A method in modeling interactive pedestrian crossing and driver yielding decisions during their interactions at intersections

Investigating pedestrian crossing and driver yielding decisions should be an important focus considering the high risks of pedestrians in exposed to motorized traffic. Limitations, however, exist in previous studies – variables considered previously have been limited; how their behavior affect each other (defined as interactive impacts) were not sufficiently considered. This paper aims to provide a methodological approach for pedestrian crossing and driver yielding decisions during their interactions, considering of different variable types including interactive impact variables, traffic condition variables, road design variables, and environment variables. A Distance-Velocity (DV) framework proposed in an earlier study is introduced for definitions and concepts in studying pedestrian-vehicle interactions. Logistic regression, support vector machines, neural networks and random forests, are introduced as candidate models. A case study involving six crosswalk locations is conducted, focusing on interactions between pedestrians and right-turn vehicles. The proposed methodological approach is applied, with the performance of the four machine learning methods compared in terms of model generalization and confusion matrix. The model with the best performance is further compared to the typical gap-based model. Results show that random forest and logistic regression models performed the best in modeling pedestrian crossing and driver yielding decisions respective, in terms of model generalization. Besides, the DV-based modeling method (average accuracy of over 90% for pedestrians and 80% for drivers) outperformed the traditional gap-based method in all test seeds. As a key finding, interactive impacts from each other (the pedestrian and the driver) act as a key contributing variable on their decisions.     

计算机化分类测验终止规则的类别、特点及应用

计算机化分类测验(Computerized Classification Testing, CCT)能够高效地对被试进行分类, 已广泛应用于合格性测验及临床心理学中。作为CCT的重要组成部分, 终止规则决定测验何时停止以及将被试最终划分到何种类别, 因此直接影响测验效率及分类准确率。已有的三大类终止规则(似然比规则、贝叶斯决策理论规则及置信区间规则)的核心思想分别为构造假设检验、设计损失函数和比较置信区间相对位置。同时, 在不同测验情境下, CCT的终止规则发展出不同的具体形式。未来研究可以继续开发贝叶斯规则、考虑多维多类别情境以及结合作答时间和机器学习算法。针对测验实际需求, 三类终止规则在合格性测验上均有应用潜力, 而临床问卷则倾向应用贝叶斯规则。     

The Secrecy Heuristic: Inferring Quality from Secrecy in Foreign Policy Contexts

Three experiments demonstrate that in the context of U.S. foreign policy decision making, people infer informational quality from secrecy. In Experiment 1, people weighed secret information more heavily than public information when making recommendations about foreign political candidates. In Experiment 2, people judged information presented in documents ostensibly produced by the Department of State and the National Security Council as being of relatively higher quality when those documents were secret rather than public. Finally, in Experiment 3, people judged a National Security Council document as being of higher quality when presented as a secret document rather than a public document and evaluated others' decisions more favorably when those decisions were based on secret information. Discussion centers on the mediators, moderators, and broader implications of this secrecy heuristic in foreign policy contexts.     

The vision heuristic: Judging music ensembles by sight alone

Team effectiveness and group performance are often defined by standards set by domain experts. Professional musicians consistently report that sound output is the most important standard for evaluating the quality of group performance in the domain of music. However, across six studies, visual information dominated rapid judgments of group performance. Participants (1062 experts and novices) were able to select the actual winners of live ensemble competitions and distinguish top-ranked orchestras from non-ranked orchestras based on 6-s silent video recordings yet were unable to do so from sound recordings or recordings with both video and sound. These findings suggest that judgments of group performance in the domain of music are driven at least in part by visual cues about group dynamics and leadership.     

