Applying matrix factorization techniques to compare experts’ categorization process during case formulation task performed by concept maps

The aim of this paper was to present a method to enable the analysis of the process of categorization of patients’ testimonials and the comparison of individual categories created by professionals. A complex diagnostic task (case conceptualization) was employed to study the categorization function in professional thinking. Two groups of psychotherapists (30 people in each group) served as subjects of the research. The main objective of the study was to find an appropriate representation of concept maps enabling a comparison of both the categories and the structures between experts. In the comparison process, only the information about the premises justifying each given category was taken into account and represented by a concept-testimonials matrix. Three different elements weighting schemes and matrix factorization-based unsupervised clustering methods were analyzed in the context of consistency and ability to establish main semantic groups of concepts common to the majority of experts. Moreover, special attention was paid to determining the number of main semantic classes. The study showed that even the used representation was similar to the task of documents indexing there was some discrepancy. The highest accuracy in generating main semantic groups was achieved using the PCA and K-Means (nKM) (the average false positive rate in clusters was 32%). This method outperformed Tempered PLSA (the average false positive rate per cluster was 52%). It was demonstrated that in analyzed task the nKM method allowed comparing the similarity of concepts even when they were created by various experts using different conceptual apparatus.     

MATRIX THINKING: AN ADAPTATION AT THE FOUNDATION OF HUMAN SCIENCE,RELIGION, AND ART

Intrigued by Robinson and Southgate's 2010 work on “entering a semiotic matrix,” we expand their model to include the juxtaposition of all signs, symbols, and mental categories, and to explore the underpinnings of creativity in science, religion, and art. We rely on an interdisciplinary review of human sentience in archaeology, evolutionary biology, the cognitive science of religion, and literature, and speculate on the development of sentience in response to strong selection pressure on the hominin evolutionary line, leaving us the “lone survivors” of complex, multiple lines of physical and cultural evolution. What we call Matrix Thinking—the creative driver of human sentience—has important cognitive and intellectual features, but also equally important characteristics traced to our intense sociability and use of emotionality in vetting rational models. Scientist, theologian, and artist create new cultural knowledge within a social context even if alone. They are rewarded by emotional validation from group members, and guided by the ever present question, “Does it feel right?”     

How to Gain Eleven IQ Points in Ten Minutes: Thinking Aloud Improves Raven's Matrices Performance in Older Adults

ABSTRACT

Few studies have examined the impact of age on reactivity to concurrent think-aloud (TA) verbal reports. An initial study with 30 younger and 31 older adults revealed that thinking aloud improves older adult performance on a short form of the Raven's Matrices (Bors & Stokes, 1998 Bors, D. A. and Stokes, T. L. 1998. Raven's Advanced Progressive Matrices: Norms for first-year university students and the development of a short form. Educational and Psychological Measurement, 58: 382–398. [Crossref], [Web of Science ®] , [Google Scholar], Educational and Psychological Measurement, 58, p. 382) but did not affect other tasks. In the replication experiment, 30 older adults (mean age = 73.0) performed the Raven's Matrices and three other tasks to replicate and extend the findings of the initial study. Once again older adults performed significantly better only on the Raven's Matrices while thinking aloud. Performance gains on this task were substantial (d = 0.73 and 0.92 in Experiments 1 and 2, respectively), corresponding to a fluid intelligence increase of nearly one standard deviation.     

幽默平面广告的适用性研究

为了探讨哪一类别商品的平面广告更适合于采用幽默的诉求方式,研究随机选取160大学生为被试,以16则改编平面广告为实验材料,以广告类型（幽默,非幽默）、商品类别为自变量,以广告评价、广告顺序为协变量,以商品的印象程度、喜爱程度、购买意向为因变量,进行平衡设计实验。结果表明,幽默广告具有较好广告效果,可以提高受众对大宗工具、小奖赏和小工具型商品的记忆;幽默平面广告更适用于低风险、功能性商品,不适用于高风险、享乐性商品。     

Plasticity associated with escalated operant ethanol self-administration during acute withdrawal in ethanol-dependent rats requires intact matrix metalloproteinase systems

Repeated cycles of ethanol intoxication and withdrawal associated with dependence induce neuroadaptations in a variety of brain systems. Withdrawal-induced negative emotional states can be ameliorated by ethanol consumption; a learned process termed negative reinforcement. Accordingly, a dependence-induced phenotype is escalated ethanol self-administration. Matrix metalloproteinases (MMPs) are proteolytic enzymes which degrade the extracellular matrix to allow for synaptic reorganization and plasticity. To test the hypothesis that an intact MMP system is required for animals to learn about the negative reinforcing effects of ethanol and display escalated self-administration during acute withdrawal when ethanol-dependent, male Wistar rats were trained to self-administer ethanol and then assigned to either acute or chronic MMP inhibition treatment groups. The chronic treatment group received intracerebroventricular (ICV) infusions of the broad spectrum MMP inhibitor FN-439 or artificial cerebrospinal fluid (aCSF) via osmotic minipumps during a 1 month ethanol dependence induction period and subsequent post-dependence induction self-administration sessions that occurred during acute withdrawal. The acute treatment group only received ICV FN-439 or aCSF on the day of self-administration sessions following dependence induction during acute withdrawal. The results showed that inhibition of MMPs attenuated escalated ethanol self-administration following chronic and acute exposure conditions. Furthermore, once learning (i.e., plasticity) had occurred, MMP inhibition had no impact on escalated response patterns and animals previously subjected to MMP inhibition that did not escalate evidenced normal escalations in operant ethanol self-administration once FN-439 treatments were terminated. Thus, the present data identified that an intact MMP system is required for the escalated responding that occurs during acute withdrawal in dependent animals and implicate such escalation as a learned response.     

