The Near-Death Experience Content (NDE-C) scale: Development and psychometric validation

As interest grows in near-death experiences (NDEs), it is increasingly important to accurately identify them to facilitate empirical research and reproducibility among assessors. We aimed (1) to reassess the psychometric properties of the NDE scale developed by Greyson (1983) and (2) to validate the Near-Death Experience Content (NDE-C) scale that quantifies NDEs in a more complete way. Internal consistency, construct and concurrent validity analyses were performed on the NDE scale. Based on those results and the most recent empirical evidence, we then developed a new 20-item scale. Internal consistency, explanatory and confirmatory factor, concurrent and discriminant validity analyses were conducted. Results revealed (1) a series of weaknesses in the NDE scale, (2) a 5-factor structure covering relevant dimensions and the very good psychometric properties of the NDE-C scale, including very good internal consistency (Cronbach α = 0.85) and concurrent validity (correlations above 0.76). This new reliable scale should facilitate future research.     

虚拟情境测验研究

该文从虚拟情境和心理测量两个方面规划了场帚式、游戏式、投射式三种虚拟情境测验样式；从科学技术层、心理情感层、操作互动层等方面对虚拟情境测验做出心理学分析；文章最后提出创立网络环境下虚拟情境测验的当代数学模型的设想。     

Follow Your Heart: Could Psychophysiology Be Associated with Political Discussion Network Homogeneity?

Most Americans are sorted into social networks that are largely politically homogeneous. A large body of political science research has explored the behavioral implications of being embedded in a politically homogeneous or heterogeneous network, but substantially less attention has been given to explaining why some people find themselves in politically homogeneous or heterogeneous social networks. In this article, we explore the psychological and physiological underpinnings of political network homogeneity. We use social network data from an original survey of 129 undergraduates paired with lab experimental evidence that measures individuals' physiological reactivity to an anticipated political discussion. Using our original survey and a separate nationally representative survey, we find suggestive evidence that individuals who are more socially anxious are more likely to share partisanship with their social network ties. Moreover, we find that individuals who experienced a greater increase in heart rate when anticipating a political discussion were more likely to be in homogeneous discussion networks, but we do not find a relationship between electrodermal activity and network homogeneity. Aversion to psychological and physiological discomfort induced by political discussions could contribute to social polarization in the American public.     

An eye-tracking attention based model for abstractive text headline

Online network platforms provide great convenience for users to obtain information. However, it’s challenging to select the required information from enormous texts. Automatic text headline generation methods not only guide users to select the information they are interested in, but also solve the problem of information overload. Nevertheless, the existing works mainly utilize the grammar rules to obtain the key information of the source text, while ignoring the dwell time of user’s attention on different text contents. To address this issue, this paper proposes an abstractive text headline generation model based on the eye-tracking attention mechanism. Specifically, this model first relies on the eye-tracking data to establish the mapping relationship between text words and the words’ reading time. Then, an eye-tracking attention mechanism is constructed to judge the importance of different words. Finally, this attention mechanism is integrated into the encoder-decoder framework to generate a high-quality headline. Experimental results obtained from different datasets demonstrate that the headline generated by our model is more concise. Moreover, our proposed model outperforms significantly the classical headline generation models on ROUGE-1, ROUGE-2 and ROUGE-L.     

社交网站中的自我呈现与青少年网络利他行为:网络社交效能和希望的作用

为考察社交网站中的积极自我呈现、真实自我呈现对青少年网络利他行为的影响以及网络社交效能和希望在其中的中介作用，采用社交网站中的积极自我呈现和真实自我呈现问卷、网络利他行为量表、网络社交效能问卷和希望量表对安徽、福建、河南、湖南、山西、天津、云南等省份八所中学的805名初中二年级学生（男生427名，女生378名；被试平均年龄为13.38岁，SD=0.63）进行问卷调查。研究显示：（1）社交网站中的积极自我呈现和真实自我呈现均能正向预测青少年网络利他行为；（2）网络社交效能在积极自我呈现与青少年的网络利他行为之间起中介作用，也在真实自我呈现与青少年的网络利他行为之间起中介作用；（3）希望在真实自我呈现与青少年的网络利他行为之间起中介作用。     

Explainability of neural networks for child language: Agent-First strategy in comprehension of Korean active transitive construction

This study investigates how neural networks address the properties of children's linguistic knowledge, with a focus on the Agent-First strategy in comprehension of an active transitive construction in Korean. We develop various neural-network models and measure their classification performance on the test stimuli used in a behavioural experiment involving scrambling and omission of sentential components at varying degrees. Results show that, despite some compatibility of these models’ performance with the children's response patterns, their performance does not fully approximate the children's utilisation of this strategy, demonstrating by-model and by-condition asymmetries. This study's findings suggest that neural networks can utilise information about formal co-occurrences to access the intended message to a certain degree, but the outcome of this process may be substantially different from how a child (as a developing processor) engages in comprehension. This implies some limits of neural networks on revealing the developmental trajectories of child language.

Research Highlights

• This study investigates how neural networks address properties of child language.
• We focus on the Agent-First strategy in comprehension of Korean active transitive.
• Results show by-model/condition asymmetries against children's response patterns.
• This implies some limits of neural networks on revealing properties of child language.