scholarly journals Individual Differences in Different Measures of Opioid Self-Administration in Rats Are Accounted for by a Single Latent Variable

2021 ◽  
Vol 12 ◽  
Author(s):  
Yayi Swain ◽  
Niels G. Waller ◽  
Jonathan C. Gewirtz ◽  
Andrew C. Harris
Keyword(s):  
Individual Differences ◽  
Latent Variables ◽  
Latent Variable ◽  
Factor Model ◽  
Abuse Liability ◽  
Human Studies ◽  
Demand Elasticity ◽  
Self Administration ◽  
Latent Factor ◽  
Widespread Application

Individual differences in vulnerability to addiction have been widely studied through factor analysis (FA) in humans, a statistical method that identifies “latent” variables (variables that are not measured directly) that reflect the common variance among a larger number of observed measures. Despite its widespread application in behavioral genetics, FA has not been used in preclinical opioid addiction research. The current study used FA to examine the latent factor structure of four measures of i.v. morphine self-administration (MSA) in rats (i.e., acquisition, demand elasticity, morphine/cue- and stress/cue-induced reinstatement). All four MSA measures are generally assumed in the preclinical literature to reflect “addiction vulnerability,” and individual differences in multiple measures of abuse liability are best accounted for by a single latent factor in some human studies. A one-factor model was therefore fitted to the data. Two different regularized FAs indicated that a one-factor model fit our data well. Acquisition, elasticity of demand and morphine/cue-induced reinstatement loaded significantly onto a single latent factor while stress/cue-induced reinstatement did not. Consistent with findings from some human studies, our results indicated a common drug “addiction” factor underlying several measures of opioid SA. However, stress/cue-induced reinstatement loaded poorly onto this factor, suggesting that unique mechanisms mediate individual differences in this vs. other MSA measures. Further establishing FA approaches in drug SA and in preclinical neuropsychopathology more broadly will provide more reliable, clinically relevant core factors underlying disease vulnerability in animal models for further genetic analyses.

scholarly journals Dispositional individual differences in cognitive effort investment: establishing the core construct

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Corinna Kührt ◽  
Sebastian Pannasch ◽  
Stefan J. Kiebel ◽  
Alexander Strobel
Keyword(s):  
Individual Differences ◽  
Latent Variables ◽  
Effortful Control ◽  
Factor Model ◽  
Cognitive Effort ◽  
Self Control ◽  
Behavioral Measures ◽  
Effort Discounting ◽  
Cognitive Motivation ◽  
Goal Directed Behavior

Abstract Background Individuals tend to avoid effortful tasks, regardless of whether they are physical or mental in nature. Recent experimental evidence is suggestive of individual differences in the dispositional willingness to invest cognitive effort in goal-directed behavior. The traits need for cognition (NFC) and self-control are related to behavioral measures of cognitive effort discounting and demand avoidance, respectively. Given that these traits are only moderately related, the question arises whether they reflect a common core factor underlying cognitive effort investment. If so, the common core of both traits might be related to behavioral measures of effort discounting in a more systematic fashion. To address this question, we aimed at specifying a core construct of cognitive effort investment that reflects dispositional differences in the willingness and tendency to exert effortful control. Methods We conducted two studies (N = 613 and N = 244) with questionnaires related to cognitive motivation and effort investment including assessment of NFC, intellect, self-control and effortful control. We first calculated Pearson correlations followed by two mediation models regarding intellect and its separate aspects, seek and conquer, as mediators. Next, we performed a confirmatory factor analysis of a hierarchical model of cognitive effort investment as second-order latent variable. First-order latent variables were cognitive motivation reflecting NFC and intellect, and effortful self-control reflecting self-control and effortful control. Finally, we calculated Pearson correlations between factor scores of the latent variables and general self-efficacy as well as traits of the Five Factor Model of Personality for validation purposes. Results Our findings support the hypothesized correlations between the assessed traits, where the relationship of NFC and self-control is specifically mediated via goal-directedness. We established and replicated a hierarchical factor model of cognitive motivation and effortful self-control that explains the shared variance of the first-order factors by a second-order factor of cognitive effort investment. Conclusions Taken together, our results integrate disparate literatures on cognitive motivation and self-control and provide a basis for further experimental research on the role of dispositional individual differences in goal-directed behavior and cost–benefit-models.

