自然选择质疑

当代对达尔文进化论的争论几乎是全方位的。尽管当今达尔文主义者的立场和论调与原先的达尔文的进化论已有很大出入，比如说，现代综合进化论对自然选择的创造性作用的突出强调，间断平衡论对达尔文随机渐变理论的重新解释等等，但现代达尔文主义者对作为达尔文进化论核心的自然选择理论一般都是坚持的。本文试图立足于现代自然科学发展的前沿，对这一理论做出再反思。     

试析达尔文“道德选择”思想

本文认为达尔文思想中隐含有与”自然选择”相对的”道德选择”概念.达尔文的”道德选择”概念指的是同时具有利己的个体本能和利他的社会性本能的人所做出的一种以群体福利为价值目标或善,在特定社会舆论的作用下发生的对利己与利他这两种相反的行为动机的自觉抉择,它是人的社会性本能能够发展到良心的关键性步骤.达尔文思想与众不同之处就在于为这两种本能或情感找到了”自然选择”的基础,即从自然选择论出发来解释这两种相反的感情的起源.自私这一面可以用”个体自然选择”来解释,社会性本能可以用”群体自然选择”来解释.人们往往没有意识到达尔文的群体自然选择思想,因而忽略了达尔文思想中所隐含的”道德选择”思想.     

达尔文,一个不可知论者

达尔文的自然选择理论,不仅摧毁了作为造物主的上帝,同时也摧毁了作为救世主的上帝。但终其一生,达尔文在宗教信仰上却是一个不可知论者。     

达尔文进化论遇到难题吗？——兼驳神创论

去年,关于“智慧设计论”是科学还是宗教的论争,甚至诉诸美国法庭。最近,关于达尔文进化论的争论,又迅速成由美国燃烧到了世界。2月21日,一份有514名科学家联合签署的声明,再次引发轩然大波,并以“500科学家质疑达尔文进化论”的报道传遍全球。由于达尔文进化论一方面关乎科学理论,另一方面关乎无神论与有神论的论争,因此不得不引起笔者的关注。关于“500科学家联名质疑达尔文进化论”“我们质疑达尔文宣称的生物随机变异能力和自然选择论来解释复杂的生命。谨慎的检验达尔文理论证据的行动应该被鼓励”。这是《世界网络日报》在今年2月21日…     

迈尔论达尔文革命的实质

达尔文在150年前提出了以自然选择为首要机制的进化理论,引发了达尔文革命.本文主要分析迈尔关于达尔文革命的有关论述,揭示这场革命的实质,即,它不仅是一场科学革命,而且也是一场思想革命.它对西方传统观念的许多重要方面(设计论、宇宙目的论、人类中心论、本质主义等)提出了挑战.同时,迈尔还提出了"种群思维"、"物理主义"等概念,有助于人们理解达尔文思想的深刻内涵,并且从-个侧面反映出迈尔的科学观和科学哲学思想.     

达尔文之后的进化论

达尔文是一个进化论者，他的自然选择理论对人的思维是一个帮助。这是典型的达尔文式学识。在１８６０年英国科学协会的年会上，牛津大教堂的威尔伯福斯主教（ＳａｍｕｅｌＷｉｌｂｅｒｆｏｒｃｅ）旗帜鲜明地反对亨利·赫胥黎（ＴｈｏｍａｓＨｅｎｒｙＨｕｘｌｅｙ），称之为达尔文的“斗犬”，当时他是伦敦煤矿学校的地质学教授。他们的争议涵盖了许多方面，包括科学，但是对宗教是主要分歧点则很少怀疑。威尔伯福斯当然不是圣经学家，但他对达尔文关于人的起源之暗示感到很不舒服；特别是对我们拥有共同祖先的人可能来自于纯粹的自然起源…     

一个基于三角模式的个体利他行为解释

达尔文(Darwin)基于常识(common sense)提出自然选择理论(natural selection),他在《物种起源》给出了考察进化所依据的四个基本特征:繁殖、遗传、变异、可继承性变异。达尔文所关注的是种群内部个体之间的选择,并认为人有利己、爱护后代及在关键时刻为亲友牺牲自身利益等道德特     

