Exploring the enactment effect from an information processing view: What can we learn from serial position analyses?

The focus of the present article was to analyze processes that determine the enactment and age effect in a multi-trial free recall paradigm by looking at the serial position effects. In an experimental study (see Schatz et al 2010), the performance-enhancing effect of enactive encoding and repeated learning was tested with older and younger participants. As expected, there was a steady improvement of memory performance as a function of repeated learning regardless of age. In addition, enactive encoding led to a better memory performance than verbal encoding in both age groups. Furthermore, younger adults outperformed the elderly regardless of type of encoding. Analyses in the present article show that encoding by enacting seems to profit especially from remembering the last items of a presented list. Regarding age differences, younger outperformed older participants in nearly all item positions. The performance enhancement after task repetition is due to a higher amount of recalled items in the middle positions in a subject performed task (SPT) and a verbal task (VT) as well as the last positions of a learned list in VT.     

Fundamental Beliefs,Origin Explanations and Perceived Effectiveness of Protection Measures: Exploring Laypersons' Chains of Reasoning About Influenza

Laypersons' chains of reasoning in explaining recent influenza outbreaks are investigated. Drawing on social representations theory, fundamental worldviews, that is, the belief in a dangerous world (BDW), are postulated to anchor explanations of disease origins, which in turn affect perceived effectiveness of protection measures. Our study, based on a longitudinal survey among the general public in Switzerland, showed that the lower people's BDW scores, the more they appeal to natural origins to explain outbreaks and the more they perceive official protection measures as effective. The higher people's BDW scores, the more they explain outbreaks via hygienic origins, which are linked with out‐group discrimination measures, and conspiracy origins, which are linked with lower perceived effectiveness of aid intervention measures. Copyright © 2013 John Wiley & Sons, Ltd.     

Situational information and the design of representative learning tasks: What impact does a scoreboard have on expert taekwondo fighters' behaviour and affective-cognitive responses?

Designing representative learning tasks is one means to enhance sports practice. Recent work has highlighted how the presence of situational information could help the design of these tasks by shaping intentions and enhancing the affective demands of practice, however this has yet to be empirically tested. This study tested this hypothesis by manipulating the presence of a scoreboard featuring time and score situational information as expert taekwondo athletes fought in practice. Nine taekwondo fighters fought with and without situational information in a counterbalanced order. Behaviour was assessed by tracking fighters' location coordinates to assess fighter-fighter dyad coordination and through notational analysis of attacking actions. Affect and cognition were assessed with mixed-methods that included perceptual scales measuring anxiety, arousal, mental effort, score perception, and post-fight video-facilitated confrontational interviews to explore how conditions differed. The results revealed that the presence of the situational information had significant effects on taekwondo players. When present, fighters reported greater cognitive anxiety (d = 0.39, p < 0.05), somatic anxiety (d = 1.11, p < 0.05) and emotion intensity (d = 0.33, p < 0.05). The enhanced affective demands were associated with behaviour changes that included fighters preferring to spend time at closer distances (d = 0.25, p < 0.05), and more predictable technique selection (d = 1.04, p < 0.05). Qualitative data supported these findings. Players also reported their intentions were coupled to the context of scoreboard. This study reveals that situational information changes the affective and behavioural demands of practice to be more like competition. Further, situational sampling affords performers the opportunity to practice attuning to the relevant affordances for a specific context.     

User gesticulation inside an automated vehicle with external communication can cause confusion in pedestrians and a lower willingness to cross

Automated vehicles are expected to require some form of communication (e.g., via LED strip or display) with vulnerable road users such as pedestrians. However, the passenger inside the automated vehicle could perform gestures or motions which could potentially be interpreted by the pedestrian as contradictory to the outside communication of the car. To explore this conflict, we conducted an online experiment (N = 59) with different message types (no message, intention, command), gestures (no gesture, wave, stop), and user positions (driver, co-driver) and measured the pedestrian’s confidence in crossing. Our results show that certain combinations (e.g., car indicates cross while the user in the driver seat gestures stop) confused the pedestrian, resulting in significantly lower confidence to cross. We further show that designing intention-based external communication led to less confusion and a significantly higher intention to cross.     

