Relational reasoning is a complex form of human cognition involving the evaluation of relations between mental representations of information. Prior studies have modified stimulus properties of relational reasoning problems and examined differences in difficulty between different problem types. While subsets of these stimulus properties have been addressed in separate studies, there has not been a comprehensive study, to our knowledge, which investigates all of these properties in the same set of stimuli. This investigative gap has resulted in different findings across studies which vary in task design, making it challenging to determine what stimulus properties make relational reasoning-and the putative formation of mental models underlying reasoning-difficult. In this article, we present the Multidimensional Relational Reasoning Task (MRRT), a task which systematically varied an array of stimulus properties within a single set of relational reasoning problems. Using a mixed-effects framework, we demonstrate that reasoning problems containing a greater number of the premises as well as multidimensional relations led to greater task difficulty. The MRRT has been made publicly available for use in future research, along with normative data regarding the relative difficulty of each problem.

A central challenge for creativity research-as for all areas of experimental psychology and cognitive neuroscience-is to establish a mapping between constructs and measures (i.e., identifying a set of tasks that best captures a set of creative abilities). A related challenge is to achieve greater consistency in the measures used by different researchers; inconsistent measurement hinders progress toward shared understanding of cognitive and neural components of creativity. New resources for aggregating neuroimaging data, and the emergence of methods for identifying structure in multivariate data, present the potential for new approaches to address these challenges. Identifying meta-analytic structure (i.e., similarity) in neural activity associated with creativity tasks might help identify subsets of these tasks that best reflect the similarity structure of creativity-relevant constructs. Here, we demonstrated initial proof-of-concept for such an approach. To build a model of similarity between creativity-relevant constructs, we first surveyed creativity researchers. Next, we used NeuroSynth meta-analytic software to generate maps of neural activity robustly associated with tasks intended to measure the same set of creativity-relevant constructs. A representational similarity analysis-based approach identified particular constructs-and particular tasks intended to measure those constructs-that positively or negatively impacted the model fit. This approach points the way to identifying optimal sets of tasks to capture elements of creativity (i.e., dimensions of similarity space among creativity constructs), and has long-term potential to meaningfully advance the ontological development of creativity research with the rapid growth of creativity neuroscience. Because it relies on neuroimaging meta-analysis, this approach has more immediate potential to inform longer-established fields for which more extensive sets of neuroimaging data are already available.