Modeling without representation

How can mathematical models which represent the causal structure of the world incompletely or incorrectly have any scientific value? I argue that this apparent puzzle is an artifact of a realist emphasis on representation in the philosophy of modeling. I offer an alternative, pragmatic methodology of modeling, inspired by classic papers by modelers themselves. The crux of the view is that models developed for purposes other than explanation may be justified without reference to their representational properties.     

Systems without a graphical causal representation

There are simple mechanical systems that elude causal representation. We describe one that cannot be represented in a single directed acyclic graph. Our case suggests limitations on the use of causal graphs for causal inference and makes salient the point that causal relations among variables depend upon details of causal setups, including values of variables.     

No representation without awareness in the lateral occipital cortex

Recent research has shown that four small dots presented in the vicinity of, but not adjacent to, a target stimulus can banish that stimulus from conscious awareness. It is thought that the mental representation of the masked stimulus is "erased" by the trailing quartet of dots. Using functional magnetic resonance adaptation, we show that there is no persisting neural representation of the successfully masked stimulus in lateral occipital cortex, a region that has been implicated in the processing of object structure. This finding rules out the alternative interpretation that a lingering neural representation is merely rendered inaccessible to consciousness, as is the fate, for example, of monocular information under conditions of binocular rivalry.     

Spectrum inversion without a difference in representation is impossible

Even if spectrum inversion of various sorts is possible, spectrum inversion without a difference in representation is not. So spectrum inversion does not pose a challenge for the intentionalist thesis that, necessarily, within a given sense modality, if two experiences are alike with respect to content, they are also alike with respect to their phenomenal character. On the contrary, reflection on variants of standard cases of spectrum inversion provides a strong argument for intentionalism. Depending on one’s views about the possibility of spectrum inversion, the impossibility of spectrum inversion without a difference in representation can also be used as an argument against a variety of reductive theories of mental representation.     

Teaching World Religions without Teaching “World Religions”

Tomoko Masuzawa and a number of other contemporary scholars have recently problematized the categories of “religion” and “world religions” and, in some cases, called for its abandonment altogether as a discipline of scholarly study. In this collaborative essay, we respond to this critique by highlighting three attempts to teach world religions without teaching “world religions.” That is, we attempt to promote student engagement with the empirical study of a plurality of religious traditions without engaging in the rhetoric of pluralism or the reification of the category “religion.” The first two essays focus on topical courses taught at the undergraduate level in self‐consciously Christian settings: the online course “Women and Religion” at Georgian Court University and the service‐learning course “Interreligious Dialogue and Practice” at St. Michael's College, in the University of Toronto. The final essay discusses the integration of texts and traditions from diverse traditions into the graduate theology curriculum more broadly, in this case at Loyola Marymount University. Such confessional settings can, we suggest, offer particularly suitable – if somewhat counter‐intuitive – contexts for bringing the otherwise covert agendas of the world religions discourse to light and subjecting them to a searching inquiry in the religion classroom.     

Knowledge representation,the World Wide Web,and the evolution of logic

It is almost universally acknowledged that first-order logic (FOL), with its clean, well-understood syntax and semantics, allows for the clear expression of philosophical arguments and ideas. Indeed, an argument or philosophical theory rendered in FOL is perhaps the cleanest example there is of “representing philosophy”. A number of prominent syntactic and semantic properties of FOL reflect metaphysical presuppositions that stem from its Fregean origins, particularly the idea of an inviolable divide between concept and object. These presuppositions, taken at face value, reflect a significant metaphysical viewpoint, one that can in fact hinder or prejudice the representation of philosophical ideas and arguments. Philosophers have of course noticed this and have, accordingly, sought to alter or extend traditional FOL in novel ways to reflect a more flexible and egalitarian metaphysical standpoint. The purpose of this paper, however, is to document and discuss how similar “adaptations” to FOL—culminating in a standardized framework known as Common Logic—have evolved out of the more practical and applied encounter of FOL with the problem of representing, sharing, and reasoning upon information on World Wide Web.     

Dissociating size representation for action and for conscious judgment: Grasping visual illusions without apparent obstacles

Visual illusions provide important evidence for the co-existence of unconscious and conscious representations. Objects surrounded by other figures (e.g., a disc surrounded by smaller or larger rings, Ebbinghaus/Titchener illusion) are consciously perceived as different in size, while the visuo-motor system supposedly uses an unconscious representation of the discs' true size for grip size scaling. Recent evidence suggests other factors than represented size, e.g., surrounding rings conceived as obstacles, affect grip size. Use of the diagonal illusion avoids visual obstacles in the path of the reaching hand. Results support the dual representation theory. Grip size scaling follows actual size independent of illusory effects, which clearly bias conscious perception in direct comparisons of lengths (Experiment 1) and in finger-thumb span indications of perceived length (Experiment 2).     

