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.     

Who’s bringing the donuts: The role of affective patterns in group decision making

Two studies examined how intragroup affective patterns influence groups’ pervasive tendency to ignore the unique expertise of their members. Using a hidden profile task, Study 1 provided evidence that groups with at least one member experiencing positive affect shared more unique information than groups composed entirely of members experiencing neutral affect. This occurred because group members experiencing positive affect were more likely to initiate unique information sharing, as well as information seeking. Study 2 built upon this base by showing that confidence mediates the relationship between positive affect and the initiation of unique information sharing. Additionally, Study 2 investigated the role of negative affect in group decision making and how negative and positive affect concurrently influence decision making when groups are composed of members experiencing each. The results are discussed in terms of the role affect plays in influencing group behavior and the resultant importance of investigating specific affective patterns.     

Children's utilization of emotion expectancies in moral decision‐making

This study investigated the relevance of emotion expectancies for children's moral decision‐making. The sample included 131 participants from three different grade levels (M = 8.39 years, SD = 2.45, range 4.58–12.42). Participants were presented a set of scenarios that described various emotional outcomes of (im)moral actions and asked to decide what they would do if they were in the protagonists' shoes. Overall, it was found that the anticipation of moral emotions predicted an increased likelihood of moral choices in antisocial and prosocial contexts. In younger children, anticipated moral emotions predicted moral choice for prosocial actions, but not for antisocial actions. Older children showed evidence for the utilization of anticipated emotions in both prosocial and antisocial behaviours. Moreover, for older children, the decision to act prosocially was less likely in the presence of non‐moral emotions. Findings suggest that the impact of emotion expectancies on children's moral decision‐making increases with age. Contrary to happy victimizer research, the study does not support the notion that young children use moral emotion expectancies for moral decision‐making in the context of antisocial actions.     

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).     

Iconic gestures prime words

Using a cross-modal semantic priming paradigm, both experiments of the present study investigated the link between the mental representations of iconic gestures and words. Two groups of the participants performed a primed lexical decision task where they had to discriminate between visually presented words and nonwords (e.g., flirp). Word targets (e.g., bird) were preceded by video clips depicting either semantically related (e.g., pair of hands flapping) or semantically unrelated (e.g., drawing a square with both hands) gestures. The duration of gestures was on average 3,500 ms in Experiment 1 but only 1,000 ms in Experiment 2. Significant priming effects were observed in both experiments, with faster response latencies for related gesture-word pairs than unrelated pairs. These results are consistent with the idea of interactions between the gestural and lexical representational systems, such that mere exposure to iconic gestures facilitates the recognition of semantically related words.     

情绪对职业决策的影响

职业决策是决策研究领域的重要课题,引起了心理学家和决策研究者的高度关注.本文在以往研究的基础上,引人情绪和职业种类变量探讨了情绪对职业决策的影响,结果显示:(1)无论是四种还是八种职业决策任务,积极情绪的被试同消极情绪的被试相比,信息搜索的时间较长,信息搜索的深度显著增加;(2)在职业选择策略上,积极情绪的被试更加倾向于基于选项的加工,而消极情绪的被试随着从四种职业决策任务到八种决策任务的转变,逐渐倾向于基于属性的加工;(3)情绪影响了决策者职业决策的信息加工能力.     

Ear Decomposition for Pair Comparison Data

An efficient graph-theoretical decomposition technique is introduced that treats inconsistencies in behavioral data as systematic adaptations rather than random errors. This technique, which is known as ear decomposition, reduces inconsistencies in any binary data set to a basis of directed cycles. Such a basis characterizes the data set in terms of inconsistencies and its size offers an improved measure of internal consistency. In two examples it is illustrated how different implementations of the ear decomposition technique can help to identify choices that are critical for violations of transitivity.     

THOG问题的演译结构及其非Wason-Brooks判定方法

本文探讨THOG问题的演绎结构及其非Wason-Brooks判断方法,据于Wason-Brooks判断程序和THOG问题逻辑结构的分析导致如下结论：1．Wason-BrooksTHOG判定方法并非判定THOG的唯一方法;2、被试选择那一种演绎判定模式取决于被试如何对原问题进行等效表征转换;3．判断失败可能意味着被试存在短时记忆容量障碍或者等效表征转换障碍。