Objectives: Deficits in semantic verbal fluency (SVF) can stem from dysfunction of an executive control system and/or of semantic knowledge. Previous analyses of SVF responses were devised to characterize these two components including switching and mean cluster size (MCS) indices, but these rely on subjective experimenter-based assessment of the words’ relatedness. To address this limitation, computational data-driven SVF indices have been developed. Our aim is to assess the validity and usefulness of these automated indices in the context of cognitive decline in Parkinson’s disease (PD). Methods: This is a retrospective study including 50 advanced PD patients with (n=28) or without (n=22) mild cognitive impairment (PD-MCI). We analyzed animal SVF outputs using an automated computational approach yielding switching, MCS, and cumulative relatedness (CuRel) indices. We compared these indices to the classic experimenter-based switching and MCS indices to assess concurrent validity, and against neuropsychological measures of executive functioning and semantic knowledge to assess construct validity. We also examined whether these indices were impaired and predicted PD-MCI. Results: Automated switching indices, but not MCS or CuRel, showed evidence of concurrent and construct validity, and characterized individual difference in advanced PD. Automated switching indices also outperformed the experimenter-dependent index in predicting the presence of PD-MCI. Conclusion: Computational methods hold promise as fine-grained, unbiased indices reflecting the executive component of SVF, but none of the methods provided valid measures of semantic knowledge in PD. Our data also confirm that SVF are not adequate tests of semantic memory in patients with executive dysfunction such as PD. (JINS, 2018, 24, 1047–1056)

There is currently a widespread interest in indexing and extracting taxonomic information from large text collections. An example is the automatic categorization of informally written medical or psychological diagnoses, followed by the extraction of epidemiological information or even terms and structures needed to formulate guiding questions as an heuristic tool for helping doctors. Vector space models have been successfully used to this end (Lee, Cimino, Zhu, Sable, Shanker, Ely & Yu, 2006; Pakhomov, Buntrock & Chute, 2006). In this study we use a computational model known as Latent Semantic Analysis (LSA) on a diagnostic corpus with the aim of retrieving definitions (in the form of lists of semantic neighbors) of common structures it contains (e.g. “storm phobia”, “dog phobia”) or less common structures that might be formed by logical combinations of categories and diagnostic symptoms (e.g. “gun personality” or “germ personality”). In the quest to bring definitions into line with the meaning of structures and make them in some way representative, various problems commonly arise while recovering content using vector space models. We propose some approaches which bypass these problems, such as Kintsch's (2001) predication algorithm and some corrections to the way lists of neighbors are obtained, which have already been tested on semantic spaces in a non-specific domain (Jorge-Botana, León, Olmos & Hassan-Montero, under review). The results support the idea that the predication algorithm may also be useful for extracting more precise meanings of certain structures from scientific corpora, and that the introduction of some corrections based on vector length may increases its efficiency on non-representative terms.

Decreased productivity on verbal fluency tasks by persons with schizophrenia has been attributed to semantic system abnormalities. Semantic structure is often assessed using multidimensional scaling (MDS) to detect normal and aberrant semantic clustering. However, MDS has limitations that may be particularly problematic for such assessments. Here, we introduce a different clustering technique, singular value decomposition (SVD), to elucidate abnormalities of the semantic system in schizophrenia. We compared 102 treated outpatients with schizophrenia to 109 healthy adults on two category-cued word fluency tasks. Patients with schizophrenia showed semantic clustering patterns that differ markedly from those of healthy adults. However, SVD revealed more detailed and critical semantic system abnormalities than previously appreciated using MDS. Patients with schizophrenia showed less coherent semantic clustering of both low- and high-frequency category exemplars than healthy adults. These results suggest the intriguing possibility that impaired automatic activation of semantic information is a key deficit in schizophrenia. (JINS, 2012, 18, 565–575)

Language plays at least two roles in design. First, language serves as representations of ideas and concepts through linguistic behaviors that represent the structure of thought during the design process. Second, language also performs actions and creates states of affairs. Based on these two perspectives on language use in design, we apply the computational linguistics tools of latent semantic analysis and lexical chain analysis to characterize how design teams engage in concept formation as the accumulation of knowledge represented by lexicalized concepts. The accumulation is described in a data structure comprised by a set of links between elemental lexicalized concepts. The folding together of these two perspectives on language use in design with the information processing theories of the mind afforded by the computational linguistics tools applied creates a new means to evaluate concept formation in design teams. The method suggests that analysis at a linguistic level can characterize concept formation even where process-oriented critiques were limited in their ability to uncover a formal design method that could explain the phenomenon.