Sequential Word Properties in Verbal Fluency: Detecting High-Proficiency Cognitive Impairment
Chang, Y.-N.; Wang, Y.-H.; Chou, C.-J.; Liu, Y.-C.; Lambon Ralph, M. A.
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Verbal fluency (VF) tasks are widely used to differentiate patients with cognitive impairment from healthy controls, but total word count produced during these tasks becomes unreliable when patients and controls exhibit comparable proficiency. This study examined, in detail, whether item-level and sequential properties of words produced during a VF task could reliably differentiate high-proficiency patients indistinguishable from controls by word count alone. Seventy-seven native Mandarin Chinese speakers (38 controls and 39 patients with mild cognitive impairment or mild dementia) completed a semantic VF task. Participants were subdivided by proficiency into four groups: high-proficiency controls (HC), low-proficiency controls (LC), high-proficiency patients (HP), and low-proficiency patients (LP). The LC and HP subgroups were matched on semantic fluency scores and thus provided a key focus for the investigation. We examined item-level properties (word frequency, contextual diversity, semantic diversity, surprisal) and sequential properties (positional frequency variation) of the words produced. Significant group differences emerged across item-level psycholinguistic properties, though these were primarily driven by the LP group, with no reliable differentiation between LC and HP. Crucially, positional frequency variation distinguished LC from HP. LC participants began their lists with high-frequency words followed by a systematic decline, whereas HP patients produced words within a consistently narrow frequency band throughout. These findings indicate that item-level psycholinguistic properties alone are insufficient to differentiate HP from LC, whereas sequential word frequency variation provides a potential index of cognitive impairment, reflecting underlying differences in semantic retrieval and memory organisation. Future work with larger samples is needed to validate generalisability.
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