Dissociated control as a signature of typological variability in high hypnotic suggestibility

This study tested the prediction that dissociative tendencies modulate the impact of a hypnotic induction on cognitive control in different subtypes of highly suggestible individuals. Low suggestible (LS), low dissociative highly suggestible (LDHS), and high dissociative highly suggestible (HDHS) participants completed the Stroop color-naming task in control and hypnosis conditions. The magnitude of conflict adaptation (faster response times on incongruent trials preceded by an incongruent trial than those preceded by a congruent trial) was used as a measure of cognitive control. LS and LDHS participants displayed marginally superior up-regulation of cognitive control following a hypnotic induction, whereas HDHS participants’ performance declined. These findings indicate that dissociative tendencies modulate the influence of a hypnotic induction on cognitive control in high hypnotic suggestibility and suggest that HS individuals are comprised of distinct subtypes with dissimilar cognitive profiles.     

Patterns of hypnotic response,revisited

It has long been speculated that there are discrete patterns of responsiveness to hypnotic suggestions, perhaps paralleling the factor structure of hypnotizability. An earlier study by Brenneman and Kihlstrom (1986), employing cluster analysis, found evidence for 12 such profiles. A new study by Terhune (2015), employing latent profile analysis, found evidence for three such patterns among highly hypnotizable subjects, and a fourth comprising subjects of medium hypnotizability. Some differences between the two studies are described. Convincing identification of discrete “types” of high hypnotizability, such as dissociative and nondissociative, may require a larger dataset than is currently available, but also data pertaining directly to divisions in conscious awareness and experienced involuntariness.     

Differential patterns of spontaneous experiential response to a hypnotic induction: A latent profile analysis

A hypnotic induction produces different patterns of spontaneous experiences across individuals. The magnitude and characteristics of these responses covary moderately with hypnotic suggestibility, but also differ within levels of hypnotic suggestibility. This study sought to identify discrete phenomenological profiles in response to a hypnotic induction and assess whether experiential variability among highly suggestible individuals matches the phenomenological profiles predicted by dissociative typological models of high hypnotic suggestibility. Phenomenological state scores indexed in reference to a resting epoch during hypnosis were submitted to a latent profile analysis. The profiles in the derived four-class solution differed in multiple experiential dimensions and hypnotic suggestibility. Highly suggestible individuals were distributed across two classes that exhibited response patterns suggesting an inward attention subtype and a dissociative subtype. These results provide support for dissociative typological models of high hypnotic suggestibility and indicate that highly suggestible individuals do not display a uniform response to a hypnotic induction.     

Methodological and practical issues regarding phenomenological subtypes of highly suggestible individuals: A response to Kumar

A hypnotic induction produces different patterns of spontaneous experiences across individuals. The magnitude and characteristics of these responses covary moderately with hypnotic suggestibility, but also differ within levels of hypnotic suggestibility. This study sought to identify discrete phenomenological profiles in response to a hypnotic induction and assess whether experiential variability among highly suggestible individuals matches the phenomenological profiles predicted by dissociative typological models of high hypnotic suggestibility. Phenomenological state scores indexed in reference to a resting epoch during hypnosis were submitted to a latent profile analysis. The profiles in the derived four-class solution differed in multiple experiential dimensions and hypnotic suggestibility. Highly suggestible individuals were distributed across two classes that exhibited response patterns suggesting an inward attention subtype and a dissociative subtype. These results provide support for dissociative typological models of high hypnotic suggestibility and indicate that highly suggestible individuals do not display a uniform response to a hypnotic induction.     

Hypnosis and belief: A review of hypnotic delusions

Hypnosis can create temporary, but highly compelling alterations in belief. As such, it can be used to model many aspects of clinical delusions in the laboratory. This approach allows researchers to recreate features of delusions on demand and examine underlying processes with a high level of experimental control. This paper reviews studies that have used hypnosis to model delusions in this way. First, the paper reviews studies that have focused on reproducing the surface features of delusions, such as their high levels of subjective conviction and strong resistance to counter-evidence. Second, the paper reviews studies that have focused on modelling underlying processes of delusions, including anomalous experiences or cognitive deficits that underpin specific delusional beliefs. Finally, the paper evaluates this body of research as a whole. The paper discusses advantages and limitations of using hypnotic models to study delusions and suggests some directions for future research.     