生存优势效应的理论解释

生存优势效应指对学习材料进行“生存加工”,其提取正确率最高。研究者试图用主题加工、一致性效应、情绪加工和自然选择等理论对生存优势效应进行解释。研究发现主题加工、一致性效应和情绪加工不足以说明生存优势现象,自然选择理论似乎可以更好地对此加以解释,但目前自然选择理论尚缺乏直接的实验证据支持。文章结合已有研究具体分析了最近的未来研究应该关注的焦点问题,例如,自然选择理论的直接证明,并对直接证明的方案等进行了尝试性的讨论。     

分子进化论研究的回顾与思考

分子进化论研究的回顾与思考中国医学科学院博士生（北京１０００２１）柏华一、传统进化论研究的局限性达尔文１８５９年发表的《物种起源》被认为是划时代的杰作,他首次用“自然选择学说”解释物种进化,认为通过变异的产生和生存的选择,使自然界的生物进化普遍存在。...     

是“滞留于决定论还是前进到选择论”

达尔文的自然选择论未能全面地突破决定论的框架。马克思通过对黑格尔辩证法的拯救和发展,形成了实践的、主体的思维方式,他关于全部所谓世界史不外是人通过人的劳动的诞生,是自然界对人说来的生成的论断,表明他已经实现了对决定论和非决定论思想的辩证综合,扬弃了外推的直线思维和内生的循环思维这两种思维方式的外在对     

What <Emphasis Type="Italic">is</Emphasis> wrong with intelligent design?

While a great deal of abuse has been directed at intelligent design theory (ID), its starting point is a fact about biological organisms that cries out for explanation, namely “specified complexity” (SC). Advocates of ID deploy three kind of argument from specified complexity to the existence of a designer: an eliminative argument, an inductive argument, and an inference to the best explanation. Only the first of these merits the abuse directed at it; the other two arguments are worthy of respect. If they fail, it is only because we have a better explanation of SC, namely Darwin’s theory of evolution by natural selection.     

Natural Properties,Necessary Connections,and the Problem of Induction

The necessitarian solution to the problem of induction involves two claims: first, that necessary connections are justified by an inference to the best explanation; second, that the best theory of necessary connections entails the timeless uniformity of nature. In this paper, I defend the second claim. My arguments are based on considerations from the metaphysics of laws, properties, and fundamentality.     

BELIEF IS NOT THE ISSUE: A DEFENCE OF INFERENCE TO THE BEST EXPLANATION

Defences of inference to the best explanation (IBE) frequently associate IBE with scientific realism, the idea that it is reasonable to believe our best scientific theories. I argue that this linkage is unfortunate. IBE does not warrant belief, since the fact that a theory is the best available explanation does not show it to be (even probably) true. What IBE does warrant is acceptance: taking a proposition as a premise in theoretical and/or practical reasoning. We ought to accept our best scientific theories since they are the theories that are most likely to lead to the goal of science, which is that of knowledge. In support of this claim I invoke Bill Lycan's Panglossian reflections regarding Mother Nature. 1     

Inference to the best explanation: does it track truth?

In the form of inference known as inference to the best explanation there are various ways to characterise what is meant by the best explanation. This paper considers a number of such characterisations including several based on confirmation measures and several based on coherence measures. The goal is to find a measure which adequately captures what is meant by ‘best’ and which also yields the truth with a high degree of probability. Computer simulations are used to show that the overlap coherence measure achieves this goal, enabling the true explanation to be identified almost as often as an approach which simply selects the most probable explanation. Further advantages to this approach are also considered in the case where there is uncertainty in the prior probability distribution.     

以貌取人可行吗？——基于面孔的特质推理及准确性

面孔—特质推理（face - traits inference）是知觉者凭借面孔推断他人特质的心理过程。面孔特征、构型以及表情是特质推理的主要线索。其心理机制可从生理、进化和认知取向等方面进行解释。以往研究发现,面孔-特质推理具有适度准确性。其准确性主要受到知觉对象的面孔吸引力,知觉者的身心特征、知识经验和文化背景的影响;情境也是影响准确性的重要因素。未来研究需在面部线索、准确性指标、准确性的影响因素以及理论框架等方面进行探讨。     

