Typicality, graded membership, and vagueness

This paper addresses theoretical problems arising from the vagueness of language terms, and intuitions of the vagueness of the concepts to which they refer. It is argued that the central intuitions of prototype theory are sufficient to account for both typicality phenomena and psychological intuitions about degrees of membership in vaguely defined classes. The first section explains the importance of the relation between degrees of membership and typicality (or goodness of example) in conceptual categorization. The second and third section address arguments advanced by Osherson and Smith (1997), and Kamp and Partee (1995), that the two notions of degree of membership and typicality must relate to fundamentally different aspects of conceptual representations. A version of prototype theory—the Threshold Model—is proposed to counter these arguments and three possible solutions to the problems of logical selfcontradiction and tautology for vague categorizations are outlined. In the final section graded membership is related to the social construction of conceptual boundaries maintained through language use

Symbol Emergence in Robotics: A Survey

Humans can learn the use of language through physical interaction with their environment and semiotic communication with other people. It is very important to obtain a computational understanding of how humans can form a symbol system and obtain semiotic skills through their autonomous mental development. Recently, many studies have been conducted on the construction of robotic systems and machine-learning methods that can learn the use of language through embodied multimodal interaction with their environment and other systems. Understanding human social interactions and developing a robot that can smoothly communicate with human users in the long term, requires an understanding of the dynamics of symbol systems and is crucially important. The embodied cognition and social interaction of participants gradually change a symbol system in a constructive manner. In this paper, we introduce a field of research called symbol emergence in robotics (SER). SER is a constructive approach towards an emergent symbol system. The emergent symbol system is socially self-organized through both semiotic communications and physical interactions with autonomous cognitive developmental agents, i.e., humans and developmental robots. Specifically, we describe some state-of-art research topics concerning SER, e.g., multimodal categorization, word discovery, and a double articulation analysis, that enable a robot to obtain words and their embodied meanings from raw sensory--motor information, including visual information, haptic information, auditory information, and acoustic speech signals, in a totally unsupervised manner. Finally, we suggest future directions of research in SER.Comment: submitted to Advanced Robotic

A functional view on prototypes

The human mind may produce prototypization within virtually any realm of cognition and behavior. A "comparative prototype-typology" might prove to be an interesting field of study – perhaps a new subfield of semiotics. This, however, would presuppose a clear view on the samenesses and differences of prototypization in these various fields. It seems realistic for the time being that the linguist first confine himself to describing prototypization within the realm of language proper. The literature on prototypes has steadily grown in the past ten years or so. I confine myself to mentioning the volume on Noun Classes and Categorization, edited by C. Craig (1986), which contains a wealth of factual information on the subject, along with some theoretical vistas. By and large, however, linguistic prototype research is still basically in a taxonomic stage - which, of course, represents the precondition for moving beyond. The procedure is largely per ostensionem, and by accumulating examples of prototypes. We still lack a comprehensive prototype theory. The following pages are intended, not to provide such, a theory, but to do the first steps in this direction. Section 2 will feature some elements of a functional theory of prototypes. They have been developed by this author within the frame of the UNITYP model of research on language universals and typology. Section 3 will bring a discussion of prototypization with regard to selected phenomena of a wide range of levels of analysis: Phonology, morphosyntax, speech acts, and the lexicon. Prototypization will finally be studied within one of the universal dimensions, that of APPREHENSION - the linguistic representation of the concepts of objects – as proposed by Seiler (1986)

Analyzing equivalalences in discourse: are discourse theory and membership categorization analysis comptatible

Facing a crucial leap from political philosophy to empirical analysis, the approach to discourse analysis that arose in the aftermath of Laclau and Mouffe (1985), and that is currently known as the Essex school of discourse theory (DT), has in recent years repeatedly been accused of suffering from a methodological deficit. This paper examines to what extent membership categorization analysis (MCA), a branch of ethnomethodology that investigates lay actors' situated descriptions-in-context as practical activity, can play a part in rendering poststructuralist DT notions such as articulation and equivalence analytically tangible in empirically observable discourse. Based on a review of Laclau and Mouffe's foundational text as well as on Glynos and Howarth's recent exposition of the framework (2007), it is argued that MCA empirically substantiates many poststructuralist claims about the indeterminacy of signification. However, MCA consistently falters - and willingly so - at the point where DT would articulate emerging equivalences between identity categories as part of a second-order explanatory concept, such as Glynos and Howarth’s notion of political logic. Nevertheless, MCA also contains the kernel of an "endogenous" notion of the political that comes fairly close to DT’s all-pervasive understanding of the concept. To support these arguments, a variety of empirical sources are mobilized, ranging from the transcript of a political talk show, a newspaper report regarding a discrimination case in a dance class, to data drawn from earlier research on the way that minority members are treated by the Belgian criminal justice system

