How Do Artificial Neural Networks Classify Musical Triads? A Case Study in Eluding Bonini's Paradox

How might artificial neural networks (ANNs) inform cognitive science? Often cognitive scientists use ANNs but do not examine their internal structures. In this paper, we use ANNs to explore how cognition might represent musical properties. We train ANNs to classify musical chords, and we interpret network structure to determine what representations ANNs discover and use. We find connection weights between input units and hidden units can be described using Fourier phase spaces, a representation studied in musical set theory. We find the total signal coming through these weighted connection weights is a measure of the similarity between two Fourier structures: the structure of the hidden unit's weights and the structure of the stimulus. This is surprising because neither of these Fourier structures is computed by the hidden unit. We then show how output units use such similarity measures to classify chords. However, we also find different types of units—units that use different activation functions—use this similarity measure very differently. This result, combined with other findings, indicates that while our networks are related to the Fourier analysis of musical sets, they do not perform Fourier analyses of the kind usually described in musical set theory. Our results show Fourier representations of music are not limited to musical set theory. Our results also suggest how cognitive psychologists might explore Fourier representations in musical cognition. Critically, such theoretical and empirical implications require researchers to understand how network structure converts stimuli into responses.     

Do goal cue and motor activity impact preschoolers’ working memory?

Preschoolers are well known for their poor working memory (WM) performance. This could result from goal neglect, which would hamper the setting of maintenance strategies. Previous studies have shown that preschoolers’ WM performance can be improved in game-like tasks, because they provide cues to support goal maintenance. However, in these studies, it was unclear what features of the task (either the main toy or the motor activity required by the game) provide efficient cues. The aim of the present study was to disentangle the two features to examine cue effects in 5- to 7-year-old children. No improvement of WM performance was observed when the toy was a potential goal cue, whereas the motor activity had a detrimental effect in all age groups. The latter effect could result from a distraction of attention from attention-based maintenance activities. Hence, preschoolers' poor WM performance would not be fundamentally due to goal neglect.     

Customer-Initiated Support and Employees’ Proactive Customer Service Performance: A Multilevel Examination of Proactive Motivation as the Mediator

Drawing on the proactive motivation model and theories of motivation, the current study examined customer-initiated support as an antecedent of employees’ proactive customer service performance (PCSP) and investigated the mediating role of proactive motivations and the moderating role of serving culture. We conducted two multilevel studies to test the proposed hypotheses. In study 1, we collected diary data from 158 nurses across 10 working days, and results showed that the relationship between nurses’ daily experience of customer-initiated support and their PCSP was mediated by self-efficacy, work meaning, and positive affect. In study 2, we collected multilevel multi-source data from 373 nurses nested within 80 units and found that nurses’ experience of customer-initiated support was positively related to their supervisor-rated PCSP via role-breadth self-efficacy and work meaning, but not through positive affect. Further, team-level serving culture strengthened the positive relationship between customer-initiated support and role-breadth self-efficacy. These findings shed light on understanding the effect of customer-initiated support as an external factor on prompting service employees’ proactive behaviors, potential motivational mediators to explain the underlying mechanisms, and potential boundary conditions inside of service organizations to strengthen the positive effects of customer-initiated support.     

睡眠不足对人际交互的影响及其认知神经机制

睡眠不足会对人的认知、情感和人际交互产生诸多影响。这种影响在社会情绪层面表现为个体情绪共情和认知共情的减少,易激惹性与愤怒情绪的增加;在社会行为层面则表现为亲社会行为的减少和攻击行为的增加。在睡眠不足状态下,情绪系统和认知系统功能连接的减弱可能是这些变化的潜在机制。未来应结合生态效度较高的睡眠操作手段,系统考察睡眠不足如何导致各种高级社会情绪的改变,以及这些社会情绪的变化如何导致社会行为的变化。     

自我损耗、情绪动机对冲动决策的影响

以大学生为被试,探讨自我损耗、情绪动机对冲动决策的影响。采用2×2×2的混合实验设计,组内变量为自我损耗,组间变量为情绪动机方向和强度,使用双任务范式操作自我损耗,使用情绪动机图片启动被试的情绪动机,使用延迟折扣任务的指标k值测量冲动决策。结果发现,高趋近动机的k值大于低趋近动机,高回避动机的k值大于低回避动机。结果表明,自我损耗、情绪动机对冲动决策有明显影响。     

An empirical Q-matrix validation method for the sequential generalized DINA model

As a core component of most cognitive diagnosis models, the Q-matrix, or item and attribute association matrix, is typically developed by domain experts, and tends to be subjective. It is critical to validate the Q-matrix empirically because a misspecified Q-matrix could result in erroneous attribute estimation. Most existing Q-matrix validation procedures are developed for dichotomous responses. However, in this paper, we propose a method to empirically detect and correct the misspecifications in the Q-matrix for graded response data based on the sequential generalized deterministic inputs, noisy ‘and’ gate (G-DINA) model. The proposed Q-matrix validation procedure is implemented in a stepwise manner based on the Wald test and an effect size measure. The feasibility of the proposed method is examined using simulation studies. Also, a set of data from the Trends in International Mathematics and Science Study (TIMSS) 2011 mathematics assessment is analysed for illustration.     

Development of a facial-expression database of Chinese Han,Hui and Tibetan people

Recently, cross-cultural facial-expression recognition has become a research hotspot, and a standardised facial-expression material system can significantly help researchers compare and demonstrate the results of other studies. We developed a facial-expression database of Chinese Han, Hui and Tibetan ethnicities. In this study, six basic human facial expressions (and one neutral expression) were collected from 200 Han, 220 Hui and 210 Tibetan participants who lived in these regions. Four experts on each ethnicity evaluated the facial-expression images according to the expressions, and only those achieving inter-rater agreement were retained. Subsequently, 240 raters evaluated these images according to the seven emotions and rated the intensity of the expressions. Consequently, 2980 images were included in the database, including 930 images of Han individuals, 962 images of Hui individuals and 1088 images of Tibetan individuals. In conclusion, the facial-expression database of Chinese Han, Hui and Tibetan people was representative and reliable with a recognition rate of over 60%, making it well-suited for cross-cultural research on emotions.     

Molecular Tumor Boards: Ethical Issues in the New Era of Data Medicine

The practice and development of modern medicine requires large amounts of data, particularly in the domain of cancer. The future of personalized medicine lies neither with “genomic medicine” nor with “precision medicine”, but with “data medicine” (DM) (big data, data mining). The establishment of this DM has required far-reaching changes, to establish four essential elements connecting patients and doctors: biobanks, databases, bioinformatic platforms and genomic platforms. The “transformation” of scientific research areas, such as genetics, bioinformatics and biostatistics, into clinical specialties has generated a new vision of care. Molecular tumor boards (MTB) are one response to these changes and are now providing better access to next-generation sequencing (NGS) and new cancer treatments to patients with inoperable or metastatic cancers, and those for whom the usual treatment has failed. However, MTB face a crucial ethical challenge: maintaining and improving the trust of patients, clinicians, researchers and industry in academic medical centers supported by private or public funding rather than providing genetic data directly to private companies. We believe that, in this era of DM, appropriate modern digital communication networks will be required to maintain this trust and to improve the organization and effectiveness of the system. There is, therefore, a need to reconsider the form and content of informed consent (IC) documents at all academic medical centers and to introduce dynamic and electronic informed consent (e-IC).