Experience-grounded semantics: a theory for intelligent systems

An experience-grounded semantics is introduced for an intelligent reasoning system, which is adaptive, and works with insufficient knowledge and resources. According to this semantics, truth and meaning are defined with respect to the experience of the system – the truth value of a statement indicates the amount of available evidence, and the meaning of a term indicates its experienced relations with other terms. The major difference between experience-grounded semantics and model-theoretic semantics is that the former does not assume the sufficiency of knowledge and resources. This approach provides new ideas to the solution of some important problems in cognitive science.     

What is the function of faith and trust in psychoanalysis?1

Unlike other concepts such as ‘illusion’, ‘capacity to tolerate frustration’ and ‘libidinal investment’, the concept of faith has not yet found a well‐defined position in psychoanalytic theory. Bion focused on faith and placed it in an unusual context: scientific work. Through the Act of Faith a researcher can give some consistency to certain ideas, hunches or intuitions that may appear during observation, though he cannot represent them by existing theory. Through the Act of Faith an analyst can ‘see’, ‘hear’ and ‘feel’ those mental phenomena, the reality of which leaves no practising psychoanalysts in doubt, even if they cannot represent them by current formulations. In this paper, the author aims to expand Bion's proposals into the clinical and therapeutic fields. In the first part, the author examines how faith and trust overlap, and how they depart from each other, and he gives an example. Faith possesses an igniting and driving force which trust doesn't possess to the same extent. In the second part, the author looks at F as a psychic function of the analyst, which aids him in supporting a depressed and hopeless patient while waiting for the return of the patient's desire to live. In the final part, he focuses on F from the patient's point of view and studies the transformations of F that may occur during an analysis.     

自尊概念辨析

自尊是个人对自我价值和自我能力的情感体验,属于自我系统中的情感成分,具有一定的评价意义。它与其相近概念,如自尊需要、自我效能、自我价值、自我概念等都有着本质的区别和联系。该文就自尊的概念及其与相关概念的区别与联系进行了尝试性探讨,以期抛砖引玉,更好地认识自尊这一人格变量。     

反应风格的测量与统计控制

反应风格是共同方法偏差的主要来源之一。本文首先讨论反应风格的定义和类型,梳理其危害,认为反应风格能使测验分数出现偏差,影响测验信效度分析和变量关系分析,有必要控制其危害。然后介绍了常用的反应风格测量方法,包括计数法和模型法两大类,对测量方法的选择给出了建议,在此基础上,就如何结合反应风格的测量方法与残差回归法、偏相关法来控制反应风格危害给出建议。     

De‐escalation after repeated negative feedback: emergent expectations of failure

Research on willingness to make marginal investments (e.g., the escalation and sunk cost literatures) has often focused on project completion decisions, such as the “radar‐blank plane.” This paper discusses a fundamentally different type of marginal investment decision, that of couples deciding whether to continue infertility treatment in the face of repeated failures. Two experiments based on this context show that when people face multiple independent chances to achieve a valued goal but are unsure about chances of success, premature quitting or “de‐escalation” is the norm. Repeated negative feedback appears to induce individuals to see each successive failure as more and more diagnostic. As a result, even a short series of failed attempts evokes beliefs that future attempts will also fail. These emergent expectations of failure, generated by causal attribution processes, associative learning, and/or discounting of ambiguous information, appear very compelling and induce people to forgo profitable marginal investments. Copyright © 2004 John Wiley & Sons, Ltd.     

优秀护士"Y-G人格测验"特异性指标的研究

采用“Y-G人格测验”问卷及自制职业自评、他评调查表,分析比较了优秀护士与一般护士的人格特质、性格类型的差异。结果表明：优秀护士与一般护士在2种性格类型、7个人格特质上存在显著差异或差异趋势;提示从“Y-G人格测验”中提取护士人格特异性指标具有可行性;优秀护士人格特质的特异性指标可折射其性格类型、对开发护士职业心理素质研究的应用工具有一定价值。     

全面深入理解“以人为本”

以人为本是一项符合人类文明发展趋势并指导当代社会发展的重要原则 ,是科学发展观的核心内容。以人为本并非只有西方人文主义那样的理解 ,它既是一种共同价值观和价值取向 ,又是一种思维方式 ,同时还是一种执政理念。     

Reasoning through doing. Epistemic mediators in scientific discovery

The recent epistemological and cognitive studies concentrate on the concept of abduction, as a means to originate and refine new ideas. Traditional cognitive science and computational accounts concerning abduction aim at illustration discovery and creativity processes in terms of theoretical and “internal” aspects, by means of computational simulations and/or abstract cognitive models. I will illustrate in this paper that some typical internal abductive processes are involved in scientific reasoning and discovery (for example through radical innovations). Nevertheless, especially concrete manipulations of the external world constitute a fundamental passage in science: by a process of manipulative abduction it is possible to build prostheses (epistemic mediators) for human minds, by interacting with external objects and representations in a constructive way. In this manner it is possible to create implicit knowledge through doing and to produce various opportunity to find, for example, anomalies and fruitful new risky perspectives. This kind of embodied and unexpressed knowledge holds a key role in the subsequent processes of scientific comprehension and discovery.     

Abductive inference in defeasible reasoning: a model for research programmes

Abduction is regarded as the inference process that goes from observations to explanations within a more general context or theoretical framework. There are good characterizations of abduction of surprising observations in monotonic theories. Also, in normal logic programs there are a tight relation among SLDNF and the abduction of negative literals. However, a problem that faces abduction is the explanation of anomalous observations, i.e., observations that are contradictory with respect to the current theory. For this reason, in this work we will consider the problem of embedding abduction of surprising and anomalous observations in defeasible (nonmonotonic) theories. We discuss some issues arising the pragmatic acceptance of abductive inferences in defeasible theories, and how to accommodate anomalous observations and characterize all the possible outcomes that a defeasible theory may face when confronted with new evidence. We explore the use of partial structures approach as a semantic foundation for our system. Finally, we discuss an application of our system as a formal device for representing the methodology of scientific research programmes. In this representation, a programme is regarded as a defeasible theory that draws predictions. When confronted with surprising or anomalous observations, the programme protects itself by means of heuristic procedures, which are represented in our system as abductive inference procedures.     

Epistemology and artificial intelligence

In this essay we advance the view that analytical epistemology and artificial intelligence are complementary disciplines. Both fields study epistemic relations, but whereas artificial intelligence approaches this subject from the perspective of understanding formal and computational properties of frameworks purporting to model some epistemic relation or other, traditional epistemology approaches the subject from the perspective of understanding the properties of epistemic relations in terms of their conceptual properties. We argue that these two practices should not be conducted in isolation. We illustrate this point by discussing how to represent a class of inference forms found in standard inferential statistics. This class of inference forms is interesting because its members share two properties that are common to epistemic relations, namely defeasibility and paraconsistency. Our modeling of standard inferential statistical arguments exploits results from both logical artificial intelligence and analytical epistemology. We remark how our approach to this modeling problem may be generalized to an interdisciplinary approach to the study of epistemic relations.