How numbers mean: Comparing random walk models of numerical cognition varying both encoding processes and underlying quantity representations

How do people derive meaning from numbers? Here, we instantiate the primary theories of numerical representation in computational models and compare simulated performance to human data. Specifically, we fit simulated data to the distributions for correct and incorrect responses, as well as the pattern of errors made, in a traditional “relative quantity” task. The results reveal that no current theory of numerical representation can adequately account for the data without additional assumptions. However, when we introduce repeated, error-prone sampling of the stimulus (e.g., Cohen, 2009) superior fits are achieved when the underlying representation of integers reflects linear spacing with constant variance. These results provide new insights into (i) the detailed nature of mental numerical representation, and, (ii) general perceptual processes implemented by the human visual system.     

Introducing human factors in pedestrian crossing behaviour models

The objective of this research is the development of pedestrian crossing choice models on the basis of road, traffic and human factors. For that purpose, a field survey was carried out, in which a panel of 75 pedestrians were asked to take 8 short walking trips (each one corresponding to a different walking and crossing scenario) in the Athens city centre in Greece, allowing to record their crossing behaviour in different road and traffic conditions. The same individuals were asked to fill in a questionnaire on their travel motivations, their mobility characteristics, their risk perceptions and preferences with respect to walking and road crossing, their opinion on drivers, etc. The walking and crossing scenarios’ data were used to develop mixed sequential logit models of pedestrian behaviour on the basis of road and traffic characteristics. The modelling results showed that pedestrian crossing choices are significantly affected by road type, traffic flow and traffic control. The questionnaire data were used to estimate human factors (components) of pedestrian crossing behaviour by means of principal component analysis. The results showed that three components of pedestrian crossing behaviour emerge, namely a “risk-taking and optimisation” component reflecting the tendency to cross at mid-block in order to save time, etc., a “conservative” component, concerning individuals with increased perceived risk of mid-block crossing, who also appear to be frequent public transport users, and a “pedestrian for pleasure” component, bringing together frequent pedestrians, walking for health or pleasure, etc. The introduction of these components as explanatory variables into the choice models resulted in improvement of the modelling results, indicating that human factors have additional explanatory power over road and traffic factors of pedestrian behaviour. Therefore, the development of integrated choice and latent variables models appears to be an appropriate field for further research.     

Fungible Correlation Matrices: A Method for Generating Nonsingular,Singular, and Improper Correlation Matrices for Monte Carlo Research

For a fixed set of standardized regression coefficients and a fixed coefficient of determination (R-squared), an infinite number of predictor correlation matrices will satisfy the implied quadratic form. I call such matrices fungible correlation matrices. In this article, I describe an algorithm for generating positive definite (PD), positive semidefinite (PSD), or indefinite (ID) fungible correlation matrices that have a random or fixed smallest eigenvalue. The underlying equations of this algorithm are reviewed from both algebraic and geometric perspectives. Two simulation studies illustrate that fungible correlation matrices can be profitably used in Monte Carlo research. The first study uses PD fungible correlation matrices to compare penalized regression algorithms. The second study uses ID fungible correlation matrices to compare matrix-smoothing algorithms. R code for generating fungible correlation matrices is presented in the supplemental materials.     

Roles of personal and environmental factors in the red light running propensity of pedestrian: Case study at the urban crosswalks

Pedestrian is vulnerable to mortality and severe injury in road crashes. Red light running violation of pedestrians is one of the leading causes to the crashes at signalized intersections, at which the crash involvement rates of pedestrians are high. Therefore, it is important to identify the factors that affect the propensity of red light running of pedestrian. In this study, effects of both personal factors (pedestrians’ demographics and behavior) and environmental factors (presence and behavior of other pedestrians, signal time, and traffic condition) on the individual decision of red light running violation are examined, using the video observation surveys at the signalized crossings that are prone to pedestrian-vehicle crashes and have moderate pedestrian and vehicular traffic volumes in the urban area. Crossing behaviors of 6320 pedestrians are captured. Results of a random parameter logit model indicate that pedestrian gender, age, number of lanes, presence of a companion, number of pedestrians around, presence of other violators in the same cycle, time to green, red time, traffic volume, and percentage of heavy vehicles all affect the propensity of red light running violation of pedestrians. Also, there are significant interaction effects by pedestrian’s gender and age, presence of other violators, with a companion, and traffic volume on the propensity. Findings are indicative to the development of effective engineering, enforcement and educational initiatives combating the red light running violation behavior of pedestrians. Therefore, pedestrian safety level at the signalized intersections can be enhanced.     

