“What Makes a Cooperative Driver?” Identifying parameters of implicit and explicit forms of communication in a lane change scenario

An automated vehicle needs to learn how human road users communicate with each other in order to avoid misunderstandings and prevent giving a negative outward image during interactions. The aim of the present work is to develop an autonomous driving system which communicates its intentions to change lanes based on implicit and explicit rules used by human drivers. To reach this goal, we aimed at gaining a deeper understanding of which aspects of lane change behaviour makes them cooperative from the perspective of other drivers. Therefore a vehicle used various lane change announcement strategies by varying combinations of driving parameters in a static driving simulator. (First study: Start indicator signal, Waittime, lane change duration; Second study: Longitudinal acceleration). It’s impact on the perception and behaviour of other road users was observed in two studies (N = 25 per study). The results showed that the earlier the merging vehicle was indicating its intentions, the more cooperative it was perceived. When turning on the indicator at a later time participants considered it as more cooperative to merge with a slower or faster lane change duration or to wait longer in the lane before starting to move to the other lane. An early longitudinal acceleration when starting to change lanes is perceived more cooperative. These findings can be used to model a lane change strategy based on human behaviour, which will eventually lead to more acceptable and safer interactions between automated and non-automated road users.     

Authentic leadership,followership, and psychological capital as antecedents of work engagement

The present study investigated authentic leadership, psychological capital, and followership behaviour influences on work engagement of employees. Respondents were 901 South African employees within the healthcare industry organisation (n = 647) and mining industry (n = 254). The employees completed questionnaires on authentic leadership, psychological capital, and followership behaviour, and work engagement. Results following structural equation modelling and mediation analysis suggest work engagement to be explained by the psychological capital of the employee rather than by authentic leadership qualities. Improving employee psychological capital has the potential to enhance the levels of work engagement of employees.     

学龄早期词语阅读习得对阅读理解能力的预测作用：初始水平和发展速率的贡献

本研究测查了183名儿童在一年级期间（学年初,学年中,学年末）的词语阅读能力（词语解码,阅读流畅性）及一年级末和二年级末的阅读理解能力,采用分层线性模型估计每名儿童在一年级期间词语阅读能力的初始水平和发展速率,以考察二者对阅读理解能力的独立预测作用。结果表明,词语识别的初始水平和增长速率都能独立预测一年级末和二年级末的阅读理解能力,阅读流畅性的初始水平和增长速率只能独立预测二年级末的阅读理解能力,但可以预测阅读理解能力在一年级到二年级的增长。     

三类多层中介效应分析方法比较

比较了贝叶斯法、Monte Carlo法和参数Bootstrap法在2-1-1多层中介分析中的表现。结果发现：1)有先验信息的贝叶斯法的中介效应点估计和区间估计都最准确;2)无先验信息的贝叶斯法、Monte Carlo法、偏差校正和未校正的参数Bootstrap法的中介效应点估计和区间估计表现相当,但Monte Carlo法在第Ⅰ类错误率和区间宽度指标上表现略优于其他三种方法,偏差校正的Bootstrap法在统计检验力上表现略优于其他三种方法,但在第Ⅰ类错误率上表现最差;结果表明,当有先验信息时,推荐使用贝叶斯法;当先验信息不可得时,推荐使用Monte Carlo法。     

Do complex models increase prediction of complex behaviours? Predicting driving ability in people with brain disorders

Prediction of complex behavioural tasks via relatively simple modelling techniques, such as logistic regression and discriminant analysis, often has limited success. We hypothesized that to more accurately model complex behaviour, more complex models, such as kernel-based methods, would be needed. To test this hypothesis, we assessed the value of six modelling approaches for predicting driving ability based on performance on computerized sensory–motor and cognitive tests (SMCTests?) in 501 people with brain disorders. The models included three models previously used to predict driving ability (discriminant analysis, DA; binary logistic regression, BLR; and nonlinear causal resource analysis, NCRA) and three kernel methods (support vector machine, SVM; product kernel density, PK; and kernel product density, KP). At the classification level, two kernel methods were substantially more accurate at classifying on-road pass or fail (SVM 99.6%, PK 99.8%) than the other models (DA 76%, BLR 78%, NCRA 74%, KP 81%). However, accuracy decreased substantially for all of the kernel models when cross-validation techniques were used to estimate prediction of on-road pass or fail in an independent referral group (SVM 73–76%, PK 72–73%, KP 71–72%) but decreased only slightly for DA (74–75%) and BLR (75–76%). Cross-validation of NCRA was not possible. In conclusion, while kernel-based models are successful at modelling complex data at a classification level, this is likely to be due to overfitting of the data, which does not lead to an improvement in accuracy in independent data over and above the accuracy of other less complex modelling techniques.     

