Exploring the landscapes of "computing": digital, neuromorphic, unconventional -- and beyond

The acceleration race of digital computing technologies seems to be steering toward impasses -- technological, economical and environmental -- a condition that has spurred research efforts in alternative, "neuromorphic" (brain-like) computing technologies. Furthermore, since decades the idea of exploiting nonlinear physical phenomena "directly" for non-digital computing has been explored under names like "unconventional computing", "natural computing", "physical computing", or "in-materio computing". This has been taking place in niches which are small compared to other sectors of computer science. In this paper I stake out the grounds of how a general concept of "computing" can be developed which comprises digital, neuromorphic, unconventional and possible future "computing" paradigms. The main contribution of this paper is a wide-scope survey of existing formal conceptualizations of "computing". The survey inspects approaches rooted in three different kinds of background mathematics: discrete-symbolic formalisms, probabilistic modeling, and dynamical-systems oriented views. It turns out that different choices of background mathematics lead to decisively different understandings of what "computing" is. Across all of this diversity, a unifying coordinate system for theorizing about "computing" can be distilled. Within these coordinates I locate anchor points for a foundational formal theory of a future computing-engineering discipline that includes, but will reach beyond, digital and neuromorphic computing.Comment: An extended and carefully revised version of this manuscript has now (March 2021) been published as "Toward a generalized theory comprising digital, neuromorphic, and unconventional computing" in the new open-access journal Neuromorphic Computing and Engineerin

Natural language acquisition and rhetoric in artificial intelligence

During the 1980s, artificial intelligence research started to undergo a quiet, but important shift in focus from research in computer science to research in the human sciences and humanities. Though in the past, artificial intelligence has primarily been researched by computer scientists, the need for input from the human sciences has invited a great amount of cross-disciplinary work by members of many different callings. Rarely do people start out in the field of artificial intelligence; rather, the dream of building an intelligent machine infects them as they see the parallels between their work and the projects being undertaken in artificial intelligence. Because artificial intelligence is, in essence, studying the qualities of humanness, few disciplines can avoid somehow being tied in

Information enforcement in learning with graphics : improving syllogistic reasoning skills

This thesis is an investigation into the factors that contribute to good choices among graphical systems used in teaching, and the feasibility of implementing teaching software that uses this knowledge.The thesis describes a mathematical metric derived from a cognitive theory of human diagram processing. The theory characterises differences among representations by their ability to express information. The theory provides the factors and relationships needed to build the metric. It says that good representations are easily processed because they are more vivid, more tractable and less expressive, than poor representations.The metric is applied to abstract systems for teaching and learning syllogistic reasoning, TARSKI'S WORLD, EULER CIRCLES, VENN DIAGRAMS and CARROLL'S GAME OF LOGIC. A rank ordering reflects the value of each system predicted by the theory and the metric. The theory, the metric and the systems are then tested in empirical studies. Five studies involving sixty-eight learners, examined the benefit of software based on these abstract systems.Studies showed the theory correctly predicted learners' success with the circle systems and poorer performance with TARSKI'S WORLD. The metric showed small but clear differences in expressivity between the circle systems. Differences between results of the learners using the circle systems contradicted the predictions of the metric.Learners with mathematical training were better equipped and more successful at learning syllogistic reasoning with the systems. Performance of learners without mathematical training declined after using the software systems. Diagrams drawn by learners together with video footage collected during problem solving, led to a catalogue of errors, misconceptions and some helpful strategies for learning from graphical systems.A cognitive style test investigated the poor performance of non-mathematically trained learners. Learners with mathematics training showed serialist and versatile learning styles while learners without this training showed a holist learning style. This is consistent with the hypothesis that non-mathematically trained learners emphasise the use of semantic cues during learning and problem solving.A card-sorting task investigated learners' preferences for parts of the graphical lexicon used in the diagram systems. Preferences for the EULER lexicon increased difficulty in explaining the system's poor results in earlier studies. Video footage of learners using the systems in the final study illustrated useful learning strategies and improved performance with EULER while individual instruction was available.Further work describes a preliminary design for an adaptive syllogism tutor and other related work