No interpretation without representation: the role of domain-specific representations and inferences in the Wason selection task

The Wason selection task is a tool used to study reasoning about conditional rules. Performance on this task changes systematically when one varies its content, and these content effects have been used to argue that the human cognitive architecture contains a number of domain-specific representation and inference systems, such as social contract algorithms and hazard management systems. Recently, however, Sperber, Cara & Girotto (Sperber, D., Cara, F., & Girotto, V. (1995). Relevance theory explains the selection task. Cognition, 57, 31-95) have proposed that relevance theory can explain performance on the selection task - including all content effects - without invoking inference systems that are content-specialized. Herein, we show that relevance theory alone cannot explain a variety of content effects - effects that were predicted in advance and are parsimoniously explained by theories that invoke domain-specific algorithms for representing and making inferences about (i) social contracts and (ii) reducing risk in hazardous situations. Moreover, although Sperber et al. (1995) were able to use relevance theory to produce some new content effects in other domains, they conducted no experiments involving social exchanges or precautions, and so were unable to determine which - content-specialized algorithms or relevance effects - dominate reasoning when the two conflict. When experiments, reported herein, are constructed so that the different theories predict divergent outcomes, the results support the predictions of social contract theory and hazard management theory, indicating that these inference systems override content-general relevance factors. The fact that social contract and hazard management algorithms provide better explanations for performance in their respective domains does not mean that the content-general logical procedures posited by relevance theory do not exist, or that relevance effects never occur. It does mean, however, that one needs a principled way of explaining which effects will dominate when a set of inputs activate more than one reasoning system. We propose the principle of pre-emptive specificity - that the human cognitive architecture should be designed so that more specialized inference systems pre-empt more general ones whenever the stimuli centrally fit the input conditions of the more specialized system. This principle follows from evolutionary and computational considerations that are common to both relevance theory and the ecological rationality approach.     

Dialogue representation

We consider question-answer dialogues between participants who may disagree with each other. The main problems are: (a) How different speech-acts affect the information in the dialogue; and (b) How to represent what was said in a dialogue, so that we can summarize it even when it involves disagreements (i.e., inconsistencies).We use a fully-typed many-sorted language L with a possible-worlds semantics. L contains nominals representing short answers. The speech-acts are uniformly represented in a dialogue language DL by focus structures, consisting of a mood operator, a topic component and a focus component. Each stage of the dialogue is associated with a set of information functions (g-functions), which are partial functions taking a topic component (representing a question raised) to a set of propositions determined by the corresponding focus component (to the set of answers given to it).Asserting is answering a question and, hence, it causes a new g-function to be defined. Asking is an attempt to cause the hearer to define a new g-function satisfying certain conditions. A question asked requests a true and complete answer. A reaction answers a question if it satisfies some of the conditions of the question. Indirect questions are viewed as indirect answers.A dialogue representation consists of: commitment sets, each representing the commitments expressed by one participant; sets of questions under discussion associated with each stage of the dialogue, and the common ground, containing the g-functions and representing consistently what was said in the dialogue.Concepts of informativeness are naturally defined within the theory. Whether an utterance is informative depends on which question it answers and how the question was answered previously. These concepts yield that uttering mathematical and logical truths is as informative as uttering a contingency.     

非符号数量表征和符号分数表征的关系

个体学习符号分数的一个关键是能对其数值形成准确表征。现有研究假设符号分数表征的认知基础是人类自婴幼儿期就具有的非符号数量表征(如表征两个集合各自的数量, 或两个数量的比例)。其证据包括表征非符号数量(尤其是非符号数量比例关系)和表征符号分数在行为和大脑神经活动层面上都表现出相关性。然而要说明非符号数量表征是符号分数表征的认知基础, 还需更多研究表明两者在数量概念上的独特相关和因果联系, 并阐明符号分数表征形成的认知机制。     

Epistemic representation, informativeness and the aim of faithful representation

In this paper, I take scientific models to be epistemic representations of their target systems. I define an epistemic representation to be a tool for gaining information about its target system and argue that a vehicle’s capacity to provide specific information about its target system—its informativeness—is an essential feature of this kind of representation. I draw an analogy to our ordinary notion of interpretation to show that a user’s aim of faithfully representing the target system is necessary for securing this feature.     

Linda问题的表象-命题双表征解释视角探究

本文首先提出了Linda问题的表象-命题双表征这一新的解释视角。该视角认为,Linda问题基于表象表征和命题表征可以有两种不同的解读与表征方式;而不同的被试在Linda问题上可能分别采取了上述表征方式之一;但由于Linda问题的特殊性,大多数被试采用了表象表征;大多数被试的这一表征取向则可能是所谓谬误判断出现的原因。本文通过4项研究,让被试在基于表象表征设计的转述版本与基于命题表征设计的转述版间选择接近自身理解的版本;并考察了将Linda问题修改成更符合命题表征的数学化表达形式能否降低所谓谬误水平;还考察了增加促使被试运用命题表征的排序项"Linda是全人类中的一员"能否降低所谓谬误水平。结果显示,在转述版本选择上,大多数被试选择了基于表象表征设计的版本;而上文所指的两个修正版Linda问题则都降低了被试的所谓谬误水平。这些结果支持了本文所提的视角。     

Topics in semantic representation

Processing language requires the retrieval of concepts from memory in response to an ongoing stream of information. This retrieval is facilitated if one can infer the gist of a sentence, conversation, or document and use that gist to predict related concepts and disambiguate words. This article analyzes the abstract computational problem underlying the extraction and use of gist, formulating this problem as a rational statistical inference. This leads to a novel approach to semantic representation in which word meanings are represented in terms of a set of probabilistic topics. The topic model performs well in predicting word association and the effects of semantic association and ambiguity on a variety of language-processing and memory tasks. It also provides a foundation for developing more richly structured statistical models of language, as the generative process assumed in the topic model can easily be extended to incorporate other kinds of semantic and syntactic structure.