The Effects of Individual Differences in Cognitive Styles on decision-Making Accuracy and Latency
Bibliographic record
Abstract
How might individuals' typical decision-making styles affect the quality and latency of their decisions? In a first study, 48 adults completed three measures of cognitive styles, including the Personal Need for Structure and Personal Fear of Invalidity scales (PNS and PFI; Thompson, Naccarato, Parker, & Moskowitz, 2001), and the Need for Cognition scale (NFC; Cacioppo & Petty, 1982). Participants then completed three trials of a medium-fidelity simulation of a naval surveillance and threat assessment task called TITAN (i.e., "Team and Individual Threat Assessment Network") that required participants to evaluate seven pieces of information for potential targets displayed in a radar space (e.g., direction, speed, bearing, etc.). After reviewing the information for each target, participants submitted their threat assessment and were provided feedback about the degree of actual threat for the target. For each session, participants were instructed to clear the radar space of as many targets as possible within a 25-minute period and to perform this operation as accurately as possible. Results showed a significant decrease in processing time across trials. Higher NFC scores predicted a significantly smaller mean decision error across trials, and higher PNS scores predicted a greater mean decision error, although the latter effect failed to reach statistical significance. None of the cognitive styles scores had a significant main effect on the mean time spent processing TITAN targets. In Study 2, 80 Canadian Forces personnel completed the three cognitive styles measures and worked in four-person teams on TANDEM 11, a simulation similar to TITAN. Each team consisted of three subordinates who separately reviewed and integrated five pieces of complex information per target before forwarding their individual threat assessments to a team leader. The team leader then assessed the veridicality of the three assessments and integrated them into a final threat assessment for each7
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".