Team adaptation to an unforeseen system failure: Limits of the potential aids of shared knowledge and standardized communication

The present study investigates the potential benefits of a team’s shared knowledge and standardized communication in adapting to an unforeseen change by combining literature on adaptation and team performance. Each of 20 teams performed a dynamic team task and was suddenly confronted with a simulated partial system breakdown. Results show that a methodological framework designed to describe performance adaptation to an unforeseen change in individuals can also be used to model performance adaptation in teams. The system failure was followed by a performance drop and a subsequent period of gradual performance recovery. Accuracy of teams’ shared knowledge correlated positively with performance before and after the change, confirming and extending the literature on shared mental models. However, the amount of knowledge similarity did not aid teams in adapting to the unforeseen system breakdown. In addition, improving teams’ standardized communication had no damping effect on the sudden performance drop and neither helped them during the subsequent recovery period. These results show that even though shared knowledge and efficient communication are of high value to team performance and success, these characteristics are limited in aiding adaptive team performance after unforeseen unique changes that force team members to update their strategies.     

Identifying expectations about the strength of causal relationships

When we try to identify causal relationships, how strong do we expect that relationship to be? Bayesian models of causal induction rely on assumptions regarding people’s a priori beliefs about causal systems, with recent research focusing on people’s expectations about the strength of causes. These expectations are expressed in terms of prior probability distributions. While proposals about the form of such prior distributions have been made previously, many different distributions are possible, making it difficult to test such proposals exhaustively. In Experiment 1 we used iterated learning—a method in which participants make inferences about data generated based on their own responses in previous trials—to estimate participants’ prior beliefs about the strengths of causes. This method produced estimated prior distributions that were quite different from those previously proposed in the literature. Experiment 2 collected a large set of human judgments on the strength of causal relationships to be used as a benchmark for evaluating different models, using stimuli that cover a wider and more systematic set of contingencies than previous research. Using these judgments, we evaluated the predictions of various Bayesian models. The Bayesian model with priors estimated via iterated learning compared favorably against the others. Experiment 3 estimated participants’ prior beliefs concerning different causal systems, revealing key similarities in their expectations across diverse scenarios.     

Spatial knowledge acquisition from direct experience in the environment: individual differences in the development of metric knowledge and the integration of separately learned places

Existing frameworks for explaining spatial knowledge acquisition in a new environment propose either stage-like or continuous development. To examine the spatial microgenesis of individuals, a longitudinal study was conducted. Twenty-four college students were individually driven along two routes in a previously unfamiliar neighborhood over 10 weekly sessions. Starting Session 4, they were also driven along a short connecting route. After each session, participants estimated spatial properties of the routes. Some participants' knowledge improved fairly continuously over the sessions, but most participants either manifested accurate metric knowledge from the first session or never manifested accurate metric knowledge. Results are discussed in light of these large individual differences, particularly with respect to the accuracy and development of integrated configurational knowledge.     

The possible benefits of difficulty: how stress can increase and decrease subjective well-being

This study explores the effects of stress, trauma, coping and growth orientation on subjective well-being. Based on cognitive stress theory, it was hypothesized that adversity may contribute to increased or decreased well-being, depending on the subsequent meaning these experiences are given. Survey data from Norwegian UN/NATO veterans (N= 142) showed that stress and well-being were negatively associated (r=-0.20, p < 0.05) at the level of zero-order correlations. However, a full structural equation model revealed that the effect of stress on well-being was mediated positively through a problem-focused coping process combined with a growth component. Stress was negatively mediated through an avoidant-focused coping process and a distress component. The effect from stress was fully mediated in the model. The hypothesis that stress can produce both increased and decreased subjective well-being was confirmed.     

Bayesian parameter estimation in the Expectancy Valence model of the Iowa gambling task

The purpose of the popular Iowa gambling task is to study decision making deficits in clinical populations by mimicking real-life decision making in an experimental context. Busemeyer and Stout [Busemeyer, J. R., & Stout, J. C. (2002). A contribution of cognitive decision models to clinical assessment: Decomposing performance on the Bechara gambling task. Psychological Assessment, 14, 253-262] proposed an “Expectancy Valence” reinforcement learning model that estimates three latent components which are assumed to jointly determine choice behavior in the Iowa gambling task: weighing of wins versus losses, memory for past payoffs, and response consistency. In this article we explore the statistical properties of the Expectancy Valence model. We first demonstrate the difficulty of applying the model on the level of a single participant, we then propose and implement a Bayesian hierarchical estimation procedure to coherently combine information from different participants, and we finally apply the Bayesian estimation procedure to data from an experiment designed to provide a test of specific influence.     

