3～6年级儿童整数数量表征与分数数量表征的关系

研究主要探讨了整数数量表征和分数数量表征的关系以及年级对两者关系的影响。实验对155名三至六年级儿童进行0~1分数数字线估计任务和0~1000整数数字线估计任务的测量。结果发现:(1)对于整数数字线估计,所有年级儿童均主要采取了线性表征;(2)对于分数数字线估计,五六年级儿童主要采取了线性表征,三四年级儿童没有明显的线性表征或对数表征的倾向;(3)整数数量表征和分数数量表征呈显著正相关,不过年级对两者的关系产生了影响,表现在只有五六年级儿童的整数数字线估计对分数数字线估计有显著预测作用。     

结合题目作答时间的计算机化自适应测验选题方法

计算机形式的测验能够记录考生在测验中的题目作答时间（Response Time, RT），作为一种重要的辅助信息来源，RT对于测验开发和管理具有重要的价值，特别是在计算机化自适应测验（Computerized Adaptive Testing, CAT）领域。本文简要介绍了RT在CAT选题方面应用并作以简评，分析了这些技术在实践中的可行性。最后，探讨了当前RT应用于CAT选题存在的问题以及可以进一步开展的研究方向。     

Audiovisual time-to-collision estimation for accelerating vehicles: The acoustic signature of electric vehicles impairs pedestrians' judgments

To avoid collisions, pedestrians intending to cross a road need to accurately estimate the time-to-collision (TTC) of an approaching vehicle. For TTC estimation, auditory information can be considered particularly relevant when the approaching vehicle accelerates. The sound of vehicles with internal combustion engine (ICEVs) provides characteristic auditory information about the acceleration state (increasing rotational speed and engine load). However, for electric vehicles (EVs), the acoustic signature during acceleration is less salient. Although the auditory detection of EVs has been studied extensively, there is no research on potential effects of the altered acoustic signature of EVs on TTC estimation. To close this gap, we compared TTC estimates for ICEVs and for EVs with and without activated acoustic vehicle alerting system (AVAS). We implemented a novel interactive audiovisual virtual-reality system for studying the human perception of approaching vehicles. Using acoustic recordings of real vehicles as source signals, the dynamic spatial sound field corresponding to a vehicle approaching in an urban setting is generated based on physical modeling of the sound propagation between vehicle and pedestrian (listener) and is presented via sound field synthesis (higher-order Ambisonics). In addition to the auditory simulations, the scene was visually presented on a head-mounted display with head tracking. Participants estimated the TTC of vehicles that either approached at a constant speed or accelerated positively. In conditions with constant speed, TTC estimates for EVs with and without AVAS were similar to those for ICEVs. In contrast, for accelerating vehicles, there was a substantial effect of the vehicle type on the TTC estimates. For the EVs, the mean TTC estimates showed a significant overestimation. Thus, subjects on average perceived the time of arrival of the EV at their position as longer than it actually was. The extent of overestimation increased with acceleration and presented TTC. This pattern is similar to a first-order TTC estimation representing a failure to consider the acceleration, which is consistently reported in the literature for visual-only presentations of accelerating objects. In comparison, the overestimation of TTC was largely reduced for the accelerating ICEVs. The AVAS somewhat improved the TTC estimates for the accelerating EVs, but without reaching the same level of accuracy as for the ICEVs. In real traffic scenarios, overestimations of the TTC of approaching vehicles might lead to risky road-crossing decisions. Therefore, our finding that pedestrians are significantly less able to use the acoustic information emitted by accelerating EVs for their TTC judgments, compared to accelerating ICEVs, has important implications for road safety and for the design of AVAS technologies.     

Iterative Spatial Updating During Forward Linear Walking Revealed Using a Continuous Pointing Task

Abstract

The continuous pointing task uses target-directed pointing responses to determine how perceived distance traveled is estimated during forward linear walking movements. To more precisely examine the regulation of this online process, the current study measured upper extremity joint angles and step-cycle kinematics in full vision and no-vision continuous pointing movements. Results show perceptual under-estimation of traveled distance in no-vision trials compared to full vision trials. Additionally, parsing of the shoulder plane of elevation trajectories revealed discontinuities that reflected this perceptual under-estimation and that were most frequently coupled with the early portion of the right foot swing phase of the step-cycle. This suggests that spatial updating may be composed of discrete iterations that are associated with gait parameters.     

