engagement (intuition, metacognition, and reason). The model has two key implications: 1) That a mere lack of deliberation and analytic thinking is a primary source of errors and 2) That when deliberation is activated, it generally reduces errors (via questioning intuitions and integrating new information) than increasing errors (via rationalization and motivated reasoning). In support of these claims, I review research showing the extensive predictive validity of measures that index individual differences in analytic cognitive style – even beyond explicit errors per se. In particular, analytic thinking is not only predictive of skepticism about a wide range of epistemically suspect beliefs (paranormal, conspiratorial, COVID-19 misperceptions, pseudoscience and alternative medicines) as well as decreased susceptibility to bullshit, fake news, and misinformation, but also important differences in people’s moral judgments and values as well as their religious beliefs (and disbeliefs). Furthermore, in some (but not all cases), there is evidence from experimental paradigms that support a causal role of analytic thinking in determining judgments, beliefs, and behaviors. The findings reviewed here provide some reason for optimism for the future: It may be possible to foster analytic thinking and therefore improve the quality of our decisions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".