The Roles of Semantic Sense and Form-Meaning Connection in Translation Priming

The Psychological Record - This study examines semantic sense and form-meaning connection across the bilingual’s languages as factors behind translation priming asymmetry, which refers to...     

记忆的顿悟优势效应

记忆的顿悟优势效应指, 与加工非顿悟问题相比, 加工顿悟问题能够产生更优的记忆保持量。该效应在顿悟的两种操作性定义以及不同实验材料中稳定出现。基于顿悟阶段观, 研究者提出两种理论解释记忆的顿悟优势效应的认知机制：认知的生成效应和表征转化理论认为, 顿悟加工过程中的认知努力、适当迁移加工以及顿悟的表征转换加工促进记忆的保持效果; 情绪的“啊哈”体验理论认为, 加工顿悟问题后的情绪对后续的信息提取具有促进作用。未来的研究可从优化顿悟的指标、巩固记忆的顿悟优势效应、评估其持续性以及更加系统探讨记忆的顿悟优势效应的认知机制等四个方面展开。     

Subtask analysis of process data through a predictive model

Response process data collected from human–computer interactive items contain detailed information about respondents' behavioural patterns and cognitive processes. Such data are valuable sources for analysing respondents' problem-solving strategies. However, the irregular data format and the complex structure make standard statistical tools difficult to apply. This article develops a computationally efficient method for exploratory analysis of such process data. The new approach segments a lengthy individual process into a sequence of short subprocesses to achieve complexity reduction, easy clustering and meaningful interpretation. Each subprocess is considered a subtask. The segmentation is based on sequential action predictability using a parsimonious predictive model combined with the Shannon entropy. Simulation studies are conducted to assess the performance of the new method. We use a case study of PIAAC 2012 to demonstrate how exploratory analysis for process data can be carried out with the new approach.     

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.     

次要任务对词类判断任务中权力空间表征激活的影响

许多证据表明权力在大脑中是以垂直空间来进行表征的, 具体而言为言语和视觉空间编码。前人研究发现两种编码具有情境依赖性, 即当前任务决定了激活哪种编码。一般的词类判断任务主要依赖于言语编码。然而仍不确定的是, 当排除言语空间编码后, 词类判断任务能否激活视觉空间编码。本研究拟借助双任务范式探讨此问题。实验结果发现单任务条件下出现权力-空间交互, 并且双任务条件下, 这一交互只受到视觉空间次要任务的干扰。这说明, 词类判断任务也可依赖视觉空间编码, 并进一步支持了权力空间表征的情境依赖性。     

外倾个体何以有更多正性情绪体验？——情绪调节自我效能感的中介作用

通过对1076名大学生进行人格特质(外倾和神经质)、情绪调节自我效能感和情绪体验的测查,本研究探讨了外倾和神经质影响情绪的可能机制。结果表明：(1) 情绪调节自我效能感与外倾和神经质之间有紧密联系;(2) 外倾与情绪调节自我效能感可以有效预测个体正性情绪的变异;(3) 外倾对正性情绪的影响部分是以情绪调节自我效能感为中介的。该发现对实践中提升个体情绪调节自我效能感、改善情绪状态并最终优化人格结构具有重要启示。     

现实预期与意愿预期在文本阅读中的保持

文本阅读中, 读者往往对事件的后续发展进行预期推理。预期推理有两种倾向, 要么是倾向于根据客观现实条件进行的现实预期, 要么是倾向于根据主观的个人意愿进行的意愿预期。3个实验通过自定步调阅读范式探讨了文本阅读中读者产生的现实预期和意愿预期的保持。结果发现, 现实预期和意愿预期在长时记忆中的保持情况差异明显, 现实预期不能在长时记忆中保持, 而意愿预期则能保持; 但意愿预期也不能单独存在, 会受到现实条件的制约, 在受到现实否定后即时消退, 不再影响读者的进一步阅读。     

基于选择反应的学习迁移效应：从个人情境到社会情境

先前习得的空间不相容联结可以减少、消除甚至反转Simon效应, 这种现象被称为学习迁移效应。研究者已经在个人情境和社会情境中发现一些影响学习迁移效应的因素, 并提出短时记忆联结解释、自下而上的启动机制和反应对立策略等相关解释。未来研究需要澄清刺激-反应迁移的双向性以及学习迁移效应的认知神经机制和发展路径。     

A reinforcement learning approach to personalized learning recommendation systems

Personalized learning refers to instruction in which the pace of learning and the instructional approach are optimized for the needs of each learner. With the latest advances in information technology and data science, personalized learning is becoming possible for anyone with a personal computer, supported by a data-driven recommendation system that automatically schedules the learning sequence. The engine of such a recommendation system is a recommendation strategy that, based on data from other learners and the performance of the current learner, recommends suitable learning materials to optimize certain learning outcomes. A powerful engine achieves a balance between making the best possible recommendations based on the current knowledge and exploring new learning trajectories that may potentially pay off. Building such an engine is a challenging task. We formulate this problem within the Markov decision framework and propose a reinforcement learning approach to solving the problem.     

威廉斯综合症及其研究现状

威廉斯综合症是一种上世纪 60年代才被人类认识的疾病。该病症有特殊的生理、行为和认知异常 ,脑发育伴随着结构和临床异常。本文对国外40年的研究成果进行了总结 ,初步介绍了威廉斯综合症的症状表现、认知特征、行为特征及诊断治疗方面的研究现状 ,最后对该领域的研究提出了展望。