Which Cognitive Functions Subserve Clustering And Switching In Category Fluency? Generalisations From An Extended Set Of Semantic Categories Using Linear Mixed-Effects Modelling

Clustering and switching are hypothesised to reflect the automatic and controlled components in category fluency, respectively, but how they are associated with cognitive functions has not been fully elucidated, due to several uncertainties. (1) The conventional scoring method that segregates responses by semantic categories could not optimally dissociate the automatic and controlled components. (2) The temporal structure of individual responses, as characterised by mean retrieval time (MRT) and mean switching time (MST), has seldom been analysed alongside the more well-studied variables, cluster size (CS) and number of switches (NS). (3) Most studies examined only one to a few semantic categories, raising concerns of generalisability. This study built upon a distance-based automatic clustering procedure, referred to as temporal-semantic distance procedure, to thoroughly characterise the category fluency performance. Linear mixed-effects (LME) modelling was applied to re-examine the differential associations of clustering and switching with cognitive functions with a sample of 80 university students. Our results revealed that although lexical retrieval speed (LRS) is clearly the determining factor for effective clustering and switching, matrix reasoning and processing speed also have significant roles to play, possibly in the processes of identifying and validating the semantic relationships. Interestingly, total fluency score was accurately predicted by the four clustering/switching indices alone; including the cognitive variables did not significantly improve the prediction. These findings underline the importance of the clustering and switching indices in explaining the category fluency performance and the cognitive demands in category fluency.