The development of an instrument to measure teachers' use of fear appeals in the GCSE classroom

Background Previous work has suggested that teachers of General Certificate of Secondary Education classes may use fear appeals as a motivational device but these may have unwanted consequences by increasing examination‐related anxiety in students. Aim To facilitate future work in this area, an instrument was developed to measure teachers' use of fear appeals in the course of normal classroom instruction. Samples Students in their final 2 years of compulsory schooling in England: 192 in Study 1 and 133 in Study 2. Method A construct validity approach was used in the development of this instrument. Study 1 reports the development and piloting of this measure. Study 2 reports refinement of this measure and relations with other constructs. Results A three‐factor structure provided a reasonable model fit and all factors demonstrated acceptable reliability. Factors 1 and 2 described the perceived frequency of fear appeals made in relation to educational/occupational consequences and the third factor described the perceived threat of fear appeals. Conclusion This instrument has demonstrated sufficient convergent and discriminant validity and reliability to be used in subsequent research, although the validation process should continue and it is hoped that the instrument will be adapted for use in other contexts.     

Appraisal Underpinnings of Affective Chronometry: The Role of Appraisals in Emotion Habituation

ABSTRACT In light of the lack of studies examining the cognitive components of affective chronometry, this research examined the appraisals associated with emotion habituation, using anger as the emotion of focus. Anger and its appraisals were assessed repeatedly over a day in the participants' naturalistic contexts. The trajectory of decline in anger over time after its first appearance was found to be a function of chronic coping styles. More importantly, the trajectories of anger-related appraisals generally corresponded to that of anger and were also moderated by coping styles in ways consistent with the moderating effects of coping styles on anger. Implications of these results for affective chronometry research and for appraisal research are discussed.     

Learning in noise: Dynamic decision-making in a variable environment

In engineering systems, noise is a curse, obscuring important signals and increasing the uncertainty associated with measurement. However, the negative effects of noise are not universal. In this paper, we examine how people learn sequential control strategies given different sources and amounts of feedback variability. In particular, we consider people’s behavior in a task where short- and long-term rewards are placed in conflict (i.e., the best option in the short-term is worst in the long-term). Consistent with a model based on reinforcement learning principles [Gureckis, T., & Love, B.C. Short term gains, long term pains: How cues about state aid learning in dynamic environments. Cognition (in press)], we find that learners differentially weight information predictive of the current task state. In particular, when cues that signal state are noisy, we find that participants’ ability to identify an optimal strategy is strongly impaired relative to equivalent amounts of noise that obscure the rewards/valuations of those states. In other situations, we find that noise and noise in reward signals may paradoxically improve performance by encouraging exploration. Our results demonstrate how experimentally-manipulated task variability can be used to test predictions about the mechanisms that learners engage in dynamic decision making tasks.     

Two pathways to stimulus encoding in category learning?

Category learning theorists tacitly assume that stimuli are encoded by a single pathway. Motivated by theories of object recognition, we evaluated a dual-pathway account of stimulus encoding. The part-based pathway establishes mappings between sensory input and symbols that encode discrete stimulus features, whereas the image-based pathway applies holistic templates to sensory input. Our experiments used rule-plus-exception structures, in which one exception item in each category violates a salient regularity and must be distinguished from other items. In Experiment 1, we found discrete representations to be crucial for recognition of exceptions following brief training. Experiments 2 and 3 involved multisession training regimens designed to encourage either part- or image-based encoding. We found that both pathways are able to support exception encoding, but have unique characteristics. We speculate that one advantage of the part-based pathway is the ability to generalize across domains, whereas the image-based pathway provides faster and more effortless recognition.     

TIME TRAVEL WITHOUT CAUSAL LOOPS

It has sometimes been suggested that backwards time travel always incurs causal loops. I show that this is mistaken, by describing worlds where backwards time travel occurs and yet no causal loops occur. Arguments that backwards time travel can occur without causal loops have been given before in the literature, but I show that those arguments are unconvincing.     

Short-term gains, long-term pains: How cues about state aid learning in dynamic environments

Successful investors seeking returns, animals foraging for food, and pilots controlling aircraft all must take into account how their current decisions will impact their future standing. One challenge facing decision makers is that options that appear attractive in the short-term may not turn out best in the long run. In this paper, we explore human learning in a dynamic decision making task which places short- and long-term rewards in conflict. Our goal in these studies was to evaluate how people’s mental representation of a task affects their ability to discover an optimal decision strategy. We find that perceptual cues that readily align with the underlying state of the task environment help people overcome the impulsive appeal of short-term rewards. Our experimental manipulations, predictions, and analyses are motivated by current work in reinforcement learning which details how learners value delayed outcomes in sequential tasks and the importance that “state” identification plays in effective learning.     

Pediatrician/Psychologist Collaboration: Opportunities for Clinical Child Psychologists

In the United States, pediatricians prescribe the majority of medications used to treat children with psychological disorders, such as ADHD. However, because pediatricians have limited background in psychology or psychiatry, opportunities exist for properly trained clinical child psychologists to assist pediatricians in assessing and treating youngsters with psychological disorders. Psychologists with additional training in psychopharmacology can make even greater contributions by helping pediatricians select and monitor appropriate medications. This paper reviews models of consultation that are especially relevant to psychologists who seek to expand their practice by consulting with pediatricians. Consultant-centered models seem most appropriate in private practice settings, but institutionally employed psychologists have opportunities to also utilize some system-centered approaches. Examples of methods in which pediatric clinical psychologists and child psychologists with psychopharmacology training can assist pediatricians in treating a child with ADHD are discussed.     

A Bayesian Nonparametric Approach to Test Equating

A Bayesian nonparametric model is introduced for score equating. It is applicable to all major equating designs, and has advantages over previous equating models. Unlike the previous models, the Bayesian model accounts for positive dependence between distributions of scores from two tests. The Bayesian model and the previous equating models are compared through the analysis of data sets famous in the equating literature. Also, the classical percentile-rank, linear, and mean equating models are each proven to be a special case of a Bayesian model under a highly-informative choice of prior distribution.