Serial Presentation of Computer Graphics: Training Effects and Strategy Development for Analytic and Holistic Cognitive Styles
Bibliographic record
Abstract
Job tasks, such as quality control, require workers to perform visual discriminations, comparing a test stimulus to a standard. In some cases, both stimuli are present and can be compared in parallel. In most cases, however, the standard stimulus is absent and workers must rely on their memory of the standard stimulus in order to make an accurate discrimination. Pratt and Sohn (2001) found that when visual discriminations are made in parallel, training content and individual differences affect strategy development and transfer performance. The present research attempts to compliment and extend Pratt and Sohn's (2001) findings by examining the role of display design and its affects on training effectiveness and strategy development. Participants were given a visual discrimination task identical to Pratt and Sohn's methodology, except stimulus sets were presented serially rather than in parallel. After training with either highly similar or highly dissimilar stimuli, participants transferred to novel stimuli of medium similarity level. Preliminary findings indicate that manipulating display design does not impact training results. Just as Pratt and Sohn discovered, training content does not influence transfer performance for individuals with an analytic cognitive style, but hard training is necessary for individuals with a holistic cognitive style to perform as well as the analytic group. Graphical design and individual differences in cognitive style are discussed.
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 | 0.001 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".