“Revealing a latent variable: Individual differences in affective response to repeated injections": Correction to Aydin, Frohmader, and Akil (2015).

2015 ◽  
Vol 129 (5) ◽  
pp. 563-563
Keyword(s):  
Individual Differences ◽  
Latent Variable ◽  
Affective Response

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.

scholarly journals Personalised Prediction of Self-Reported Emotion Responses to Music Stimuli

2021 ◽  
Author(s):  
◽  
Kameron Christopher
Keyword(s):  
Deep Learning ◽  
Individual Differences ◽  
Latent Variable ◽  
Emotional Experience ◽  
Emotional Responses ◽  
Stimulus Selection ◽  
Emotion Induction ◽  
Musical Background ◽  
Musical Tempo ◽  
The Many

<p>In this thesis I develop a robust system and method for predicting individuals’ emotional responses to musical stimuli. Music has a powerful effect on human emotion, however the factors that create this emotional experience are poorly understood. Some of these factors are characteristics of the music itself, for example musical tempo, mode, harmony, and timbre are known to affect people's emotional responses. However, the same piece of music can produce different emotional responses in different people, so the ability to use music to induce emotion also depends on predicting the effect of individual differences. These individual differences might include factors such as people's moods, personalities, culture, and musical background amongst others. While many of the factors that contribute to emotional experience have been examined, it is understood that the research in this domain is far from both a) identifying and understanding the many factors that affect an individual’s emotional response to music, and b) using this understanding of factors to inform the selection of stimuli for emotion induction. This unfortunately results in wide variance in emotion induction results, inability to replicate emotional studies, and the inability to control for variables in research.  The approach of this thesis is to therefore model the latent variable contributions to an individual’s emotional experience of music through the application of deep learning and modern recommender system techniques. With each study in this work, I iteratively develop a more reliable and effective system for predicting personalised emotion responses to music, while simultaneously adopting and developing strong and standardised methodology for stimulus selection. The work sees the introduction and validation of a) electronic and loop-based music as reliable stimuli for inducing emotional responses, b) modern recommender systems and deep learning as methods of more reliably predicting individuals' emotion responses, and c) novel understandings of how musical features map to individuals' emotional responses.  The culmination of this research is the development of a personalised emotion prediction system that can better predict individuals emotional responses to music, and can select musical stimuli that are better catered to individual difference. This will allow researchers and practitioners to both more reliably and effectively a) select music stimuli for emotion induction, and b) induce and manipulate target emotional responses in individuals.</p>

Individual Differences in Resilience and Affective Response during Simulated UAV Operations

2015 ◽  
Vol 59 (1) ◽  
pp. 751-755 ◽  
Author(s):  
Ryan W. Wohleber ◽  
Gerald Matthews ◽  
Lauren E. Reinerman-Jones ◽  
April Rose Panganiban ◽  
David Scribner
Keyword(s):  
Individual Differences ◽  
Affective Response

A Quantitative Analysis and Qualitative Explanation of the Individual Differences in Affective Responses to Prescribed and Self-Selected Exercise Intensities

2007 ◽  
Vol 29 (3) ◽  
pp. 281-309 ◽  
Author(s):  
Elaine A. Rose ◽  
Gaynor Parfitt
Keyword(s):  
Individual Differences ◽  
Mixed Method ◽  
Lactate Threshold ◽  
Treadmill Exercise ◽  
Affective Response ◽  
Affective Responses ◽  
Affective Valence ◽  
Dual Mode ◽  
The Individual ◽  
Method Approach

Using a mixed-method approach, the aim of this study was to explore affective responses to exercise at intensities below-lactate threshold (LT), at-LT, and above-LT to test the proposals of the dual-mode model (Ekkekakis, 2003). These intensities were also contrasted with a self-selected intensity. Further, the factors that influenced the generation of those affective responses were explored. Nineteen women completed 20 min of treadmill exercise at each intensity. Affective valence and activation were measured, pre-, during and postexercise. Afterward, participants were asked why they had felt the way they had during each intensity. Results supported hypotheses showing affect to be least positive during the above-LT condition and most positive during the self-selected and below-LT conditions. Individual differences were greatest in the below-LT and at-LT conditions. Qualitative results showed that factors relating to perceptions of ability, interpretation of exercise intensity, exercise outcomes, focus of concentration, and perceptions of control influenced the affective response and contributed to the individual differences shown in the quantitative data.

