The mind beyond our immediate awareness: Freudian, Jungian, and cognitive models of the unconscious

Abstract: Several branches of cognitive science now focus on the nature of the unconscious. This paper explores some of the findings and models from this research. By introducing formulations based on non‐clinical data, the cognitive scientists—in neural linguistics, computational modelling, and neuroscience—may depart from the older psychoanalytic formulations. An understanding of unconscious neural processes is nevertheless emerging showing how synapses are modified by experience and how learning, conscious and unconscious, is due to this important aspect of brain plasticity. Freud and Jung's formulations about the unconscious psyche, representing the main tenets of depth psychology, are also based on a conception of the mind as extending beyond immediate awareness. However, their models are more hypothetical in that their data, almost exclusively, come from treatments of psychotherapy patients and their verbal accounts. So how do these two conceptions of the unconscious match, where do they differ? And how does the neural understanding in the present research support theories and practices of analytic treatments?     

An experimentally confirmed statistical model on arm movement

The purpose of this research work was to develop a methodology to model arm movement in normal subjects and neurologically impaired individuals through the application of a statistical modelling method. Thirteen subjects with Parkinson's disease and 29 normal controls were recruited to participate in an arm motor task. An infrared optoelectronic kinematic movement analysis system was employed to record arm movement at 50 times per second. This study identified the modified extended Freundlich model as one that could be used to describe this task. Results showed that this model fit the data well and that it has a good correspondence between the observed and the predicted data. However, verification of the model showed that the residuals contained a sizeable autocorrelation factor. The Cochrane and Orcutt method was applied to remove this factor, which improved the fit of the model. Results showed that Parkinson's disease subjects had a higher autocorrelation coefficient than the normal subjects for this task. A positive correlation (r(s) = 0.72, p < 0.001) was found between the Langton-Hewer stage and the autocorrelation coefficient of PD subjects. This finding suggests that if autocorrelation is positively correlated with disease progression, clinicians in their clinical practice might use the autocorrelation value as a useful indicator to quantify the progression of a subjects' disease. Significant differences in model parameters were seen between normal and Parkinson's disease subjects. The use of such a model to represent and quantify movement patterns provides an important base for future study.     

Using Structural Analysis in Natural Settings: A Responsive Functional Assessment Strategy

Since the reauthorization of IDEA (1997), the use of functional assessments to examine factors that are related to students challenging behaviors has gained increasing attention in the literature. Over the past five years, several groups of investigators have conducted systematic analyses of the current status of the use of functional assessment in relation to students with or at risk for emotional/behavioral disorders (EBD). Their findings outline a number of critical, applied research issues for the field to consider. The purpose of this paper is to respond to a number of these issues by highlighting the relevance of structural analysis in addition to and in some cases in lieu of more traditional functional assessments. A brief review of current trends in the functional assessment research is presented as well as a rational for the use of structural analysis to examine the occurrence of appropriate behavior for students with EBD. Finally, illustrations of studies conducting structural analyses in classrooms settings are provided.     

Nonlocal effects in the continuum theory of shear localisation in 2d foams

Goyon et al. [J. Goyon, A. Colin, G. Ovarlez, A. Ajdari and L. Bocquet, Nature 454 (2008) p. 84] have shown that nonlocal effects in the rheology of foams may be accounted for by a modification of the standard (Herschel–Bulkley) model. Here we consider the effects of this modification on the continuum theory of 2d shear localisation. We compute results for various examples, showing that the localisation length is increased, and explore the limiting cases of zero and infinite nonlocality length ξ. Velocity profiles are shown to take an exponential form in the case where ξ is large. As the formulation of the nonlocal continuum model presented in this article is general, it may also be directly applicable to other complex fluids.     

A structural model for surface-enhanced stabilization in some metallic glass formers

A structural model for surface-enhanced stabilization in some metallic glass formers is proposed. In this model, the alloy surface structure is represented by five-layer Kagomé-net-based lateral ordering. Such surface structure has intrinsic abilities to stabilize icosahedral-like short-range order in the bulk, acting as ‘a cloak of liquidity’. In particular, recent experimental observations of surface-induced lateral ordering and a very high glass forming ability of the liquid alloy Au₄₉Ag_5.5Pd_2.3Cu_26.9Si_16.3 can be united using this structural model. This model may be useful for the interpretation of surface structure of other liquid alloys with a high glass forming ability. In addition, it suggests the possibility of guiding the design of the surface coating of solid containers for the stabilization of undercooled liquids.     

