化人文科学传统为己有——论卡尔·雅斯贝尔斯精神病理学的基本特征

卡尔·雅斯贝尔斯的精神病理学思想对20世纪精神病学和现代许多国家的临床精神病理学产生了广泛而持久的影响。雅斯贝尔斯重新审视人文科学的特殊性,化人文科学传统为己有,致力于人文科学的理解的心理学,将精神病科医生作为理解和体验的个体一同纳入精神病理学中,描述了静力学的理解、理性的理解、心理学的理解、爱的理解等,从而清晰地勾画了理解的全过程,建立了精神病理学中的理解的心理学。     

对电源模型等效变换理解存在的歧义

根据对两种电源模型在内阻趋于零和无穷大时伏安特性变化趋势的分析,指出通常对电源模型等效变换理解存在的歧义,全面、准确的理解应是理想电压源不能等效变换为电流源,理想电流源不能等效变换为电压源。     

语义神经网络神经元的面向对象的实现

根据语义神经网络的特征，在分析了汉语语义关系的特点，人工神经元的特性和面向对象编程技术的基础上，将概念类内部知识结构与神经元的外部结构很好的融合在一起，并在此基础上，提出将语义神经网络神经元作为面向对象的类来处理，利用面向对象技术中的继承，重载，多态性等机制来刻画汉语间的语义关系，实现了语义神经网络神经元的面向对象的描述。     

Using computational models to discover and understand mechanisms

Areas of biology such as cell and molecular biology have been dominated by research directed at constructing mechanistic explanations that identify parts and operations that when organized appropriately are responsible for the various phenomena they investigate. Increasingly the mechanisms hypothesized involve non-sequential organization of non-linear operations and so exceed the ability of researchers to mentally rehearse their behavior. Accordingly, scientists rely on tools of computational modeling and dynamical systems theory in advancing dynamic mechanistic explanations. Using circadian rhythm research as an exemplar, this paper explores the variety of roles computational modeling is playing. They serve not just to determine whether the mechanism will produce the desired behavior, but in the discovery process of hypothesizing mechanisms and in understanding why proposed mechanisms behave as they do.     

Understanding as explanatory knowledge: The case of Bjorken scaling

In this paper, we develop and refine the idea that understanding is a species of explanatory knowledge. Specifically, we defend the idea that S understands why p if and only if S knows that p, and, for some q, S’s true belief that q correctly explains p is produced/maintained by reliable explanatory evaluation. We then show how this model explains the reception of James Bjorken’s explanation of scaling by the broader physics community in the late 1960s and early 1970s. The historical episode is interesting because Bjorken’s explanation initially did not provide understanding to other physicists, but was subsequently deemed intelligible when Feynman provided a physical interpretation that led to experimental tests that vindicated Bjorken’s model. Finally, we argue that other philosophical models of scientific understanding are best construed as limiting cases of our more general model.     

Explanation,understanding, and unrealistic models

How can false models be explanatory? And how can they help us to understand the way the world works? Sometimes scientists have little hope of building models that approximate the world they observe. Even in such cases, I argue, the models they build can have explanatory import. The basic idea is that scientists provide causal explanations of why the regularity entailed by an abstract and idealized model fails to obtain. They do so by relaxing some of its unrealistic assumptions. This method of ‘explanation by relaxation’ captures the explanatory import of some important models in economics. I contrast this method with the accounts that Daniel Hausman and Nancy Cartwright have provided of explanation in economics. Their accounts are unsatisfactory because they require that the economic model regularities obtain, which is rarely the case. I go on to argue that counterfactual regularities play a central role in achieving ‘understanding by relaxation.’ This has a surprising implication for the relation between explanation and understanding: Achieving scientific understanding does not require the ability to explain observed regularities.     

求知听力教学点滴

本文旨在说明求知听力教学模式能极大转变传统的应试听力教学所导致的被动局面,大幅度提高英语听力课的教学效果,有利于培养学生的英语综合能力。     

大学新生英语口语的强化训练策略

本文拟从实用角度出发探讨以“学生为中心”以语音为突破点对新生进行英语所说强化训练的教学方法，指出利用认知法原理对新生进行强化训练的可行性，并且强调：在具体操作中要注意调动学生的积极性和提高分析问题和解决问题的能力。     

科学解释与科学理解——重思亨普尔覆盖律模型

根据一种传统的观点,科学的目标是解释而非理解.然而,包括爱因斯坦和亨普尔在内的许多著名科学家和哲学家都认为科学的目标是理解.但一些人坚持认为,即使这些著名科学家和哲学家用到"理解"这个词,他们只是随便一说;"解释是科学的目标"才是他们严肃认真的看法,至少对于亨普尔是如此.本文反驳了这一观点,探讨了"科学的目标是理解"这...     

Understanding climate phenomena with data-driven models

In climate science, climate models are one of the main tools for understanding phenomena. Here, we develop a framework to assess the fitness of a climate model for providing understanding. The framework is based on three dimensions: representational accuracy, representational depth, and graspability. We show that this framework does justice to the intuition that classical process-based climate models give understanding of phenomena. While simple climate models are characterized by a larger graspability, state-of-the-art models have a higher representational accuracy and representational depth. We then compare the fitness-for-providing understanding of process-based to data-driven models that are built with machine learning. We show that at first glance, data-driven models seem either unnecessary or inadequate for understanding. However, a case study from atmospheric research demonstrates that this is a false dilemma. Data-driven models can be useful tools for understanding, specifically for phenomena for which scientists can argue from the coherence of the models with background knowledge to their representational accuracy and for which the model complexity can be reduced such that they are graspable to a satisfactory extent.