The relationship between multilevel models and non‐parametric multilevel mixture models: Discrete approximation of intraclass correlation,random coefficient distributions,and residual heteroscedasticity

Multilevel data structures are common in the social sciences. Often, such nested data are analysed with multilevel models (MLMs) in which heterogeneity between clusters is modelled by continuously distributed random intercepts and/or slopes. Alternatively, the non‐parametric multilevel regression mixture model (NPMM) can accommodate the same nested data structures through discrete latent class variation. The purpose of this article is to delineate analytic relationships between NPMM and MLM parameters that are useful for understanding the indirect interpretation of the NPMM as a non‐parametric approximation of the MLM, with relaxed distributional assumptions. We define how seven standard and non‐standard MLM specifications can be indirectly approximated by particular NPMM specifications. We provide formulas showing how the NPMM can serve as an approximation of the MLM in terms of intraclass correlation, random coefficient means and (co)variances, heteroscedasticity of residuals at level 1, and heteroscedasticity of residuals at level 2. Further, we discuss how these relationships can be useful in practice. The specific relationships are illustrated with simulated graphical demonstrations, and direct and indirect interpretations of NPMM classes are contrasted. We provide an R function to aid in implementing and visualizing an indirect interpretation of NPMM classes. An empirical example is presented and future directions are discussed.     

The neural correlates of movement intentions: A pilot study comparing hypnotic and simulated paralysis

The distinct feeling of wanting to act and thereby causing our own actions is crucial to our self-perception as free human agents. Disturbances of the link between intention and action occur in several disorders. Little is known, however, about the neural correlates of wanting or intending to act. To investigate these for simple voluntary movements, we used a paradigm involving hypnotic paralysis and functional magnetic resonance imaging. Eight healthy women were instructed to sequentially perform left and right hand movements during a normal condition, as well as during simulated weakness, simulated paralysis and hypnotic paralysis of the right hand. Right frontopolar cortex was selectively hypoactivated for attempted right hand movement during simulated paralysis while it was active in all other conditions. Since simulated paralysis was the only condition lacking an intention to move, the activation in frontopolar cortex might be related to the intention or volition to move.     

Multidimensional motivation for exercise: A latent profile and transition analysis

ObjectivesTo: a) identify motivational profiles for exercise, using Self-Determination Theory as a theoretical framework, among a sample of parents of UK primary school children; b) explore the movement between motivational profiles over a five year period; and c) examine differences across these profiles in terms of gender, physical activity and BMI.DesignData were from the B-Proact1v cohort.Methods2555 parents of British primary school children participated across three phases when the child was aged 5–6, 8–9, and 10–11. Parents completed a multidimensional measure of motivation for exercise and wore an ActiGraph GT3X + accelerometer for five days in each phase. Latent profile and transition analyses were conducted using a three-step approach in MPlus.ResultsSix profiles were identified, comprising different combinations of motivation types. Between each timepoint, moving between profiles was more likely than remaining in the same one. People with a more autonomous profile at a previous timepoint were unlikely to move to more controlled or amotivated profiles. At all three timepoints, more autonomous profiles were associated with higher levels of MVPA and lower BMI.ConclusionsThe results show that people’s motivation for exercise can be described in coherent and consistent profiles which are made up of multiple and simultaneous types of motivation. More autonomous motivation profiles were more enduring over time, indicating that promoting more autonomous motivational profiles may be central to facilitating longer-term physical activity engagement.     

Hypnotic suggestibility predicts the magnitude of the imaginative word blindness suggestion effect in a non-hypnotic context

The present study investigated how the magnitude the word blindness suggestion effect on Stroop interference depended on hypnotic suggestibility when given as an imaginative suggestion (i.e. not post-hypnotic suggestion) and under conditions in which hypnosis was not mentioned. Hypnotic suggestibility is shown to be a significant predictor of the magnitude of the imaginative word blindness suggestion effect under these conditions. This is therefore the first study to show a linear relationship between the imaginative word blindness suggestion effect and hypnotic suggestibility across the whole hypnotizability spectrum. The results replicate previous findings showing that highs respond to the word blindness suggestion to a greater extent than lows but extend previous work by showing that the advantage for those higher on the hypnotizability spectrum occurs even in a non-hypnotic context. Negative attitudes about hypnosis may not explain the failure to observe similar effects of the word blindness suggestion in less hypnotizable individuals.     

Implementing continuous non-normal skewed distributions in latent growth mixture modeling: An assessment of specification errors and class enumeration

Abstract

Recent advances have allowed for modeling mixture components within latent growth modeling using robust, skewed mixture distributions rather than normal distributions. This feature adds flexibility in handling non-normality in longitudinal data, through manifest or latent variables, by directly modeling skewed or heavy-tailed latent classes rather than assuming a mixture of normal distributions. The aim of this study was to assess through simulation the potential under- or over-extraction of latent classes in a growth mixture model when underlying data follow either normal, skewed-normal, or skewed-t distributions. In order to assess this, we implement skewed-t, skewed-normal, and conventional normal (i.e., not skewed) forms of the growth mixture model. The skewed-t and skewed-normal versions of this model have only recently been implemented, and relatively little is known about their performance. Model comparison, fit, and classification of correctly specified and mis-specified models were assessed through various indices. Findings suggest that the accuracy of model comparison and fit measures are dependent on the type of (mis)specification, as well as the amount of class separation between the latent classes. A secondary simulation exposed computation and accuracy difficulties under some skewed modeling contexts. Implications of findings, recommendations for applied researchers, and future directions are discussed; a motivating example is presented using education data.     

