时间信息加工与信息加工时间特性双视角下的重要时间参数及其证据

时间既是人类信息加工的对象, 也是(非时间)信息加工的制约因素。数十毫秒至数秒之间的时间加工与人类日常生活关联紧密, 譬如主观计时、演奏及言语等活动。根据以往文献分析可知, 在该时间区域内, 20~ 60 ms、1/3~1 s、2~3 s是研究者关注的重要时间参数, 但是支持这些参数的证据尚存分歧。首先从“时间信息加工”和“信息加工的时间特性”的视角介绍时间参数的基本观点及其提出背景, 然后基于“时间信息加工”视角从行为学研究、脑损伤研究、神经药理学研究, 脑电研究、脑成像研究、经颅磁刺激研究、经颅直流电刺激研究等领域介评了1/3~1 s和2~3 s分界区域的证据, 接着基于“信息加工的时间特性”视角从时序知觉阈限研究、感觉运动同步研究、主观节奏研究、言语行为研究、知觉逆转研究、返回抑制研究及失匹配负波研究等领域介评了20~60 ms和2~3 s时间窗口的证据。未来研究既要注意构建基于分界区域与时间窗口的更强解释力的理论假说, 也要厘清分界区域与时间窗口的联系与区别。     

反馈间隔影响反馈加工：整合行为和电生理研究的视角

反馈在现实生活中扮演着重要角色, 通过反馈信息进行学习是人类获取知识和技能的有效手段。反馈间隔是指个体行为发生到反馈刺激呈现之间的时间间隔。在反馈加工过程中, 反馈间隔是一个重要影响因素, 但反馈间隔影响反馈加工的研究结果不一。对反馈间隔影响反馈加工的行为和电生理研究分别做了介绍, 对结果出现差异的原因进行了分析。未来的研究应考虑结合行为研究与电生理研究, 并统一反馈间隔的操作定义。     

统计学习的认知神经机制及其与语言的关系

统计学习是指个体在连续刺激流中发现转移概率等统计规律的过程, 在Saffran等(1996)的经典婴儿语音切分研究中首次被提出。大量研究证实了统计学习的普遍存在, 近期学界开始关注统计学习的特异性及其对认知的影响, 尤其是从学习过程及其特异性两个方面阐述统计学习的认知神经机制并揭示其和语言的交互作用。未来应从脑和行为的多模态数据视角, 丰富统计学习结果的行为和神经指标, 考察不同类型统计学习过程的动态神经活动模式, 建立统计学习行为和脑的关联, 深化对统计学习认知神经机制的认识, 在统计学习与语言交互作用的基础上, 从成人二语学习切入结合音乐统计学习训练探讨促进语言学习的统计学习干预手段。     

医疗信息的风险感知

从信息加工视角探究个体对医疗信息的风险感知过程, 通过医疗信息来源、医疗信息内容与呈现形式和信息加工主体三个方面归纳其影响因素, 并基于经验-分析式加工理论和字面-要义加工理论探讨医疗信息风险感知的认知机制。今后研究可更注重医疗信息风险感知的通用研究与特殊主题研究的平衡、风险感知测量工具的规范性与特异性的结合, 并立足于中国医疗体系建立具有循证依据的预防措施和配套政策。     

时变效应模型及在密集追踪数据分析中的应用

密集追踪数据通常蕴含了心理过程的详细变化信息,反映了某些心理的复杂变化过程。时变效应模型用函数替代恒定的系数,可描述密集追踪数据中随时间推移心理的动态变化过程和时变效应,是分析复杂心理过程的有效方法。在介绍时变效应模型的原理后,通过模拟研究考察模型的表现,结果显示:(1)样本量增加可降低函数估计的误差;(2)惩罚样条法的节点数选择与函数的复杂度有关,函数越复杂,所需节点越多;(3)样本量与节点数对函数估计误差的交互效应不显著。进一步应探讨测量次数、数据分布形态、数据缺失等如何影响模型的表现。     

瞳孔是心灵的窗口吗?——瞳孔在心理学研究中的应用及测量

瞳孔大小是眼动研究中一个重要的参数指标, 在一定程度上能够反映人的心理活动并影响他人的心理与行为。一方面, 瞳孔变化受到自上而下的知觉与注意、情绪与动机、心理努力、社会认知与发展等因素的影响, 另一方面, 大瞳孔能引起他人更多的积极评价和积极行为。瞳孔的神经机制(蓝斑-去甲肾上腺素系统)和自适应增益理论解释了瞳孔与心理之间存在紧密联系的原因。瞳孔测量(pupillometry)作为一种有效的眼动追踪方法有助于理解瞳孔与心理的关系, 研究者在测量瞳孔时需要关注无关变量(如亮度、注视位置), 原始数据处理(如基线校正、眨眼处理)及瞳孔指标选取(如瞳孔直径、震颤频率)等问题, 未来研究应继续探讨瞳孔与其他心理之间的关系, 并探索更有效地处理和使用瞳孔指标的方法。     

Is there a natural order for expressing semantic relations?

