Adaptations in driver deceleration behaviour with automatic incident detection: A naturalistic driving study

Traffic congestion and crash rates can be reduced by introducing variable speed limits (VSLs) and automatic incident detection (AID) systems. Previous findings based on loop detector measurements have revealed that drivers reduce their speeds while approaching traffic congestion when the AID system is active. Notwithstanding these behavioural effects, most microscopic traffic flow models assessing the impact of VSLs do not describe driver response accurately.This study analyses the main factors that influence driver deceleration behaviour while approaching traffic congestion with and without VSLs. The Dutch VSL database was linked to the driver behaviour data collected in the UDRIVE naturalistic driving study. Driver engagement in secondary tasks and glance behaviour were extracted from the video data. Linear mixed-effects models predicting the characteristics of deceleration events were estimated.The results show that the maximum deceleration is high when approaching a slower leader, when driving at high speeds and short distance headways, and close to the beginning of traffic congestion. The minimum time headway is short when driving at high speeds and changing lanes. Certain drivers showed higher decelerations and shorter time headways than others. Controlled for these main factors, smaller maximum decelerations were found when the VSLs were present and visible, and when the gantries were within close proximity. These factors could be incorporated into microscopic traffic simulations to evaluate the impact of AID systems on traffic congestion more realistically. Further research is needed to clarify the link between engagement in secondary tasks, glance behaviour and deceleration behaviour.     

Trust and intention to use autonomous vehicles: Manufacturer focus and passenger control

Autonomous Vehicle (AV) research has focused on public acceptance of and intention to use AVs, with trust emerging as important to these variables. Research on AV trust has centered on trust in vehicle performance. However, trust evaluation may include AV manufacturers and developers, and regulation pertaining to AVs; thus, we expand our measurement of trust to include beliefs based on manufacturers and regulation. In this experiment, we manipulate information regarding manufacturer focus (i.e., an emphasis on standards, regulations, and research (SRR) versus speed to market) to determine its effect on trust. When information focused on SRR, we found higher levels of trust in AV performance and in manufacturers, compared to when it focused on speed to market. In addition, information regarding passenger control (i.e., the ability to take over for the vehicle and to determine privacy settings) was manipulated to yield either high or low passenger control conditions. Behavioral Intention (BI) to use AVs was lowest when speed to market was emphasized and when passenger control was low. Furthermore, trust was tested as a mediator between perceived risk and BI. Trust in AV performance partially mediated the relationship between perceived AV performance risk and BI. In addition, trust in AV regulation partially mediated the relationship between AV privacy/security risk and BI. Researchers should continue to examine trust beyond the AV itself to encompass trust in manufacturers and regulations. Those designing and marketing AVs should carefully consider decisions that influence manufacturer/developer reputation and passenger control due to their effect on intention to use AVs.     

Learning algorithm for an intelligent decision making system based on multi-agent neurocognitive architectures

The paper presents the formalism of an intelligent decision-making system based on multi-agent neurocognitive architectures, which has an architectural similarity to the human brain. An invariant of the organizational and functional structure of the intellectual decision-making process based on the multi-agent neurocognitive architecture is developed. An algorithm for teaching intelligent decision-making systems based on the self-organization of the invariant of multi-agent neurocognitive architectures is presented. Using this algorithm, an intelligent agent was trained and the architecture of the learning process was built on the basis of an invariant of neurocognitive architecture. Further research is related to training an intelligent agent in more complex behavior and expanding the capabilities of an intelligent decision-making system based on multi-agent neurocognitive architectures.     

