Intuition versus Analytical Thinking and Impairment Testing
Bibliographic record
Abstract
ABSTRACT We examine the use of intuition versus analytical thinking in auditor risk assessment using a task that requires auditors to assess a group of impairment indicators. We expect that auditor intuition, rooted in the subconscious, more likely reacts to impairment indicator risk than does auditor analytical thinking. Results from two different experiments support this expectation for less‐experienced audit seniors. These seniors are more likely to assess step‐zero impairment indicators as signaling potential impairment when prompted to thinkintuitivelyversusanalytically. In contrast, a third experiment finds that experienced seniors are more likely to assess step‐zero impairment indicators as signaling potential impairment when prompted to thinkanalyticallyversusintuitively. This is consistent with the more experienced but still non‐expert seniors possessing developed analytical thinking, but struggling to effectively use their intuition. Our results inform theory by suggesting under what conditions auditor intuition and analytical thinking produce differential risk sensitivity. Furthermore, our results inform practice, given regulators' stated focus on auditor skepticism and impairment assessments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".