Contrast effects in typicality judgements: A hierarchical Bayesian approach

We examine the influence of contrast categories on the internal graded membership structure of everyday concepts using computational models proposed in the artificial category learning tradition. In particular, the generalized context model (Nosofsky, 1986), which assumes that only members of a given category contribute to the typicality of a category member, is contrasted to the similarity–dissimilarity generalized context model (SD-GCM; Stewart & Brown, 2005), which assumes that members of other categories are also influential in determining typicality. The models are compared in a hierarchical Bayesian framework in their account of the typicality gradient of five animal categories and six artefact categories. For each target category, we consider all possible relevant contrast categories. Three separate issue are examined: (a) whether contrast effects can be found, (b) which categories are responsible for these effects, and (c) whether more than one category influences the typicality. Results indicate that the internal category structure is codetermined by dissimilarity towards potential contrast categories. In most cases, only a single contrast category contributed to the typicality. The present findings suggest that contrast effects might be more widespread than has previously been assumed. Further, they stress the importance of characteristics particular of everyday concepts, which require careful consideration when applying computational models of representation of the artificial category learning tradition to everyday concepts.     

When and for whom do frequencies facilitate performance? On the role of numerical literacy

The thesis that the mind is better prepared to process frequencies—as compared to other numerical formats—continues to be debated. A recent aspect of this issue is the role of numeracy (numerical literacy; one's ability to understand and work with numerical information) and specifically the argument that individual differences in numeracy interact with numerical formats. This interaction, either that frequencies improve performance only for those of low numeracy or that frequencies work only for those of high numeracy, would suggest that better performance using frequencies could be due to (nonevolutionary) numeracy effects. The three present studies revisited prior work with cumulative probability, Bayesian reasoning, and scenario risk assessments to study the effects of numeracy on frequency facilitation. Results from these experiments consistently failed to replicate previous findings of interactions; however, a more consistent finding emerged of a straightforward frequency effect. The lack of interactions and observations of frequency main effects lend support to the evolutionary explanation of the frequency effect. In addition, some possible statistical processes are proposed to explain the observation of interactions in past studies.     

Attention and memory protection: Interactions between retrospective attention cueing and interference

Visual working memory (VWM) and attention have a number of features in common, but despite extensive research it is still unclear how the two interact. Can focused attention improve VWM precision? Can it protect VWM from interference? Here we used a partial-report, continuous-response orientation memory task to examine how attention and interference affect different aspects of VWM and how they interact with one another. Both attention and interference were orthogonally manipulated during the retention interval. Attention was manipulated by presenting informative retro-cues, whereas interference was manipulated by introducing a secondary interfering task. Mixture-model analyses revealed that retro-cues, compared to uninformative cues, improved all aspects of performance: Attention increased recall precision and decreased guessing rate and swap-errors (reporting a wrong item in memory). Similarly, performing a secondary task impaired all aspects of the VWM task. In particular, an interaction between retro-cue and secondary task interference was found primarily for swap-errors. Together these results suggest that both the quantity and quality of VWM representations are sensitive to attention cueing and interference modulations, and they highlight the role of attention in protecting the feature–location associations needed to access the correct items in memory.     

Inconsistency effects in source memory and compensatory schema-consistent guessing

The attention–elaboration hypothesis of memory for schematically unexpected information predicts better source memory for unexpected than expected sources. In three source-monitoring experiments, the authors tested the occurrence of an inconsistency effect in source memory. Participants were presented with items that were schematically either very expected or very unexpected for their source. Multinomial processing tree models were used to separate source memory, item memory, and guessing bias. Results show an inconsistency effect in source memory accompanied by a compensatory schema-consistent guessing bias when expectancy strength is high, that is, when items are very expected or very unexpected for their source.