Multidimensional Cross-Recurrence Quantification Analysis (MdCRQA) – A Method for Quantifying Correlation between Multivariate Time-Series

In this paper, Multidimensional Cross-Recurrence Quantification Analysis (MdCRQA) is introduced. It is an extension of Multidimensional Recurrence Quantification Analysis (MdRQA), which allows to quantify the (auto-)recurrence properties of a single multidimensional time-series. MdCRQA extends MdRQA to bi-variate cases to allow for the quantification of the co-evolution of two multidimensional time-series. Moreover, it is shown how a Diagonal Cross-Recurrence Profile (DCRP) can be computed from the MdCRQA output that allows to capture time-lagged coupling between two multidimensional time-series. The core concepts of these analyses are described, as well as practical aspects of their application. In the supplementary materials to this paper, implementations of MdCRQA and the DCRP as MatLab- and R-functions are provided.     

Withdrawal Aversion and the Equivalence Test

If a doctor is trying to decide whether or not to provide a medical treatment, does it matter ethically whether that treatment has already been started? Health professionals sometimes find it harder to stop a treatment (withdraw) than to refrain from starting the treatment (withhold). But does that feeling correspond to an ethical difference? In this article, we defend equivalence—the view that withholding and withdrawal of treatment are ethically equivalent when all other factors are equal. We argue that preference for withholding over withdrawal could represent a form of cognitive bias—withdrawal aversion. Nevertheless, we consider whether there could be circumstances in which there is a moral difference. We identify four examples of conditional nonequivalence. Finally, we reflect on the moral significance of diverging intuitions and the implications for policy. We propose a set of practical strategies for helping to reduce bias in end-of-life decision making, including the equivalence test.     

Transformative Learning: A Case for Using Grounded Theory as an Assessment Analytic

Transformative Learning Theory and pedagogies leverage disruptive experiences as catalysts for learning and teaching. By facilitating processes of critical analysis and reflection that challenge assumptions, transformative learning reframes what counts as knowledge and the sources and processes for gaining and producing it. Students develop a broader range of perspectives on and entry points for learning and behavior change engaging cognition, embodiment, aesthetics, emotions, and ethics (see Mezirow 1991 and Figures 1 and 2). The open‐inquiry, multi‐modal nature of transformative learning defies most traditional assessment strategies. This article demonstrates that grounded theory offers the rigorous qualitative analysis needed to document and track transformative learning outcomes in practice. By applying a grounded theory approach to data from over eighty student portfolios across several iterations of a Religion and Ecology course at Emory University, this article demonstrates a successful and replicable assessment of transformative learning pedagogies.     

Reproducing affective norms with lexical co-occurrence statistics: Predicting valence,arousal, and dominance

Human ratings of valence, arousal, and dominance are frequently used to study the cognitive mechanisms of emotional attention, word recognition, and numerous other phenomena in which emotions are hypothesized to play an important role. Collecting such norms from human raters is expensive and time consuming. As a result, affective norms are available for only a small number of English words, are not available for proper nouns in English, and are sparse in other languages. This paper investigated whether affective ratings can be predicted from length, contextual diversity, co-occurrences with words of known valence, and orthographic similarity to words of known valence, providing an algorithm for estimating affective ratings for larger and different datasets. Our bootstrapped ratings achieved correlations with human ratings on valence, arousal, and dominance that are on par with previously reported correlations across gender, age, education and language boundaries. We release these bootstrapped norms for 23,495 English words.     

Automatic gender detection of dream reports: A promising approach

A computer program was developed in an attempt to differentiate the dreams of males from females. Hypothesized gender predictors were based on previous literature concerning both dream content and written language features. Dream reports from home-collected dream diaries of 100 male (144 dreams) and 100 female (144 dreams) adolescent Anglophones were matched for equal length. They were first scored with the Hall and Van de Castle (HVDC) scales and quantified using DreamSAT. Two male and two female undergraduate students were asked to read all dreams and predict the dreamer’s gender. They averaged a pairwise percent correct gender prediction of 75.8% (κ = 0.516), while the Automatic Analysis showed that the computer program’s accuracy was 74.5% (κ = 0.492), both of which were higher than chance of 50% (κ = 0.00). The prediction levels were maintained when dreams containing obvious gender identifiers were eliminated and integration of HVDC scales did not improve prediction.     

