A “conceptual spaces” approach is used to formalize Aristotle’s main intuitions about time and change, and other ideas about temporal points of view. That approach has been used in earlier studies about points of view. Properties of entities are represented by locations in multidimensional conceptual spaces; and concepts of entities are identified with subsets or regions of conceptual spaces. The dimensions of the spaces, called “determinables”, are qualities in a very general sense. A temporal element is introduced by adding a (...) time variable to state functions that map entities into conceptual spaces. That way, states may have some permanency or stability around time instances. Following Aristotle’s intuitions, changes and events will not be necessarily instant phenomena, instead they could be processual and interval dependent. Change is defined relatively to the interval during which the change is taking place. Time intervals themselves are taken to represent points of view. To have a point of view is to look at the world as it is in the selected interval. Many important concepts are relativized to intervals, for instance change, events, identity, ontology, potentiality, etc. The definition of points of view as intervals allows to compare points of view in relation to all these concepts. The conceptual space approach has an immediate semantic and structural character, but it is tempting to develop also logics to describe them. A formal language is introduced to show how this could be done. (shrink)

One of the best known arguments against the connectionist approach to artificial intelligence and cognitive science is that neural networks are black boxes, i.e., there is no understandable account of their operation. This difficulty has impeded efforts to explain how categories arise from raw sensory data. Moreover, it has complicated investigation about the role of symbols and language in cognition. This state of things has been radically changed by recent experimental findings in artificial deep learning research. Two kinds of artificial (...) deep learning networks, namely the convolutional neural network and the generative adversarial network have been found to possess the capability to build internal states that are interpreted by humans as complex visual categories, without any specific hints or any grammatical processing. This emergent ability suggests that those categories do not depend on human knowledge or the syntactic structure of language, while they do rely on their visual context. This supports a mild form of empiricism, while it does not assume that computational functionalism is true. Some consequences are extracted regarding the debate about amodal and grounded representations in the human brain. Furthermore, new avenues for research on cognitive science are open. (shrink)

Points of view are a central phenomenon in human cognition. Although the concept of point of view is ambiguous, there exist common elements in different notions. A point of view is a certain way to look at things around us. In conceptual points of view, things are looked at or interpreted through conceptual lenses. Conceptual points of view are important for epistemology, cognitive science, and philosophy of science. In this article, a new method to formalize conceptual points of view is (...) introduced. It is based on the conceptual space approach, where concepts are regions of multi-dimensional quality spaces. Points of view, as defined in this article, consist of a selection of relevant dimensions, referred to here as determinables, and of a certain supposition, referred to here as a theory, about the subject content. After considering some early efforts to formalize points of view, the notion of conceptual space is defined and explored. One concept of point of view is defined and developed in the framework of conceptual space, and a new logic for points of view is also outlined. The problem of mind–body correlation is discussed as an example of the application of the notion of points of view. To conclude, the meaning and applications of the new concepts and tools developed in the article are discussed. (shrink)

Concepts are the building blocks of higher-order cognition and consciousness. Building on Conceptual Spaces Theory (CST) and proceeding from the assumption that concepts are inherently dynamic, this paper provides historical context to and significantly elaborates the previously offered Iterative Subdivision Model (ISDM) with the goal of pushing it toward empirical testability. The paper describes how agents in continuous interaction with their environment adopt an intentional orientation, estimate the utility of the concept(s) applicable to action in the current context, engage in (...) practical action, and adopt any new concepts that emerge: a largely pre-intellectual cycle that repeats essentially without interruption over the conceptual agent’s lifetime. This paper elaborates utility optimization by establishing three constraints on concept formation/evaluation – non-redundancy, distinctiveness, and proportionality – embedding them in a quasi-mathematical model intended for development into a formal logic. The notion of a distinctor – a quality dimension of the conceptual space in focus at any given time, used for making what we call a difference distinction – is key. The primary contribution of the revised ISDM is the way it relates concepts to action via utility optimization/actualization and the way it describes the emergence of quality dimensions through trial-by-action (trial and error), something previous presentations of CST have failed to address. (shrink)