1.1. Different Theories of Creative Cognition
1.2. The Be-Creative Effect: Support for the Controlled-Attention Theory?
1.3. The Present Study
2. Methods and Materials
2.2. Power Analysis
2.4.1. Divergent Thinking Tasks
2.4.2. Digit-Tracking Task
2.4.3. Ideational Behavior
2.6. Scoring Divergent Thinking Tasks
2.7. Analytical Approach
2.7.1. Testing of Hypotheses
2.7.2. Validation of Divergent Thinking Task Procedure
2.7.3. Exploratory Analyses
- Serial order effect: In addition to the linear mixed-effects model that included only effects of time as predictors, we explored the changes in model fit that occurred when we considered the factors workload and instruction. To track differences between conditions over time, various combinations of additive and interactive terms of both predictors were added as fixed effects into further models. As in the above analysis, intercepts for participants were the random effects.
- Correlations: Finally, we explored correlations between fluency, creative quality, dose (i.e., the number of digits presented during the AUTs in the load condition), accuracy (i.e., the percentage of correct reactions on an event within the digit-tracking task), and RIBS scores. The correlations were calculated separately for each experimental condition.
3.1. Testing of Hypotheses
3.2. Validation of Divergent Thinking Task Procedure
3.3. Exploratory Analyses
3.3.1. Serial Order Effect—Creative Quality
3.3.2. Serial Order Effect—Flexibility
4.1. Further Interesting Findings
4.2. Strengths and Limitations
4.3. Future Directions
Institutional Review Board Statement
Conflicts of Interest
- 3—7—3 → Press Space-key
- 5—9—4 → Don´t do anything
- Name one use for each object that is presented to you.
- Track the digits and press the SPACE-key if you observed 3 digits in a row.
- 1st of each 8 participants: 1 → 2 → 3 → 4
- 2nd of each 8 participants: 3 → 4 → 1 → 2
- 3rd of each 8 participants: 1 → 2 → 4 → 3
- 4th of each 8 participants: 3 → 4 → 2 → 1
- 5th of each 8 participants: 2 → 1 → 3 → 4
- 6th of each 8 participants: 4 → 3 → 1 → 2
- 7th of each 8 participants: 2 → 1 → 4 → 3
- 8th of each 8 participants: 4 → 3 → 2 → 1
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For the practice trials including an AUT, the objects knife and ball as well as stone and cup served as stimuli. These differed from the experimental task stimuli and were not controlled in terms of their lemma frequency.
Furthermore, the be-fluent instruction was used for these AUTs.
In practice trials including digit tracking, 12 digits were presented whereby participants had to react on two events. A mistake could be made by either pressing the space bar in the absence of an event or by missing an event.
The ICC was calculated on the basis of the final dataset. Therefore, excluded ideas and ideas from excluded participants were not considered for the calculation. ICC scores, however, did only differ marginally from the unadjusted dataset.
Cohen’s d was calculated using the average standard deviation of both repeated measures (see for example Lakens 2013).
Workload affected fluency, but not in the intended manner (see Table 1).
The impact of workload on creative quality seemed to be greater in the be-fluent instruction condition than in the be-creative instruction condition (see Table 1). This finding contradicted our prediction.
This methodological inadequacy was also visible in the enormously high correlation between dose and fluency in the load conditions.
The SAT (=Scholastic Assessment Test) is a college admission test that is used in the United States of America.
|Be-fluent||No Load||49.314 (15.253)||1.949 (0.490)||0.303 (0.073)|
|Load||49.608 (15.416)||1.915 (0.469)||0.312 (0.081)|
|Be-creative||No Load||28.726 (10.962)||2.931 (0.609)||0.485 (0.133)|
|Load||30.314 (10.855)||2.927 (0.561)||0.457 (0.105)|
|Covariate||Model 1||Model 2||Model 3||Model 4||Model 5||Model 6||Model 7||Model 8||Model 9|
|β (SE)||β (SE)||β (SE)||β (SE)||β (SE)||β (SE)||β (SE)||β (SE)||β (SE)|
|Intercept||1.448 (0.049) ***||1.445|
|Time × load||−0.020|
|Time2 × load||0.003|
|Time × instruction||0.029|
|Time2 × instruction||−0.001|
|Instruction × load||0.040|
|Time × load × instruction||0.006|
|Time2 × load × instruction||−0.001|
|(df)||402.169 (2) ***,a||0.182 (1) b||7.417 (2) *,c||2309.459 (1) ***,d||12.066 (2) **,e||1.029 (1) f||8.998 (2) *,g||11.981 (2) **,h||1.649 (3) i|
|Covariate||Model 1||Model 2||Model 3||Model 4||Model 5||Model 6||Model 7||Model 8||Model 9|
|β (SE)||β (SE)||β (SE)||β (SE)||β (SE)||β (SE)||β (SE)||β (SE)||β (SE)|
|Time × load||0.041|
|Time2 × load||0.029|
|Time × instruction||0.238|
|Time2 × instruction||0.076|
|Instruction × load||−0.132|
|Time × load × instruction||−0.018|
|Time2 × load × instruction||0.008|
|(df)||843.31 (2) ***,a||0.37 (1) b||1.15 (2) c||240.75 (1) ***,d||25.92 (2) ***,e||0.21 (1) f||1.09 (2) g||25.85 (2) ***,h||1.43 (3) i|
|Measure||BF (No Load)||BF (Load)||BC (No Load)||BC (Load)|
|1. Dose BF||51.588||15.946||0.515 ***||0.173||0.274||−0.008|
|2. Dose BC||31.882||10.360||−0.008||0.357 *||0.099|
|3. Accuracy BF||0.781||0.153||0.222||0.149|
|4. Accuracy BC||0.752||0.257||0.082|
|5. RIBS a||3.458||0.629|
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