Scientific theories of consciousness identify its contents with the spatiotemporal structure of neural population activity. We follow up on this approach by stating and motivating Dynamical Emergence Theory, which defines the amount and structure of experience in terms of the intrinsic topology and geometry of a physical system’s collective dynamics. Specifically, we posit that distinct perceptual states correspond to coarse-grained macrostates reflecting an optimal partitioning of the system’s state space—a notion that aligns with several ideas and results from computational neuroscience (...) and cognitive psychology. We relate DET to existing work, offer predictions for empirical studies, and outline future research directions. (shrink)

Simple idealized models seem to provide more understanding than opaque, complex, and hyper-realistic models. However, an increasing number of scientists are going in the opposite direction by utilizing opaque machine learning models to make predictions and draw inferences, suggesting that scientists are opting for models that have less potential for understanding. Are scientists trading understanding for some other epistemic or pragmatic good when they choose a machine learning model? Or are the assumptions behind why minimal models provide understanding misguided? In (...) this paper, using the case of deep neural networks, I argue that it is not the complexity or black box nature of a model that limits how much understanding the model provides. Instead, it is a lack of scientific and empirical evidence supporting the link that connects a model to the target phenomenon that primarily prohibits understanding. (shrink)

In Rules and Representations, first published in 1980, Noam Chomsky lays out many of the concepts that have made his approach to linguistics and human cognition so instrumental to our understanding of language.Chomsky arrives at his well-known position that there is a universal grammar, structured in the human mind and common to all human languages. Based on Chomsky's 1978 Woodbridge Lectures, this edition contains revised versions of the lectures and two new essays.

Emotions play a significant role in human relations, decision-making, and the motivation to act on those decisions. There are ongoing attempts to use artificial intelligence (AI) to read human emotions, and to predict human behavior or actions that may follow those emotions. However, a person’s emotions cannot be easily identified, measured, and evaluated by others, including automated machines and algorithms run by AI. The ethics of emotional AI is under research and this study has examined the emotional variables as well (...) as the perception of emotional AI in two large random groups of college students in an international university in Japan, with a heavy representation of Japanese, Indonesian, Korean, Chinese, Thai, Vietnamese, and other Asian nationalities. Surveys with multiple close-ended questions and an open-ended essay question regarding emotional AI were administered for quantitative and qualitative analysis, respectively. The results demonstrate how ethically questionable results may be obtained through affective computing and by searching for correlations in a variety of factors in collected data to classify individuals into certain categories and thus aggravate bias and discrimination. Nevertheless, the qualitative study of students’ essays shows a rather optimistic view over the use of emotional AI, which helps underscore the need to increase awareness about the ethical pitfalls of AI technologies in the complex field of human emotions. (shrink)

Social robots are designed to perform intelligent, emotional, and autonomous behavior in order to establish intimate relationships with humans, for instance, in the context of elderly care. However, the imitation of qualities usually assumed to be necessary for human reciprocal interaction may impact our understanding of social interaction. Against this background, we compare the technical operations based on which social robots imitate human-like behavior with the concepts of emotionality, intelligence, and autonomy as usually attached to humans. In doing so, we (...) aim at making a conceptual contribution to the intuition that the technical imitation of emotional, intelligent, and autonomous behavior differs qualitatively from human forms of emotionality, intelligence, and autonomy. We conclude that the increasing tendency of social robots to imitate human characteristics and the thus increased human tendency of anthropomorphizing technical systems put forth new forms of human–machine interactions (HMI) that call for a critical examination. Thus, the emerging forms of interaction may lead, for instance, to false, reinforced, and perplexing assumptions regarding reciprocity. Therefore, studies are needed that critically examine the potential consequences and psychological effects that may be associated with the use of social robots, especially with regard to vulnerable groups such as children or the elderly. (shrink)

The book from which these sections are excerpted is concerned with the prospects for assimilating the study of human intelligence and its products to the natural sciences through the investigation of cognitive structures, understood as systems of rules and representations that can be regarded as “mental organs.” These mental structui′es serve as the vehicles for the exercise of various capacities. They develop in the mind on the basis of an innate endowment that permits the growth of rich and highly articulated (...) structures along an intrinsically determined course under the triggering and partially shaping effect of experience, which fixes parameters in an intricate system of predetermined form. It is argued that the mind is modular in character, with a diversity of cognitive structures, each with its specific properties arid principles. Knowledge of language, of the behavior of objects, and much else crucially involves these mental structures, and is thus not characterizable in terms of capacities, dispositions, or practical abilities, nor is it necessarily grounded in experience in the standard sense of this term.Various types of knowledge and modes of knowledge acquisition are discussed in these terms. Some of the properties of the language faculty are investigated. The basic cognitive relation is “knowing a grammar”; knowledge of language is derivative and, correspondingly, raises further problems. Language as commonly understood is not a unitary phenomenon but involves a number of interacting systems: the “computational” system of grammar, which provides the representations of sound and meaning that permit a rich range of expressive potential, is distinct from a conceptual system with its own properties; knowledge of language must be distinguished from knowledge of how to use a language; and the various systems that enter into the knowledge and use of language must be further analyzed into their specific subcomponents. (shrink)

The article presents problems of intellectual intuition in metaphysics from a semiotic point of view. There are various types of intuition in philosophy: rational intuition, irrational intuition, and sensual intuition. All of them are immediate ways of cognition. Classical metaphysics uses intellectual intuition as its main method to find out and justify its statements. The main problem of intellectual intuition is an intersubjective approach to the object of metaphysics. The main aim of this paper is the objectivization and rationalization of (...) intellectual intuition in language. The semantic notion of meaning and the pragmatic notion of understanding are the fundamental tools which are used to translate the issue of intuition from the subject-ma$er level into the language level. This operation allows to look at intuition in a non-psychological manner. It enables the objectivization of the method of intellectual intuition in the light of the understanding of meanings. (shrink)