All languages rely to some extent on word order to signal relational information. Why? We address this question by exploring communicative and cognitive factors that could lead to a reliance on word order. In Study 1, adults were asked to describe scenes to another using their hands and not their mouths. The question was whether this home-made "language" would contain gesture sentences with consistent order. In addition, we asked whether reliance on order would be influenced by three communicative factors (whether the communication partner is permitted to give feedback; whether the information to be communicated is present in the context that recipient and gesturer share; whether the gesturer assumes the role of gesture receiver as well as gesture producer). We found that, not only was consistent ordering of semantic elements robust across the range of communication situations, but the same non-English order appeared in all contexts. Study 2 explored whether this non-English order is found only when a person attempts to share information with another. Adults were asked to reconstruct scenes in a non-communicative context using pictures drawn on transparencies. The adults picked up the pictures for their reconstructions in a consistent order, and that order was the same non-English order found in Study 1. Finding consistent ordering patterns in a non-communicative context suggests that word order is not driven solely by the demands of communicating information to another, but may reflect a more general property of human thought.     

On normal theory based inference for multilevel models with distributional violations

Data in social and behavioral sciences are often hierarchically organized though seldom normal, yet normal theory based inference procedures are routinely used for analyzing multilevel models. Based on this observation, simple adjustments to normal theory based results are proposed to minimize the consequences of violating normality assumptions. For characterizing the distribution of parameter estimates, sandwich-type covariance matrices are derived. Standard errors based on these covariance matrices remain consistent under distributional violations. Implications of various covariance estimators are also discussed. For evaluating the quality of a multilevel model, a rescaled statistic is given for both the hierarchical linear model and the hierarchical structural equation model. The rescaled statistic, improving the likelihood ratio statistic by estimating one extra parameter, approaches the same mean as its reference distribution. A simulation study with a 2-level factor model implies that the rescaled statistic is preferable.This research was supported by grants DA01070 and DA00017 from the National Institute on Drug Abuse and a University of North Texas faculty research grant. We would like to thank the Associate Editor and two reviewers for suggestions that helped to improve the paper.     

A unified approach to exploratory factor analysis with missing data,nonnormal data,and in the presence of outliers

Factor analysis is regularly used for analyzing survey data. Missing data, data with outliers and consequently nonnormal data are very common for data obtained through questionnaires. Based on covariance matrix estimates for such nonstandard samples, a unified approach for factor analysis is developed. By generalizing the approach of maximum likelihood under constraints, statistical properties of the estimates for factor loadings and error variances are obtained. A rescaled Bartlett-corrected statistic is proposed for evaluating the number of factors. Equivariance and invariance of parameter estimates and their standard errors for canonical, varimax, and normalized varimax rotations are discussed. Numerical results illustrate the sensitivity of classical methods and advantages of the proposed procedures.This project was supported by a University of North Texas Faculty Research Grant, Grant #R49/CCR610528 for Disease Control and Prevention from the National Center for Injury Prevention and Control, and Grant DA01070 from the National Institute on Drug Abuse. The results do not necessarily represent the official view of the funding agencies. The authors are grateful to three reviewers for suggestions that improved the presentation of this paper.     

Case markers as clause boundary inducers in Japanese

The present paper provides evidence that the processing of verb final clauses proceeds incrementally based on local information that becomes available with each word. The results of three self-paced reading experiments are reported in support of the proposal that NPs in Japanese are associated within clauses before a verb is processed. It is argued that a clause boundary is posited whenever case markers prevent two NPs from being part of the same clause, and slow reading times at the second NP are used as supporting evidence. Moreover, clause boundaries induced by case marking can facilitate processing at later points in the sentence as attested to by faster reading times at relative-clause heads. Contrary to previous findings that argued against a subclass of head-driven parsers, the present results are not easily reconcilable with any type of model that delays parsing decisions until a verb is available in the input sentence.