Confirmatory reinforcement learning changes with age during adolescence

Understanding how learning changes during human development has been one of the long-standing objectives of developmental science. Recently, advances in computational biology have demonstrated that humans display a bias when learning to navigate novel environments through rewards and punishments: they learn more from outcomes that confirm their expectations than from outcomes that disconfirm them. Here, we ask whether confirmatory learning is stable across development, or whether it might be attenuated in developmental stages in which exploration is beneficial, such as in adolescence. In a reinforcement learning (RL) task, 77 participants aged 11–32 years (four men, mean age = 16.26) attempted to maximize monetary rewards by repeatedly sampling different pairs of novel options, which varied in their reward/punishment probabilities. Mixed-effect models showed an age-related increase in accuracy as long as learning contingencies remained stable across trials, but less so when they reversed halfway through the trials. Age was also associated with a greater tendency to stay with an option that had just delivered a reward, more than to switch away from an option that had just delivered a punishment. At the computational level, a confirmation model provided increasingly better fit with age. This model showed that age differences are captured by decreases in noise or exploration, rather than in the magnitude of the confirmation bias. These findings provide new insights into how learning changes during development and could help better tailor learning environments to people of different ages.

Research Highlights

• Reinforcement learning shows age-related improvement during adolescence, but more in stable learning environments compared with volatile learning environments.
• People tend to stay with an option after a win more than they shift from an option after a loss, and this asymmetry increases with age during adolescence.
• Computationally, these changes are captured by a developing confirmatory learning style, in which people learn more from outcomes that confirm rather than disconfirm their choices.
• Age-related differences in confirmatory learning are explained by decreases in stochasticity, rather than changes in the magnitude of the confirmation bias.

     

Analysis of Interactions and Nonlinear Effects with Missing Data: A Factored Regression Modeling Approach Using Maximum Likelihood Estimation

Abstract

When estimating multiple regression models with incomplete predictor variables, it is necessary to specify a joint distribution for the predictor variables. A convenient assumption is that this distribution is a multivariate normal distribution, which is also the default in many statistical software packages. This distribution will in general be misspecified if predictors with missing data have nonlinear effects (e.g., x²) or are included in interaction terms (e.g., x·z). In the present article, we introduce a factored regression modeling approach for estimating regression models with missing data that is based on maximum likelihood estimation. In this approach, the model likelihood is factorized into a part that is due to the model of interest and a part that is due to the model for the incomplete predictors. In three simulation studies, we showed that the factored regression modeling approach produced valid estimates of interaction and nonlinear effects in regression models with missing values on categorical or continuous predictor variables under a broad range of conditions. We developed the R package mdmb, which facilitates a user-friendly application of the factored regression modeling approach, and present a real-data example that illustrates the flexibility of the software.     

Spatio-temporal accident analysis for accident prevention in relation to behavioral factors in driving: The case of South Anatolian Motorway

Analyzing the pattern of traffic accidents on road segments can highlight the hazardous locations where the accidents occur frequently and help to determine problematic parts of the roads. The objective of this paper is to utilize accident hotspots to analyze the effect of different measures on the behavioral factors in driving. Every change in the road and its environment affects the choices of the driver and therefore the safety of the road itself. A spatio-temporal analysis of hotspots therefore can highlight the road segments where measures had positive or negative effects on the behavioral factors in driving. In this paper 2175 accidents resulted in injury or death on the South Anatolian Motorway in Turkey for the years between 2006 and 2009 are considered. The network-based kernel density estimation is used as the hotspot detection method and the K-function and the nearest neighbor distance methods are taken into account to check the significance of the hotspots. A chi-square test is performed to find out whether temporal changes on hotspots are significant or not. A comparison of characteristics related driver attributes like age, experience, etc. for accidents in hotspots vs. accidents outside of hotspots is performed to see if the temporal change of hotspots is caused by structural changes on the road. For a better understanding of the effects on the driver characteristics, the accidents are analyzed in five groups based on three different grouping schemes. In the first grouping approach, all accident data are considered. Then the accident data is grouped according to direction of the traffic flow. Lastly, the accident data is classified in terms of the vehicle type. The resultant spatial and temporal changes in the accident patterns are evaluated and changes on the road structure related to behavioral factors in driving are suggested.