Human age classification using facial skin aging features and artificial neural network

In this paper a novel method based on facial skin aging features and Artificial Neural Network (ANN) is proposed to classify the human face images into four age groups. The facial skin aging features are extracted by using Local Gabor Binary Pattern Histogram (LGBPH) and wrinkle analysis. The ANN classifier is designed by using two layer feedforward backpropagation neural networks. The proposed age classification framework is trained and tested with face images from PAL face database and shown considerable improvement in the age classification accuracy up to 94.17% and 93.75% for male and female respectively.     

Neural implementation of probabilistic models of cognition

Bayesian models of cognition hypothesize that human brains make sense of data by representing probability distributions and applying Bayes’ rule to find the best explanation for available data. Understanding the neural mechanisms underlying probabilistic models remains important because Bayesian models provide a computational framework, rather than specifying mechanistic processes. Here, we propose a deterministic neural-network model which estimates and represents probability distributions from observable events—a phenomenon related to the concept of probability matching. Our model learns to represent probabilities without receiving any representation of them from the external world, but rather by experiencing the occurrence patterns of individual events. Our neural implementation of probability matching is paired with a neural module applying Bayes’ rule, forming a comprehensive neural scheme to simulate human Bayesian learning and inference. Our model also provides novel explanations of base-rate neglect, a notable deviation from Bayes.     

Concepts as Semantic Pointers: A Framework and Computational Model

The reconciliation of theories of concepts based on prototypes, exemplars, and theory‐like structures is a longstanding problem in cognitive science. In response to this problem, researchers have recently tended to adopt either hybrid theories that combine various kinds of representational structure, or eliminative theories that replace concepts with a more finely grained taxonomy of mental representations. In this paper, we describe an alternative approach involving a single class of mental representations called “semantic pointers.” Semantic pointers are symbol‐like representations that result from the compression and recursive binding of perceptual, lexical, and motor representations, effectively integrating traditional connectionist and symbolic approaches. We present a computational model using semantic pointers that replicates experimental data from categorization studies involving each prior paradigm. We argue that a framework involving semantic pointers can provide a unified account of conceptual phenomena, and we compare our framework to existing alternatives in accounting for the scope, content, recursive combination, and neural implementation of concepts.     

Book Reviews

Scruton, Roger Modern Philosophy: An Introduction and Survey
Fraser, Nancy and Bartky, Sandra Lee (eds) Revaluing French Feminisms: Critical Essays on Difference, Agency and Culture
Friedman, Marilyn What are Friends For? Feminist Perspectives on Personal Relationships and Moral Theory     

Directions in Connectionist Research: Tractable Computations Without Syntactically Structured Representations

Should connectionists abandon the quest for tractably computable cognitive transition functions while retaining syntactically structured mental representations? We argue, in opposition to Horgan, that it should not. We argue that the case against tractably computable functions, based upon the claimed isotropic and Quinean character of cognition, fails since cognition is not as isotropic and Quinean as Fodor and Horgan contend. Moreover, we illustrate how current research in both connectionism and cognitive neuroscience suggests that tractability can be preserved through division of overall computations into modular sub-processes, each of which is tractable. As to syntactically structured representations, we argue that they are unneeded for most cognitive tasks organisms confront, and that when they are needed, they may be provided by external representational media such as natural language. Moreover, we note that increasingly cognitive linguistics has become the ally of connectionism and that the research program of cognitive linguistics suggests that abilities to use natural languages may be developed without requiring syntactically structured mental representations to exist prior to natural language.     

Nearest vertex attraction for actively reducing loss

In recent years the neural networks have received considerable success on classification tasks. The randomly initialized network is generally expected to adjust the weights for reducing the loss, which is also indicated as the total distance from the samples to the corresponding vertices. However, the output space of the conventional neural networks suffers from the fixed relation between the labels and vertices during learning. This case forces the mapped points around the unexpected vertex across the decision boundary into the neighborhood of the correct vertex, and simultaneously the boundary points cannot obtain the substantial rectification. Therefore, this study proposes a novel nearest vertex attraction (NVA) to actively adjust the relation between the categories and the output vertices for improving the neural network classifiers. The best relation allows that the data points can be attracted by the nearest vertices to minimize the total moving distances. In this way, the mapped points that are near to the vertices and the decision boundaries obtain the decent management. We evaluated the NVA with several conventional classification techniques and other neural network classifiers on 12 public UCI datasets. The numerical experiments demonstrate that the proposed method improves performance of the neural network classifiers on the involved benchmarks.     