An Electroencephalogram Investigation of Two Modes of Reasoning

The use of electroencephalography (EEG) to exam the electrical brain activity associated with reasoning provides an opportunity to quantify the functional and temporal aspects of this uniquely human capability, and at the same time expand our knowledge about what a given event-related potential (ERP) might measure. The question of what form of mental representation and transformational processes underlie human reasoning has been a central theme in cognitive psychology since its inception (Chomsky, 1957; McCarthy, 1955; Miller, 1956; Newell, Shaw, Simon, 1958). Two prominent, but competing views remain at the forefront of the discussion, one positing that human inference making is principally syntactic (Braine & O'Brien, 1998; Fodor, 1975; Pylyshyn, 1984; Rips, 1994), and the other that it is, fundamentally, semantic in nature (Gentner & Stevens, 1983; Johnson-Laird, 1983). The purpose of the proposed study is to investigate the neurophysiology of mental model (MM) and mental rule (MR) reasoning using high-density electroencephalography (EEG), with the goal of providing a characterization of the time course and a general estimate of the spatial dimensions of the brain activations correlated with these specific instances of two classic views of reasoning. The research is motivated by two questions: 1) Will violations of expectancy established by the devised MM and MR reasoning strategies evoke the N400 and P600 ERPs, respectively, and 2) Will topographical scalp distributions associated with each reasoning strategy suggest distinct psychological representations and processes? A finding of a N400 response in the MM condition suggests that reasoning about the relations between entities in the type of problems presented engages a network of cortical areas previously shown to be involved in processing violations of semantic expectancies in studies of language comprehension. By comparison, incongruent events in the MR condition are expected to evoke a bilateral anterior P600, a component previously associated with recognizing and restructuring syntactic anomalies or incongruities in sentence comprehension. If the hypothesized results are obtained they would provide potentially insightful information about the chronometry of mental processes associated with the different representations and inference making mechanisms postulated to support each mode of reasoning, and as well, broaden our understanding of the neural functionality associated with the N400 and P600 ERP

Human reasoning and cognitive science

In the late summer of 1998, the authors, a cognitive scientist and a logician, started talking about the relevance of modern mathematical logic to the study of human reasoning, and we have been talking ever since. This book is an interim report of that conversation. It argues that results such as those on the Wason selection task, purportedly showing the irrelevance of formal logic to actual human reasoning, have been widely misinterpreted, mainly because the picture of logic current in psychology and cognitive science is completely mistaken. We aim to give the reader a more accurate picture of mathematical logic and, in doing so, hope to show that logic, properly conceived, is still a very helpful tool in cognitive science. The main thrust of the book is therefore constructive. We give a number of examples in which logical theorizing helps in understanding and modeling observed behavior in reasoning tasks, deviations of that behavior in a psychiatric disorder (autism), and even the roots of that behavior in the evolution of the brain

Operational research from Taylorism to Terabytes: a research agenda for the analytics age

The growing attention and prominence afforded to analytics presents a genuine challenge for the operational research community. Many in the community have recognised this growth and sought to align themselves with analytics. For instance, the US operational research society INFORMS now offers analytics related conferences, certification and a magazine. However, as shown in this research, the volume of analytics-orientated studies in journals associated with operational research is comparatively low. This paper seeks to address this paradox by seeking to better understand what analytics is, and how operational research is related to it. To do so literature from a range of academic disciplines is analysed, in what is conceived as concurrent histories in the shared tradition of a management paradigm spread over the last 100 years. The findings of this analysis reveal new insights as to how operational research exists within an ecosystem shared with several other disciplines, and how interactions and ripple effects diffuse knowledge and ideas between each. Whilst this ecosystem is developed and evolved through interdisciplinary collaborations, individual disciplines are cast into competition for the attention of the same business users. These findings are further explored by discussing the implication this has for operational research, as well as considering what directions future research may take to maximise the potential value of these relationships

Mediated Cognition: Information Technologies and the Sciences of Mind

This dissertation investigates the interconnections between minds, media, and the cognitive sciences. It asks what it means for media to have effects upon the mind: do our tools influence the ways that we think? It considers what scientific evidence can be brought to bear on the question: how can we know and measure these effects? Ultimately, it looks to the looping pathways by which science employs technological media in understanding the mind, and the public comes to understand and respond to these scientific discourses. I contend that like human cognition itself, the enterprise of cognitive science is a deeply and distinctively mediated phenomenon. This casts a different light on contemporary debates about whether television, computers, or the Internet are changing our brains, for better or for worse. Rather than imagining media effects as befalling a fictive natural mind, I draw on multiple disciplines to situate mind and the sciences thereof as shaped from their origins through interaction with technology. Our task is then to interrogate the forms of cognition and attention fostered by different media, alongside their attendant costs and benefits. The first chapter positions this dissertation between the fields of media studies and STS, developing a case for the reality of media effects without the implication of technological determinism. The second considers the history of technological metaphor in scientific characterizations of the mind. The third section consists of three separate chapters on the history of cognitive science, presenting the core of my case for its uniquely mediated character. Across three distinct eras, what unifies cognitive science is the quest to understand the mind using computational systems, operating by turns as generative metaphors and tangible models. I then evaluate the contemporary cognitive-scientific research on the question of media effects, and the growing role of electronic media in science. My fifth and final section develops a content analysis: what is said in the media about the popular theory that media themselves, in one way or another, are causing attention deficit disorders? The work concludes with a summary and some reflections on mind, culture, technoscience and markets as recursively interwoven causal systems