利/害条件下归纳推理的心理效应分离

研究设计了两类包含内容的归纳推理任务, 一类是对获得收益的可能性的推理; 另一类是避免伤害的推理。实验显示, 不同内容的归纳推理结果有显著差异。其中, 避免伤害的归纳推理表现出将伤害可能过度推延的特点; 而获得收益的归纳推理则表现出对收益可能推延不足。此外, 不同的自我卷入水平——自我卷入式推理或者非自我卷入式推理——也会对推理产生显著影响。在避害条件下, 自我卷入式推理比非自我卷入式推理表现出更强烈的过度推延的特点; 在获利条件下, 自我卷入式推理比非自我卷入式推理表现出更严重的推延不足。研究经过分析提出, 这种差异可能暗示了归纳推理的领域特殊性特征。     

Inference-based Teacher Professionalism: How Professional Practitioners in Religious Education Reflect in Action

A theory of professional action is developed based on the 3 modes of inference: abduction, induction, and deduction. The theory explains how professionals reflect in action by combining a single case to their professional repertoire. With teachers in religious education (RE), the problem is focused on how they link students' new and individual religious semantics to traditional Christianity to make religious communication possible. Therefore, the professional teacher action in religious education starts with the religious change in a plural society. Next professionals react to this change by means of the modes of inference. As part of this idea, a theory of inference-based professionalism is created, which relates Schön's theory of reflection-in-action to the modes of inference. The theory is grounded or based on qualitative empirical research. Third, research design and its results are presented and, finally, an outlook on transformative professionalism concerning planning, carrying out, and evaluating religious education-lessons is discussed.     

The sound-board account of reasoning: A one-system alternative to dual-process theory

In order to explain the effects found in the heuristics and biases literature, dual-process theories of reasoning claim that human reasoning is of two kinds: Type-1 processing is fast, automatic, and associative, while Type-2 reasoning is slow, controlled, and rule based. If human reasoning is so divided, it would have important consequences for morality, epistemology, and philosophy of mind. Although dual-process theorists have typically argued for their position by way of an inference to the best explanation, they have generally failed to consider alternative hypotheses. Worse still, it is unclear how we might test dual-process theories. In this article, I offer a one-system theory, which I call the Sound-Board Account of Reasoning, according to which there is one reasoning system which is flexible, allowing the properties used to distinguished Type-1 and Type-2 reasoning to cross-cut one another. I empirically distinguish my theory from the two dominant versions of dual-process theory (parallel-competitive and default-interventionist dual-process theory) and argue that my theory’s predictions are confirmed over both of these versions of dual-process theory.     

The inference to the best explanation

In a situation in which several explanations compete, is the one that is better qua explanation also the one we should regard as the more likely to be true? Realists usually answer in the affirmative. They then go on to argue that since realism provides the best explanation for the success of science, realism can be inferred to. Nonrealists, on the other hand, answer the above question in the negative, thereby renouncing the inference to realism. In this paper I separate the two issues. In the first section it is argued that a rationale can be provided for the inference to the best explanation; in the second, that this rationale cannot justify an inference to realism. The defence of the inference rests on the claim that our standards of explanatory power are subject to critical examination, which, in turn, should be informed by empirical considerations. By means of a comparison of the realist's explanation for the success of science with that of conventionalism and instrumentalism it is then shown that realism does not offer a superior explanation and should not, therefore, be inferred to.     

Reasoning as simulation

The theory that human cognition proceeds through mental simulations, if true, would provide a parsimonious explanation of how the mechanisms of reasoning and problem solving integrate with and develop from mechanisms underlying forms of cognition that occur earlier in evolution and development. However, questions remain about whether simulation mechanisms are powerful enough to exhibit human-level reasoning and inference. In order to investigate this issue, we show that it is possible to characterize some of the most powerful modern artificial intelligence algorithms for logical and probabilistic inference as methods of simulating alternate states of the world. We show that a set of specific human perceptual mechanisms, even if not implemented using mechanisms described in artificial intelligence, can nevertheless perform the same operations as those algorithms. Although this result does not demonstrate that simulation theory is true, it does show that whatever mechanisms underlie perception have at least as much power to explain non-perceptual human reasoning and problem solving as some of the most powerful known algorithms.