Thoughts About Disordered Thinking: Measuring and Quantifying the Laws of Order and Disorder

Elvevåg, Brita; Foltz, Peter W.; Rosenstein, Mark; Ferrer-i-Cancho, Ramon; Deyne, Simon De; Mizraji, Eduardo; Cohen, Alex S.

doi:10.1093/schbul/sbx040

Cited by 30 publications

(20 citation statements)

References 49 publications

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“…Personality, demographics, professional training, and interest in the subject matter are examples of factors that seem to affect the degree to which respondents comply with what is semantically given (Arnulf, Larsen, Martinsen, et al, ). LSA has already been used to identify schizophrenic from nonschizophrenic subjects in psychiatric diagnoses based on the patients' level of semantic predictability (Elvevag et al, ). This is information that we so far have not tapped systematically, and that can possibly be used to illuminate different sample characteristics.…”

Section: Discussionmentioning

confidence: 99%

Measuring semantic components in training and motivation: A methodological introduction to the semantic theory of survey response

Arnulf

Dysvik

Larsen

2018

Human Resource Dev Quarterly

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This is a methodological presentation of the relationship between semantics and survey statistics in human resource development (HRD) research. This study starts with an introduction to the semantic theory of survey response (STSR) and proceeds by offering a guided approach to conducting such analyses. The reader is presented with two types of semantic algorithms and a brief overview of how they are calculated and how they can be accessed by interested researchers. Subsequently, we use semantic data to reanalyze a previously published study on the relationships between perceptions of a trainee program, intrinsic motivation, and work outcomes. The semantic algorithms can explain between 31 and 55% of the variation in the observed correlations. This article shows how the statistical models originally used to explore the survey data can be replicated using semantics either alone or as an identifiable source of variation in the data. All the steps are presented in detail, and the datasets as well as the statistical syntax necessary to perform the analyses are made available to the readers. Implications for methodology and the improvement of predictive validity in HRD research are discussed.

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Section: Discussionmentioning

confidence: 99%

Measuring semantic components in training and motivation: A methodological introduction to the semantic theory of survey response

Arnulf

Dysvik

Larsen

2018

Human Resource Dev Quarterly

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“…A cursory review of the literature reveals that “validity” has been established, in that modest convergence is documented between various computationally‐derived semantic speech features and “gold‐standard” clinical symptom ratings. This approach to validation seems inappropriate when one considers the mismatch in resolution between these measures – with the former being derived from systematic analysis of brief language samples procured during a fairly‐contrived clinical interaction or cognitive task, and the latter representing an ordinal rating assigned by a clinician based on an extended clinical interview. These ratings reflect very different temporal and spatial characteristics, and hence, failures to find large convergence is unsurprising.…”

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confidence: 99%

“…These ratings reflect very different temporal and spatial characteristics, and hence, failures to find large convergence is unsurprising. While machine learning‐based algorithms connecting digital phenotyping technologies and clinical ratings have shown impressive accuracy, they have generally also ignored the overt resolution mismatch between these variables and have not demonstrated generalizability to new samples, speaking tasks or clinical measures.…”

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confidence: 99%

Validating digital phenotyping technologies for clinical use: the critical importance of “resolution”

Cohen

Schwartz

et al. 2020

World Psychiatry

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“…Importantly, these packages minimize the barriers for researchers to apply SemNA to their own data, opening doors to applications of SemNA in new domains. One promising avenue for future research is to understand how the structure and processes of semantic memory are associated with psychological disorders (Elvevåg et al, 2017;Holmlund, Cheng, Foltz, Cohen, & Elvevåg, 2019;Kenett & Faust, 2020;Kenett et al, 2016b) and personality traits (Christensen et al, 2018b). To date, there have been only a handful of studies that have quantitatively examined the cognitive processes that underlie these phenomena in psychology.…”

Section: Discussionmentioning

confidence: 99%

Semantic Network Analysis (SemNA): A Tutorial on Preprocessing, Estimating, and Analyzing Semantic Networks

Christensen¹,

Kenett²

2019

Preprint

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To date, the application of semantic network methodologies to study cognitive processes in psychological phenomena has been limited in scope. One barrier to broader application is the lack of resources for researchers unfamiliar with the approach. Another barrier, for both the unfamiliar and knowledgeable researcher, is the tedious and laborious preprocessing of semantic data. In this article, we aim to minimize these barriers by offering detailed descriptions of one approach to a semantic network analysis pipeline (preprocessing, estimating, and analyzing networks), and an associated R tutorial that uses a suite of R packages to accommodate this pipeline. Two of these packages, SemNetDictionaries and SemNetCleaner, promote an efficient, reproducible, and transparent approach to preprocessing verbal fluency data. The third package, SemNeT, provides methods and measures for analyzing and statistically comparing semantic networks. Using real-world data, we present a start-to-finish pipeline from raw data to semantic network analysis results. This article aims to provide resources for researchers, both the unfamiliar and knowledgeable, that minimize some of the barriers for conducting semantic network analysis.

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Thoughts About Disordered Thinking: Measuring and Quantifying the Laws of Order and Disorder

Cited by 30 publications

References 49 publications

Measuring semantic components in training and motivation: A methodological introduction to the semantic theory of survey response

Measuring semantic components in training and motivation: A methodological introduction to the semantic theory of survey response

Validating digital phenotyping technologies for clinical use: the critical importance of “resolution”

Semantic Network Analysis (SemNA): A Tutorial on Preprocessing, Estimating, and Analyzing Semantic Networks

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