Deriving semantic structure from category fluency: clustering techniques and their pitfalls.
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2014-05-29
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Abstract
Assessing verbal output in category fluency tasks provides a sensitive indicator of cortical dysfunction The most common metrics are the overall number of words produced and the number of errors Two main observations have been made about the structure of the output first that there is a temporal component to it with words being generated in spurts and second that the clustering pattern may reflect a search for meanings such that the clustering is attributable to the activation of a specific semantic field in memory A number of sophisticated approaches to examining the structure of this clustering have been developed and a core theme is that the similarity relations between category members will reveal the mental semantic structure of the category underlying an individual s responses which can then be visualized by a number of algorithms such as MDS hierarchical clustering ADDTREE ADCLUS or SVD Such approaches have been applied to a variety of neurological and psychiatric populations and the general conclusion has been that the clinical condition systematically distorts the semantic structure in the patients as compared to the healthy controls In the present paper we explore this approach to understanding semantic structure using category fluency data On the basis of a large pool of patients with schizophrenia n 204 and healthy control participants n 204 we find that the methods are problematic and unreliable to the extent that it is not possible to conclude that any putative difference reflects a systematic difference between the semantic representations in patients and controls Moreover taking into account the unreliability of the methods we find that the most probable conclusion to be made is that no difference in underlying semantic representation exists The consequences of these findings to understanding semantic structure and the use of category fluency data in cortical dysfunction are discussed