A longitudinal study of student-teacher relationship quality, difficult temperament, and risky behavior from childhood to early adolescence

This study examines the mediating role of student-teacher relationship quality (conflict and closeness) in grades 4, 5, and 6 on the relation between background characteristics, difficult temperament at age 4½ and risky behavior in 6th grade. The longitudinal sample of participants (N = 1156) was from the NICHD Study of Early Child Care and Youth Development. Structural equation modeling was used to estimate paths from (a) background characteristics to student-teacher relationship quality and risky behavior, (b) temperament to student-teacher relationship quality and risky behavior, and (c) student-teacher relationship quality to risky behavior. Findings indicate that students' family income, gender, receipt of special services, and more difficult temperament were associated with risky behavior. In addition, student-teacher conflict was a mediator. Students with more difficult temperaments were more likely to report risky behavior and to have conflict in their relationships with teachers. More conflict predicted more risky behavior. Closer student-teacher relationships were associated with less risky behavior. Results suggest negative relationships, specifically student-teacher relationships, may increase the risk that certain adolescents will engage in risky behavior.     

School profiles of at-risk student concentration: Differential growth in oral reading fluency

The present study provides a data-driven approach to identifying groups of schools based on the concentration of at-risk students the school serves. The percentage of English language learners, minority students, and students eligible for free or reduced priced lunch were used as indicators in a latent profile analysis of 569 schools. The goal of the present study was to determine whether school-level average student reading performance varied as a function of the groups identified in the latent profile analysis. To do so, groups extracted by the latent profile analysis were used as school-level predictors of growth in oral reading fluency, which was modeled at the within-student level of a three-level hierarchical growth curve model. Oral reading fluency was measured at four points during the year in a large cross-sectional sample of first-, second-, and third-grade students. Results indicated that schools were able to be classified into four distinct groups based on their concentrations and types of at-risk students. Further, in all three grades, there were significant differences between the four identified groups observed in average reading fluency scores at the beginning of the year, the end of the year, and growth during the year indicating that groups based on school-concentration of at-risk students were significantly related to average student achievement in reading ability.     

Learning letter names and sounds: Effects of instruction, letter type, and phonological processing skill

Preschool-age children (N = 58) were randomly assigned to receive instruction in letter names and sounds, letter sounds only, or numbers (control). Multilevel modeling was used to examine letter name and sound learning as a function of instructional condition and characteristics of both letters and children. Specifically, learning was examined in light of letter name structure, whether letter names included cues to their respective sounds, and children’s phonological processing skills. Consistent with past research, children receiving letter name and sound instruction were most likely to learn the sounds of letters whose names included cues to their sounds regardless of phonological processing skills. Only children with higher phonological skills showed a similar effect in the control condition. Practical implications are discussed.     

Beta-MPT: Multinomial processing tree models for addressing individual differences

Traditionally, multinomial processing tree (MPT) models are applied to groups of homogeneous participants, where all participants within a group are assumed to have identical MPT model parameter values. This assumption is unreasonable when MPT models are used for clinical assessment, and it often may be suspect for applications to ordinary psychological experiments. One method for dealing with parameter variability is to incorporate random effects assumptions into a model. This is achieved by assuming that participants’ parameters are drawn independently from some specified multivariate hyperdistribution. In this paper we explore the assumption that the hyperdistribution consists of independent beta distributions, one for each MPT model parameter. These beta-MPT models are ‘hierarchical models’, and their statistical inference is different from the usual approaches based on data aggregated over participants. The paper provides both classical (frequentist) and hierarchical Bayesian approaches to statistical inference for beta-MPT models. In simple cases the likelihood function can be obtained analytically; however, for more complex cases, Markov Chain Monte Carlo algorithms are constructed to assist both approaches to inference. Examples based on clinical assessment studies are provided to demonstrate the advantages of hierarchical MPT models over aggregate analysis in the presence of individual differences.