内源性注意与外源性注意对数字加工的不同影响

分别采用内源性注意和外源性注意实验范式,材料为小数（1－4）和大数（6－9）的中文与阿拉伯数字,任务为判断数字是否大于5,考察在注意与非注意条件下不同大小数字加工的距离效应和符号效应。结果发现：⑴在内源性线索和外源性线索的注意条件下,大小数都出现了距离效应;但在非注意条件下,内源性线索时的大小数都出现了距离效应,而外源性线索时只有小数出现了距离效应,大数的距离效应明显减弱或消失。⑵在内源性线索和外源性线索的注意条件下,大小数都没有出现符号效应;但在非注意条件下,大数没有出现符号效应,而小数出现了符号效应（阿拉伯数字的绩效比中文数字差）,并且内源性线索时的符号效应强度小于外源性线索。     

不同注意条件下的空间－数字反应编码联合效应

考察在无线索、内源性线索与外源性线索时不同符号数字在注意与非注意条件下的空间－数字的反应编码联合效应（Spatial Numerical Association of Response Codes,简称SNARC效应）。采用1到9的中文与阿拉伯数字为材料,以判断数字奇偶为任务。实验结果表明：⑴无线索时注意条件下阿拉伯和中文数字都出现了SNARC效应,而非注意条件下则都没有出现,并且受影响的主要是较大的数字（8、9）;⑵外源性线索和内源性线索时,我们得到一个逐渐递减的SNARC效应,受影响的也主要是较大的数字（8、9）。在内源性线索的注意条件阿拉伯和中文数都有SNARC效应,而在非注意条件只有阿拉伯数有SNARC效应;在外源性线索的注意条件只有阿拉伯数有SNARC效应,而在非注意条件阿拉伯和中文都没有SNARC效应,说明外源性注意的影响比内源性注意更大,中文数字所受的影响比阿拉伯数字更大。     

仪式动作、象征意义和积极情绪增强控制感：双路径机制

以中国普米族人的传统文化仪式“敬锅庄”为例, 采用回忆任务、创设新颖仪式等方法考察仪式动作、象征意义和积极情绪对普米族青少年及成人的控制感的影响。结果表明, 熟悉敬锅庄仪式的动作、象征意义或者具有更多情感体验的青少年的控制感更强。仪式动作与象征意义对控制感的影响存在双路径机制：仪式动作直接增强个体的控制感, 象征意义通过积极情绪间接增强个体的控制感。象征意义与控制感的关系还因仪式主体不同有所区别：祈求庇佑通过积极情绪间接增强青少年的控制感, 表达感恩通过积极情绪间接增强成年人的控制感。研究结果对探究仪式动作、象征意义和积极情绪对个体控制感的影响有重要启示。     

多级评分聚类诊断法的影响因素

从测验和被试两个层面探讨了属性数目、属性层级关系、被试知识状态分布、属性层级误设和Q矩阵误设等因素对GRCDM的影响, 以进一步考察GRCDM的特性。研究发现：(1)GRCDM对属性数目无依赖, 随属性数目的增多判准率反而增高; (2)被试知识状态分布对GRCDM判准率高低无影响; (3)属性层级误设对GRCDM的影响与属性层级类型有关, 当属性层级为无结构型和发散型时, “属性层级关系错乱”的判准率降幅最大; (4)Q矩阵误设对GRCDM的影响因层级关系而异, 收敛型和发散型受影响较小, 无结构型和线型的判准率在属性既冗余又缺失时降幅最大。     

汉字加工的基本单元:来自笔画数效应和部件数效应的证据

用命名作业和真假字判断作业考察了笔画数和部件数对汉字加工的影响,发现,当部件数保持恒定时,笔画数少的汉字较笔画数多的汉字加工快,这种笔画数效应和字频之间不存在交互作用;当笔画数被控制时,部件数效应也存在,但和字频有交互作用,表现在低频少部件汉字的加工要快于低频多部件汉字的加工,而高频汉字中的部件数效应不显著。实验还发现独体字（只有一个部件）的加工和合体字（部件数大于等于二）的加工有差异。作者认为,汉字的加工要经过笔画、部件和整字三个层次,其中单位部件的笔画数和部件数影响着汉字加工时间     

结构对称性汉字视觉识别特点的实验研究

用命名作业通过二个实验考察了结构对称性汉字的视觉识别特点,结果表明,汉字识别具有显著的结构对称性效应,识别结构对称性汉字的加工明显快于非对称性汉字,对称性效应与字频效应,笔画效应具有显著的交互作用,进一步的简单效应的分析表明,这种结构对称性效应在低频字中起作用,而在高频字中不起显著作用;在多笔画字中起作用,而在少毛画字中不起显著作用,这表明,对称性汉字中的具有冗余信息的格式塔部件促进地识别汉字的加     

仪表数量与信号间隔时距对监视作业效绩的影响

用信号察觉反应时与信号误报率作指标 ,对监视作业中监视仪表数量与信号间隔时距对监视作业效绩的影响问题进行研究。结果表明 ,监视作业的效绩会随仪表增多和信号间隔时距增大而呈下降趋势。当监视仪表达到 8个时 ,监视作业的效绩会因负荷过重而明显下降。信号出现间隔时距增长时 ,会因警戒水平下降使监视仪表作业的效绩明显降低。     

Factor Retention Using Machine Learning With Ordinal Data

Determining the number of factors in exploratory factor analysis is probably the most crucial decision when conducting the analysis as it clearly influences the meaningfulness of the results (i.e., factorial validity). A new method called the Factor Forest that combines data simulation and machine learning has been developed recently. This method based on simulated data reached very high accuracy for multivariate normal data, but it has not yet been tested with ordinal data. Hence, in this simulation study, we evaluated the Factor Forest with ordinal data based on different numbers of categories (2–6 categories) and compared it to common factor retention criteria. It showed higher overall accuracy for all types of ordinal data than all common factor retention criteria that were used for comparison (Parallel Analysis, Comparison Data, the Empirical Kaiser Criterion and the Kaiser Guttman Rule). The results indicate that the Factor Forest is applicable to ordinal data with at least five categories (typical scale in questionnaire research) in the majority of conditions and to binary or ordinal data based on items with less categories when the sample size is large.     

Delayed stimulus control: recall for single and relational stimuli.

In a discrete-trial symbolic matching-to-sample procedure, pigeons' left-key responses were reinforced following presentation of one center-key sample, and right-key responses were reinforced following presentation of another. Recallability was measured by the difference between log ratios of left to right responses following each sample. In Experiment 1, samples were successively presented same or different wavelengths in the relational discrimination, or individual wavelengths in the single discrimination. The rate at which recallability decreased with increasing delay since sample presentation was the same for single and relational discriminations, but the initial level of performance differed, indicating that the relational discrimination was more difficult. In Experiment 2, recall functions for easy and difficult discriminations between individual wavelengths also differed in levels of initial performance but not in rate of decrement of recallability over time. Recall for stimuli differing in complexity may therefore reflect differences in discrimination difficulty.