Virtual Reality as a Selfobject Function: Towards Reclaiming Unlived Potentialities

ABSTRACT

In this article, I illustrate how virtual reality may promote reclaiming unlived potentialities, unconscious or disavowed. Using Kohut’s concept of the Virtual Self, I suggest that virtual aspects of the self, its potential capability, are unconscious (or unknown) until met by someone or something that promotes their transformation into a conscious state of mind and supports their realization. In this sense, the virtual experiences of the self are drawn from the preconscious. Three vignettes demonstrate the transformative potential of virtual experiences when they are perceived as recognizing, and responsive to, the Self’s needs. Two vignettes are drawn from ancient myths and illuminate how the virtual zone has inspired transformation and expansion of self awareness already in ancient times. A third vignette is presented to illustrate how, at the advent of social media, disavowed and dissociated needs are channeled into cyberspace as communications that are unconsciously geared to reclaim unlived potentialities. Cyber reality converses with conventional reality and, thus, is appreciated as a mode of psychic advance toward a new kind of self consciousness.     

精神分裂症的潜伏抑制动物模型

精神分裂症动物模型的建立,是当前研究精神分裂症的神经生物学机制以及开发新的抗精神病药物的一个重要课题。文章系统地阐述了以潜伏抑制为行为模式的精神分裂症动物模型的理论以及神经机制,并重点介绍了精神分裂症的潜伏抑制动物模型的建立方法,包括（1）调节某些与精神分裂症相关的中枢神经递质的传递活动;（2）对与精神分裂症有关的中脑-伏隔核神经环路进行结构损伤或药物性干预;（3）在发育早期给予免疫刺激或环境应激     

Capturing Shadows: On Photography,Causation, and Absences

Many photographs seem to be images of absences: for instance, a photograph of a shadow seems to be an image of an absence, as shadows are plausibly thought of as being absences of light. Absence photography is puzzling, however, as, first, it is a common idea that photographs can only be images of things that have caused them, and, second, it is unclear whether absences can cause anything. In this paper, I look at various ways to unravel the puzzle. Along the way, I also hope to cast some light on the idea that photography is a causal medium.     

Latent Profiles of Comorbid Depression as Predictors of PTSD Treatment Outcome

Posttraumatic stress disorder (PTSD) frequently co-occurs with major depressive disorder, and empirically supported PTSD treatments consistently improve depression. However, both diagnoses are heterogeneous and specific patterns of symptom overlap may be related to worse treatment outcome. Two hundred individuals with chronic PTSD participated in a doubly randomized preference trial comparing prolonged exposure and sertraline. Latent Profile Analysis was used to identify classes based on PTSD and depression symptoms prior to starting treatment. A three-class model best fit the data, with a high depression and PTSD severity class (distressed), a moderate depression and low PTSD avoidance class (depressive), and a low depression and high PTSD avoidance class (avoidant). The avoidant class showed the lowest rates of major depressive disorder diagnosis and transdiagnostic vulnerabilities to depression. Patients in the distressed class experienced more robust PTSD treatment response, with no differences between prolonged exposure and sertraline. These findings highlight the role of avoidance in nondepressed PTSD presentations while also demonstrating that co-occurring depression is not contraindicated in evidence-based PTSD treatment.     

Optimization-Based Model Fitting for Latent Class and Latent Profile Analyses

Statisticians typically estimate the parameters of latent class and latent profile models using the Expectation-Maximization algorithm. This paper proposes an alternative two-stage approach to model fitting. The first stage uses the modified k-means and hierarchical clustering algorithms to identify the latent classes that best satisfy the conditional independence assumption underlying the latent variable model. The second stage then uses mixture modeling treating the class membership as known. The proposed approach is theoretically justifiable, directly checks the conditional independence assumption, and converges much faster than the full likelihood approach when analyzing high-dimensional data. This paper also develops a new classification rule based on latent variable models. The proposed classification procedure reduces the dimensionality of measured data and explicitly recognizes the heterogeneous nature of the complex disease, which makes it perfect for analyzing high-throughput genomic data. Simulation studies and real data analysis demonstrate the advantages of the proposed method.     

Measuring Latent Quantities

A distinction is proposed between measures and predictors of latent variables. The discussion addresses the consequences of the distinction for the true-score model, the linear factor model, Structural Equation Models, longitudinal and multilevel models, and item-response models. A distribution-free treatment of calibration and error-of-measurement is given, and the contrasting properties of measures and predictors are examined.     

Latent partition analysis

Latent partition analysis has been formulated to study the relationships between two or more partitions of the same set of items. The major structural hypothesis is that a latent partition underlies the manifest partitions; that is, it is assumed that each item belongs to a latent category and that the manifest categories are derived by dividing and combining the latent categories. We have found that by examining manifest categories it is possible to reconstruct information about the latent partition and about its relation to the manifest partitions.The research reported herein was originally supported through the U. S. O. E. Cooperative Research Project 5-1005-2-12-1, directed by Donald M. Miller, at the Instructional Research Laboratory, University of Wisconsin. Further work has been supported by the National Science Foundation Grant No. GS-1025 at The University of Chicago. The author wishes to acknowledge the invaluable assistance of Richard G. Wolfe in the final statement of the theory and the assistance of Robert M. Pruzek in certain early formulations.     

Latent variables should remain as such: Evidence from a Monte Carlo study

Use of subject scores as manifest variables to assess the relationship between latent variables produces attenuated estimates. This has been demonstrated for raw scores from classical test theory (CTT) and factor scores derived from factor analysis. Conclusions on scores have not been sufficiently extended to item response theory (IRT) theta estimates, which are still recommended for estimation of relationships between latent variables. This is because IRT estimates appear to have preferable properties compared to CTT, while structural equation modeling (SEM) is often advised as an alternative to scores for estimation of the relationship between latent variables. The present research evaluates the consequences of using subject scores as manifest variables in regression models to test the relationship between latent variables. Raw scores and three methods for obtaining theta estimates were used and compared to latent variable SEM modeling. A Monte Carlo study was designed by manipulating sample size, number of items, type of test, and magnitude of the correlation between latent variables. Results show that, despite the advantage of IRT models in other areas, estimates of the relationship between latent variables are always more accurate when SEM models are used. Recommendations are offered for applied researchers.     

Latent curve analysis

As a method for representing development, latent trait theory is presented in terms of a statistical model containing individual parameters and a structure on both the first and second moments of the random variables reflecting growth. Maximum likelihood parameter estimates and associated asymptotic tests follow directly. These procedures may be viewed as an alternative to standard repeated measures ANOVA and to first-order auto-regressive methods. As formulated, the model encompasses cohort sequential designs and allow for period or practice effects. A numerical illustration using data initially collected by Nesselroade and Baltes is presented.The authors wish to thank John Nesselroade for providing us the data for our illustration and Karen Paul and Connie Tilse for assisting in the data analysis. This research was supported by a grant (No. AG03164) from the National Institute on Aging to the senior author.