Preservation of Craig interpolation by the product of matrix logics

The product of matrix logics, possibly with additional interaction axioms, is shown to preserve a slightly relaxed notion of Craig interpolation. The result is established symbolically, capitalizing on the complete axiomatization of the product of matrix logics provided by their meet-combination. Along the way preservation of the metatheorem of deduction is also proved. The computation of the interpolant in the resulting logic is proved to be polynomially reducible to the computation of the interpolants in the two given logics. Illustrations are provided for classical, intuitionistic and modal propositional logics.     

SDDRS: Stacked Discriminative Denoising Auto-Encoder based Recommender System

Recommender systems are widely used in our life for automatically recommending items relevant to our preference. Collaborative Filtering (CF) is one of the most successful methods in recommendation field. Matrix Factorization (MF) based recommender system is designed according to the basic strategy of the CF algorithm, which is widely adopted recently. However, the rating matrix utilized by these models is usually sparse, so it is of vital significance to integrate the side information to provide relatively effective knowledge for modeling the user or item features. The key problem is to extract effective features from the noisy side information. However, the side information contains a lot of noise except rating knowledge, which makes it a challenging issue for extracting effective features. In this paper, we propose Stacked Discriminative Denoising Auto-Encoder based Recommender System (SDDRS) by integrating deep learning model with MF based recommender system to effectively incorporate side information with rating information. Extensive top-N recommendation experiments conducted on three real-world datasets empirically demonstrate that SDDRS outperforms several state-of-the-art methods.     

小学生图形推理策略个体差异

选取一至六年级儿童145名为对象,根据项目中图形关系的规则,把瑞文测验的项目分成6类,选择出14道题目为实验材料,探讨小学生图形推理策略的个体差异.结果发现,数学能力不同的小学生在解决简单图形推理问题时,基本上使用知觉算法策略,没有表现出差异.但在较难图形推理问题时差异显著,中高数学水平儿童主要使用分析策略和知觉分析策略;而低数学水平儿童基本上使用知觉匹配策略;除了格式塔类型的题目以外,推理水平高的儿童在解决这五类题目时主要使用分析策略和知觉分析策略,而推理水平低的儿童主要使用知觉匹配策略;推理水平高的儿童在解决较为简单的图形推理问题时的策略使用很集中,随着题目难度的加大,策略变得越来越分散,而推理水平低的儿童则无论题目难易如何变化,他们的策略主要使用知觉匹配策略,而且表现出随机性特点.     

Geometric analogue of holographic reduced representation

Holographic reduced representations (HRRs) are distributed representations of cognitive structures based on superpositions of convolution-bound n-tuples. Restricting HRRs to n-tuples consisting of ±1, one reinterprets the variable binding as a representation of the additive group of binary n-tuples with addition modulo 2. Since convolutions are not defined for vectors, the HRRs cannot be directly associated with geometric structures. Geometric analogues of HRRs are obtained if one considers a projective representation of the same group in the space of blades (geometric products of basis vectors) associated with an arbitrary n-dimensional Euclidean (or pseudo-Euclidean) space. Switching to matrix representations of Clifford algebras, one can always turn a geometric analogue of an HRR into a form of matrix distributed representation. In typical applications the resulting matrices are sparse, so that the matrix representation is less efficient than the representation directly employing the rules of geometric algebra. A yet more efficient procedure is based on ‘projected products’, a hierarchy of geometrically meaningful n-tuple multiplication rules obtained by combining geometric products with projections on relevant multivector subspaces. In terms of dimensionality the geometric analogues of HRRs are in between holographic and tensor-product representations.     

Applicability of the Somatomorphic Matrix computer program in Japanese and Australian Caucasian males in relation to measured body composition

The Somatomorphic Matrix (SM) is a computer-based body image assessment program that allows assessment of the individual's body image in relation to known body composition values. To date, no study has compared the SM results to the measured body composition and assessed the usefulness in multi-ethnic samples. Japanese and Australian Caucasian males underwent testing to examine the SM body image results to their measured body composition. Japanese males showed significant (p < 0.01) discrepancy in their measured and the perceived ‘current’ muscularity compared with Australians. Both groups showed wide variability in the perception of their ‘current’ body composition in relation to the measured values, indicating an incomparability of the SM results to the measured results. The study also showed Japanese males to have lower consistency in the SM results than Australian males after a 12-month interval. In order to use the program in multi-ethnic populations further development of the program is recommended.