Uncertainty and risk during the COVID-19 pandemic: A latent profile analysis

In the present study, we examine how subgroups of people are characterized by different profiles of uncertainty surrounding COVID-19, susceptibility, and recovery. Participants (N = 199) were U.S. residents recruited online for a longitudinal study during the summer of 2020. We first, identified groups using latent profile analysis (LPA) and then examined whether these profiles predicted differences in COVID-related risky and preventative behaviors. LPA identified five distinct profiles of people representing a combination of low and high uncertainty and low, moderate, and high risk perceptions. Results revealed that latent-profile group membership predicted intention to interact with others outside of the household, intention to engage in non-essential shopping, intention to attend an in-person religious gathering, intention to wear a mask in public, and self-reported physical distancing in the past week. Profile membership did not predict intentions to: dine out, go to the nail/hair salon, go to the gym, nor physically distance from others in the future, nor did it predict handwashing in the past week.     

A latent profile analysis of the Five Factor Model of personality: Modeling trait interactions

Interactions among the dimensions of the Five Factor Model (FFM) have not typically been evaluated in mental health research, with the extant literature focusing on bivariate relationships with psychological constructs of interest. This study used latent profile analysis to mimic higher-order interactions to identify homogenous personality profiles using the FFM, and also examined relationships between resultant profiles and affect, self-esteem, depression, anxiety, and coping efficacy. Participants (N = 371) completed self-report and daily diary questionnaires. A 3-profile solution provided the best fit to the data; the profiles were characterized as well-adjusted, reserved, and excitable. The well-adjusted group reported better psychological functioning in validation analyses. The reserved and excitable groups differed on anxiety, with the excitable group reporting generally higher anxiety than the reserved group. Latent profile analysis may be a parsimonious way to model personality heterogeneity.     

A latent profile analysis of the Five Factor Model of personality: Modeling trait interactions

Interactions among the dimensions of the Five Factor Model (FFM) have not typically been evaluated in mental health research, with the extant literature focusing on bivariate relationships with psychological constructs of interest. This study used latent profile analysis to mimic higher-order interactions to identify homogenous personality profiles using the FFM, and also examined relationships between resultant profiles and affect, self-esteem, depression, anxiety, and coping efficacy. Participants (N = 371) completed self-report and daily diary questionnaires. A 3-profile solution provided the best fit to the data; the profiles were characterized as well-adjusted, reserved, and excitable. The well-adjusted group reported better psychological functioning in validation analyses. The reserved and excitable groups differed on anxiety, with the excitable group reporting generally higher anxiety than the reserved group. Latent profile analysis may be a parsimonious way to model personality heterogeneity.     

Doubling‐down on deprivation: Using latent profile analysis to evaluate an age‐old assumption in relative deprivation theory

Theorists have long argued that two forms of relative deprivation exist: individual‐based relative deprivation (IRD) whereby a person feels deprived relative to other individuals and group‐based relative deprivation (GRD) whereby a person feels his/her ingroup is deprived relative to other groups. Combinations of IRD and GRD are therefore assumed to produce four response profiles: (i) high on IRD and GRD (i.e. ‘doubly deprived’); (ii) high on IRD, low on GRD; (iii) low on IRD, high on GRD; or (iv) low on IRD and GRD. The existence of these profiles, however, has never been assessed. We address this oversight by using latent profile analysis to identify distinct response patterns to measures of IRD and GRD. Across two studies, we found no support for this typology, nor the oft‐assumed doubly deprived profile. Rather, response patterns showed moderate levels of IRD across discrete profiles accompanied by considerable variability in GRD.     

潜在剖面分析在组织行为领域中的应用

以个体为中心的研究路径将各个变量看作是相互依赖的一个系统, 基于多项特征(变量)将被试分为多个子群体, 分析子群体的前因与影响。以个体为中心的研究路径理解更加直观、更贴近实践, 受到越来越多的关注。潜在剖面分析(latent profile analysis, LPA)是以个体为中心研究路径的典型分析技术。在总结归纳以个体与以变量为中心两种研究路径异同、LPA与传统以个体为中心的分析技术差异后, 系统梳理了LPA在组织行为学领域的应用主题, 并从研究主题选取、样本要求、理论使用、剖面数量确定等方面归纳了LPA应用的步骤与注意事项。最后, 提出了未来研究的方向。     

Physical activity in employees with differing occupational stress and mental health profiles: A latent profile analysis

