scholarly journals Individual differences in the production of disfluency: A latent variable analysis of memory ability and verbal intelligence

2018 ◽  
Vol 72 (5) ◽  
pp. 1084-1101 ◽  
Author(s):  
Paul E Engelhardt ◽  
Mhairi EG McMullon ◽  
Martin Corley
Keyword(s):  
Individual Differences ◽  
Latent Variable ◽  
Language Production ◽  
Structural Equation ◽  
Task Demands ◽  
Verbal Intelligence ◽  
Complex Sentences ◽  
Latent Variable Analysis ◽  
Different Types ◽  
Underlying Causes

Recent work has begun to focus on the role that individual differences in executive function and intelligence have on the production of fluent speech. However, isolating the underlying causes of different types of disfluency has been difficult given the speed and complexity of language production. In this study, we focused on the role of memory abilities and verbal intelligence, and we chose a task that relied heavily on memory for successful performance. Given the task demands, we hypothesised that a substantial proportion of disfluencies would be due to memory retrieval problems. We contrasted memory abilities with individual differences in verbal intelligence as previous work highlighted verbal intelligence as an important factor in disfluency production. A total of 78 participants memorised and repeated 40 syntactically complex sentences, which were recorded and coded for disfluencies. Model comparisons were carried out using hierarchical structural equation modelling. Results showed that repetitions were significantly related to verbal intelligence. Unfilled pauses and repairs, in contrast, were marginally ( p < .09) related to memory abilities. The relationship in all cases was negative. Conclusions explore the link between different types of disfluency and particular problems arising in the course of production, and how individual differences inform theoretical debates in language production.

Neuroimaging of Individual Differences: A Latent Variable Modeling Perspective

2018 ◽  
Author(s):  
Shelly Renee Cooper ◽  
Joshua James Jackson ◽  
Deanna Barch ◽  
Todd Samuel Braver
Keyword(s):  
Individual Differences ◽  
Individual Difference ◽  
Latent Variable ◽  
Structural Equation ◽  
Behavioral Measure ◽  
Equation Modeling ◽  
Latent Variable Modeling ◽  
Psychometric Theory ◽  
Human Connectome Project ◽  
Neuroimaging Data

Neuroimaging data is being increasingly utilized to address questions of individual difference. When examined with task-related fMRI (t-fMRI), individual differences are typically investigated via correlations between the BOLD activation signal at every voxel and a particular behavioral measure. This can be problematic because: 1) correlational designs require evaluation of t-fMRI psychometric properties, yet these are not well understood; and 2) bivariate correlations are severely limited in modeling the complexities of brain-behavior relationships. Analytic tools from psychometric theory such as latent variable modeling (e.g., structural equation modeling) can help simultaneously address both concerns. This review explores the advantages gained from integrating psychometric theory and methods with cognitive neuroscience for the assessment and interpretation of individual differences. The first section provides background on classic and modern psychometric theories and analytics. The second section details current approaches to t-fMRI individual difference analyses and their psychometric limitations. The last section uses data from the Human Connectome Project to provide illustrative examples of how t-fMRI individual differences research can benefit by utilizing latent variable models.

Quantifying the Effects of Multi-Tasking on Processing Efficiency

2018 ◽  
Vol 62 (1) ◽  
pp. 1717-1721
Author(s):  
Elizabeth L. Fox ◽  
Joseph W. Houpt
Keyword(s):  
Individual Differences ◽  
System Design ◽  
Task Demands ◽  
Task Characteristics ◽  
Baseline Model ◽  
Processing Efficiency ◽  
Different Types ◽  
Unique Individual ◽  
And Task ◽  
Task Environments

The type and amount of task demands that humans must simultaneously process and respond to influences how efficient they are in completing the tasks. Capturing how and to what degree human efficiency changes in different task environments is crucial to inform an appropriate system design. An individual-based analytic approach is necessary to accurately capture performance changes and lend practical suggestions. We can provide designers with the amount and type of task demands that we expect a person to sustain adequate performance given their unique underlying cognitive properties. We develop a metric, multi-tasking throughput (MT), that provides the extent to which a person processes tasks more efficiently, the same, or less efficiently when required to complete several different types of tasks at once. This is a cognitive-based, standardized metric; meaning it yields the relative degree of change from a baseline model that is created to accommodate to unique individual differences, numbers of tasks, and task characteristics. We quantify MT by using transformations of RTs to predict the extent that external demands of multi-tasking exceeds what the cognitive system can accommodate to thereby hindering performance. We use a real world dual-task application to highlight the apparent differences in strategy and ability across individuals and alternative task environments.

scholarly journals Competing complexity metrics and adults' production of complex sentences

