Stimulating performance: A scoping review on transcranial electrical stimulation effects on olympic sports

Transcranial electrical stimulation (tES) is widely used to explore the role of various cortical regions involved in a multitude of motor and cognitive processes. Recently, tES has been discussed as being able to potentially enhance performance in sports and even been suggested as a potential way of boosting performance in competitions. In this scoping review, we systematically investigate the literature on tES-induced performance modulations on Olympic sports. The aim is to create a critical overview of the emerging field of tES-enhanced sports performance. Further, methodological approaches, research desiderata, and potential directions for future research will be identified. The final dataset shows a large variation in methodology and a lack of research into the area. Further, partially sub-optimal choices in study design, methodology, and lacking consistency in reporting procedures may impede valid conclusions and obscured the effects of tES on Olympic sports. Consequently, we outline future directions and areas to improve research.     

不同认知成分在图形推理测验项目难度预测中的作用

理解项目难度变异的来源是实现计算机自动化项目生成的第一步。通过文献综述, 总结出影响图形推理测验项目难度的四个方面的因素, 再通过操控构图元素熟悉性、属性的抽象性、知觉组织的和谐性以及规则类型与数目这些因素, 编制8套图形推理测验, 共包含112个与高级瑞文推理类似的项目。采用铆测验等值设计, 在每套测验中嵌入10个高级瑞文推理测验项目为铆题, 通过网络施测于6323名被试。使用BILOG MG估算项目参数, 并使用IRTEQ进行测验等值, 将后七套测验上所有项目的项目参数都转换到第一套测验的单位系统上。以项目难度为因变量, 项目题干特征变量为预测变量进行回归分析, 结果发现这四个因素均对项目难度有显著预测作用。优势分析的结果显示记忆负荷(即规则类型与数目的组合)是项目难度的最重要的预测变量, 其他依次为属性的抽象性、知觉组织的和谐性和构图元素熟悉性。     

Category assignment and relatedness in the group ideation process

When groups gather to generate creative ideas on a particular topic, they can consider many aspects or components of the problem. Because such a multitude of alternatives can be overwhelming, groups may find it helpful to focus on specific aspects or categories of the problem. However, it is not clear whether it is best for group members to focus on the same components of the problem at the same time or whether it is better for group members to focus on different components of the problem. Furthermore, the impact of this type of focus may depend on the extent to which the different components of the problem are closely related semantically. It may be easier to generate ideas in semantically related areas but semantically unrelated areas may stimulate generation of more creative ideas. The present study provided a comprehensive test of the effects of task components or category assignment and the degree of relatedness of the assigned categories on the creativity of groups. Groups that were jointly assigned a small set of categories to focus on at the beginning of their session generated more ideas, explored more categories and exhibited higher clustering of similar ideas than the groups whose members were assigned their own unique category. The groups assigned with low related categories surveyed more categories than those assigned with categories of high relatedness. This study suggests that interdisciplinary or diverse groups or teams should have some common focus in the initial phase of their creative sessions and focus on unrelated aspects of the problem in this phase.     

Manipulating attentional load in sequence learning through random number generation

Implicit learning is often assumed to be an effortless process. However, some artificial grammar learning and sequence learning studies using dual tasks seem to suggest that attention is essential for implicit learning to occur. This discrepancy probably results from the specific type of secondary task that is used. Different secondary tasks may engage attentional resources differently and therefore may bias performance on the primary task in different ways. Here, we used a random number generation (RNG) task, which may allow for a closer monitoring of a participant's engagement in a secondary task than the popular secondary task in sequence learning studies: tone counting (TC). In the first two experiments, we investigated the interference associated with performing RNG concurrently with a serial reaction time (SRT) task. In a third experiment, we compared the effects of RNG and TC. In all three experiments, we directly evaluated participants' knowledge of the sequence with a subsequent sequence generation task. Sequence learning was consistently observed in all experiments, but was impaired under dual-task conditions. Most importantly, our data suggest that RNG is more demanding and impairs learning to a greater extent than TC. Nevertheless, we failed to observe effects of the secondary task in subsequent sequence generation. Our studies indicate that RNG is a promising task to explore the involvement of attention in the SRT task.     