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.

Exploring the Relations Among Executive Functions, Fluid Intelligence, and Personality

2009 ◽  
Vol 30 (4) ◽  
pp. 194-200 ◽  
Author(s):  
Nash Unsworth ◽  
Joshua D. Miller ◽  
Chad E. Lakey ◽  
Diana L. Young ◽  
J. Thadeus Meeks ◽  
...  
Keyword(s):  
Working Memory ◽  
Individual Differences ◽  
Personality Disorder ◽  
Executive Functions ◽  
Latent Variables ◽  
Fluid Intelligence ◽  
Factor Model ◽  
Five Factor Model ◽  
Unified Field ◽  
Goal Directed Behavior

Executive functions (EFs) are important for goal-directed behavior and have been linked with a number of important constructs like intelligence. The current study examined the link between EFs and aspects of normal and abnormal personality. Latent variables of working memory, fluency, response inhibition, and vigilance EFs were examined along with fluid intelligence (gF). It was found that the EFs were separate yet correlated, and that each was related to gF. Furthermore, it was found that aspects of personality as measured by the Five-Factor Model and the BIS/BAS were differentially related to the EFs and gF. Examination of personality disorder measures also demonstrated differential relationships with the EFs and gF. The results suggest a number of systematic and important links between EFs and personality and suggest the need for a more unified field of individual differences.

scholarly journals Individual differences in musical training and executive functions: A latent variable approach

2018 ◽  
Author(s):  
Brooke Okada ◽  
L. Robert Slevc
Keyword(s):  
Working Memory ◽  
Individual Differences ◽  
Executive Functions ◽  
Latent Variables ◽  
Latent Variable ◽  
Musical Training ◽  
Continuous Measure ◽  
Memory Updating ◽  
Variable Approach ◽  
Working Memory Updating

Learning and performing music draw on a host of cognitive abilities, and previous research postulates that musicians might have advantages in related cognitive processes. One such aspect of cognition that may be related to musical training is executive functions (EFs), a set of top-down processes that regulate behavior and cognition according to task demands. Previous studies investigating the link between musical training and EFs have yielded mixed results and are difficult to compare. In part, this is because most studies look at only one specific cognitive process, and even studies looking at the same process use different experimental tasks. Furthermore, most correlational studies use different categorizations of “musician” and “non-musician” for comparisons, so generalizing findings is difficult. The current study provides a more comprehensive assessment of how individual differences in musical training relate to latent measures of three separable aspects of EFs. We administered a well-validated EF battery containing multiple tasks tapping EF components of inhibition, shifting, and working memory updating (Friedman et al., 2008) and a comprehensive, continuous measure of musical training and sophistication (Müllensiefen et al., 2014). Musical training correlated with some individual EF tasks involving inhibition and working memory updating, but not individual tasks involving shifting. However, musical training only predicted the latent variable of working memory updating, but not latent variables of inhibition or shifting after controlling for IQ, socioeconomic status, and handedness. Although these data are correlational, they nonetheless suggest that musical experience places particularly strong demands specifically on working memory updating processes.

scholarly journals Can Variances of Latent Variables be Scaled in Such a Way That They Correspond to Eigenvalues?

2017 ◽  
Vol 6 (6) ◽  
pp. 35 ◽  
Author(s):  
Karl Schweizer ◽  
Stefan Troche ◽  
Siegbert Reiß
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Simulation Study ◽  
Latent Variables ◽  
Latent Variable ◽  
Factor Model ◽  
Estimation Method ◽  
Factor Loadings ◽  
Confirmatory Factor ◽  
Theoretical Foundations

The paper reports an investigation of whether sums of squared factor loadings obtained in confirmatory factor analysis correspond to eigenvalues of exploratory factor analysis. The sum of squared factor loadings reflects the variance of the corresponding latent variable if the variance parameter of the confirmatory factor model is set equal to one. Hence, the computation of the sum implies a specific type of scaling of the variance. While the investigation of the theoretical foundations suggested the expected correspondence between sums of squared factor loadings and eigenvalues, the necessity of procedural specifications in the application, as for example the estimation method, revealed external influences on the outcome. A simulation study was conducted that demonstrated the possibility of exact correspondence if the same estimation method was applied. However, in the majority of realized specifications the estimates showed similar sizes but no correspondence. 

