Reasoning about rights and duties: mental models,world knowledge and pragmatic interpretation

We address the way verb-based and rule-content knowledge are combined in understanding institutional deontics. Study 1 showed that the institutional regulations used in our studies were readily categorised into one of two content groups: rights or duties. Participants perceived rights as benefiting the addressees identified by the rule, whereas they perceived duties as benefiting the collective that imposed the rule. Studies 2, 3, and 4 showed that rule content (rights vs. duties) had clear effects on perceptions of violations and relevance of cases for explaining the rule, even when controlling for deontic verb, phrasing of the action permitted by a right, or the formality of the deontic verb. These effects are incompatible with a simple pragmatic disambiguation approach to pragmatic modulation, as they often induce permissibility judgments that contradict the core semantic meanings of the deontic verbs. Other ways of reconciling verb meaning with rule content should be considered in a fuller theory of the interpretation of institutional rules.     

“Put the blame on…others!”: The battle of cyclists against pedestrians and car drivers at the urban environment. A cyclists’ perception study

The main hypothesis of the paper is that cyclists tend to blame primarily car drivers, for the conflict events they have experienced with cars, but they do not have the same intention for the conflict events with pedestrians. For this purpose, 306 cyclists were interviewed through a revealed questionnaire survey and 64% revealed that they had experienced a conflict with a pedestrian whereas 55% revealed that they had experienced a conflict with a car. From the responses, two linear regression models were developed, with the perceived responsibility considered as the dependent variable.The cycling–pedestrian model indicated that cyclists who also have car accessibility, tend to blame primarily the pedestrians for the incidents, compared to cyclists who do not possess a car. Further, cyclists tend to give less responsibility to pedestrians for incidents occurred at sidewalks, crosswalks, etc. compare to incidents occurred at shared use paths. In addition, cyclists do not blame pedestrians for conflicts occurred primarily at sidewalks and crosswalks; places were pedestrians are considered to have the priority. Finally, cyclists aged between 55 and 64 years old, are giving less responsibility to pedestrians for the incident, compare to cyclists aged between 25 and 39 years old.On the other hand, the cycling–car model showed that an illegal cyclist’s movement at a road segment can reduce by half the responsibility the cyclists give to the car drivers, compare to the responsibility they give when the latter have an aggressive driving behavior. It was also found that cyclists, who tend to cycle less than 1 h, do not blame so much the car drivers for the incident, compared to cyclists that cycle for longer. Finally, cyclists who prefer the bicycle lane to be located along the road instead at the sidewalk, tend to accuse less the drivers for the incident.     

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.     

Neural implementation of probabilistic models of cognition

Bayesian models of cognition hypothesize that human brains make sense of data by representing probability distributions and applying Bayes’ rule to find the best explanation for available data. Understanding the neural mechanisms underlying probabilistic models remains important because Bayesian models provide a computational framework, rather than specifying mechanistic processes. Here, we propose a deterministic neural-network model which estimates and represents probability distributions from observable events—a phenomenon related to the concept of probability matching. Our model learns to represent probabilities without receiving any representation of them from the external world, but rather by experiencing the occurrence patterns of individual events. Our neural implementation of probability matching is paired with a neural module applying Bayes’ rule, forming a comprehensive neural scheme to simulate human Bayesian learning and inference. Our model also provides novel explanations of base-rate neglect, a notable deviation from Bayes.     

Fitting the Reduced RUM with Mplus: A Tutorial

The Reduced Reparameterized Unified Model (Reduced RUM) is a diagnostic classification model for educational assessment that has received considerable attention among psychometricians. However, the computational options for researchers and practitioners who wish to use the Reduced RUM in their work, but do not feel comfortable writing their own code, are still rather limited. One option is to use a commercial software package that offers an implementation of the expectation maximization (EM) algorithm for fitting (constrained) latent class models like Latent GOLD or Mplus. But using a latent class analysis routine as a vehicle for fitting the Reduced RUM requires that it be re-expressed as a logit model, with constraints imposed on the parameters of the logistic function. This tutorial demonstrates how to implement marginal maximum likelihood estimation using the EM algorithm in Mplus for fitting the Reduced RUM.     

CHRM3基因与孤独症谱系障碍

孤独症谱系障碍是一类具有遗传基础的儿童发展障碍疾病。近些年, 研究者们从分子病理学层面发现中枢胆碱能神经系统异常与孤独症患者认知和行为异常存在相关性。尸检研究、临床案例、动物模型研究均发现毒蕈碱型(M型)乙酰胆碱受体异常和孤独症的发生有着密切的关系。在以小鼠为模型的行为学研究中, 编码毒蕈碱型乙酰胆碱受体Ⅲ亚型的CHRM3基因突变会导致小鼠出现认知障碍、刻板行为等孤独症样表现。深入了解CHRM3基因的功能将能够帮助研究者进一步解释孤独症的相关行为特征, 为孤独症儿童教育方案的制定提供新的思路和方法。     

题目位置效应的概念及检测

题目位置效应(Item Position Effect, IPE)是指在剔除随机误差的影响之后, 同一道题目在不同测验间因题目位置的变化而导致题目参数的变化。IPE的存在会严重威胁依赖于项目反应理论参数不变性特征的相关应用, 比如测验等值和计算机化自适应测验。目前关于这一领域的研究主要集中于对IPE的检测, 而对所检测到的效应进行进一步的解释, 则是今后的研究重点。另外, 在不同的研究情境下深入探讨IPE, 对于基础研究领域和实践领域都具有重要意义。     

Minimax d-optimal designs for item response theory models

Various different item response theory (IRT) models can be used in educational and psychological measurement to analyze test data. One of the major drawbacks of these models is that efficient parameter estimation can only be achieved with very large data sets. Therefore, it is often worthwhile to search for designs of the test data that in some way will optimize the parameter estimates. The results from the statistical theory on optimal design can be applied for efficient estimation of the parameters.A major problem in finding an optimal design for IRT models is that the designs are only optimal for a given set of parameters, that is, they are locally optimal. Locally optimal designs can be constructed with a sequential design procedure. In this paper minimax designs are proposed for IRT models to overcome the problem of local optimality. Minimax designs are compared to sequentially constructed designs for the two parameter logistic model and the results show that minimax design can be nearly as efficient as sequentially constructed designs.