B - 72 Neuropsychological Problem-Solving Styles on the Tower of London Drexel
Bibliographic record
Abstract
Abstract Objective The Tower of LondonDX (2nd Edition TOLDX, Culbertson & Zillmer, 2005) provides a standardized measure of executive planning and problem solving. To enhance the measure’s clinical and empirical utility, four contrasting Executive Planning Styles (EPS), were identified based on two different, but moderately related (i.e., rho = −0.37), TOLDX variables, Time to First Move and Total Move Score. Data Selection The TOLDX normative data (Technical Manual) for participants ranging in age from 7 to 80 years (n = 990) were utilized for study. The participants were drawn from sites in the United States and Canada. An individual’s assignment to an EPS was based on initiation time and performance scores either one standard deviation above or below the mean for that individual’s normative group. Using these criteria, four contrasting EPS were identified. Data Synthesis The four Executive Efficiency constructs are labeled as: Q1 Efficient-Fast, exhibiting rapid, advanced planning skills; Q2 Efficient-Slow, suggesting careful, deliberate, and strategic planning; Q3 Inefficient-Fast, indicating pervasive impulsivity, nonstrategic planning, and an increased likelihood of poor rule-governed behavior; and Q4 Inefficient-Slow, suggesting limitations in executive planning, trial-and-error problem-solving, and possible cognitive limitations. Conclusions The purpose of this analysis was to further differentiate the executive planning process of an established measure of executive function by combing two constructs (initiation time and performance) into four Executive Planning Styles. Executive planning is a dimension of complex human behavior, and the four-quadrant conceptualization of Executive Efficiency expands our understanding of this very important neuropsychological construct.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".