FN400和LPC对熟悉性和回想的特异性指向分析

双加工理论认为熟悉性和回想是情景记忆的两个独立过程,其重要证据是ERP研究中FN400和LPC可以分别指示熟悉性和回想。但也有研究证明FN400是概念启动和熟悉性共同作用的结果,而LPC也不能准确的反映回想加工,因此,将其作为双加工理论证据的可靠性需进一步讨论。未来需进一步明确概念启动和熟悉性影响FN400的前提条件,并在提高生态效度和控制额外变量的同时明晰LPC的指向性,同时探究熟悉性和回想在再认过程中的具体作用方式。     

眼跳的双相调节理论的行为证据

为了研究眼跳的双相调节理论是否适用于人类的视觉系统,本研究测量了人类被试对分别呈现在三种眼跳时间段（基线、眼跳抑制和眼跳增强）内的光栅的朝向辨别准确率。研究发现：相对于光栅呈现在基线时间段内,被试对呈现在抑制（或增强）时间段内的光栅的朝向辨别准确率显著地更低（或更高）（实验1）;另外,只有使用低或中等空间频率光栅作为测试刺激时,才有这种双相调节作用（实验2）。这些结果表明：人类的视觉系统在眼跳过程中存在双相调节机制,并且这种双相调节机制具有刺激选择性。     

象似性和手语学习经验对中国手语-汉语跨模态语义启动效应的影响

采用启动条件下的词汇判断任务,考察熟练手语使用者和无手语经验成年听人的跨模态语义启动效应。结果发现：1)在象似词条件下,两组被试判断汉语语义相关词的反应时均快于语义无关词,说明手语象似词和汉语词之间存在跨模态语义启动效应。2)在非象似词条件下,仅手语熟练被试判断汉语语义相关词的反应时快于语义无关词,无手语经验被试判断汉语语义相关词和无关词的速度没有差异。这是由于前者心理词库中的手语词和口语词共享语义表征,而后者主要依赖手语象似词的视觉模拟性。整个研究表明,中国手语和汉语间存在跨模态语义启动效应,但该效应受到手语词象似性和手语学习经历的调节。     

混合情绪能促进心理健康吗？

摘 要 积极和消极情绪的同时体验,即混合情绪,是生活中常见的情绪状态。健康应对的共激活模型、情绪动态模型和积极比模型一致认为混合情绪与心理健康之间呈现非线性的关系,适当的混合情绪有利于个体在困境中全面加工和整合信息,寻求意义,获得控制感,进而实现健康应对和心理症状改善,获得幸福感。未来研究应关注对非线性关系的精确描述;低压力或非压力情境下混合情绪的意义;进一步研究混合情绪促进心理健康的内部机制和影响因素。     

心理因素与学业表现：机器学习分类预测模型

随着高等教育规模的扩大,学业表现不良逐渐成为一个不容忽视的现象,对学业表现不良的学生进行预测并提早给予干预可降低退学率并减少教育资源的损失。由于导致学业表现不良的因素众多且关系复杂,传统的基于相关分析的研究方法很难建立早期预测模型并进行应用。本研究旨在利用机器学习算法,对数据进行挖掘,并建立学业表现预测模型。研究对653名大一新生的心理健康状况、应对方式、人格、内外控倾向和相关人口统计学信息进行了收集,并在一年后采集了其学业成绩,利用随机森林(RF)、K邻近（KNN）、支持向量机（SVM）、决策树（DT）、朴素贝叶斯（NB）等机器学习算法建立了分类模型。结果显示,随机森林算法在识别学业表现不良学生时有最好的表现,其中准确率95.86%, 召回率91.83%,f1分数为93.80%。特征重要性分析显示,前10个对模型有最高贡献度的特征包括：年龄、性别、是否为独生子、内外控倾向、神经质倾向、积极应对倾向、宜人性倾向、一般症状指数、开放性倾向和焦虑水平。为避免过度拟合问题,本研究在一年后收集的166名新生样本中进行了模型验证,结果显示模型在新数据集上有较好的泛化表现,其中f1分数90.90％,准确率92.60％,召回率89.26％。研究提示基于人口统计学和心理测评信息,机器学习算法有助于及早识别学业表现不良学生并为开展早期干预提供启示。     

A systematic review of the effectiveness of machine learning for predicting psychosocial outcomes in acquired brain injury: Which algorithms are used and why?

