Sex with children is abuse: Comment on Rind, Tromovitch, and Bauserman (1998).

B. Rind, P. Tromovitch, and R. Bauserman (1998) reported a meta-analysis of the relation between sexual abuse in childhood and adolescence and psychological functioning among college students. Several aspects of their work have proven to be highly controversial, including their assertion that the relation between child sexual abuse and adjustment is quite small and their questioning of whether child sexual abuse should be labeled abuse in scientific inquiry. In this commentary, the authors summarize the controversy that has ensued, place it in a historical context, discuss the limitations of B. Rind et al.'s findings, and critique the manner in which those findings are presented. The authors also argue for the appropriateness of the term abuse and for scientific terminology that reflects rather than contradicts consensual public morality.     

Deconstructing antenatal depression: What is it that matters for neonatal behavioral functioning?

This prospective study examined the variability within clinical characteristics of antenatal maternal depression and cortisol levels for associations with newborn infant behavior using the Neonatal Behavioral Assessment Scale (NBAS; T.B. Brazelton, 1984 ). Participants were 81 pregnant women at risk for perinatal depression given their histories of depression prior to pregnancy. We took into consideration not only whether the woman experienced antenatal depression but also whether the depression met diagnostic criteria and variability in timing (onset and occurrence) of antenatal depression and symptom severity. Infants of mothers who became depressed during pregnancy scored less optimally on a subset of the NBAS scales, specifically those scales related to infant neuroregulation. Among the clinical characteristics of depression, the fetus' overall exposure to mothers' depression (reflected in the mean) was most often and most strongly associated with NBAS scales. In terms of timing, third‐trimester exposure was significantly related to newborn behavior. The findings are discussed within the S.H. Goodman and I.H. Gotlib ( 1999 ) model for transmission of psychopathology to offspring of depressed mothers.     

Using the Interactive Systems Framework to Support a Quality Improvement Approach to Dissemination of Evidence-Based Strategies to Promote Early Detection of Breast Cancer: Planning a Comprehensive Dynamic Trial

Dissemination efforts must optimize interventions for new settings and populations. As such, dissemination research should incorporate principles of quality improvement. Comprehensive Dynamic Trial (CDT) designs examine how information gained during dissemination may be used to modify interventions and improve performance. Although CDT may offer distinct advantages over static designs, organizing the many necessary roles and activities is a significant challenge. In this article, we discuss use of the Interactive Systems Framework for Dissemination and Implementation to systematically implement a CDT. Specifically, we describe “Bronx ACCESS”, a program designed to disseminate evidence‐based strategies to promote adherence to mammography guidelines. In Bronx ACCESS, the Intervention Delivery System will elicit information needed to adapt strategies to specific settings and circumstances. The Intervention Synthesis and Translation System will use this information to test changes to strategies through “embedded experiments”. The Intervention Support System will build local capacities found to be necessary for intervention institutionalization. Simulation modeling will be used to integrate findings across systems. Results will inform on‐going policy debate about interventions needed to promote population‐level screening. More generally, this project is intended to advance understanding of research paradigms necessary to study dissemination. Special Issue: Advances in Bridging Research and Practice Using the Interactive System Framework for Dissemination and Implementation; Guest Editors: Abraham Wandersman, Paul Flaspohler, Catherine A. Lesesne, Richard Puddy; Action Editor: Emilie Phillips Smith     

Practicing Safe Text: the Impact of Texting on Walking Behavior

Sending and receiving text messages on cell phones is increasingly common. Texting while walking or driving, however, increases the likelihood of accidents. One possible cause is that distracted individuals are unable to detect safe opportunities for action. To investigate this, we asked participants to walk through doorways of differing widths while holding or texting on their phone. We measured walking speed, doorway‐to‐shoulder‐width ratio, and the number of bumps into the doorframes. Our results revealed that texters were more cautious than non‐texters; they walked slower and rotated their body through doorways they could have safely walked straight through. There were no significant differences, however, in the number of bumps into the doorframes. If texters in the real world behave like those in our laboratory, then the number of texting‐related accidents reported in other studies might suggest that being overcautious while texting does not actually decrease the likelihood of accidents. Copyright © 2012 John Wiley & Sons, Ltd.     

Theory learning as stochastic search in the language of thought

We present an algorithmic model for the development of children's intuitive theories within a hierarchical Bayesian framework, where theories are described as sets of logical laws generated by a probabilistic context-free grammar. We contrast our approach with connectionist and other emergentist approaches to modeling cognitive development. While their subsymbolic representations provide a smooth error surface that supports efficient gradient-based learning, our symbolic representations are better suited to capturing children's intuitive theories but give rise to a harder learning problem, which can only be solved by exploratory search. Our algorithm attempts to discover the theory that best explains a set of observed data by performing stochastic search at two levels of abstraction: an outer loop in the space of theories and an inner loop in the space of explanations or models generated by each theory given a particular dataset. We show that this stochastic search is capable of learning appropriate theories in several everyday domains and discuss its dynamics in the context of empirical studies of children's learning.     

One and Done? Optimal Decisions From Very Few Samples

In many learning or inference tasks human behavior approximates that of a Bayesian ideal observer, suggesting that, at some level, cognition can be described as Bayesian inference. However, a number of findings have highlighted an intriguing mismatch between human behavior and standard assumptions about optimality: People often appear to make decisions based on just one or a few samples from the appropriate posterior probability distribution, rather than using the full distribution. Although sampling‐based approximations are a common way to implement Bayesian inference, the very limited numbers of samples often used by humans seem insufficient to approximate the required probability distributions very accurately. Here, we consider this discrepancy in the broader framework of statistical decision theory, and ask: If people are making decisions based on samples—but as samples are costly—how many samples should people use to optimize their total expected or worst‐case reward over a large number of decisions? We find that under reasonable assumptions about the time costs of sampling, making many quick but locally suboptimal decisions based on very few samples may be the globally optimal strategy over long periods. These results help to reconcile a large body of work showing sampling‐based or probability matching behavior with the hypothesis that human cognition can be understood in Bayesian terms, and they suggest promising future directions for studies of resource‐constrained cognition.     

Inexact Knowledge without Improbable Knowing

Abstract

In a series of recent papers, Timothy Williamson has argued for the surprising conclusion that there are cases in which you know a proposition in spite of its being overwhelmingly improbable given what you know that you know it. His argument relies on certain formal models of our imprecise knowledge of the values of perceptible and measurable magnitudes. This paper suggests an alternative class of models that do not predict this sort of improbable knowing. I show that such models are motivated by independently plausible principles in the epistemology of perception, the epistemology of estimation, and concerning the connection between knowledge and justified belief.     

Books Received

Abstract

Early childhood deprivation, attachment chaos, repeated medical traumata, and abuse set up neural networks of dread, shame, worthlessness, and hopelessness: the phenomenology of abject experience that resides in implicit memory. Abject interactions with others condense yearning and despair into complex enactments of abject suffering. Working in abject subgroups dilutes the intensity of abject material as ownership of toxic affects is broadened. Affective engagement and a relational perspective facilitate the management and transformation of self-abjection into more optimal relatedness in combined group and individual treatment.