scholarly journals The Scientific Study of Passive Thinking: The Methodology of Mind Wandering Research

2020 ◽  
Author(s):  
Samuel Murray ◽  
Zachary Irving ◽  
Kristina Krasich
Keyword(s):  
Machine Learning ◽  
Empirical Study ◽  
Eye Tracking ◽  
Mind Wandering ◽  
Self Report ◽  
Machine Learning Techniques ◽  
Objective Measures ◽  
Learning Techniques ◽  
Self Reports ◽  
Direct Sensitivity

In this chapter, we survey methodological challenges in the empirical study of mind wandering and provide a metaphysical framework that begins to address these challenges. We argue that mind wandering is a passive manifestation of agency—passive because people cannot mind wander on command and a manifestation of agency because the onset, progression, and content of mind wandering often exhibits direct sensitivity to personal concerns and plans. To measure passive thinking, researchers must ask, “Is your mind wandering?” Worries about this self-report methodology have encouraged researchers to develop “objective” measures of mind wandering through eye tracking and machine learning techniques. These “objective” measures, however, are validated in terms of how well they predict self-reports, which means that purportedly objective measures of mind wandering retain a subjective core. To assuage worries about self-report (and, ultimately, vindicate objective measures of mind wandering), we offer a metaphysical account of mind wandering that generates several predictions about its causes and consequences. This account also justifies different methods for measuring mind wandering.

scholarly journals Breathlessness in COPD: linking symptom clusters with brain activity

2021 ◽  
pp. 2004099
Author(s):  
Sarah L. Finnegan ◽  
Olivia K. Harrison ◽  
Catherine J. Harmer ◽  
Mari Herigstad ◽  
Najib M. Rahman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Functional Neuroimaging ◽  
Brain Activity ◽  
Symptom Burden ◽  
Functional Brain Imaging ◽  
Self Report ◽  
Machine Learning Techniques ◽  
Chronic Obstructive ◽  
Unsupervised Machine Learning ◽  
Learning Techniques

RationaleCurrent models of breathlessness often fail to explain disparities between patients' experiences of breathlessness and objective measures of lung function. While a mechanistic understanding of this discordance has thus far remained elusive, factors such as mood, attention and expectation have all been implicated as important modulators of breathlessness. Therefore, we have developed a model to better understand the relationships between these factors using unsupervised machine learning techniques. Subsequently we examined how expectation-related brain activity differed between these symptom-defined clusters of participants.MethodsA cohort of 91 participants with mild-to-moderate chronic obstructive pulmonary disease (COPD) underwent functional brain imaging, self-report questionnaires and clinical measures of respiratory function. Unsupervised machine learning techniques of exploratory factor analysis and hierarchical cluster modelling were used to model brain-behaviour-breathlessness links.ResultsWe successfully stratified participants across four key factors corresponding to mood, symptom burden and two capability measures. Two key groups resulted from this stratification, corresponding to high and low symptom burden. Compared to the high symptom load group, the low symptom burden group demonstrated significantly greater brain activity within the anterior insula, a key region thought to be involved in monitoring internal bodily sensations (interoception).ConclusionsThis is the largest functional neuroimaging study of COPD to date and is the first to provide a clear model linking brain, behaviour and breathlessness expectation. Furthermore, it was possible to stratify participants into groups, which then revealed differences in brain activity patterns. Together, these findings highlight the value of multi-modal models of breathlessness in identifying behavioural phenotypes, and for advancing understanding of differences in breathlessness burden.

Compound or phrase or in between? Testing linguistic criteria for compoundhood in English

2020 ◽  
Vol 13 (2) ◽  
pp. 250-281
Author(s):  
Patrick Ziering ◽  
Lonneke van der Plas
Keyword(s):  
Machine Learning ◽  
Empirical Study ◽  
European Parliament ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Nominal Compounds ◽  
Definition Of

In this paper, we present an empirical study on the definition of compounds in English, the graded nature of the phenomenon and its correlations with the commonly used linguistic criteria for compoundhood. We create a resource that includes a diverse set of nominal compounds identified by two trained independent annotators in sentences from the proceedings of the European Parliament. In addition, the annotators provide ratings on the compoundhood of the identified compounds, and ratings for the applicability of six prominent linguistic criteria of compoundhood for each item. We show the controversy of defining compounds in practice by comparing the annotations of two annotators, and the graded nature of compoundhood. By measuring the correlation between compoundhood and the six diverse linguistic criteria using machine learning techniques, we show that some linguistic criteria are stronger predictors of compoundhood than others.

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