Clinicians working in the field of acquired brain injury (ABI, an injury to the brain sustained after birth) are challenged to develop suitable care pathways for an individual client’s needs. Being able to predict psychosocial outcomes after ABI would enable clinicians and service providers to make advance decisions and better tailor care plans. Machine learning (ML, a predictive method from the field of artificial intelligence) is increasingly used for predicting ABI outcomes. This review aimed to examine the efficacy of using ML to make psychosocial predictions in ABI, evaluate the methodological quality of studies, and understand researchers’ rationale for their choice of ML algorithms. Nine studies were reviewed from five databases, predicting a range of psychosocial outcomes from stroke, traumatic brain injury, and concussion. Eleven types of ML were employed with a total of 75 ML models. Every model was evaluated as having high risk of bias, unable to provide adequate evidence for predictive performance due to poor methodological quality. Overall, there was limited rationale for the choice of ML algorithms and poor evaluation of the methodological limitations by study authors. Considerations for overcoming methodological shortcomings are discussed, along with suggestions for assessing the suitability of data and suitability of ML algorithms for different ABI research questions.     

The Natural Selection of Prediction Heuristics: Anchoring and Adjustment versus Representativeness

There are several heuristics which people use in making numerical predictions and these heuristics compete for the determination of prediction output. Some of them (e.g. representativeness) lead to excessively extreme predictions while others (e.g. anchoring and adjustment) lead to regressive (and even over-regressive) predictions. In this paper we study the competition between these two heuristics by varying the representation of predictor and outcome. The results indicate that factors which facilitate reliance on representativeness (e.g. compatibility between predictor and outcome) indeed lead to an increase in extremity, while factors that facilitate reliance on anchoring and adjustment (e.g. increased salience of a potential anchor) lead to a decrease in extremity.     

Termination of information evaluation in auditing

A screening decision is about whether a candidate fails to meet a decision maker's criteria and must be rejected, or whether it meets the criteria and can be accepted. Image theory contends that screening decisions are based on a simple, noncompensatory process that focuses solely upon the candidate's negative attributes. This contention has received empirical support in a job screening context in which students role-played a hypothetical person seeking a job. But how robust is image theory's prediction? The present work was done in the context of professional auditors screening prospective clients. The client screening context is a particularly interesting setting to test the implications of image theory. If auditors reject clients with negative attributes, as predicted by image theory, this will indicate that some clients are being denied access to a socially useful service. Further, auditors can accept prospective clients with negative attributes and simply adjust the audit plan and fees to compensate for the client's riskiness. Thus, it is not clear that the preliminary findings obtained in a job screening context will generalize to the audit context. On the other hand, results consistent with the image theory prediction would suggest a robust theory. Even in the client screening context, where auditors used their own criteria to screen prospective clients, we found overwhelming support for image theory's view of screening. The results indicate the potential payoffs of image theory as a robust framework to address audit and other substantive problems. Implications of a noncompensatory, negative oriented, client screening strategy are discussed.     

Why do aggressive movies make people aggressive? an attempt to explain short-term effects of the depiction of violence on the observer

An explanation-approach within the frame of a universal and integrating theory of aggression is favored for understanding the effects of exposure to violence. Kornadt's Motivation Theory of Aggression is used to generate several hypotheses concerning the effects of aggressive movies on a viewer's expectancy-value-cognitions and these are tested in an experiment. The experiment suggests that aggressive stimulation is able to change aggression-related expectancy-value-cognitions in an aggression-facilitating direction. Based on these results, an attempt is made to explain increases in the aggression of viewers in terms of the Motivation Theory of Aggression. © 1993 Wiley-Liss, Inc.