Prolonged subjective duration near the hands: Effects of hand proximity on temporal reproduction

Psychonomic Bulletin & Review - It has been reported that human visual perception and attention are altered when the hands are nearby. Previous studies indicate that placing hands near stimuli...     

初级视觉皮层在注意振荡中的作用

注意离散性是注意间歇性地采集外界信息的特性, 这种特性在行为中的表现被称为注意振荡。尽管有研究使用经典的线索靶子范式和视觉搜索范式发现行为数据中存在注意振荡, 证明了注意是离散的, 但其相应的神经机制, 即注意离散性与哪些脑区有关, 尚不明确。研究采用高时间分辨率的线索靶子范式和双眼分视技术, 基于人眼视觉通路的解剖学特性, 比较了线索靶子出现在双眼(实验1)、同眼和异眼(实验2)条件下的注意振荡, 考察初级视觉皮层对注意振荡的影响。结果发现：1)双眼分视会对注意振荡发生的频段产生影响, 非双眼分视条件下, 注意振荡出现在低频(2 Hz和8 Hz)。而双眼分视条件下, 注意振荡则出现在更高的频段(12.5 Hz)。2)无论线索靶子出现在同眼还是异眼, 注意振荡的频段无显著差异。结果表明V1区双眼视觉通路或者更高级的视觉区可能参与注意振荡, 为探明注意振荡的神经机制提供了重要的行为学证据。     

孝的多维心理结构：取向之异与古今之变

孝的内在结构具有多面性, 但以往孝的量表存在信效度不高、掺入研究者主观意图、遗漏重要因素等弊端。研究通过经典文本改编和被试自由生成而编制条目, 通过因素分析与效标分析等方法考察孝的结构( 总样本量 = 1725), 发现孝包含尊敬安乐父母、服从父母、和颜对待父母、守身不辱父母、陪伴父母、扬名以显父母、思慕父母、不干涉父母、劝谏父母, 九因素结构具有良好的信效度。不同因素与效标变量(家庭自我概念、家庭弹性、父母教养方式、亲子边界不当、自立人格、服从倾向)具有不同的关联模式。这些发现反映出儒学论孝的敬慕之别、敬顺之变。此外讨论了不同因素的古今变迁。     

两人和三人最后通牒博弈任务中嗓音吸引力对决策的影响

本研究用两人和三人最后通牒博弈任务探究男性嗓音吸引力对决策的影响。实验1发现, 高吸引力的嗓音会提高被试对不公平分配方案的接受率。实验2发现, 即使分配方案对第三方接受者公平, 对被试不公平, 第三方接受者的高吸引力嗓音仍然会提高被试对方案的接受率。综上, 嗓音吸引力可以诱发类似面孔吸引力的“美貌津贴”效应。     

问卷调查中被试不认真作答的控制与识别

问卷调查是心理与教育领域十分常见的数据收集方法, 而被试的不认真作答可能导致问卷数据失真。回顾已有研究发现:(a)不认真作答可以从外在作答模式和内在产生原因两个方向进行定义; (b)不认真作答的常见事前控制方法主要包括降低任务难度以及提高被试作答动机两大类; (c)事后识别方法主要包括嵌入识别量表、作答模式识别、反应时识别三大类。今后的研究中应基于作答机制的研究优化与开发控制方法, 检验作答识别方法的跨情境适用性并开发新方法, 并对局部不认真的识别与处理进行更深入的探讨。     

中外两种主要推理模型的实验再比较研究

本研究采用2X2两因素(自变量分别为“心理模型数量”和不同“内容性质”)重复测量拉丁方实验方法,在范畴三段论推理题的内在结构为“形式正确”,且“推理者已经掌握相应的形式逻辑推理规则”的前提条件下,对西方学者提出的“心理模型”与中国学者提出的“推理题和推理者的推理知识双重结构模型”这两种理论模型再次进行了实验比较研究。结果表明,对于“正确形式—正确内容”和“正确形式—错误内容”两种不同结构的推理题,若按照形式逻辑规则来判定推理者“对各推理题的结论能否从两个前提中推论出来的推论结果”是否正确,则有:(1)当推理题含有错误内容的情况下,即使该推理题只是由“一个心理模型”所构成,也会使推理者降低对其结论判定为“正确”的可能性;(2)当推理题是由正确内容所构成时,即使该推理题是由三个心理模型所构成,对其结论判定为“正确”的可能性也要比“单模型——错误内容”构成的推理题的可能性更高。由此得到的比较结论是:在本实验条件下,“推理题和推理者的推理知识双重结构模型”对实验结果所做的解释要比“心理模型”理论所做的解释更为贴切。     

A Bayesian Power Analysis Procedure Considering Uncertainty in Effect Size Estimates from a Meta-analysis

In conventional frequentist power analysis, one often uses an effect size estimate, treats it as if it were the true value, and ignores uncertainty in the effect size estimate for the analysis. The resulting sample sizes can vary dramatically depending on the chosen effect size value. To resolve the problem, we propose a hybrid Bayesian power analysis procedure that models uncertainty in the effect size estimates from a meta-analysis. We use observed effect sizes and prior distributions to obtain the posterior distribution of the effect size and model parameters. Then, we simulate effect sizes from the obtained posterior distribution. For each simulated effect size, we obtain a power value. With an estimated power distribution for a given sample size, we can estimate the probability of reaching a power level or higher and the expected power. With a range of planned sample sizes, we can generate a power assurance curve. Both the conventional frequentist and our Bayesian procedures were applied to conduct prospective power analyses for two meta-analysis examples (testing standardized mean differences in example 1 and Pearson's correlations in example 2). The advantages of our proposed procedure are demonstrated and discussed.     

Reevaluating the two-representation model of numerical magnitude processing

One debate in mathematical cognition centers on the single-representation model versus the two-representation model. Using an improved number Stroop paradigm (i.e., systematically manipulating physical size distance), in the present study we tested the predictions of the two models for number magnitude processing. The results supported the single-representation model and, more importantly, explained how a design problem (failure to manipulate physical size distance) and an analytical problem (failure to consider the interaction between congruity and task-irrelevant numerical distance) might have contributed to the evidence used to support the two-representation model. This study, therefore, can help settle the debate between the single-representation and two-representation models.