Situational information and the design of representative learning tasks: What impact does a scoreboard have on expert taekwondo fighters' behaviour and affective-cognitive responses?

Designing representative learning tasks is one means to enhance sports practice. Recent work has highlighted how the presence of situational information could help the design of these tasks by shaping intentions and enhancing the affective demands of practice, however this has yet to be empirically tested. This study tested this hypothesis by manipulating the presence of a scoreboard featuring time and score situational information as expert taekwondo athletes fought in practice. Nine taekwondo fighters fought with and without situational information in a counterbalanced order. Behaviour was assessed by tracking fighters' location coordinates to assess fighter-fighter dyad coordination and through notational analysis of attacking actions. Affect and cognition were assessed with mixed-methods that included perceptual scales measuring anxiety, arousal, mental effort, score perception, and post-fight video-facilitated confrontational interviews to explore how conditions differed. The results revealed that the presence of the situational information had significant effects on taekwondo players. When present, fighters reported greater cognitive anxiety (d = 0.39, p < 0.05), somatic anxiety (d = 1.11, p < 0.05) and emotion intensity (d = 0.33, p < 0.05). The enhanced affective demands were associated with behaviour changes that included fighters preferring to spend time at closer distances (d = 0.25, p < 0.05), and more predictable technique selection (d = 1.04, p < 0.05). Qualitative data supported these findings. Players also reported their intentions were coupled to the context of scoreboard. This study reveals that situational information changes the affective and behavioural demands of practice to be more like competition. Further, situational sampling affords performers the opportunity to practice attuning to the relevant affordances for a specific context.     

潜变量交互效应标准化估计：方法比较与选用策略

标准化估计对模型的解释和效应大小的比较有重要作用。虽然潜变量交互效应的恰当标准化估计公式已经面世超过10年, 国内外都在使用和引用, 但至今未见到关于不同估计方法得到的恰当标准化估计的系统比较。通过模拟实验, 比较了乘积指标法、潜调节结构方程(LMS)、无先验信息和有先验信息的贝叶斯法的潜变量交互效应标准化估计在不同条件下的表现。结果发现, 在正态条件下, LMS和有信息贝叶斯法表现较好; 而在非正态条件下, 乘积指标法比较稳健, 但需要较大的样本(不小于500), 小样本且外生潜变量之间相关很低时可使用无信息贝叶斯法。     

认知诊断缺失数据处理方法的比较：零替换、多重插补与极大似然估计法

数据缺失在测验中经常发生, 认知诊断评估也不例外, 数据缺失会导致诊断结果的偏差。首先, 通过模拟研究在多种实验条件下比较了常用的缺失数据处理方法。结果表明：(1)缺失数据导致估计精确性下降, 随着人数与题目数量减少、缺失率增大、题目质量降低, 所有方法的PCCR均下降, Bias绝对值和RMSE均上升。(2)估计题目参数时, EM法表现最好, 其次是MI, FIML和ZR法表现不稳定。(3)估计被试知识状态时, EM和FIML表现最好, MI和ZR表现不稳定。其次, 在PISA2015实证数据中进一步探索了不同方法的表现。综合模拟和实证研究结果, 推荐选用EM或FIML法进行缺失数据处理。     

首词素位置概率信息未参与汉语阅读的词切分：基于副中央凹加工的证据

本研究采用边界范式,考察汉语阅读中读者是否在副中央凹处利用首词素的位置概率信息进行词切分和词识别。采用2（首词素位置概率：高、低）×２（预视类型：相同预视、假字预视）的被试内实验设计,以双字词为目标词,记录大学生阅读句子时的眼动轨迹。结果发现,在跳读率和第一遍阅读的眼动指标中,被试在高、低首词素位置概率条件中获得的预视效益量无显著差异。该结果表明,读者并未在副中央凹处加工首词素的位置概率信息。结合前期研究推断,首词素位置概率信息没有作用于汉语阅读的词切分和词识别。     

Sometimes it does hurt to ask: The constructive role of articulating impressions

Decisions can sometimes have a constructive role, so that the act of, for example, choosing one option over another creates a preference for that option (e.g., , ,  and ). In this work we explore the constructive role of just articulating an impression, for a presented visual stimulus, as opposed to making a choice (specifically, the judgments we employ are affective evaluations). Using quantum probability theory, we outline a cognitive model formalizing such a constructive process. We predict a simple interaction, in relation to how a second image is evaluated, following the presentation of a first image, depending on whether there is a rating for the first image or not. The interaction predicted by the quantum model was confirmed across three experiments and a variety of control manipulations. The advantages of using quantum probability theory to model the present results, compared with existing models of sequence order effects in judgment (e.g., Hogarth & Einhorn, 1992) or other theories of constructive processes when a choice is made (e.g.,  and ) are discussed.     

On the logical structure of de Finetti's notion of event

This paper sheds new light on the subtle relation between probability and logic by (i) providing a logical development of Bruno de Finetti's conception of events and (ii) suggesting that the subjective nature of de Finetti's interpretation of probability emerges in a clearer form against such a logical background. By making explicit the epistemic structure which underlies what we call Choice-based probability we show that whilst all rational degrees of belief must be probabilities, the converse doesn't hold: some probability values don't represent decision-relevant quantifications of uncertainty.     

建构反应题中能力估计准确性的影响因素:评分者人数和项目个数的交互作用

采用康春花、孙小坚和曾平飞(2016)提出的等级反应多水平侧面模型探讨了评分者人数和项目个数对被试能力估计准确性的影响。模拟研究的结果表明:(1)随着项目个数的增加,估计值与真值之间的相关也不断增加;(2)评分者人数和项目个数在平均绝对偏差(MAB)和误差均方根(RMSE)上的主效应均显著,两者间的交互效应也显著;(3)简单效应分析发现,当项目较少时,3个评分者条件下的能力估计准确性最好; 随着项目个数的增加,4个评分者的估计误差迅速下降,且表现变为最好。     

Two-Stage maximum likelihood estimation in the misspecified restricted latent class model

The maximum likelihood classification rule is a standard method to classify examinee attribute profiles in cognitive diagnosis models (CDMs). Its asymptotic behaviour is well understood when the model is assumed to be correct, but has not been explored in the case of misspecified latent class models. This paper investigates the asymptotic behaviour of a two-stage maximum likelihood classifier under a misspecified CDM. The analysis is conducted in a general restricted latent class model framework addressing all types of CDMs. Sufficient conditions are proposed under which a consistent classification can be obtained by using a misspecified model. Discussions are also provided on the inconsistency of classification under certain model misspecification scenarios. Simulation studies and a real data application are conducted to illustrate these results. Our findings can provide some guidelines as to when a misspecified simple model or a general model can be used to provide a good classification result.