A dual-process account of female facial attractiveness preferences: Sexual and nonsexual routes

The current study conceptualizes facial attractiveness as a dual-process judgment, combining sexual and aesthetic value. We hypothesized that holistic face processing is more integral to perceiving aesthetic preference and feature-based processing is more integral to sexual preference. In order to manipulate holistic versus feature-based processing, we used a variation of the composite face paradigm. Previous work indicates that slightly shifting the top from the bottom half of a face disrupts holistic processing and enhances feature-based processing. In the present study, while nonsexual judgments best explained facial attraction in whole-face images, a reversal occurred for split-face images such that sexual judgments best explained facial attraction, but only for mate-relevant faces (i.e., other-sex). These findings indicate that disrupting holistic processing can decouple sexual from nonsexual judgments of facial attraction, thereby establishing the presence of a dual-process.     

Global Model Analysis of Cognitive Variability

Residual fluctuations produced in typical experimental methodologies are examined as correlated noises. The effective range of the correlations was assessed by determining whether the decay over look-back time is better described as a power law or exponential. Both of these decay laws contain free parameters and it is argued that it is not possible to distinguish their models on the basis of simple measures of goodness-of-fit. Global analyses that evaluate models on the basis of how well they generalize are conducted. The models are examined in terms of three constructs that all bear on generalization: cross-validity, flexibility, and representativeness. Quantitative assessment of a large ensemble of data suggests that the correlations decay over time as a power law. The conclusion is that human residual fluctuation is a correlated fractal.     

多级评分题计算机自适应测验选题策略比较

研究比较了多级评分题计算机化自适应测验五种选题策略的优劣。应用的IRT模型是Samejima的等级反应模型。参加比较的选题策略有难度均值与能力匹配法、难度中值与能力匹配法、信息量最大法和两种A分层法。比较指标采用了能力估计值返回真值偏差、能力估计标准差、人均用题数和试题调用次数标准差四个。研究采用蒙特卡罗模拟法,结果显示每种方法各有优劣,在分层得当情况下,A分层法(中)的综合效果最佳     

初级军官心理选拔的预测性

目的：通过建立院校学员胜任特征及初级军官评价模型,探讨初级军官心理选拔检测系统的预测性。方法：某军校340名男性本科学员入校时完成初级军官心理选拔检测,毕业前进行院校胜任特征评价,对162名学员毕业后1~5年进行部队工作表现追踪评价。结果：中国MBTI-G人格类型测验对院校学员管理能力、影响力、约束力、口头表达、军人气质、人际关系等有较好的预测性,预测符合率72.13%;中国军人明尼苏达个性调查表6项精神障碍维度对情绪稳定性有较好的预测,预测符合率71.90%;院校学员一般能力倾向测验对学业成绩、文字表达、口头表达、人际关系和决策能力等胜任特征也有一定的预测性,预测符合率66.70%;三项测验院校胜任特征总预测符合率达81.70%,部队胜任特征总预测符合率76~87%。结论：为我军初级军官心理选拔提供了实用工具,为提高心理选拔预测准确性提供了科学依据     

职业选拔情境下人格测验作假研究

在职业选拔情境下被试很容易对人格测验作假,从而制约了人格测验在企业中的应用。许多研究者在努力解决作假问题,分别就应聘者是否会作假,作假给人格测验带来的负面影响、应聘者如何作假以及如果应对作假等问题进行了深入的探讨。经过几十年的发展,该研究领域已经形成了包括实验诱导设计、已知群体设计和量表设计等几种特定的研究范式。研究结果显示,大多数应聘者会作假,但其负面影响并不严重;作假不同于社会称许性反应,它是一种工作称许性反应。目前的几种应对作假的方法尚存在一些问题,其有效性有待提高。总之,人格测验的作假作用明显,其研究难度较大,有待革新性理论和方法的出现     

四卡问题解决中的内容与逻辑分析效应

选取条件概率(P(Q|P))由低到高的四个命题作为四卡问题中的检验规则,探讨了大学生被试对四张卡片的逻辑证明作用的推断能力及其对解决四卡问题的影响。结果发现:(1)不同条件概率的命题之间正确选择P-Q的人数百分比不存在显著差异,命题的条件概率因素对四卡问题的正确解决没有影响。(2)逻辑分析过程对四卡问题的正确解决产生了一定的抑制作用,这可能是因为被试不能从整体上思考四张卡片在命题检验中的逻辑作用的缘故。(3)一些被试即使在逻辑分析过程中表现出知道-Q卡片的证伪作用,仍然倾向于选择卡片Q而非-Q,这一现象再次证实了人类思维的非形式逻辑的一面。     

进化心理学择偶心理机制假设的跨文化检验

通过对404例天津和432例Boston征婚启事的内容分析,结果发现：（1）男性更关注未来异性配偶的相貌、身材信息,而女性更关注对方的资源与承诺信息,但天津男女征婚者在这两方面都表现得更为强烈;（2）男性随年龄增长,会倾向于更大程度地寻找比自己小的女性,而中美女性则较稳定;（3）Boston人的配偶选择年龄空间较天津人广;（4）在个性特质方面,天津人更关注承诺性,Boston人更关注精神性与“享乐性”。结果表明,进化心理学对男女间差异有较大解释力,但对中美之间的文化差异却缺乏足够说服力。     

从知觉负载理论来理解选择性注意

知觉负载理论被认为解决了选择性注意研究的早选择和晚选择观点之争。当前任务对注意资源的耗用程度决定了与任务无关的干扰刺激得到多少加工,从而导致在低知觉负载下,注意资源自动溢出去加工干扰刺激（晚选择）,而在高知觉负载下,注意资源被当前任务耗尽而无法加工干扰刺激（早选择）。知觉负载理论提出后,研究者进行了一系列研究。一部分工作专注于知觉负载对选择性注意的调节作用;另外一些工作则关注其他认知过程如何影响注意资源的分配,其中知觉负载与工作记忆负载的关系是当前关注重点。     

汉语词汇产生中语义、字形和音韵激活的时间进程

通过图画－词汇干扰实验范式探索汉语单字词产生中语义、字形和语音激活的时间进程。实验一和实验二的结果综合表明：当SOA为-300 ms~0 ms时,存在语义抑制效应,当SOA为-100 ms~100 ms时,存在字形促进效应,当SOA为100 ms时,存在语音促进效应。实验结果支持交互激活理论的观点。实验成功地分离了字形促进效应和语音促进效应,确定了语义、字形和语音这三种信息在汉语口语词汇产生中通达的先后次序,讨论了字形信息在词汇产生中的重要作用,以及被试是否在实验中使用了策略。     

A variable-selection heuristic for K-means clustering

One of the most vexing problems in cluster analysis is the selection and/or weighting of variables in order to include those that truly define cluster structure, while eliminating those that might mask such structure. This paper presents a variable-selection heuristic for nonhierarchical (K-means) cluster analysis based on the adjusted Rand index for measuring cluster recovery. The heuristic was subjected to Monte Carlo testing across more than 2200 datasets with known cluster structure. The results indicate the heuristic is extremely effective at eliminating masking variables. A cluster analysis of real-world financial services data revealed that using the variable-selection heuristic prior to the K-means algorithm resulted in greater cluster stability.