MARKETS AND MORALITY

For most of human history, economic systems were personal in nature—people normally interacted with people they knew personally and knew well. Today's modern market economies are impersonal—people normally interact with people they do not know personally. The historical movement from personal to impersonal systems was necessary for societies to develop the specialization of labor needed for modern production technologies. That is, the high standards of living in the developed world are due to these impersonal systems. However, the ethical systems theologians apply to modern market systems were developed for personal societies and are often inappropriate for impersonal societies. In this article, we develop the differences between personal and impersonal systems, examine the writings of a number of theologians who are very critical of market economies, and attempt to show how a different ethical approach is needed.     

Wellness,Emotion Regulation,and Relapse During Substance Use Disorder Treatment

Using a cross-sectional study design, the authors investigated the impact of wellness and emotion regulation on relapse in 179 individuals currently enrolled in substance use disorder treatment. An integration of the indivisible self model of wellness (Myers & Sweeney, 2004) and the covert antecedents model of relapse (Marlatt, 1985) informed the study hypothesis that emotion regulation mediates the wellness-relapse relationship. Binomial logistic regressions revealed that Creative Self wellness (odds ratio [OR] = 1.09, p = .002), reappraisal (OR = 1.08, p = .008), difficulties in emotion regulation (OR = 1.03, p = .007), and Physical Self wellness (OR = 0.95, p < .001) were associated with the odds of relapse on any given day during treatment. Sobel tests indicated 2 occurrences of full mediation, including that difficulties in emotion regulation mediated the relationship between Coping Self wellness and relapse (OR = 0.97, p = .015).     

A small-to-large unit progression in metaphonological awareness and reading?

The paper reports a series of studies of reading and metaphonological processing by children in their second year in primary school(aged 6 years). An earlier study had established that, in the first year of learning, performance was characterized by a small-unit approach in which graphemes and phonemes were emphasized. In the second year, reading became more sensitiveto the frequencies of rime structures in the lexicon. Capacity to generate word analogies for nonwords also showed increasing commitment to rime-based responses, and this trend was strongly linked to reading age. The present results suggest that a small-unit approach to reading is augmented by a large-unit approach as development proceeds. This trend was reflected in performance on a test of explicit phonological awareness. When asked to report the segment of sound shared by two spoken words, Primary 1 children were poor in reporting shared rimes but relatively adept in reporting shared phonemes. During Primary 2 there was an improvement in ability to report shared rimes, and this trend was also related to reading age. These results are discussed in relation to the influence of instruction and the nature of the orthography in determining the course of reading development.     

ASMR-Experience Questionnaire (AEQ): A data-driven step towards accurately classifying ASMR responders

Autonomous sensory meridian response (ASMR) describes an atypical multisensory experience of calming, tingling sensations that originate in the crown of the head in response to a specific subset of audio-visual triggers. There is currently no tool that can accurately classify both ASMR-Responders and non-responders, while simultaneously identifying False-Positive cases that are similar sensory-emotional experiences. This study sought to fill this gap by developing a new online psychometric tool – the ASMR-Experiences Questionnaire (AEQ). Participants watched a series of short ASMR videos and answered sensory-affective questions immediately afterwards. Using a k-means clustering approach, we identified five data-driven groupings, based on tingle- and affect-related scores. ASMR-Responders differentiate based on ASMR propensity and intensity (ASMR-Strong; ASMR-Weak); non-responders differentiate based on response valence (Control+; Control−; False-Positive). Recommendations for how the AEQ and the respective output groups can be best utilized to enhance ASMR research are discussed.     

The psychometric function: II. Bootstrap-based confidence intervals and sampling

The psychometric function relates an observer’s performance to an independent variable, usually a physical quantity of an experimental stimulus. Even if a model is successfully fit to the data and its goodness of fit is acceptable, experimenters require an estimate of the variability of the parameters to assess whether differences across conditions are significant. Accurate estimates of variability are difficult to obtain, however, given the typically small size of psychophysical data sets: Traditional statistical techniques are only asymptotically correct and can be shown to be unreliable in some common situations. Here and in our companion paper (Wichmann & Hill, 2001), we suggest alternative statistical techniques based on Monte Carlo resampling methods. The present paper’s principal topic is the estimation of the variability of fitted parameters and derived quantities, such as thresholds and slopes. First, we outline the basic bootstrap procedure and argue in favor of the parametric, as opposed to the nonparametric, bootstrap. Second, we describe how the bootstrap bridging assumption, on which the validity of the procedure depends, can be tested. Third, we show how one’s choice of sampling scheme (the placement of sample points on the stimulus axis) strongly affects the reliability of bootstrap confidence intervals, and we make recommendations on how to sample the psychometric function efficiently. Fourth, we show that, under certain circumstances, the (arbitrary) choice of the distribution function can exert an unwanted influence on the size of the bootstrap confidence intervals obtained, and we make recommendations on how to avoid this influence. Finally, we introduce improved confidence intervals (bias corrected and accelerated) that improve on the parametric and percentile-based bootstrap confidence intervals previously used. Software implementing our methods is available.