Sequential sampling models of human text classification

Text classification involves deciding whether or not a document is about a given topic. It is an important problem in machine learning, because automated text classifiers have enormous potential for application in information retrieval systems. It is also an interesting problem for cognitive science, because it involves real world human decision making with complicated stimuli. This paper develops two models of human text document classification based on random walk and accumulator sequential sampling processes. The models are evaluated using data from an experiment where participants classify text documents presented one word at a time under task instructions that emphasize either speed or accuracy, and rate their confidence in their decisions. Fitting the random walk and accumulator models to these data shows that the accumulator provides a better account of the decisions made, and a “balance of evidence” measure provides the best account of confidence. Both models are also evaluated in the applied information retrieval context, by comparing their performance to established machine learning techniques on the standard Reuters‐21578 corpus. It is found that they are almost as accurate as the benchmarks, and make decisions much more quickly because they only need to examine a small proportion of the words in the document. In addition, the ability of the accumulator model to produce useful confidence measures is shown to have application in prioritizing the results of classification decisions.     

Differential contributions of set-shifting and monitoring to dual-task interference

It is commonly argued that complex behaviour is regulated by a number of “executive functions”, which work to coordinate the operation of disparate cognitive systems in the service of an overall goal. However, the identity, roles, and interactions of specific putative executive functions remain contentious, even within widely accepted tests of executive function. The authors present two experiments that use dual-task interference to provide further support for multiple distinct executive functions and to establish the differential contributions of those functions in two relatively complex executive tasks—random generation and the Wisconsin Card Sorting Test. Results are interpreted in terms of process models of the complex executive tasks.     

随机支持模型:青少年主观概率校准的特点

随机支持模型试图以假设支持为随机变量的方式描述主观概率的校准。采用随机配对的60个中国城市户籍人口的比较任务验证了该模型对初中、高中和大学生的适用性。研究还表明青少年主观概率校准的发展特点:青少年的正确率随年龄的增长而提高;主观概率判断(信心)不同年龄有显著差异,大学生信心最强,其次是初中生、再次是高中生;大学生主观概率判断的极端性最强,其次是初中生,再次是高中生;青少年的主观概率校准是过度自信的。     

Criteria instability and the isolated option effect

In two-stage choices, decision makers often compare a new (isolated) option with the winner from the first stage. Previous research has identified a choice advantage for an isolated option, ostensibly due to loss aversion. We propose an alternative mechanism suggesting that instability of the criteria used in each choice stage is the main driver of the isolated option effect. Results from a series of experiments support the criteria instability account and not loss aversion as the explanation for the isolated option effect.     

An open measurement problem of interest

This open problem urges a seach for a nice “union” of algbraic measurement models having two or more operations and probabilistic choice models. The difficulty lies in arriving at probabilistic formulations of linking properties, such as distribution properties, between the two operations. The goal is a formulation that leads to a representation theorem in terms of a random variable representation and that reduces to a known algebraic representation when there is no noise.     

青少年网络游戏体验与网络游戏成瘾的交叉滞后分析

该研究采用青少年网络游戏体验量表和网络游戏成瘾量表,对北京某中学初一、初二、初三和高二年级247名中学生进行为期四个月的追踪研究,考察被试在追踪期间网络游戏成瘾倾向的变化,以及网络游戏体验与网络游戏成瘾的因果关系及其内在心理机制。结果表明:(1)青少年的网络游戏成瘾倾向在追踪研究期间保持相对稳定。(2)网络游戏体验与网络游戏成瘾相关显著。(3)交叉滞后分析结果表明,网络游戏体验是网络游戏成瘾的预测变量。进一步的回归分析表明,网络游戏体验对青少年网络游戏成瘾为同时性的预测。