scholarly journals Making Cognitive Latent Variables Manifest: Distinct Neural Networks for Fluid Reasoning and Processing Speed

2015 ◽  
Vol 27 (6) ◽  
pp. 1249-1258 ◽  
Author(s):  
Christian Habeck ◽  
Jason Steffener ◽  
Daniel Barulli ◽  
Yunglin Gazes ◽  
Qolamreza Razlighi ◽  
...  
Keyword(s):  
Individual Differences ◽  
Cognitive Ability ◽  
Cognitive Abilities ◽  
Latent Variables ◽  
Latent Variable ◽  
Cognitive Tasks ◽  
Neural Activation ◽  
Strongly Correlated ◽  
Activation Patterns ◽  
Fluid Reasoning

Cognitive psychologists posit several specific cognitive abilities that are measured with sets of cognitive tasks. Tasks that purportedly tap a specific underlying cognitive ability are strongly correlated with one another, whereas performances on tasks that tap different cognitive abilities are less strongly correlated. For these reasons, latent variables are often considered optimal for describing individual differences in cognitive abilities. Although latent variables cannot be directly observed, all cognitive tasks representing a specific latent ability should have a common neural underpinning. Here, we show that cognitive tasks representing one ability (i.e., either perceptual speed or fluid reasoning) had a neural activation pattern distinct from that of tasks in the other ability. One hundred six participants between the ages of 20 and 77 years were imaged in an fMRI scanner while performing six cognitive tasks, three representing each cognitive ability. Consistent with prior research, behavioral performance on these six tasks clustered into the two abilities based on their patterns of individual differences and tasks postulated to represent one ability showed higher similarity across individuals than tasks postulated to represent a different ability. This finding was extended in the current report to the spatial resemblance of the task-related activation patterns: The topographic similarity of the mean activation maps for tasks postulated to reflect the same reference ability was higher than for tasks postulated to reflect a different reference ability. Furthermore, for any task pairing, behavioral and topographic similarities of underlying activation patterns are strongly linked. These findings suggest that differences in the strengths of correlations between various cognitive tasks may be because of the degree of overlap in the neural structures that are active when the tasks are being performed. Thus, the latent variable postulated to account for correlations at a behavioral level may reflect topographic similarities in the neural activation across different brain regions.

scholarly journals Psychometric Models of Individual Differences in Reading Comprehension: A Reanalysis of Freed, Hamilton, and Long (2017)

2019 ◽  
Author(s):  
Sara Anne Goring ◽  
Christopher J. Schmank ◽  
Michael J. Kane ◽  
Andrew R. A. Conway
Keyword(s):  
Reading Comprehension ◽  
Individual Differences ◽  
Cognitive Abilities ◽  
Latent Variable ◽  
Convergent Validity ◽  
Cognitive Task ◽  
Network Modeling ◽  
Network Models ◽  
Measurement Model ◽  
Common Cause

Individual differences in reading comprehension have often been explored using latent variable modeling (LVM), to assess the relative contribution of domain-general and domain-specific cognitive abilities. However, LVM is based on the assumption that the observed covariance among indicators of a construct is due to a common cause (i.e., a latent variable; Pearl, 2000). This is a questionable assumption when the indicator variables are measures of performance on complex cognitive tasks. According to Process Overlap Theory (POT; Kovacs &amp; Conway, 2016), multiple processes are involved in cognitive task performance and the covariance among tasks is due to the overlap of processes across tasks. Instead of a single latent common cause, there are thought to be multiple dynamic manifest causes, consistent with an emerging view in psychometrics called network theory (Barabási, 2012; Borsboom &amp; Cramer, 2013). In the current study, we reanalyzed data from Freed et al. (2017) and compared two modeling approaches: LVM (Study 1) and psychometric network modeling (Study 2). In Study 1, two exploratory LVMs demonstrated problems with the original measurement model proposed by Freed et al. Specifically, the model failed to achieve discriminant and convergent validity with respect to reading comprehension, language experience, and reasoning. In Study 2, two network models confirmed the problems found in Study 1, and also served as an example of how network modeling techniques can be used to study individual differences. In conclusion, more research, and a more informed approach to psychometric modeling, is needed to better understand individual differences in reading comprehension.

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