Ambivalence under scrutiny: Returning to the future

Abstract

This Special Issue on the study of ambivalent processes in psychology integrates issues from contemporary evolutionary, cognitive, and cultural psychology with new directions of formal models that are available in qualitative mathematics. Tribute is paid to the pioneer of the study of ambivalence—Else Frenkel-Brunswik. Her work antedates most of our contemporary efforts in this field. Becoming free from the limits of its obsession with numbers in lieu of “measurement”, psychology at our time faces the challenge of investigation of dynamic psychological complexity. Contemporary mathematics—which is qualitative in its nature—provides new opportunities for psychology. New mathematical models—based on topology (Morse functions) and from intuitionistic formal logic (theory of locales)—are shown to provide promising new directions for future research on ambivalence. The emphasis on mathematical tools as enablement devices for psychological theorizing leads psychology to the need to create new kinds of generalized understanding of complex psychological processes.     

Examining Spirituality Over Time: Latent Growth Curve and Individual Growth Curve Analyses

SUMMARY

Research on spirituality and religiousness has gained growing attention in recent years; however, most studies have used cross-sectional designs. As research on this topic evolves, there has been increasing recognition of the need to examine these constructs and their effects through the use of longitudinal designs. Beyond repeated-measures ANOVA and OLS regression models, what tools are available to examine these constructs over time? The purpose of this paper is to provide an overview of two cutting-edge statistical techniques that will facilitate longitudinal investigations of spirituality and religiousness: latent growth curve analysis using structural equation modeling (SEM) and individual growth curve models. The SEM growth curve approach examines change at the group level, with change over time expressed as a single latent growth factor. In contrast, individual growth curve models consider longitudinal change at the level of the person. While similar results may be obtained using either method, researchers may opt for one over the other due to the strengths and weaknesses associated with these methods. Examples of applications of both approaches to longitudinal studies of spirituality and religiousness are presented and discussed, along with design and data considerations when employing these modeling techniques.     

Models for the Detection of Deviations from the Expected Processing Strategy in Completing the Items of Cognitive Measures

This paper presents confirmatory factor models with fixed factor loadings that enable the identification of deviations from the expected processing strategy. The instructions usually define the expected processing strategy to a considerable degree. Simplification is a deviation from instructions that is likely to occur in complex cognitive measures. Since simplification impairs the validity of the measure, its identification is important. Models representing simplicity and instruction-based processing strategies were considered in investigating the data of 345 participants obtained by a working memory measure in order to find out whether and how the use of these strategies influences model-data fit. As expected, the consideration of simplicity strategies improved the model-data fit achieved for the instruction-based strategy.     

Narrowing the Creativity Gap: The Moderating Effects of Perceived Support for Creativity

This article examines the role of 3 types of perceived support for creativity in moderating the relation between creative self-efficacy and self-perceived creativity. The findings suggest significant interaction effects for perceived work-group support and supervisor support, but not for perceived organizational support. This study is among the first to (a) examine the importance of perceived support for creativity in unlocking creative potential and increasing creativity in organizations and (b) use interaction terms in structural equation modeling (SEM) to investigate moderator effects in an applied research setting. These results imply that organizational interventions focused on training supervisors and work-group members to support creativity in the workplace may be more effective than broader and less focused interventions at the organizational level.     

A Computationally More Efficient and More Accurate Stepwise Approach for Correcting for Sampling Error and Measurement Error

Over the last decade or two, multilevel structural equation modeling (ML-SEM) has become a prominent modeling approach in the social sciences because it allows researchers to correct for sampling and measurement errors and thus to estimate the effects of Level 2 (L2) constructs without bias. Because the latent variable modeling software Mplus uses maximum likelihood (ML) by default, many researchers in the social sciences have applied ML to obtain estimates of L2 regression coefficients. However, one drawback of ML is that covariance matrices of the predictor variables at L2 tend to be degenerate, and thus, estimates of L2 regression coefficients tend to be rather inaccurate when sample sizes are small. In this article, I show how an approach for stabilizing covariance matrices at L2 can be used to obtain more accurate estimates of L2 regression coefficients. A simulation study is conducted to compare the proposed approach with ML, and I illustrate its application with an example from organizational research.