Mirroring Neurostimulation Outcomes Through Behavioral Interventions to Improve Creative Performance
Bibliographic record
Abstract
Abstract Creativity, a key component of engineering design, is not a static trait, but a skill that can be strategically enhanced. Neurostimulation methods, e.g., using electrical current to stimulate brain areas, have been reliably shown to improve creative performance. However, safety and ethical concerns present obstacles to the direct implementation of such methods in the engineering-design process. Thus, the current work explores whether creative performance can be enhanced using behavioral tasks that recruit the same brain regions targeted in neurostimulation studies. Study participants were 30 undergraduate students enrolled in an introductory psychology course. Two intervention tasks, a Stroop task and a finger-tapping pattern-matching task, each with easy and hard versions, were used in a 2 (task type) x 3 (task difficulty) within-subjects design. Relative to the pretest period, difficulty was manipulated by using versions of tasks with 1) predictable responses (easy) and 2) unpredictable responses (hard). Creativity in each experimental condition was assessed via the well-validated Alternative Uses Test (AUT). A multilevel analysis revealed a significant increase in fluency (number of alternative uses) as task difficulty increased regardless of task type. Flexibility (number of alternative-uses categories) also increased with task difficulty, but the effect was stronger for the Stroop task. These results suggest that high-difficulty versions of the selected tasks may be more effective in increasing AUT performance. Between the two tasks studied, the Stroop task has greater potential as a candidate to adapt as a behavioral intervention to improve creativity. Beyond the Stroop task, other behaviors, which activate brain regions that respond favorably to neurostimulation, may also be explored as the bases of interventions to improve creative performance in engineering design.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".