scholarly journals Effect of Dichotomization on the Latent Structure of Data

2021 ◽  
Vol 7 ◽  
Author(s):  
Karl Schweizer ◽  
Andreas Gold ◽  
Dorothea Krampen
Keyword(s):  
Simulation Study ◽  
Latent Variables ◽  
Latent Variable ◽  
Factor Model ◽  
Latent Structure ◽  
Factor Loadings ◽  
Dichotomous Data ◽  
Level Data ◽  
Confirmatory Factor

We investigated whether dichotomous data showed the same latent structure as the interval-level data from which they originated. Given constancy of dimensionality and factor loadings reflecting the latent structure of data, the focus was on the variance of the latent variable of a confirmatory factor model. This variance was shown to summarize the information provided by the factor loadings. The results of a simulation study did not reveal exact correspondence of the variances of the latent variables derived from interval-level and dichotomous data but shrinkage. Since shrinkage occurred systematically, methods for recovering the original variance were fleshed out and evaluated.

Probability-Based and Measurement-Related Hypotheses With Full Restriction for Investigations by Means of Confirmatory Factor Analysis

Methodology ◽  
2011 ◽  
Vol 7 (4) ◽  
pp. 157-164
Author(s):  
Karl Schweizer
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Cognitive Processing ◽  
Latent Variables ◽  
Repeated Measures ◽  
Latent Variable ◽  
Model Fit ◽  
Repeated Measures Data ◽  
Confirmatory Factor ◽  
And Performance

Probability-based and measurement-related hypotheses for confirmatory factor analysis of repeated-measures data are investigated. Such hypotheses comprise precise assumptions concerning the relationships among the true components associated with the levels of the design or the items of the measure. Measurement-related hypotheses concentrate on the assumed processes, as, for example, transformation and memory processes, and represent treatment-dependent differences in processing. In contrast, probability-based hypotheses provide the opportunity to consider probabilities as outcome predictions that summarize the effects of various influences. The prediction of performance guided by inexact cues serves as an example. In the empirical part of this paper probability-based and measurement-related hypotheses are applied to working-memory data. Latent variables according to both hypotheses contribute to a good model fit. The best model fit is achieved for the model including latent variables that represented serial cognitive processing and performance according to inexact cues in combination with a latent variable for subsidiary processes.

Conceptualizing Protective Family Context and Its effect on Substance Use: Comparisons Across Diverse Ethnic-Racial Youth

2019 ◽  
Author(s):  
Kevin Constante ◽  
Edward Huntley ◽  
Emma Schillinger ◽  
Christine Wagner ◽  
Daniel Keating
Keyword(s):  
Substance Use ◽  
Measurement Invariance ◽  
Latent Variables ◽  
Latent Variable ◽  
Path Model ◽  
Family Context ◽  
Partial Metric ◽  
Racial Groups ◽  
Protective Methods ◽  
Family Variables

Background: Although family behaviors are known to be important for buffering youth against substance use, research in this area often evaluates a particular type of family interaction and how it shapes adolescents’ behaviors, when it is likely that youth experience the co-occurrence of multiple types of family behaviors that may be protective. Methods: The current study (N = 1716, 10th and 12th graders, 55% female) examined associations between protective family context, a latent variable comprised of five different measures of family behaviors, and past 12 months substance use: alcohol, cigarettes, marijuana, and e-cigarettes. Results: A multi-group measurement invariance assessment supported protective family context as a coherent latent construct with partial (metric) measurement invariance among Black, Latinx, and White youth. A multi-group path model indicated that protective family context was significantly associated with less substance use for all youth, but of varying magnitudes across ethnic-racial groups. Conclusion: These results emphasize the importance of evaluating psychometric properties of family-relevant latent variables on the basis of group membership in order to draw appropriate inferences on how such family variables relate to substance use among diverse samples.

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.

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