Analysis and meta-analysis of single-case designs with a standardized mean difference statistic: A primer and applications

This article presents a d-statistic for single-case designs that is in the same metric as the d-statistic used in between-subjects designs such as randomized experiments and offers some reasons why such a statistic would be useful in SCD research. The d has a formal statistical development, is accompanied by appropriate power analyses, and can be estimated using user-friendly SPSS macros. We discuss both advantages and disadvantages of d compared to other approaches such as previous d-statistics, overlap statistics, and multilevel modeling. It requires at least three cases for computation and assumes normally distributed outcomes and stationarity, assumptions that are discussed in some detail. We also show how to test these assumptions. The core of the article then demonstrates in depth how to compute d for one study, including estimation of the autocorrelation and the ratio of between case variance to total variance (between case plus within case variance), how to compute power using a macro, and how to use the d to conduct a meta-analysis of studies using single-case designs in the free program R, including syntax in an appendix. This syntax includes how to read data, compute fixed and random effect average effect sizes, prepare a forest plot and a cumulative meta-analysis, estimate various influence statistics to identify studies contributing to heterogeneity and effect size, and do various kinds of publication bias analyses. This d may prove useful for both the analysis and meta-analysis of data from SCDs.     

The dark side of Facebook: Semantic representations of status updates predict the Dark Triad of personality

Using Latent Semantic Analysis, we quantified the semantic representations of Facebook status updates of 304 individuals in order to predict self-reported personality. We focused on, besides Neuroticism and Extraversion, the Dark Triad of personality: Psychopathy, Narcissism, and Machiavellianism. The semantic content of Facebook updates predicted Psychopathy and Narcissism. These updates had a more “odd” and negatively valanced content. Furthermore, Neuroticism, number of Facebook friends, and frequency of status updates were predictable from the status updates. Given that Facebook allows individuals to have major control in how they present themselves and draw benefits from these interactions, we conclude that the Dark Triad, involving socially malevolent behavior such as self-promotion, emotional coldness, duplicity, and aggressiveness, is manifested in Facebook status updates.     

“I’ve got you under my skin”: Relational therapists’ experiences of patients who occupy their inner world

The aim of this study was to conduct an in-depth exploration of therapists’ experiences of patients who affect them more than others and occupy their inner world beyond the context of therapy sessions. A phenomenological analysis was performed on semi-structured interviews with five relational therapists. All the therapists had a strong experience of a particular patient getting “under their skin.” In all these cases, the patient was a traumatized woman. The distinctive characteristic of the phenomenon was a sense of blurred or too permeable boundaries between the therapist and the patient. This was associated with fear and anxiety, but also with feelings of love. The therapists’ reactions to having a patient “under their skin” varied from resistance to symbiotic relatedness. The therapists’ ideas of their professional role influenced how the experience of carrying the patient’s suffering was interpreted. The phenomenon of the patient’s presence in the therapist’s representational world might be interpreted as a distinct countertransference phenomenon when working in a more “thin boundary” manner with particular cases. The therapists’ ability to effectively manage their vulnerabilities, activated in the countertransference, seems to be crucial for therapeutic progress. Implications for research, clinical practice, and training are discussed.     

Correlation between functional mobility and cognitive performance in older adults with cognitive impairment

Association between cognitive impairment and gait performance occurs in mild cognitive impairment (MCI) and Alzheimer‘s disease (AD), particularly under “divided attention” conditions, leading to a greater risk of falls. We studied 36 controls, 42 MCI, and 26 mild AD patients, using the Timed Up-and-Go test (TUG) under four conditions: TUG single – TUG1; TUG cognitive – TUG2; TUG manual –TUG3; TUG cognitive and manual – TUG4. Cognition was assessed using the MMSE, SKT, Exit25, and TMT (A and B). We found significant correlations between cognitive scores and TUG2 [r values (MMSE: –0.383, TMT-A: 0.430, TMT-B: 0.386, Exit25: 0.455, SKT: 0.563)] and TUG4 [(MMSE: –0.398, TMT-A: 0.384, TMT-B: 0.352,Exit25: 0.466, SKT: 0.525)] in the AD group, and between all TUG modalities and SKT in MCI and AD. Our results revealed that functional mobility impairment in cognitive dual tasks correlated to cognitive decline in AD patients and to attention and memory impairment in MCI.