Decision making
What makes some people better at thinking up options for decisions?
Open access · cc by · source: Europe PMC
People who were good at brainstorming unusual uses for everyday objects also generated more options for real-world problems, and this held even after accounting for their memory, executive control and intelligence.
Study at a glance
- Design
- Cross-sectional — Two-session individual-differences study: three timed ill-structured option generation problems plus a battery of executive-function, memory, verbal fluency, intelligence, cognitive reflection, alternative uses and decision-rule tasks, analysed with hierarchical regression and path analysis
- N
- N=141 · 141 undergraduate students completed both sessions; all analyses are on this single sample.
- Population
- Italian undergraduate students at the University of Trieste, mostly young and female
- Outcome
- Option generation fluency and diversity, mean and maximum rated quality of generated options, and rated quality of the chosen option, for each of three decision problems
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Key findings
Ideation fluency on the alternative uses test was the strongest predictor of how many and how varied the options were in all three problems, adding predictive power after memory, control, intelligence, verbal fluency and domain knowledge were entered; the full models explained roughly 20 to 30 percent of the variance. By contrast, the structured decision-rule task was predicted by memory, control and intelligence but not by ideation, a double dissociation in two of the three problems. Generating more options tended to lower the average quality of the other options (less is more) but to raise the quality of the best option (quantity breeds quality), and both average and best quality related to better final choices, though these indirect effects were small and varied by problem.
Methodology
Undergraduates had 6 minutes each to list as many ways as they could to solve three open-ended problems: city parking, fund raising for a children's charity, and saving energy at home, then picked their best option. Two trained raters counted the options, sorted them into categories and rated each option's usefulness. In a second session the same students completed tests of working memory updating, inhibition (Stroop), task switching, cued recall, category and letter fluency, fluid intelligence (Raven's matrices), cognitive reflection, a structured decision-rule task, and the alternative uses test of ideation fluency.
Limitations
Because this is a correlational individual-differences design, it cannot show that ideation ability causes better option generation, and the authors call for experimental follow-ups. The sample was young, educated undergraduates, which restricts the range of ability and limits generalisation. Several predictor tasks had low reliability (for example letter-memory and the switch cost), which can hide real relationships with memory and control. The less-is-more result depended on statistically controlling for the best option's quality, and the authors note that without this control the fluency–mean-quality correlations were not significant; results also varied across the three problems.
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