精加工推理中的文章结构清晰度效应

本研究旨在探讨文章结构清晰度对读者在阅读时所构建的精加工推理的水平和阶段的影响。研究采用让被试阅读后做推理测试题的方法 ,通过分析其答题成绩得出的结果表明 :文章的结构越清晰 ,越有利于读者在阅读的过程中进行精加工推理。     

Co-PoeTryMe: Interactive poetry generation

PoeTryMe is a poetry generation system that autonomously produces poems from a set of initial parameters. After using it, creative writers and other users of this system expressed the desire to partake in the creative process by directly interacting with PoeTryMe. This paper is motivated by the previous impressions. It illustrates some of the challenges of automatic poetry generation, highlights limitations of PoeTryMe, and reports on recent efforts to address user feedback and provide alternative ways of using this system. First, making some functionalities available via a web API enabled their exploitation by other systems. Those include the generation of single lines as well as retrieval of related words, allowing for additional constraints on the number of syllables, sentiment and rhyme. On top of this API, a co-creative interface, Co-PoeTryMe, has been developed. Co-PoeTryMe allows users to either start from scratch or from a generated draft. Extensive editing functionality has been incorporated, in particular, allowing lines and words to be switched or replaced by newly generated alternatives. Co-PoeTryMe, its interface and the co-creative process for producing poetry are described, along with a user study that provided useful feedback. Users pointed out the strengths of this new system, including its capacities in providing inspiration, and giving the user the ability to customize an existing draft and visualize the changes, but also pointed out weaknesses, primarily by identifying potential improvements in the user interface.     

Evaluating Fund Performance by Compromise Programming with Linear‐Quadratic Composite Metric: An Actual Case on The CaixaBank in Spain

This paper proposes an additive measure on the basis of compromise programming to evaluate fund performance from multiple criteria, of which the most usual are profitability and risk. This proposal is motivated by the fact that compromise programming is a sound decision support model to obtain scores of alternatives by minimizing weighted distances to an ideal point, the weights reflecting the investor's preferences for the criteria. To define the distance objective function, the linear‐quadratic composite metric is used, which combines advantages of linear and non‐linear objective functions. A critical advantage of compromise programming for fund performance evaluation is that the model can be extended to more than two financial criteria while other measures currently used (either ratio‐based or leverage‐based measures) only consider two criteria, say, profitability and risk. In the application, three investor's profiles are defined, which involve different weighting systems and lead to different fund rankings. These rankings are compared with domination relationships, the latter formulating if a fund is dominated or non‐dominated by convex combinations of other funds. Numerical tables are provided with data, computational process and results, which are analysed. Copyright © 2012 John Wiley & Sons, Ltd.     

Generation and evaluation of user tailored responses in multimodal dialogue

When people engage in conversation, they tailor their utterances to their conversational partners, whether these partners are other humans or computational systems. This tailoring, or adaptation to the partner takes place in all facets of human language use, and is based on a mental model or a user model of the conversational partner. Such adaptation has been shown to improve listeners’ comprehension, their satisfaction with an interactive system, the efficiency with which they execute conversational tasks, and the likelihood of achieving higher level goals such as changing the listener’s beliefs and attitudes. We focus on one aspect of adaptation, namely the tailoring of the content of dialogue system utterances for the higher level processes of persuasion, argumentation and advice-giving. Our hypothesis is that algorithms that adapt content for these processes, according to a user model, will improve the usability, efficiency, and effectiveness of dialogue systems. We describe a multimodal dialogue system and algorithms for adaptive content selection based on multi-attribute decision theory. We demonstrate experimentally the improved efficacy of system responses through the use of user models to both tailor the content of system utterances and to manipulate their conciseness.