ObjectivesTo examine whether employees with differing occupational stress and mental health profiles differ in their self-reported levels of physical activity.DesignCross-sectional survey data.MethodThe sample consisted of 2660 Swedish health care workers and social insurance officers (85% women, M = 46.3 years). Latent profile analysis was performed to identify classes. Between-class-differences in physical activity were tested via χ²-tests and multinominal logistic regression analyses using sex, age, BMI, marital status, children at home, caregiving, and smoking as covariates.ResultsLatent profile analysis resulted in a six-profile solution. Two pairs of classes had equal stress levels, one pair with high stress, one pair with moderate stress. Within each pair, one group showed some resilience (i.e. only moderate mental health problems despite high stress or good mental health despite moderate stress), whereas the other did not. The other two classes were characterized by either low stress and good mental health or moderate-to-high stress and elevated mental health problems. Participants who were resilient to high or moderate stress were more active than participants of the corresponding non-resilient classes. Participants with low stress and good mental health reported the highest physical activity levels, participants with high stress and high mental health problems reported the lowest physical activity levels.ConclusionsThe findings suggest that physical activity is associated with resilience to occupational stress, and that beyond primary prevention efforts to make work less stressful regular physical activity should be a target variable for health professionals working in the occupational setting.     

分类精确性指数Entropy在潜剖面分析中的表现:一项蒙特卡罗模拟研究

本研究通过蒙特卡洛模拟考查了分类精确性指数Entropy及其变式受样本量、潜类别数目、类别距离和指标个数及其组合的影响情况。研究结果表明:(1)尽管Entropy值与分类精确性高相关,但其值随类别数、样本量和指标数的变化而变化,很难确定唯一的临界值;(2)其他条件不变的情况下,样本量越大,Entropy的值越小,分类精确性越差;(3)类别距离对分类精确性的影响具有跨样本量和跨类别数的一致性;(4)小样本(N=50~100)的情况下,指标数越多,Entropy的结果越好;(5)在各种条件下Entropy对分类错误率比其它变式更灵敏。     

Maximum likelihood estimation of latent interaction effects with the LMS method

In the context of structural equation modeling, a general interaction model with multiple latent interaction effects is introduced. A stochastic analysis represents the nonnormal distribution of the joint indicator vector as a finite mixture of normal distributions. The Latent Moderated Structural Equations (LMS) approach is a new method developed for the analysis of the general interaction model that utilizes the mixture distribution and provides a ML estimation of model parameters by adapting the EM algorithm. The finite sample properties and the robustness of LMS are discussed. Finally, the applicability of the new method is illustrated by an empirical example. This research has been supported by a grant from the Deutsche Forschungsgemeinschaft, Germany, No. Mo 474/1 and Mo 474/2. The data for the empirical example have been provided by Andreas Thiele of the University of Frankfurt, Germany. The authors are indebted to an associate editor and to three anonymous reviewers ofPsychometrika whose comments and suggestions have been very helpful.     

Exploring the peer status prototypes: A large-scale latent profile analysis on high-school students from four European countries

Peer status – the regard other group members have of an individual – is fundamental for youth development. Different research traditions developed independent theoretical frameworks conceiving the dimensions underlying social status, and this led to identifying a variety of peer status prototypes. In this work, we explored whether a classification based on the four dimensions of popularity, aggression, dislike, and victimization could integrate the scattered peer status profiles found in the different traditions. A latent profile analysis on 16,224 European students identified the peer status prototypes of popular, bullies, disliked, victims, and average students. Both the peer- and self-reported correlates supported that the five profiles accounted for the large variety of the students' profiles in the literature. These findings suggest that the adoption of a multidimensional approach supported by advanced statistical procedures could identify students' peer status profiles more effectively, replacing classifications based on cutoffs, and leading to a unified students' classification.     

A Bayesian Model of Biases in Artificial Language Learning: The Case of a Word‐Order Universal

In this article, we develop a hierarchical Bayesian model of learning in a general type of artificial language‐learning experiment in which learners are exposed to a mixture of grammars representing the variation present in real learners’ input, particularly at times of language change. The modeling goal is to formalize and quantify hypothesized learning biases. The test case is an experiment ( Culbertson, Smolensky, & Legendre, 2012 ) targeting the learning of word‐order patterns in the nominal domain. The model identifies internal biases of the experimental participants, providing evidence that learners impose (possibly arbitrary) properties on the grammars they learn, potentially resulting in the cross‐linguistic regularities known as typological universals. Learners exposed to mixtures of artificial grammars tended to shift those mixtures in certain ways rather than others; the model reveals how learners’ inferences are systematically affected by specific prior biases. These biases are in line with a typological generalization—Greenberg's Universal 18—which bans a particular word‐order pattern relating nouns, adjectives, and numerals.