人类性别加工的认知神经机制

性别信息是人类重要的生物和社会属性, 对性别快速而准确的识别对人类的生存和繁衍具有重要意义。人类的性别加工具有自动化、刻板化以及不对称性等特点, 并受性别加工主体、其他性别信息、社会类别信息以及高级认知调节的影响。聚焦于面孔和声音这两个性别信息的主要来源, 综述了性别加工的行为和神经机制研究, 并提出了两阶段性别加工模型, 早期阶段为对性别信息物理属性的特异性加工, 晚期阶段为对抽象性别信息的一般性加工。未来的研究可以重点关注性别加工的系统性研究、性别分类和机器学习、以及性别的二相性问题等方面。     

夜间进食综合征的研究进展

夜间进食综合征是一种表现为早晨没有食欲、晚饭后过量进食或夜间醒来进食的饮食障碍,并且与睡眠问题相关。在国内外文献进行分析研究的基础上,本文先后介绍了夜食症的概念内涵、发病的生理神经机制及主要的干预措施和治疗手段,并对现有研究的不足进行总结,提出了未来研究的方向,旨在为研究者在此领域的进一步探索提供参考。     

Implicit and explicit processing in deep dyslexia: Semantic blocking as a test for failure of inhibition in the phonological output lexicon

The failure of inhibition hypothesis posits a theoretical distinction between implicit and explicit access in deep dyslexia. Specifically, the effects of failure of inhibition are assumed only in conditions that have an explicit selection requirement in the context of production (i.e., aloud reading). In contrast, the failure of inhibition hypothesis proposes that implicit processing and explicit access to semantic information without production demands are intact in deep dyslexia. Evidence for intact implicit and explicit access requires that performance in deep dyslexia parallels that observed in neurologically intact participants on tasks based on implicit and explicit processes. In other words, deep dyslexics should produce normal effects in conditions with implicit task demands (i.e., lexical decision) and on tasks based on explicit access without production (i.e., forced choice semantic decisions) because failure of inhibition does not impact the availability of lexical information, only explicit retrieval in the context of production. This research examined the distinction between implicit and explicit processes in deep dyslexia using semantic blocking in lexical decision and forced choice semantic decisions as a test for the failure of inhibition hypothesis. The results of the semantic blocking paradigm support the distinction between implicit and explicit processing and provide evidence for failure of inhibition as an explanation for semantic errors in deep dyslexia.     

The Role of Network Support and Interference in Women's Perception of Romantic,Friend, and Parental Relationships

This study examined the association between support/interference from the best friend and closest parent to women's (a) satisfaction with the parent–daughter relationship, (b) satisfaction with the friendship, and (c) love for the romantic partner. The respondents (n = 162 females; 84% Caucasian, 1% Asian American, 10% Hispanic, 4% African American, and 1% Multiracial) completed a questionnaire packet to assess each of the factors. Results revealed that romantic love was unrelated to friend support, friend interference, or parental interference, but positively related to parental support. Parent support was a significant correlate of parent satisfaction, and a similar pattern emerged between friend support/friendship satisfaction. Further, best friend support moderated the relationship between friend interference and friendship satisfaction, such that interference was negatively related to satisfaction in low support conditions. Overall, the results suggested that network reactions to romance played a limited role in romantic affection, but were more strongly associated with network satisfaction.     

Sensitivity of MRQAP Tests to Collinearity and Autocorrelation Conditions

Multiple regression quadratic assignment procedures (MRQAP) tests are permutation tests for multiple linear regression model coefficients for data organized in square matrices of relatedness among n objects. Such a data structure is typical in social network studies, where variables indicate some type of relation between a given set of actors. We present a new permutation method (called “double semi-partialing”, or DSP) that complements the family of extant approaches to MRQAP tests. We assess the statistical bias (type I error rate) and statistical power of the set of five methods, including DSP, across a variety of conditions of network autocorrelation, of spuriousness (size of confounder effect), and of skewness in the data. These conditions are explored across three assumed data distributions: normal, gamma, and negative binomial. We find that the Freedman–Lane method and the DSP method are the most robust against a wide array of these conditions. We also find that all five methods perform better if the test statistic is pivotal. Finally, we find limitations of usefulness for MRQAP tests: All tests degrade under simultaneous conditions of extreme skewness and high spuriousness for gamma and negative binomial distributions. Special thanks go to Cajo Ter Braak, Philip Hans Franses, Patrick Houweling, Pierre Legendre, three anonymous reviewers, the associate editor, and the editor for comments.     

等级效标分数的概率神经网络预测方法研究

针对基于统计学的等级效标分数预测存在的问题,提出了运用概率神经网络进行等级效标分数预测的方法。在20种条件下进行了计算机模拟实验,包括测验分数为单变量和多变量,以及各种水平的白噪声干扰条件,结果表明在测验分数为多变量的情况下,或者在有白噪声干扰的条件下,运用概率神经网络方法可以比统计学方法更好地对等级效标分数进行预测。     

从对框架效应的分析看风险决策的神经基础

对风险决策的研究近年来一直是微观经济学和心理学的热门,但是从传统的决策理论到主张有限理性的预期理论大多是对决策行为的研究,决策的心理机制一直是无法解释的黑箱子。令人兴奋的是近几年来神经科学家及其他领域的学者开始用先进的神经心理学的方法来研究风险决策,试图理解神经过程调节风险决策行为的方式。文章从分析违背“不变性”原则的框架效应入手,从正反框架、确定选择和风险选择、风险规避和风险寻求、高估小概率事件几个方面,全面分析风险决策可能的神经基础。在总结已有研究的基础上提出了把风险决策研究扩展到不确定决策,把风险决策中的一次决策研究扩展到多次决策的研究思路     

内隐学习的人工神经网络模型

近年来,人工神经网络模型常被用来模拟各种心理活动,从而为心理学的一些相关理论提供丰富的证据,内隐学习也不例外。基于权重调整来学习正确反应的人工神经网络模型和内隐学习的两大本质特征间有着极为相应的匹配,因此,人工神经网络模型特别适用于内隐学习研究。到目前为止,针对两种较为普遍的内隐学习任务,已经相应地出现了两种使用较为广泛的神经网络模型——自动联系者和简单循环网络     

情绪神经回路的可塑性

从两个方面介绍了近年来关于情绪神经回路可塑性的研究进展：（1）情绪神经回路中各主要脑区（包括前额叶、杏仁核等）的可塑性及其相互影响;（2）情绪神经回路可塑性的影响因素,包括情绪学习、早期环境和经历以及抚养者的情绪状态等。文章最后还对情绪神经回路可塑性的研究方法及未来的研究方向进行了探讨     

An exploratory analysis of the latent structure of process data via action sequence autoencoders

Computer simulations have become a popular tool for assessing complex skills such as problem-solving. Log files of computer-based items record the human–computer interactive processes for each respondent in full. The response processes are very diverse, noisy, and of non-standard formats. Few generic methods have been developed to exploit the information contained in process data. In this paper we propose a method to extract latent variables from process data. The method utilizes a sequence-to-sequence autoencoder to compress response processes into standard numerical vectors. It does not require prior knowledge of the specific items and human–computer interaction patterns. The proposed method is applied to both simulated and real process data to demonstrate that the resulting latent variables extract useful information from the response processes.