1992 ◽  
Vol 13 (1) ◽  
pp. 53-76 ◽  
Author(s):  
Hintat Cheung ◽  
Susan Kemper
Keyword(s):  
Individual Differences ◽  
Structural Equation ◽  
Equation Model ◽  
Equation Modeling ◽  
Group Differences ◽  
Age Group ◽  
Complex Sentences ◽  
Complexity Metrics ◽  
Modeling Techniques

ABSTRACTThe adequacy of 11 metrics for measuring linguistic complexity was evaluated by applying each metric to language samples obtained from 30 different adult speakers, aged 60–90 years. The analysis then determined how well each metric indexed age-group differences in complexity. In addition, individual differences in the complexity of adults' language were examined as a function of these complexity metrics using structural equation modeling techniques. In a follow-up study, judges listened to sentences in noise, rated their comprehensibility, and attempted to recall each sentence verbatim. Hierarchical multiple regression was used to evaluate the structural equation model, derived from the language samples, with respect to sentence comprehensibility and recall. While most of the metrics provided an adequate account of age-group and individual differences in complexity, the amount of embedding and the type of embedding proved to predict how easily sentences are understood and how accurately they are recalled.

scholarly journals Acute alcohol effects on set-shifting and its moderation by baseline individual differences: a latent variable analysis

Addiction ◽  
2016 ◽  
Vol 112 (3) ◽  
pp. 442-453 ◽  
Author(s):  
Ozlem Korucuoglu ◽  
Kenneth J. Sher ◽  
Phillip K. Wood ◽  
John Scott Saults ◽  
Lee Altamirano ◽  
...  
Keyword(s):  
Individual Differences ◽  
Latent Variable ◽  
Set Shifting ◽  
Latent Variable Analysis ◽  
Alcohol Effects ◽  
Variable Analysis

scholarly journals How do the relationships among health phenomena explain the nursing students’ quality of life?

2021 ◽  
Vol 12 (5) ◽  
pp. 26
Author(s):  
Rodrigo Marques da Silva ◽  
Ana Lúcia Siqueira Costa ◽  
Margareth Heitkemper ◽  
Fernanda Carneiro Mussi ◽  
Karla Melo Batista ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Depressive Symptoms ◽  
Nursing Students ◽  
Sleep Quality ◽  
Latent Variable ◽  
Structural Equation ◽  
Protective Role ◽  
Latent Variable Analysis ◽  
One Year

Background and objective: To know the direct relationships between stress, sleep quality, depressive symptoms, resiliency, and quality of life of nursing students. Less is known about how the simultaneous relationships between these variables may explain the nursing students’ quality of life remains unclear. We assessed how the simultaneous causal relationships among stress, depressive symptoms, sleep quality, and resilience explain the nursing students’ quality of life one year after starting a nursing degree program.Methods: This was a one-year longitudinal study. Data were gathered with validated tools from first university-year nursing students enrolled in two public Brazilian universities at the beginning (n = 117) and end (n = 100) of March 2016. The latent variable analysis- a complement of the R statistical package- was used to estimate the Structural Equation Modelling.Results: The final model showed good fitness and residues quality. Stress decreased sleep quality and increased the intensity of the depressive symptoms. Both of these, directly and indirectly, reduced the quality of life. Resiliency decreased stress levels and depressive symptoms and improved sleep quality.Conclusions: The academic environment has the potential for illnesses, impacting the quality of life. On other hand, resiliency plays a protective role on nursing students by reducing stress and its negative effects. Education institutions need to rethink their curricular elements, promote resilience and create actions to promote students’ health.

scholarly journals Neither measurement error nor speed-accuracy trade-offs explain the difficulty of establishing attentional control as a psychometric construct: Evidence from a latent-variable analysis using diffusion modeling

2021 ◽  
Author(s):  
Alodie Rey-Mermet ◽  
Henrik Singmann ◽  
Klaus Oberauer
Keyword(s):  
Measurement Error ◽  
Attentional Control ◽  
Latent Variable ◽  
Structural Equation ◽  
Equation Modeling ◽  
Large Set ◽  
Diffusion Modeling ◽  
Latent Variable Analysis ◽  
Trade Offs ◽  
Speed Accuracy

Attentional control refers to the ability to maintain and implement a goal and goal-relevant information when facing distraction. So far, previous research has failed to substantiate strong evidence for a psychometric construct of attentional control. This has been argued to result from two methodological shortcomings: (a) the neglect of individual differences in speed-accuracy trade-offs when only speed or accuracy is used as dependent variable, and (b) the difficulty of isolating attentional control from measurement error. To overcome both issues, we combined hierarchical-Bayesian Wiener diffusion modeling with structural equation modeling. We re-analyzed the dataset from Rey-Mermet, Gade, and Oberauer (2018), which includes data from a large set of attentional-control tasks from young and older adults. Even when accounting for speed-accuracy trade-offs and removing measurement error, measures of attentional control failed to correlate with each other and to load on a latent variable. These results emphasize the necessity of rethinking attentional control.

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