Semantic Modeling of Analytic-based Relationships with Direct Qualification

Successfully modeling state and analytics-based semantic relationships of documents enhances representation, importance, relevancy, provenience, and priority of the document. These attributes are the core elements that form the machine-based knowledge representation for documents. However, modeling document relationships that can change over time can be inelegant, limited, complex or overly burdensome for semantic technologies. In this paper, we present Direct Qualification (DQ), an approach for modeling any semantically referenced document, concept, or named graph with results from associated applied analytics. The proposed approach supplements the traditional subject-object relationships by providing a third leg to the relationship; the qualification of how and why the relationship exists. To illustrate, we show a prototype of an event-based system with a realistic use case for applying DQ to relevancy analytics of PageRank and Hyperlink-Induced Topic Search (HITS).Comment: Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015

LOGICAL AND PSYCHOLOGICAL PARTITIONING OF MIND: DEPICTING THE SAME MAP?

The aim of this paper is to demonstrate that empirically delimited structures of mind are also differentiable by means of systematic logical analysis. In the sake of this aim, the paper first summarizes Demetriou's theory of cognitive organization and growth. This theory assumes that the mind is a multistructural entity that develops across three fronts: the processing system that constrains processing potentials, a set of specialized structural systems (SSSs) that guide processing within different reality and knowledge domains, and a hypecognitive system that monitors and controls the functioning of all other systems. In the second part the paper focuses on the SSSs, which are the target of our logical analysis, and it summarizes a series of empirical studies demonstrating their autonomous operation. The third part develops the logical proof showing that each SSS involves a kernel element that cannot be reduced to standard logic or to any other SSS. The implications of this analysis for the general theory of knowledge and cognitive development are discussed in the concluding part of the paper

Power as Control and the Therapeutic Effects of Hegel’s Logic

Rather than approaching the question of the constructive or therapeutic character of Hegel’s Logic through a global consideration of its argument and its relation to the rest of Hegel’s system, I want to come at the question by considering a specific thread that runs through the argument of the Logic, namely the question of the proper understanding of power or control. What I want to try to show is that there is a close connection between therapeutic and constructive elements in Hegel’s treatment of power. To do so I will make use of two deep criticisms of Hegel’s treatment from Michael Theunissen. First comes Theunissen’s claim that in Hegel’s logical scheme, reality is necessarily dominated by the concept rather than truly reciprocally related to it. Then I will consider Theunissen’s structurally analogous claim that for Hegel, the power of the concept is the management of the suppression of the other. Both of these claims are essentially claims about the way in which elements of the logic of reflection are modified and yet continue to play a role in the logic of the concept

Rapid Visual Categorization is not Guided by Early Salience-Based Selection

The current dominant visual processing paradigm in both human and machine research is the feedforward, layered hierarchy of neural-like processing elements. Within this paradigm, visual saliency is seen by many to have a specific role, namely that of early selection. Early selection is thought to enable very fast visual performance by limiting processing to only the most salient candidate portions of an image. This strategy has led to a plethora of saliency algorithms that have indeed improved processing time efficiency in machine algorithms, which in turn have strengthened the suggestion that human vision also employs a similar early selection strategy. However, at least one set of critical tests of this idea has never been performed with respect to the role of early selection in human vision. How would the best of the current saliency models perform on the stimuli used by experimentalists who first provided evidence for this visual processing paradigm? Would the algorithms really provide correct candidate sub-images to enable fast categorization on those same images? Do humans really need this early selection for their impressive performance? Here, we report on a new series of tests of these questions whose results suggest that it is quite unlikely that such an early selection process has any role in human rapid visual categorization.Comment: 22 pages, 9 figure

Computational physics of the mind

In the XIX century and earlier such physicists as Newton, Mayer, Hooke, Helmholtz and Mach were actively engaged in the research on psychophysics, trying to relate psychological sensations to intensities of physical stimuli. Computational physics allows to simulate complex neural processes giving a chance to answer not only the original psychophysical questions but also to create models of mind. In this paper several approaches relevant to modeling of mind are outlined. Since direct modeling of the brain functions is rather limited due to the complexity of such models a number of approximations is introduced. The path from the brain, or computational neurosciences, to the mind, or cognitive sciences, is sketched, with emphasis on higher cognitive functions such as memory and consciousness. No fundamental problems in understanding of the mind seem to arise. From computational point of view realistic models require massively parallel architectures