An aphasia database on the internet: a model for computer-assisted analysis in aphasiology

A web-based software model was developed as an example for data mining in aphasiology. It is used for educating medical and engineering students. It is based upon a database of 254 aphasic patients which contains the diagnosis of the aphasia type, profiles of an aphasia test battery (Aachen Aphasia Test), and some further clinical information. In addition, the cerebral lesion profiles of 147 of these cases were standardized by transferring the coordinates of the lesions to a 3D reference brain based upon the ACPC coordinate system. Two artificial neural networks were used to perform a classification of the aphasia type. First, a coarse classification was achieved by using an assessment of spontaneous speech of the patient which produced correct results in 87% of the test cases. Data analysis tools were used to select four features of the 30 available test features to yield a more accurate diagnosis. This classifier produced correct results in 92% of the test cases. The neural network approach is similar to grouping performed in group studies, while the nearest-neighbor method shows a design more similar to case studies. It finds the neurolinguistic and the lesion data of patients whose AAT profiles are most similar to the user's input. This way lesion profiles can be compared to each other interindividually. The Aphasia Diagnoser is available on the Web address http://fuzzy.iau.dtu.dk/aphasia.nsf and thus should facilitate a discussion about the reliability and possibilities of data-mining techniques in aphasiology.     

人工语法中的内隐学习实验研究

用Reber等人发明的人工语法和人工语法学习程序,研究在复杂规则学习中的内隐学习与外显学习过程.实验结果发现内隐学习中启动效应存在,内隐学习效应明显,支持Reber等人提出的内隐学习理论;对内隐记忆与外显记忆关系的研究,支持杨治良等(1998)提出的内隐和外显记忆的"钢筋水泥"结构性模型的假设.     

Embodied AI, Creation, and Cog

This is a reply to comments on my paper Cog, a Humanoid Robot, and the Questions of the Image of God ; one was written by Mary Gerhart and Allan Melvin Russell, and another one by Helmut Reich. I will start with the suggested analogy of the relationship between God and us and the one between us and the humanoid robot Cog and will show why this analogy is not helpful for the dialogue between theology and artificial intelligence (AI). Such a dialogue can succeed only if both our fascination for humanoids and our fear of them are equally accepted. Any avoidance of these emotions, as well as any rejection of the possibility that Cog might one day be humanlike, destroy the dialogue. The interpretation of both scientific theories and religious metaphors as stories replaces seemingly "rational" arguments with the confession of the respective commitments to a body of stories and opens up a space for exchange and friendship between AI-researchers and theologians—an option that usually remains closed.     

What is the Thematic Structure of Science?

The paper justifies the concept of “thematic structure” or “order of knowledge” over the traditional “classification of sciences” due to the uncertainty of many classification criteria. The thematic structure of science has, of course, various levels and various dimensions. Arguments against any forms of separating the humanities from sciences in the traditional sense of the term are presented and discussed. Equally unacceptable are attempts at sharp separation of technical disciplines and humanities. The thematic structure of humanities is not created by some material aspects but rather through the interest — or problem-oriented aspects. In addition to the natural sciences and the humanities there exists an important sphere of sciences on artefacts or, using the term by H. Simon, the sciences of the artificial. For the contemporary research activities is typical what could be denoted as “interdisciplinary or multidisciplinary complex.” The paper traces a set of epistemological criteria for the justification of the relative independence of a scientific discipline. This revised version was published online in August 2006 with corrections to the Cover Date.     

Transdisciplinarity,neuro-techno-philosophy,and the future of philosophy

Philosophy and science have always striven to make sense of the world, continuously influencing each other in the process. Their interplay paved the way for neurophilosophy, which harnesses neuroscientific insights to address traditionally philosophical questions. Given the rapid neuroscientific and technological advances in recent years, this paper argues that philosophers who wish to tackle intractable philosophical problems and influence public discourse and policies should engage in neuro-techno-philosophy. This novel type of inquiry describes the transdisciplinary endeavor of philosophers, (neuro)scientists, and others to anticipate the societal implications of the impending transformations of subjects and theorizers. While human enhancement is likely to irreversibly change what it means to be human, disruptive technologies might lead to the emergence of artificial intelligent agents and human-machine hybrids. The paper predicts that neuro-techno-philosophy will be indispensable to understanding and engaging with these game-changing innovations and thus play a pivotal role in the future of philosophy.     

An examination of indexes for determining the number of clusters in binary data sets

The problem of choosing the correct number of clusters is as old as cluster analysis itself. A number of authors have suggested various indexes to facilitate this crucial decision. One of the most extensive comparative studies of indexes was conducted by Milligan and Cooper (1985). The present piece of work pursues the same goal under different conditions. In contrast to Milligan and Cooper's work, the emphasis here is on high-dimensional empirical binary data. Binary artificial data sets are constructed to reflect features typically encountered in real-world data situations in the field of marketing research. The simulation includes 162 binary data sets that are clustered by two different algorithms and lead to recommendations on the number of clusters for each index under consideration. Index results are evaluated and their performance is compared and analyzed.Author names are listed in alphabetical order.This piece of research was supported by the Austrian Science Foundation (FWF) under grant SFB#010 (Adaptive Information Systems and Modeling in Economics and Management Science).The authors would like to thank the anonymous reviewers and especially the associate editor for their helpful comments and suggestions.     

比较治疗专家系统在临床诊断中的应用

比较治疗专家系统是比较治疗学在临床应用的一个方向,它也是辅助医生临床诊断的一种智能化的工具。本文比较了现有各种知识表示方法的优缺点,简要介绍了比较治疗专家系统在医疗诊断中的研究现状;通过分析现存问题提出了一个可行性的研究方向。     

内隐学习的人工神经网络模型

近年来,人工神经网络模型常被用来模拟各种心理活动,从而为心理学的一些相关理论提供丰富的证据,内隐学习也不例外。基于权重调整来学习正确反应的人工神经网络模型和内隐学习的两大本质特征间有着极为相应的匹配,因此,人工神经网络模型特别适用于内隐学习研究。到目前为止,针对两种较为普遍的内隐学习任务,已经相应地出现了两种使用较为广泛的神经网络模型——自动联系者和简单循环网络     

人工语法学习中习得知识的分离:基于信号检测论和结构知识的视角

习得知识的意识觉知问题一直以来是内隐学习领域的研究热点和难点。以加工分离程序来分解击中率和虚报率、以结构知识中意识和无意识测量为视角,采用2（测验方式：SDTT,SKT）×2（学习程度：30试次,60试次）混合设计,试图去分离人工语法学习中习得的意识知识和无意识知识。结果发现：（1）学习程度影响到习得的无意识知识,但还未影响到习得的意识知识;（2）在分离人工语法学习的习得知识上,SDTT的敏感性高于SKT;（3）SKT夸大了元认知中的意识成分。     

An Analysis of Fundamental Issues in the Research of Artificial Intelligence Ethical Risk Prevention

While receiving widespread attention, the fundamental issues in the research of artificial intelligence (AI) ethical risk prevention need to be probed and articulated. Although the relevant research is still at an initial stage with long-term tasks, it has a very important social significance. The strategic goals of AI ethical risk prevention research are clarifying ethical risks, taking flexible and effective measures, and providing ethical support for the development of AI technology. This paper argues that key tasks must be of the top priority, such as broadening the theoretical foundation, evaluating ethical risks, realizing ethical design and solving core ethical risks. Therefore, the research of AI ethical risk prevention can be substantially promoted.