Children Use Temporal Cues to Learn Causal Directionality

The ability to learn the direction of causal relations is critical for understanding and acting in the world. We investigated how children learn causal directionality in situations in which the states of variables are temporally dependent (i.e., autocorrelated). In Experiment 1, children learned about causal direction by comparing the states of one variable before versus after an intervention on another variable. In Experiment 2, children reliably inferred causal directionality merely from observing how two variables change over time; they interpreted Y changing without a change in X as evidence that Y does not influence X. Both of these strategies make sense if one believes the variables to be temporally dependent. We discuss the implications of these results for interpreting previous findings. More broadly, given that many real‐world environments are characterized by temporal dependency, these results suggest strategies that children may use to learn the causal structure of their environments.     

SAwSu: An Integrated Model of Associative and Reinforcement Learning

Successfully explaining and replicating the complexity and generality of human and animal learning will require the integration of a variety of learning mechanisms. Here, we introduce a computational model which integrates associative learning (AL) and reinforcement learning (RL). We contrast the integrated model with standalone AL and RL models in three simulation studies. First, a synthetic grid‐navigation task is employed to highlight performance advantages for the integrated model in an environment where the reward structure is both diverse and dynamic. The second and third simulations contrast the performances of the three models in behavioral experiments, demonstrating advantages for the integrated model in accounting for behavioral data.     

The Effects of Prior Learned Strategies on Updating an Opponent's Strategy in the Rock,Paper, Scissors Game

To explore how model building adapts to changing environments, we had participants play “rock‐paper‐scissors” against a computer that played a frequency bias or a player‐dependent bias and then switched. Participants demonstrated their use of prior experience in how quickly they recognized and exploited changes in the computer's play strategy; in general, the more similar the strategies, the more efficient the updating. These findings inform our understanding of previously reported updating impairments in right‐brain damaged patients.     

多级评分模型下的题库结构对CAT的影响分析

在多级评分模型下,项目的难度参数或步骤参数有多个,在对多级评分模型进行选题时,通常对项目的多个难度参数用一个综合的指标来概括.当对每个项目的难度参数进行有效的综合后,综合后的难度参数分布发生了改变,这时如果增加适量的平均难度较难或较易的项目进入题库,测验的精度和项目的曝光率都有一定的改善.     

大学生的基本兴趣及其与专业承诺的关系

该研究调查了1304名来自18个不同专业领域的大学生的基本兴趣和专业承诺,并用回归法分析了特定基本兴趣与专业承诺之间的关系.结果发现：（1）大学生的31种基本兴趣在二维空间中呈现近似圆形的排列,且基本兴趣结构上的性别差异不明显;（2）在14个专业领域内大学生的特定基本兴趣能显著预测其专业承诺,所解释的变异量平均为20％,接近中等水平的效果大小;（3）在不同专业领域内基本兴趣对专业承诺的影响效果大小有所不同.     

The driver behaviour questionnaire in South-East Europe countries: Bulgaria,Romania and Serbia

Using data from three samples and more than 1000 participants, this study have examined the psychometric properties of the Driver Behaviour Questionnaire (DBQ) in three countries from South-East Europe. Differences in driving behaviour between countries were also investigated. Exploratory factor analysis results supported the distinction between errors and violations in all three countries. Furthermore, the positive associations of both errors and violations with self-reported traffic accidents were also consistent in all three samples. In terms of differences in driving behaviour, Romanian drivers scored higher on many error and violation items in comparison to the other two countries. Also, speeding violations were the most common violations in all three countries. Overall, our results provide further support for using the DBQ to measure aberrant (i.e. errors and violations) driver behaviour.     

Bidirectional feature selection with global and local structure preservation for small size samples

Collecting samples is a challenging task for face recognition, especially for some real-world applications such as law enhancement and ID card identification, where there is usually single sample per person (SSPS) used to train a face recognition system. To extract discriminative features from the small size samples, in this paper we propose virtual samples via bidirectional feature selection with global and local structure preservation (VS-BFS-GL) to augment the number of training samples. In VS-BFS-GL, bidirectional feature selection is developed, which introduces L_2,1 norm to explore the face variations from both horizontal and vertical directions. Further, to include more variations in the virtual images, the global structure information and sample-specified local structure information of the SSPP training set are considered. By integrating bidirectional feature selection, global and local structure, the limited training samples are fully utilized and more knowledge are mined. To further improve the effectiveness of VS-BFS-GL, an auxiliary database containing different face variations can be used to explore the local structure information. We extensively evaluated the proposed approach on AR and FERET database. The promising recognition results demonstrate that VS-BFS-GL is robust to expression, pose and partial occlusion variations in the faces.     

School Perceptions of Children Raised by Grandparents

Increasing numbers of children raised by grandparents are students in schools. Their substitute family structure and precursors to the emergence of this family structure have implications for the children's school performance. Research suggests teachers view these children as at risk for difficult school functioning. The aforementioned judgment is problematic because teacher expectations, attributions, and biases are associated with stereotype threat that impacts student performance adversely. The author compares children raised by grandparents with children raised by parents and foster parents. The author examines the children's perceptions of their teachers' views of their school performance. He also examines the children's general feelings about school. Participants in the study were 6,550 students in Grades 6–8 from the nationally representative Health Behaviour in School-aged Children survey. Nonparametric statistical analyses reveal children raised by grandparents believe teachers' view their school performance negatively when compared with children living with both parents. Strategies are described to improve the children's performance in school and to foster favorable teacher perceptions.     

The Influence of Rater Effects in Training Sets on the Psychometric Quality of Automated Scoring for Writing Assessments

Automated essay scoring engines (AESEs) are becoming increasingly popular as an efficient method for performance assessments in writing, including many language assessments that are used worldwide. Before they can be used operationally, AESEs must be “trained” using machine-learning techniques that incorporate human ratings. However, the quality of the human ratings used to train the AESEs is rarely examined. As a result, the impact of various rater effects (e.g., severity and centrality) on the quality of AESE-assigned scores is not known. In this study, we use data from a large-scale rater-mediated writing assessment to examine the impact of rater effects on the quality of AESE-assigned scores. Overall, the results suggest that if rater effects are present in the ratings used to train an AESE, the AESE scores may replicate these effects. Implications are discussed in terms of research and practice related to automated scoring.     

Investigating How Test-Takers Change Their Strategies to Handle Difficulty in Taking a Reading Comprehension Test: Implications for Score Validation

This article investigates how test-takers change their strategies to handle increased test difficulty. An adult sample reported their test-taking strategies immediately after completing the tasks in a reading test. Data were analyzed using structural equation modeling specifying a measurement-invariant, ability-moderated, latent transition analysis in Mplus (Muthén & Asparouhov, 2011). It was found that almost half of the test-takers (47%) changed their strategies when encountering increased task-difficulty. The changes were characterized by augmenting comprehending-meaning strategies with score-maximizing and test-wiseness strategies. Moreover, test-takers' ability was the driving influence that facilitated and/or buffered the changes. The test outcomes, when reviewed in light of adjusted test-taking strategies, demonstrated a form of process-based validity evidence.