Choice‐making,Expectations, and Treatment Positivity: How and When Choosing Shapes Aversive Experiences

Every day we use products and treatments with unknown but expected effects, such as using medication to manage pain. In many cases, we have a choice over which products or treatments to use; however, in other cases, people choose for us or choices are unavailable. Does choosing (versus not choosing) have implications for how a product or treatment is experienced? The current experiments examined the role of choice‐making in facilitating so‐called expectation assimilation effects—or situations in which a person's experiences (e.g., discomfort and pain) are evaluated in a manner consistent with their expectations. In Experiment 1, participants were initially exposed to a baseline set of aversive stimuli (i.e., sounds). Next, some participants were given expectations for two “treatments” (i.e., changes in screen display) that could ostensibly reduce discomfort. Critically, participants were either given a choice or not about which of the two treatments they preferred. Participants in a control condition were not provided with treatment expectations. Results revealed that discomfort experiences assimilated to expectations only when participants were provided with choice. Experiment 2 replicated this finding and provided evidence against the idea that demand characteristics and choice‐making unrelated to the core task (i.e., choices without associated expectations) could account for the results. Further, Experiment 2 showed that choosing reduced discomfort because of increased positivity about the treatment. Results are discussed in the context of extant research on choice‐making and expectation effects. Copyright © 2013 John Wiley & Sons, Ltd.     

Extreme Outcomes Sway Risky Decisions from Experience

Whether buying stocks or playing the slots, people making real‐world risky decisions often rely on their experiences with the risks and rewards. These decisions, however, do not occur in isolation but are embedded in a rich context of other decisions, outcomes, and experiences. In this paper, we systematically evaluate how the local context of other rewarding outcomes alters risk preferences. Through a series of four experiments on decisions from experience, we provide evidence for an extreme‐outcome rule, whereby people overweight the most extreme outcomes (highest and lowest) in a given context. As a result, people should be more risk seeking for gains than losses, even with equally likely outcomes. Across the experiments, the decision context was varied so that the same outcomes served as the high extreme, low extreme, or neither. As predicted, people were more risk seeking for relative gains, but only when the risky option potentially led to the high‐extreme outcome. Similarly, people were more risk averse for relative losses, but only when the risky option potentially led to the low‐extreme outcome. We conclude that in risky decisions from experience, the biggest wins and the biggest losses seem to matter more than they should. Copyright © 2013 John Wiley & Sons, Ltd.     

Police Overestimation of Criminal Career Homogeneity

Police presumptions about criminal career trajectories have been little studied. The exploratory study reported here involved 42 police staff of varying rank and experience. Participants were asked to complete a questionnaire that asked them to predict the type of offence that an individual with a specified prior record would most probably commit next. Participating police personnel substantially overstated the homogeneity of criminal careers, that is, the nature of prior offences determined their prediction of their next offence more than available official data would deem reasonable. An incidental finding was that officers who rated the probability of further offending highest were also those who thought criminal careers most specialised. The implications for operational police decision‐making were discussed and held to be profound. Copyright © 2013 John Wiley & Sons, Ltd.     

A neural cognitive model of argumentation with application to legal inference and decision making

Formal models of argumentation have been investigated in several areas, from multi-agent systems and artificial intelligence (AI) to decision making, philosophy and law. In artificial intelligence, logic-based models have been the standard for the representation of argumentative reasoning. More recently, the standard logic-based models have been shown equivalent to standard connectionist models. This has created a new line of research where (i) neural networks can be used as a parallel computational model for argumentation and (ii) neural networks can be used to combine argumentation, quantitative reasoning and statistical learning. At the same time, non-standard logic models of argumentation started to emerge. In this paper, we propose a connectionist cognitive model of argumentation that accounts for both standard and non-standard forms of argumentation. The model is shown to be an adequate framework for dealing with standard and non-standard argumentation, including joint-attacks, argument support, ordered attacks, disjunctive attacks, meta-level attacks, self-defeating attacks, argument accrual and uncertainty. We show that the neural cognitive approach offers an adequate way of modelling all of these different aspects of argumentation. We have applied the framework to the modelling of a public prosecution charging decision as part of a real legal decision making case study containing many of the above aspects of argumentation. The results show that the model can be a useful tool in the analysis of legal decision making, including the analysis of what-if questions and the analysis of alternative conclusions. The approach opens up two new perspectives in the short-term: the use of neural networks for computing prevailing arguments efficiently through the propagation in parallel of neuronal activations, and the use of the same networks to evolve the structure of the argumentation network through learning (e.g. to learn the strength of arguments from data).