An information - expectation framework for decisions under uncertainty

Despite being in existence for many decades, normative decision theory has not become a commonly used tool for real-world decisions. This paper considers the reasons for this situation and suggestions for circumventing them. The main suggestion involves a two-stage framework in terms of the information available to the decision maker and his/her expectations under the available acts. This framework is well suited for decisions with incomplete structure, which is typical of real decision situations. Within this framework a specific multiplicative model is also discussed.     

Sex differences in the perceived causal structure of date rape: A preliminary report

The present study examines the perceived causes of date rape by the use of network analysis. The 142 subjects in the study were presented with grids containing 9 causes listed by row and column. They were asked to indicate if they belived there was a causal link between each of the 72 causal pairings. The resulting network placed emphasis on male and female drunkenness, norms of dating, and society's attitude to date rape as distal causes of date rape. Misunderstanding of behavior by men, male attitude that date rape is unimportant, and need by males for dominance were seen as proximal causes. Further networks for each sex were constructed: these showed clear division of opinion on the causes of date rape. © 1995 Wiley-Liss, Inc.     

The danger of conflating level-specific effects of control variables when primary interest lies in level-2 effects

In the multilevel modelling literature, methodologists widely acknowledge that a level-1 variable can have distinct within-cluster and between-cluster effects, and that failing to disaggregate these can yield a slope estimate that is an uninterpretable, conflated blend of the two. Methodologists have stated, however, that including conflated slopes of level-1 variables in a model is not problematic if substantive interest lies only in effects of level-2 predictors. Researchers commonly follow this advice and use methods that do not disaggregate effects of level-1 control variables (e.g., grand mean centering) when examining effects of level-2 predictors. The primary purpose of this paper is to show that this is a dangerous practice. When level-specific effects of level-1 variables differ, failing to disaggregate them can severely bias estimation of level-2 predictor slopes. We show mathematically why this is the case and highlight factors that can exacerbate such bias. We corroborate these findings with simulations and present an empirical example, showing how such distortions can severely alter substantive conclusions. We ultimately recommend that simply including the cluster mean of the level-1 variable as a control will alleviate the problem.     

Timing the “magical number seven”: Presentation rate and regularity affect verbal working memory performance

The informative value of time and temporal structure often remains neglected in cognitive assessments. However, next to information about stimulus identity we can exploit temporal ordering principles, such as regularity, periodicity, or grouping to generate predictions about the timing of future events. Such predictions may improve cognitive performance by optimising adaptation to dynamic stimuli. Here, we investigated the influence of temporal structure on verbal working memory by assessing immediate recall performance for aurally presented digit sequences (forward digit span) as a function of standard (1000 ms stimulus-onset-asynchronies, SOAs), short (700 ms), long (1300 ms) and mixed (700–1300 ms) stimulus timing during the presentation phase. Participant's digit spans were lower for short and mixed SOA presentation relative to standard SOAs. This confirms an impact of temporal structure on the classic “magical number seven,” suggesting that working memory performance can in part be regulated through the systematic application of temporal ordering principles.     

Belief in Hell and Parenting Priorities Concerning Child Independence and Obedience: Does Economic Context Matter?

This study examines the ways that belief in hell is associated with parental goals concerning child independence and obedience. Further, it assesses how this association is contingent upon economic circumstances of a nation, reflected by a country's Gross Domestic Product (GDP) per capita. Multilevel analyses with data from the sixth wave of the World Values Survey (2010-2014) show that belief in hell is associated with a greater likelihood of opposing independence and endorsing obedience. Yet, these observed associations differ across national economic context. Specifically, the negative association between belief in hell and parental endorsement of independence is greater in countries that are more economically developed. Similarly, the positive association between belief in hell and parental endorsement of obedience is greater in countries that are more economically developed. The theoretical implications of these findings are discussed regarding the interplay of religion, parental values about child socialization, and social context.     

A brief review of technology-based antecedent training procedures

This brief review summarizes recent research on technology-based antecedent staff training procedures, highlights key methodological components, and concludes with potential directions for future research.     

Neuroticism as the intensity,reactivity, and variability in day-to-day affect

Neuroticism has been linked to typical levels of affect, affect reactivity to negative events, and variability in affect over time. However, the intercorrelations among these characteristics make it unclear whether neuroticism reflects unique variance in each of these aspects of emotional life. Data from two daily-diary samples revealed that neuroticism was associated with average levels and variability of positive and negative affect and reactivity of negative affect to stressors, but was only uniquely related to mean levels of positive and negative affect. Findings highlight the substantial overlap in affect indices, suggesting that mean levels of affect, at the very least, are at the core of neuroticism, and reveal the need for further research using more nuanced approaches.     

Inherited and Environmental Moderators of Mother-Child Behavioral Contingency and Contingent Negativity at 27 Months

Previous research has shown that as depressive symptomology increases, mothers tend to show withdrawn or harsh interaction patterns with their children, and the quality of these interaction patterns have subsequently been linked to child behavior problems. However, little research has examined bidirectional influences between mothers and their children, and how these moment-to-moment contingencies differ based on heritable and environmental characteristics. We used data from the Early Growth and Development Study a prospective adoption study to examine how adoptive mothers’ depressive symptoms and children’s heritable tendencies for negative affectivity interact to predict the quality of mother-child interactions at child age 27-months. Results detected two distinct dyadic interaction patterns. The first was a withdrawn interaction style and was observed in children with a high heritable tendency for negative affect. In that style, mother and child interactions were not contingent upon each other, suggesting a lack of joint engagement. The second was a volatile interaction style, observed in children with a low heritable tendency for negative affect. In these cases, mother and child interactions were highly contingent but negative. Our findings demonstrate essential differences in how dyadic interaction patterns vary according to level of depressive symptomology and heritable tendency for negative affect.     

EARSHOT: A Minimal Neural Network Model of Incremental Human Speech Recognition

Despite the lack of invariance problem (the many-to-many mapping between acoustics and percepts), human listeners experience phonetic constancy and typically perceive what a speaker intends. Most models of human speech recognition (HSR) have side-stepped this problem, working with abstract, idealized inputs and deferring the challenge of working with real speech. In contrast, carefully engineered deep learning networks allow robust, real-world automatic speech recognition (ASR). However, the complexities of deep learning architectures and training regimens make it difficult to use them to provide direct insights into mechanisms that may support HSR. In this brief article, we report preliminary results from a two-layer network that borrows one element from ASR, long short-term memory nodes, which provide dynamic memory for a range of temporal spans. This allows the model to learn to map real speech from multiple talkers to semantic targets with high accuracy, with human-like timecourse of lexical access and phonological competition. Internal representations emerge that resemble phonetically organized responses in human superior temporal gyrus, suggesting that the model develops a distributed phonological code despite no explicit training on phonetic or phonemic targets. The ability to work with real speech is a major advance for cognitive models of HSR.