Comparing instructor-led,video-model,and no-instruction control tutorials for creating single-subject graphs in Microsoft Excel: A systematic replication and extension

Visual inspection of single-subject data is the primary method for behavior analysts to interpret the effect of an independent variable on a dependent variable; however, there is no consensus on the most suitable method for teaching graph construction for single-subject designs. We systematically replicated and extended Tyner and Fienup (2015) using a repeated-measures between-subjects design to compare the effects of instructor-led, video-model, and no-instruction control tutorials on the graphing performance of 81 master's students with some reported Microsoft Excel experience. Our mixed-design analysis revealed a statistically significant main effect of pretest, tutorial, and posttest submissions for each tutorial group and a nonsignificant main effect of tutorial group. Tutorial group significantly interacted with submissions, suggesting that both instructor-led and video-model tutorials may be superior to providing graduate students with a written list of graphing conventions (i.e., control condition). Finally, training influenced performance on an untrained graph type (multielement) for all tutorial groups.     

老年人整体运动知觉衰退的视觉机制——基于知觉模板模型

整体运动知觉存在年龄相关的衰退,然其老化的视觉机制仍不清楚。本研究分别招募34名青年人和老年人为研究对象,采用随机点阵范式,首次以动态速度为外部噪声考察了这一问题。结果发现,老年组整体运动一致性阈值显著高于青年组,且中等速度下的差异较小,证明整体运动知觉存在老化效应,且老化程度受到速度的影响。对两组数据进行知觉模板拟合发现,以青年组为基准,老年组表征内部加法噪声和排除外部噪声能力的 A_a 和 A_f 均大于1,且 A_a >> A_f ,提示老年人整体运动知觉的衰退可能源于其内部加法噪声的增强和排除外部噪声能力的减弱,其中内部加法噪声的增强更为显著。     

Impact of time pressure on acceleration behavior and crossing decision at the onset of yellow signal

This study investigates acceleration behavior and crossing decision of the drivers under increasing time pressure driving conditions. A typical urban route was designed in a fixed-base driving simulator consisting of four signalized intersections with varying time to stop line (4 s and 6 s) and maneuver type (right-turn and go-through). 97 participants’ data were obtained under No Time Pressure (NTP), Low Time Pressure (LTP), and High Time Pressure (HTP) driving conditions. The acceleration behavior was examined at the onset of yellow signal in four ways: continuous deceleration, acceleration-deceleration, deceleration-acceleration, and continuous acceleration. A random forest model was used to build an acceleration behavior prediction model for identifying the significant explanatory variables based on variable importance ranking. Further, a Mixed Effects Multinomial Logit (MEML) model was developed using the explanatory variables obtained from a random forest model. Additionally, a generalized linear mixed model was incorporated for estimating the likelihood of crossing an intersection by considering all the explanatory variables. A MEML model result revealed that the odds of adopting acceleration-deceleration, deceleration-acceleration, and continuous acceleration instead of continuous deceleration increased by 63 %, 123 %, and 77 %, respectively under HTP driving conditions. Moreover, the likelihood of crossing a signalized intersection increased by 2.73 times and 4.26 times when the drivers were under LTP and HTP driving conditions, respectively as compared to NTP driving condition. Apart from this, time to stop line (reference: 6 s) and age showed negative association with crossing probability. Overall, the findings from this study revealed that drivers altered their acceleration behavior for executing risky driving decisions under increasing time pressure driving conditions.     

Public acceptance of connected vehicles: An extension of the technology acceptance model

To examine the public acceptance of connected vehicles (CVs), this study developed a novel connected vehicle acceptance model (CVAM) extending the technology acceptance model (TAM). The model was built based on a questionnaire survey of 2400 US adults. Perceived data privacy and security associated with the technology was found to shape the trust, attitude, and behavioral intention to use CVs, in addition to the predictors of original TAM: perceived usefulness and ease of use. Results revealed that trust mediates the effect of perceived data privacy and security on CV acceptance, though its directional relationship with perceived usefulness and perceived ease of use is unclear. Socio-demographic and other characteristics of respondents associated with CV acceptance and its predictors were explored. A number of theoretical and practical implications of the study findings are discussed.     

Exploring behavioral validity of driving simulator under time pressure driving conditions of professional drivers

Driving simulators have become an important tool in human factors research, given that they are appropriately validated. Therefore, this study aims to explore the behavioral (absolute and relative) validity of a fixed-base driving simulator by analyzing different driving behavior measures such as speed, longitudinal acceleration, lateral acceleration, and brake pedal force. Thirty professional drivers participated in the experiment and the data was collected in real and simulated worlds under No Time Pressure (NTP) and Time Pressure (TP) driving conditions. Initially, comparative analyses were conducted on different driving behavior measures using Wilcoxon-signed rank test to examine absolute validity of the driving simulator. Finally, Generalized Linear Mixed (GLM) models were developed for computing the effective distance between real and simulated worlds by quantifying the parameters and for establishing relative validity. In general, the continuous profiles of driving behavior measures followed similar trends in real and simulated worlds and comparative analyses indicated relative validity of the driving simulator. The GLM models showed significant interaction effect of driving environments (real-world and simulated world) and driving conditions (NTP and TP) where high driving speed, high brake pedal force, and low lateral acceleration were observed in simulated world under TP driving condition than real-world under TP driving condition. Overall, the statistical analyses showed qualitative correspondence (relative validity) of the driving behavior measures in between real and simulated worlds. The findings from the current study showed expediency of the driving simulator and its effectiveness in conducting research on human factors and driver safety.     

