Is personality stable in adolescence? Disentangling time-invariant and time-varying factors of personality in a trait–state–occasion model

This study applied trait–state–occasion (TSO) modelling to investigate the extent to which inter-individual differences in personality were accounted for by time-invariant and time-varying factors during adolescence. The participants were 753 high school students, and only 661 students were followed for 4 years. The mean age in the first year was 15.23 years and 52.8% were females. The results showed that the full TSO model had the best representation of adolescent personality, and the means of the proportions of variance explained by trait factors for the Big Five ranged between 56% and 83%, suggesting that inter-individual differences in personality are mainly accounted for by stable trait variance over the course of adolescence. Furthermore, the five dimensions of personality differed not only in the amounts of trait variance but also in the stability of time-varying factors. These findings have significant implications for personality research.     

The argument from surprise

I develop an account of productive surprise as an epistemic virtue of scientific investigations which does not turn on psychology alone. On my account, a scientific investigation is potentially productively surprising when (1) results can conflict with epistemic expectations, (2) those expectations pertain to a wide set of subjects. I argue that there are two sources of such surprise in science. One source, often identified with experiments, involves bringing our theoretical ideas in contact with new empirical observations. Another, often identified with simulations, involves articulating and bringing together different parts of our knowledge. Both experiments and simulations, then, can surprise.     

Naïve and Robust: Class‐Conditional Independence in Human Classification Learning

Humans excel in categorization. Yet from a computational standpoint, learning a novel probabilistic classification task involves severe computational challenges. The present paper investigates one way to address these challenges: assuming class‐conditional independence of features. This feature independence assumption simplifies the inference problem, allows for informed inferences about novel feature combinations, and performs robustly across different statistical environments. We designed a new Bayesian classification learning model (the dependence‐independence structure and category learning model, DISC‐LM) that incorporates varying degrees of prior belief in class‐conditional independence, learns whether or not independence holds, and adapts its behavior accordingly. Theoretical results from two simulation studies demonstrate that classification behavior can appear to start simple, yet adapt effectively to unexpected task structures. Two experiments—designed using optimal experimental design principles—were conducted with human learners. Classification decisions of the majority of participants were best accounted for by a version of the model with very high initial prior belief in class‐conditional independence, before adapting to the true environmental structure. Class‐conditional independence may be a strong and useful default assumption in category learning tasks.     

Distinguishing the Ratio Bias from Unsystematic Error: Situation and Individual‐difference Effects

People often perceive the occurrence of events to be less likely when the likelihood of the event is expressed in ratios consisting of smaller numbers versus larger numbers, an effect known as the ratio bias. This work presents a theoretical framework for the conditions that need to be met for the ratio bias to occur. In doing so, we contrast effects on the ratio bias to those on unsystematic error, which have often been confounded in previous research. We find that the ratio bias is weaker (1) when both sets of numbers are relatively large than when both sets of numbers are relatively small; (2) for scenarios involving lottery tickets than for scenarios involving drawing balls from a bin; and (3) when a physical display depicting the numbers is provided to participants. Each of these factors reduced the ratio bias without reducing unsystematic error. Additionally, we show that unsystematic error is lower among people who (1) reason on the basis of proportions rather than on the basis of the numerator and denominator individually; (2) score higher on the rational scale of the Rational–Experiential Inventory; and (3) are of higher numeracy. We use these results to distinguish causes of error generally from those on the ratio bias specifically and discuss the implications for our understanding of when the ratio bias occurs. Copyright © 2018 John Wiley & Sons, Ltd.     

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.