The Scientific Untraceability of Phenomenal Consciousness

It is a common conviction among philosophers who hold that phenomenal properties, qualia, are distinct from any cognitive, intentional, or functional properties, that it is possible to trace the neural correlates of these properties. The main purpose of this paper is to present a challenge to this view, and to show that if “non-cognitive” phenomenal properties exist at all, they lie beyond the reach of neuroscience. In the final section it will be suggested that they also lie beyond the reach of psychology, so that they may be said to lie beyond the reach of science.

Synchrnoic consciousness from a neurological point of view: the philosophical foundations for neuroethics

Daniel Kolak’s theory of synchronic consciousness according to which the entire range of dissociative phenomena, from pathologies such as MPD and schizophrenia to normal dream states, are best explained in terms of consciousness becoming simultaneously identified as many selves, has revolutionary therapeutic implications for neurology and psychiatry. All these selves, according to Kolak—even the purely imaginary ones that exist as such only in our dreams—are not just conscious but also self-conscious, with beliefs, intentions, living lives informed by memories (confabulatory, in the case of the fictional ones) and personal histories. Kolak’s derivation of psychiatrically relevant aspects of his theory—a neurological rendition of a Kantian transcendental argument—can be given a straightforward neurological, and therefore open to scientific scrutiny, interpretation that would then more easily lend itself to the clinical setting in which these perplexing phenomena, along with their purveyors, must live and cope. This will be the main focus of this paper.     

Fragile X syndrome: Neural network models of sequencing and memory

A comparative framework of memory processes in males with fragile X syndrome (FXS) and typically developing (TYP) mental age-match children is presented. Results indicate a divergence in sequencing skills, such that males with FXS recall sequences similarly to TYP children around five and a half years of age, but the males with FXS recall significantly worse when compared to TYP children around seven and a half years of age. Performance on one working memory measure, an n-back Card Task, is modeled with a neural network. To date, no network models explicate the sequencing and memory processes in those with FXS. Noise was added to various levels (weight matrices) in the FXS model and outputs approximated human FXS performance. Three models were compared: (1) FXS; (2) younger mental age-TYP matches; (3) older reading level-TYP matches. Modeling can help to reify conceptualizations of deficits and to guide in the creation of more valid, science-based remediations. The FXS model suggested that the levels of phonological representation and sequencing in memory were candidates for targeted therapies in males with FXS.     

从个体关系的角度看文化对社会脑功能的塑造

摘 要 尽管社会认知的内容丰富多样,但其核心在于人们对“自我”、“他人”及两者关系的理解。文化作为一种独特的社会现象,对社会认知有着广泛影响,这一点集中体现在文化对“自我” 与“他人” 信息加工及其大脑机制的影响上。文化神经科学的研究表明：文化显著影响自我相关记忆、自我表征、自我觉知等自我认知过程。这可能主要来源于不同文化人群自我建构方式的不同。上述差异的神经机制主要体现为不同文化人群自我相关加工时,其内侧前额叶功能性变化的不同。与此相对应的是,文化同样显著影响人们对他人,尤其是对他人情绪的认知。这一点集中表现为表情认知的文化优势效应及共情过程的文化差异。在神经机制上这一差异主要体现为杏仁核功能的文化可塑性。文化神经科学的未来研究,可继续探讨主流文化、区域文化、宗教文化等各种形式的文化差异：1）对自我认知与情绪认知相互作用的影响与神经基础;2）对共情（empathy）、社会比较（social comparison）、心理理论（theory of mind）与协同行为（joint action）等多种社会认知过程的影响及其神经机制。 关键词 自我建构 文化神经科学 情绪认知 自我表征 共情