A Computational Theory of Learning Causal Relationships

I present a cognitive model of the human ability to acquire causal relationships. I report on experimental evidence demonstrating that human learners acquire accurate causal relationships more rapidly when training examples are consistent with a general theory of causality. This article describes a learning process that uses a general theory of causality as background knowledge. The learning process, which I call theory-driven learning (TDL), hypothesizes causal relationships consistent both with observed data and the general theory of causality. TDL accounts for data on both the rate at which human learners acquire causal relationships, and the types of causal relationships they acquire. Experiments with TDL demonstrate the advantage of TDL for acquiring causal relationships over similarity-based approaches to learning: Fewer examples are required to learn an accurate relationship.     

塔斯基：语义性真理论与符合论

塔斯基语义性真理论是否为符合论是一个争论的热点。目前有两种观点认为它是符合论,其一是根据对象语言与元语言的区分,到目前为止此观点已受到激烈批评,其二是根据“满足”概念的递归定义,此观点到目前为止还没有被人详细地阐明。本文认为塔斯基的语义性真理论是符合论,为此将首先对第一种观点及其反对意见进行深入分析,并得出结论认为该观点不能成立,继而详细阐明第二种观点,表明它是塔斯基本人的意图。在讨论中本文将对一系列的逻辑哲学问题进行探讨。     

隐喻在文章语境中的理解--概念隐喻理论探讨

概念隐喻理论认为,语言系统中存在着广泛的隐喻一致性,人们对于隐喻的理解是通过将其匹配到基本的概念隐喻来进行的。但有研究者指出,除了一些新颖的隐喻,惯用隐喻的理解并不需要概念匹配。本研究认为,概念隐喻已经储存在长时记忆中,在文章语境中,无论惯用隐喻还是新颖隐喻的理解都可能产生概念匹配。三个实验支持了这一假设。实验一通过目标句的阅读反应时探讨不同文章版本下目标句的理解情况;实验二和实验三从事后与即时两方面对各种文章版本下的概念匹配情况进行比较。研究结果支持了概念隐喻理论。     

Dispositions Indisposed: Semantic Atomism and Fodor's Theory of Content

According to Jerry Fodor's atomistic theory of content, subjects' dispositions to token mentalese terms in counterfactual circumstances fix the contents of those terms. I argue that the pattern of counterfactual tokenings alone does not satisfactorily fix content; if Fodor's appeal to patterns of counterfactual tokenings has any chance of assigning correct extensions, Fodor must take into account the contents of subjects' various mental states at the times of those tokenings. However, to do so, Fodor must abandon his semantic atomism. And while Fodor has recently qualified his atomism, the cognitively holistic nature of dispositions continues to undermine his view.     

The Place of Semantic Theory

Abstract: Some twenty years since its publication Putnam's model‐theoretic argument is still much discussed. The present paper aims to defend a reconstruction of the argument but begins by attempting to clarify the form of the argument. Usually, and with good textual grounds, the argument is treated as a reductio argument against metaphysical realism. I argue instead that it should be treated as developing a paradox. I go on to claim that the most promising response to this paradox is to be able to provide a theory of (Fregean) sense, in the style recommended by Dummett. So, according to this reasoning, the argument is not an argument against metaphysical realism but an argument against positions which reject the notion of sense.     

语义结构理论与临床推理

临床推理是医生的核心能力之一。临床推理的语义结构理论认为,语义轴由临床意义相反的两个词构成,能精确地表征临床知识并促进医生精确地回忆知识;语义轴是正确临床诊断的必要条件,医生使用语义轴的数量与其临床诊断的正确性呈正相关。应用语义结构理论进行医学教学改革可能有益于医学生临床推理能力的迅速提高。     

Pain and its Metaphors: A Dialogical Approach

Most health professionals are unaware of the extent to which aspects of language, such as metaphor, influence their practice. Sensitivity to metaphor can deepen our understanding of healthcare and, arguably, improve its quality. This is because metaphors, and the linguisticality of which they are a part, shape medical practice in important ways. Examples are the metaphors used in pain management. By exploring the dialogical tension between such metaphors, we can better understand the ways in which they influence medical practice.     

Semantic Information and the Correctness Theory of Truth

Semantic information is usually supposed to satisfy the veridicality thesis: p qualifies as semantic information only if p is true. However, what it means for semantic information to be true is often left implicit, with correspondentist interpretations representing the most popular, default option. The article develops an alternative approach, namely a correctness theory of truth (CTT) for semantic information. This is meant as a contribution not only to the philosophy of information but also to the philosophical debate on the nature of truth. After the introduction, in Sect. 2, semantic information is shown to be translatable into propositional semantic information (i). In Sect. 3, i is polarised into a query (Q) and a result (R), qualified by a specific context, a level of abstraction and a purpose. This polarization is normalised in Sect. 4, where [Q + R] is transformed into a Boolean question and its relative yes/no answer [Q + A]. This completes the reduction of the truth of i to the correctness of A. In Sects. 5 and 6, it is argued that (1) A is the correct answer to Q if and only if (2) A correctly saturates Q by verifying and validating it (in the computer science’s sense of “verification” and “validation”); that (2) is the case if and only if (3) [Q + A] generates an adequate model (m) of the relevant system (s) identified by Q; that (3) is the case if and only if (4) m is a proxy of s (in the computer science’s sense of “proxy”) and (5) proximal access to m commutes with the distal access to s (in the category theory’s sense of “commutation”); and that (5) is the case if and only if (6) reading/writing (accessing, in the computer science’s technical sense of the term) m enables one to read/write (access) s. Sect. 7 provides some further clarifications about CTT, in the light of semantic paradoxes. Section 8 draws a general conclusion about the nature of CTT as a theory for systems designers not just systems users. In the course of the article all technical expressions from computer science are explained.     

Computational Theory and Cognition in Representational Momentum and Related Types of Displacement: A reply to Kerzel

Kerzel’s (2006) commentary on Hubbard’s (2005) re-view of the literature on representational momentum and related types of displacement highlights differences of interpretation between Hubbard and Kerzel, but also contains mischaracterizations of Hubbard’s position. In this reply to Kerzel, these differences and mischaracterizations are addressed. Issues are briefly considered that involve the following: whether displacement involves multiple processes; the Marr (1982) framework; the relationship of displacement to internalized physics, goodness of perceived motion, apparent motion, and oculomotor overshoot; generalization from flashed objects to moving objects; necessary and sufficient criteria for displacement; and whether the existence of auditory representational momentum provides evidence for a supramodal account of displacement.     

Neural Computation and the Computational Theory of Cognition

We begin by distinguishing computationalism from a number of other theses that are sometimes conflated with it. We also distinguish between several important kinds of computation: computation in a generic sense, digital computation, and analog computation. Then, we defend a weak version of computationalism—neural processes are computations in the generic sense. After that, we reject on empirical grounds the common assimilation of neural computation to either analog or digital computation, concluding that neural computation is sui generis. Analog computation requires continuous signals; digital computation requires strings of digits. But current neuroscientific evidence indicates that typical neural signals, such as spike trains, are graded like continuous signals but are constituted by discrete functional elements (spikes); thus, typical neural signals are neither continuous signals nor strings of digits. It follows that neural computation is sui generis. Finally, we highlight three important consequences of a proper understanding of neural computation for the theory of cognition. First, understanding neural computation requires a specially designed mathematical theory (or theories) rather than the mathematical theories of analog or digital computation. Second, several popular views about neural computation turn out to be incorrect. Third, computational theories of cognition that rely on non‐neural notions of computation ought to be replaced or reinterpreted in terms of neural computation.