聋生1秒以下与以上的视时距知觉研究

采用时距二分任务,探讨聋生在视觉通道的1s以下和1s以上时距知觉的特点。结果发现,在1s以下条件下,聋生时距判断准确性低于普通学生;聋生CNV峰值与LPCt波幅低于普通学生,CNV潜伏期短于普通学生,LPCt峰值潜伏期长于普通学生。在1s以上条件下,聋生时距判断准确性高于普通学生;聋生N1、P2、CNV与LPCt成分各项指标与普通学生均没有显著差异。这说明,听觉丧失损伤了聋生1s以下的时距记忆与决策过程,支持了普遍缺陷假设;但听觉丧失未对聋生1s以上时距加工产生显著影响,支持了感觉补偿机制。因此,时距长度在听觉丧失对视觉时距知觉的影响中具有调节作用,为时间认知的分段综合模型提供了新的支持证据。     

Modeling the effects of perceived intuitiveness and urgency of various auditory warnings on driver takeover performance in automated vehicles

Existing driver models mainly account for drivers’ responses to visual cues in manually controlled vehicles. The present study is one of the few attempts to model drivers’ responses to auditory cues in automated vehicles. It developed a mathematical model to quantify the effects of characteristics of auditory cues on drivers’ response to takeover requests in automated vehicles. The current study enhanced queuing network-model human processor (QN-MHP) by modeling the effects of different auditory warnings, including speech, spearcon, and earcon. Different levels of intuitiveness and urgency of each sound were used to estimate the psychological parameters, such as perceived trust and urgency. The model predictions of takeover time were validated via an experimental study using driving simulation with resultant R squares of 0.925 and root-mean-square-error of 73 ms. The developed mathematical model can contribute to modeling the effects of auditory cues and providing design guidelines for standard takeover request warnings for automated vehicles.     

Bio-inspired cognitive architecture of episodic memory

Memory and learning are essential functions in human beings as they allow us to acquire and store in the brain representations of thoughts, experiences, and behaviors, which are required for problem-solving in our daily life and mainly for survival. Episodic memory is a type of memory that provides the ability to re-experience events in one’s life, and it is associated with their conscious recollection. Since episodic memory can represent our experiences about the environment, similar to a mental journey, it is desired in systems that attempt to create human-like behavior. Currently, the main problem is that state of the art proposals do not consider neuroscientific evidence like memory dynamics for forgetting or bottom-up inputs, and most of them do not consider episodic memory as a different memory but as part of general declarative memory. We consider these omissions to limit the generation of human-like behavior. In this work, we propose a bio-inspired cognitive architecture of episodic memory. Neuroscientific evidence provides us with the brain structures associated with this type of memory, the connections, and the operations these areas perform. We hypothesize that virtual entities endowed with our episodic memory cognitive architecture will plan and make decisions in a more human-like fashion. To test the capabilities of the proposal, we endowed a virtual creature with a distributed and concurrent implementation of our architecture, and it was given two tasks. The first task validated the functions of the memory independently, and in the second task, the creature used episodic memory to solve a planning problem. From the results of these experiments, we validate our proposal and show that it is possible to create a system that behaves as the human brain does.     

Research on parking choice behavior of shared autonomous vehicle services by measuring users’ intention of usage

Shared autonomous vehicles (SAVs) are one of the important development directions of smart and green transportation. However, relevant researches are not sufficient at present. The factors influencing the intention to use SAVs and their parking choice behaviors need to be further analyzed. First, in order to better explain, predict, and improve travelers’ intention to use SAVs, the conceptual framework based on technology acceptance model was developed to establish the relationships between the travelers’ intention to use SAVs, social influence of SAVs, attitude toward behavior of SAVs, perceived risk of SAVs, perceived usefulness of SAVs and perceived ease of these use. Then structural equation model (SEM) was established to analyze the relationship between various variables. The results show that the perceived usefulness, behavior attitude, social influence, perceived ease of use, and perceived risk are the main factors that determine the intention to use SAVs. Through the test of direct effect, indirect effect, and total effect in the model, it is found that perceived usefulness has the largest total impact on intention to use SAVs, with a standardized coefficient of 0.765, followed by behavior attitude (0.732), social influence (0.597), perceived ease of use (0.462) and perceived risk of SAVs (−0.452). In addition, through the study of observed indicator variables ATB2 and BI3, it is found that perceived usefulness, perceived ease of use, social influence, perceived risk, attitude toward behavior, and behavior intention all have an impact on parking behavior. In order to study the specific influencing factors of parking choice behavior, a multinomial logit (MNL) model was established to analyze the relationships between travelers’ parking choice behaviors and the influential factors, which include travelers’ individual characteristics, travel attributes, and parking modes’ attributes by extracting from a questionnaire. The results show that the travel time, travel fees, parking charge, cruising fees, parking time and traffic emission are the main factors that determine travelers’ choices of parking. This paper provides advice for operators of SAVs.     

Computational modeling of organisational learning by self-modeling networks

Within organisational learning literature, mental models are considered a vehicle for both individual learning and organizational learning. By learning individual mental models (and making them explicit), a basis for formation of shared mental models for the level of the organization is created, which after its formation can then be adopted by individuals. This provides mechanisms for organizational learning. These mechanisms have been used as a basis for an adaptive computational network model. The model is illustrated by a not too complex but realistic case study.