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Abstract
Abstract. The idea that language mediates our thoughts and enables abstract cognition has been a key idea in socio-cultural psychology. However, it is not clear what mechanisms support this process of abstraction. Peirce argued that one mechanism by which language enables abstract thought is hypostatic abstraction, the process through which a predicate (e.g., dark) turns into an object (e.g., darkness). By using novel computational tools we tested Peirce’s idea. Analysis of the data provides empirical support for Peirce's mechanism and evidence of the way the use of signs enables abstraction. These conclusions are supported by the in-depth analysis of two case studies concerning the abstraction of sweet and dark. The paper concludes by discussing the findings from a broad and integrative theoretical perspective and by pointing to computational cultural psychology as a promising perspective for addressing long-lasting questions of the field.
The idea that abstract words are grounded in our sensorimotor experience is gaining support and popularity, as observed in the increasing number of studies dealing with "neurosemantics." Therefore, it is important to form models that explain how to bridge the gap between basic bodily experiences and abstract language. This paper focuses on the embodiment of connotations, such as 'sweet' in 'sweet baby', where the adjective has been abstracted from its concrete and embodied sense. We summarize several findings from recent studies in neuroscience and the cognitive sciences suggesting that emotion, body, and language are three factors required for understanding the emergence of abstract words, and (1) propose a model explaining how these factors contribute to the emergence of connotations, (2) formulate a computational model instantiating our theoretical model and (3) test our model in a task involving the automatic identification of connotations. The results support our model pointing to the role of embodiment in the formation of connotations.
Educational Studies in Mathematics
Diagrammatic Reasoning As the Basis for Developing Concepts: A Semiotic Analysis of Students2005 •
Proceedings of the …
Literal and metaphorical sense identification through concrete and abstract context2011 •
Abstract: Metaphor is ubiquitous in text, even in highly technical text. Correct inference about textual entailment requires computers to distinguish the literal and metaphorical senses of a word. Past work has treated this problem as a classical word sense disambiguation task. In this paper, we take a new approach, based on research in cognitive linguistics that views metaphor as a method for transferring knowledge from a familiar, well-understood, or concrete domain to an unfamiliar, less understood, or more abstract domain. This view ...
In recent years, semiotics has become an innovative theoretical framework in mathematics education. The purpose of this article is to show that semiotics can be used to explain learning as a process of experimenting with and communicating about one's own representations (in particular ‘diagrams') of mathematical problems. As a paradigmatic example, we apply a Peircean semiotic framework to answer the question of how students develop a notion of ‘distribution' in a statistics course by ‘diagrammatic reasoning' and by forming ‘hypostatic abstractions', that is by forming new mathematical objects which can be used as means for communication and further reasoning. Peirce's semiotic terminology is used as an alternative to concepts such as modeling, symbolizing, and reification. We will show that it is a precise instrument of analysis with regard to the complexity of learning and communicating in mathematics classrooms.
Mentalization describes the process through which we understand the mental states of oneself and others. In this paper, I present a computational semiotic model of mentalization and illustrate it through a worked-out example. The model draws on classical semiotic ideas, such as abductive inference and hypostatic abstraction, but pour them into new ideas and tools from natural language processing, machine learning and neural networks, to form a novel model of language-mediated-mentalization.
2007 •
Teaching Philosophy
Review of A General Introduction to the Semeiotic of Charles Sanders Peirce1998 •

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