Developing a neurally informed ontology of creativity measurement

Area of Science:

• Cognitive Neuroscience
• Psychology
• Neuroimaging

Background:

• Creativity research faces challenges in mapping constructs to measures and ensuring measurement consistency.
• Inconsistent measures hinder shared understanding of creativity's cognitive and neural components.
• Neuroimaging data aggregation and multivariate data analysis offer new approaches.

Purpose of the Study:

• To demonstrate a proof-of-concept for using neuroimaging meta-analysis to identify structure in creativity-relevant constructs.
• To develop a model of similarity between creativity constructs and associated neural activity.
• To identify optimal task sets for measuring creativity and advance ontological development in creativity research.

Main Methods:

• Surveyed creativity researchers to build a model of construct similarity.
• Utilized NeuroSynth software for meta-analysis of neuroimaging data from creativity tasks.
• Applied representational similarity analysis to assess model fit and identify impactful constructs/tasks.

Main Results:

• Identified specific creativity constructs and tasks that influenced the similarity model fit.
• Demonstrated the feasibility of linking neural activity patterns to construct similarity.
• Provided a method to evaluate the alignment between creativity tasks and theoretical constructs.

Conclusions:

• The proposed approach can identify optimal task sets for capturing creativity dimensions.
• This method has potential to advance the ontological development of creativity neuroscience.
• Leveraging neuroimaging meta-analysis offers immediate benefits for established fields with extensive data.