Modeling talent development pathways in alpine ski racers

We explore developmental pathways to excellence in sub-elite adolescent alpine ski racing by exploring athletes' engagement in practice activities that characterize sport specialization and/or diversification. Moreover, we extend previous research by using more refined linear mixed-effects models to examine changes in practice and performance data over time rather than relying on the less sensitive repeated measures ANOVAs typically used in previous research. Altogether, 174 individuals (M = 15.9 yrs, women = 91) from professional development academies in the United States completed a sport-specific Practice History Questionnaire (PHQ). We recorded national rankings in technical events to assess performance. Mixed-effects models indicated that hours in ski-related activities generally increased over time (linear β = 1.38, p's < 0.001), except time spent in play, which decreased over time (β = 102.8, p = 0.002). Performance rankings also improved over time (β = 30.99, p < 0.001), with more time spent in ski-activity associated with greater rates of improvement, particularly in younger skiers (β = 9.77, p's < 0.001). The effects were largely driven by time spent in coach-led group practice (β = 2.16, p < 0.001). Overall, an earlier age of first engagement was associated with better rankings (β = −15.40, p = 0.024), and accumulating more hours in other sports was also associated with greater rates of performance improvement (β = −8.87, p = 0.014). Sub-elite alpine ski racers engage early in the sport, with higher-ranked athletes spending more time in ski-specific practice, particularly group practice, during development. However, these athletes generally continued to participate in other sports across development, providing greater support for the early engagement hypothesis rather than early specialization per se.     

EDITORIAL Building bridges with mixed methods research?

Mixed methods refers to the use of both qualitative and quantitative approaches and methods in one study or sequentially in two or more studies. Without a mixed methods approach there is – to our minds ‐ a risk that only part of a phenomenon or experience is being explored. Our own involvement in mixed methods research has grown out of a shared interest in the concept ‘research‐practitioner’. Psychotherapy is a notoriously complex and ever‐developing field, and our growing sense has been that mixed methods research can contribute to a more complete – both broad and deep ‐ sense of knowledge and understanding.     

多级评分认知诊断模型述评

认知诊断评估旨在探讨个体内部的知识掌握结构,并提供关于学生优缺点的详细诊断信息,以促进个体的全面发展。当前研究者已开发了大量0-1评分的认知诊断模型,但对于多级评分认知诊断模型的研究还比较少。本文对已有的多级评分认知诊断模型进行了归纳,介绍了模型的假设,计量特征以及适用范围,为实际应用者和研究者在多级评分认知诊断模型的比较和选用上提供借鉴和参考。最后,对未来关于多级评分诊断模型的研究方向进行了展望。     

混合情绪能促进心理健康吗？

摘 要 积极和消极情绪的同时体验,即混合情绪,是生活中常见的情绪状态。健康应对的共激活模型、情绪动态模型和积极比模型一致认为混合情绪与心理健康之间呈现非线性的关系,适当的混合情绪有利于个体在困境中全面加工和整合信息,寻求意义,获得控制感,进而实现健康应对和心理症状改善,获得幸福感。未来研究应关注对非线性关系的精确描述;低压力或非压力情境下混合情绪的意义;进一步研究混合情绪促进心理健康的内部机制和影响因素。     

Action representation deficits in adolescents with developmental dyslexia

Developmental dyslexia (DD), a severe and frequent disorder of reading acquisition, is characterized by a diversity of cognitive and motor deficits whose interactions still remain under debate. Although deficits in the automatization of sensorimotor control have been highlighted, internal action representation allowing prediction has never before been investigated. In this study, we considered action representation of 18 adolescents with pure DD and 18 age-matched typical readers. Participants actually and mentally performed a visually guided pointing task involving strong spatiotemporal constraints (speed/accuracy trade-off paradigm). While actual and mental movement times of typical readers were isochronous and both conformed to Fitts’ law, the movement times of dyslexics differed between conditions, and only the actual movement times conformed to Fitts’ law. Furthermore, the quality of motor imagery correlated with word reading abilities. This suggests that the process of action representation is impaired in pure DD and supports the sensorimotor perspective of DD. Theoretical implications are discussed.     

Using Lexicographic Parametric Programming for Searching a Non-dominated Set in Multiple-Objective Linear Programming

In this paper we propose an approach which makes it possible to search non-dominated and only non-dominated solutions in multiple-objective linear programming. The approach is based on the use of a reference direction and lexicographic parametric programming. The requisite theory is developed in the paper. The use of the approach is illustrated with a numerical example.     

Generating interior search directions for multiobjective linear programming

A new multiobjective linear programming (MOLP) algorithm is presented. The algorithm uses a variant of Karmarkar's interior-point algorithm known as the affine-scaling primal algorithm. Using this single-objective algorithm, interior search directions are generated and used to provide an approximation to the gradient of the (implicitly known) utility function. The approximation is guided by assessing locally relevant preference information for the various interior directions through interaction with a decision maker (DM). The resulting algorithm is an interactive approach that makes its progress towards the solution through the interior of the constraints polytope.