Assessing State Mental Contamination: Development and Preliminary Validation of the State Mental Contamination Scale

This 2-part study described the development and preliminary validation of the State Mental Contamination Scale (SMCS), a multi-item self-report measure of state mental contamination. In Study 1, community adults (N = 211) in the United States were randomly assigned to a pathogen disgust, moral disgust, or neutral condition and then completed the SMCS. Study 1 results supported a 1-factor structure of the SMCS items. Scores on the SMCS items evidenced good internal consistency and strong interitem correlations. SMCS scores were significantly greater following the 2 disgust conditions relative to the neutral condition and a full range of SMCS scores were found in the disgust conditions. An independent sample of community adults (N = 121) from the United States participated in Study 2 to extend Study 1 results. Study 2 results indicated that SMCS scores were manipulated independently of trait mental contamination, more strongly related to convergent than a discriminant measure, and shared unique variance with a criterion measure after controlling for convergent measures. The future use of the SMCS is discussed.     

Patterns of emotional availability between mothers and young children: Associations with risk factors for borderline personality disorder

Emotional availability (EA) characterizes a warm, close relationship between caregiver and child. We compared patterns (clusters) of EA on risk factors, including those for borderline personality disorder (BPD). We sampled 70 children aged 4 to 7 years from low socio‐economic backgrounds: 51% of whose mothers had BPD. We coded filmed interactions for EA: mothers' sensitivity, structuring, non‐intrusiveness, non‐hostility, and children's responsiveness to, and involvement of, mothers. We additionally coded children's over‐responsiveness and over‐involvement. Using person‐centred analyses, we identified four clusters: high functioning, low functioning, asynchronous (mothers above average on two of four dimensions and children below), and below average. Mothers in the low‐functioning cluster had lower income, less social support, more of the borderline feature of negative relationships, and more depression than did mothers in the high‐functioning cluster. The children in the low‐functioning group had more risk factors for BPD (physical abuse, neglect, and separation from, or loss of caregivers, and negative narrative representations of the mother–child relationship in their stories) than did children in the high‐functioning group. The asynchronous group included older girls who were over‐responsive and over‐involving with their mothers in an apparent role reversal. Interventions targeting emotional availability may provide a buffer for children facing cumulative risks and help prevent psychopathology.

Highlights

• This paper investigated how mother‐child emotional availability (warmth and closeness) relates to risk factors for borderline personality disorder, including mother‐child role reversal.
• In filmed mother‐child interactions, low emotional availability was associated with risk for borderline personality disorder and role reversal was more likely for older girls.
• Findings support the cumulative risk hypothesis and may inform interventions to improve mother‐child emotional availability to prevent the development of psychopathology.

     

Isolated words enhance statistical language learning in infancy

Infants are adept at tracking statistical regularities to identify word boundaries in pause-free speech. However, researchers have questioned the relevance of statistical learning mechanisms to language acquisition, since previous studies have used simplified artificial languages that ignore the variability of real language input. The experiments reported here embraced a key dimension of variability in infant-directed speech. English-learning infants (8-10 months) listened briefly to natural Italian speech that contained either fluent speech only or a combination of fluent speech and single-word utterances. Listening times revealed successful learning of the statistical properties of target words only when words appeared both in fluent speech and in isolation; brief exposure to fluent speech alone was not sufficient to facilitate detection of the words' statistical properties. This investigation suggests that statistical learning mechanisms actually benefit from variability in utterance length, and provides the first evidence that isolated words and longer utterances act in concert to support infant word segmentation.     

Interactions between statistical and semantic information in infant language development

Infants can use statistical regularities to form rudimentary word categories (e.g. noun, verb), and to learn the meanings common to words from those categories. Using an artificial language methodology, we probed the mechanisms by which two types of statistical cues (distributional and phonological regularities) affect word learning. Because linking distributional cues vs. phonological information to semantics make different computational demands on learners, we also tested whether their use is related to language proficiency. We found that 22-month-old infants with smaller vocabularies generalized using phonological cues; however, infants with larger vocabularies showed the opposite pattern of results, generalizing based on distributional cues. These findings suggest that both phonological and distributional cues marking word categories promote early word learning. Moreover, while correlations between these cues are important to forming word categories, we found infants' weighting of these cues in subsequent word-learning tasks changes over the course of early language development.     

Nano-Technology, Ethics, and Risks

Nanotechnology is developing far faster than our understanding of its effects. This lapping of our understanding by speedy development is typical of new technologies, and in the United States we let development occur, introducing new artifacts into the world, without any serious attempt to understand beforehand their effects, long-term or short-term. We have been willing to pay the price of pushing the technological envelope, but pushing the nanotechnological envelope has some special risks, requiring more caution.     

Simple rules for detecting depression

Depressive disorders are major public health issues worldwide. We tested the capacity of a simple lexicographic and noncompensatory fast and frugal tree (FFT) and a simple compensatory unit-weight model to detect depressed mood relative to a complex compensatory logistic regression and a naïve maximization model. The FFT and the two compensatory models were fitted to the Beck Depression Inventory (BDI) score of a representative sample of 1382 young women and cross validated on the women's BDI score approximately 18 months later. Although the FFT on average inspected only approximately one cue, it outperformed the naïve maximization model and performed comparably to the compensatory models. The heavier false alarms were weighted relative to misses, the better the FFT and the unit-weight model performed. We conclude that simple decision tools—which have received relatively little attention in mental health settings so far—might offer a competitive alternative to complex weighted assessment models in this domain.