The influence of task demands and experience on diagnostic accuracy: Investigating the assumptions of a default interventionist dual systems model
Bibliographic record
Abstract
There are various dual process models of human cognition. While many models of cognitive control propose processes that are selected exclusively or in combination, a default-interventionist model of reasoning assumes that processing occurs in serial stages. System 1 processes are believed to recruit unconscious memory retrieval processes by default and precede System 2 processes (Evans & Stanovich, 2013; Kahneman, 2011). System 1 processes are also considered to be overly sensitive to the automatic influences of the environment and thereby also to various cognitive biases and errors; hence System 1 is inferior. On the other hand System 2, which represent conscious logic and normative reasoning processes, is not considered susceptible to such automatic influences and thereby capable of overriding errors made through System 1 reasoning; hence System 2 is superior. This default-interventionist model has become highly influential in theories about best practices in medical education (Croskerry, 2009; 2003; Klein, 2005; Redelmeier, 2005), and has encouraged a view that increased conscious processing and reflective thought will improve performance. Such a view is in stark contrast to models of human memory in psychology that suggest contextual or automatic influences of the environment are not only critical for learning, but also critical for adaptive processing and the development of expertise (Yonelinas, 2002; Larsen & Roediger, 2012). In this thesis I investigate and critique several assumptions of the default-interventionist model by testing the relationship between processing time, reflective thought, experience and accuracy. The results of two large studies do not support basic assumptions presented in the literature and instead demonstrate that experience and knowledge are better predictors of performance.
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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 | 0.029 | 0.244 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".