External communication of automated vehicles in mixed traffic: Addressing the right human interaction partner in multi-agent simulation

The urban traffic system is most likely to change in the next years to a mixed traffic with human drivers, vulnerable road users, and automated vehicles. In the past, the development of external communication approaches for automated vehicles focused on scenarios where an automated vehicle communicates with either a pedestrian or a human driver. However, interactions with more than one traffic partner are more realistic. Therefore, a study with 42 participants was conducted with a multi-agent simulation in which an automated vehicle interacted simultaneously with two participants, a pedestrian and a driver of a manual vehicle. In this study, two main scenarios were investigated in order to evaluate the safety and efficiency of the interactions and to determine whether the human road users feel correctly addressed. In one scenario, the pedestrian had to cross the road in front of the automated and the manual vehicle, which were approaching from different sides. In the other, the manual vehicle had to drive through a bottleneck in front of the oncoming automated vehicle, while the pedestrian had to cross the road after both vehicles passed. The communication approach of the automated vehicle consisted of implicit signals using a speed profile and lateral offset within its lane, and explicit signals using an external human–machine interface. The results of the study show that no collisions were observed in terms of safety and no significant negative effects on efficiency were measured. However, in contrast to single agent interactions, a majority of participants felt wrongly addressed in situations where the automated vehicle signals the right-of-way to the other human road user. It can be concluded that the communication approach of the automated vehicle needs to be modified in order to address certain road users more clearly.     

项目反应理论观察分数核等值的影响因素

探究带宽选择方法、样本量、题目数量、等值设计、数据模拟方式对项目反应理论观察分数核等值的影响。通过两种数据模拟方式,获得研究数据,并计算局部与全域评价指标。研究发现,在随机组设计中,带宽选择方法表现相似;考生样本量和题目数量影响甚微。在非等组设计中,惩罚法与Silverman经验准则表现优异;增加题目量可降低百分相对误差和随机误差;增加样本量导致百分相对误差变大,随机误差减小。数据模拟方式可影响等值评价。未来应重点关注等值系统评估。     

社会创造性的微观变化：基于虚拟仿真技术的研究

本研究将微观发生法设计和虚拟仿真技术相结合,考察了不同特质社会创造性大学生跨情境、跨时间的状态社会创造性的变化特点。结果表明：（1）在四种具体社会问题情境中,个体策略新颖性的变化表现出了跨情境的多样性特点,而策略实用性的变化则主要表现出了跨情境的一致性特点;（2）个体特质社会创造性对其状态社会创造性的表现的影响主要体现在策略的实用性上。     

Single-case effect size calculation: Comparing regression and non-parametric approaches across previously published reading intervention data sets

Growing from demands for accountability and research-based practice in the field of education, there is recent focus on developing standards for the implementation and analysis of single-case designs. Effect size methods for single-case designs provide a useful way to discuss treatment magnitude in the context of individual intervention. Although a standard effect size methodology does not yet exist within single-case research, panel experts recently recommended pairing regression and non-parametric approaches when analyzing effect size data. This study compared two single-case effect size methods: the regression-based, Allison-MT method and the newer, non-parametric, Tau-U method. Using previously published research that measured the Words read Correct per Minute (WCPM) variable, these two methods were examined by comparing differences in overall effect size scores and rankings of intervention effect. Results indicated that the regression method produced significantly larger effect sizes than the non-parametric method, but the rankings of the effect size scores had a strong, positive relation. Implications of these findings for research and practice are discussed.     

Using generalized additive (mixed) models to analyze single case designs

This article shows how to apply generalized additive models and generalized additive mixed models to single-case design data. These models excel at detecting the functional form between two variables (often called trend), that is, whether trend exists, and if it does, what its shape is (e.g., linear and nonlinear). In many respects, however, these models are also an ideal vehicle for analyzing single-case designs because they can consider level, trend, variability, overlap, immediacy of effect, and phase consistency that single-case design researchers examine when interpreting a functional relation. We show how these models can be implemented in a wide variety of ways to test whether treatment is effective, whether cases differ from each other, whether treatment effects vary over cases, and whether trend varies over cases. We illustrate diagnostic statistics and graphs, and we discuss overdispersion of data in detail, with examples of quasibinomial models for overdispersed data, including how to compute dispersion and quasi-AIC fit indices in generalized additive models. We show how generalized additive mixed models can be used to estimate autoregressive models and random effects and discuss the limitations of the mixed models compared to generalized additive models. We provide extensive annotated syntax for doing all these analyses in the free computer program R.