What am I? Virtual Machines and the Mind/Body Problem

When your word processor or email program is running on your computer, this creates a "virtual machine” that manipulates windows, files, text, etc. What is this virtual machine, and what are the virtual objects it manipulates? Many standard arguments in the philosophy of mind have exact analogues for virtual machines and virtual objects, but we do not want to draw the wild metaphysical conclusions that have sometimes tempted philosophers in the philosophy of mind. A computer file is not made of epiphenomenal ectoplasm. I argue instead that virtual objects are "supervenient objects". The stereotypical example of supervenient objects is the statue and the lump of clay. To this end I propose a theory of supervenient objects. Then I turn to persons and mental states. I argue that my mental states are virtual states of a cognitive virtual machine implemented on my body, and a person is a supervenient object supervening on his cognitive virtual machine

Scientific requirements for an engineered model of consciousness

The building of a non-natural conscious system requires more than the design of physical or virtual machines with intuitively conceived abilities, philosophically elucidated architecture or hardware homologous to an animal’s brain. Human society might one day treat a type of robot or computing system as an artificial person. Yet that would not answer scientific questions about the machine’s consciousness or otherwise. Indeed, empirical tests for consciousness are impossible because no such entity is denoted within the theoretical structure of the science of mind, i.e. psychology. However, contemporary experimental psychology can identify if a specific mental process is conscious in particular circumstances, by theory-based interpretation of the overt performance of human beings. Thus, if we are to build a conscious machine, the artificial systems must be used as a test-bed for theory developed from the existing science that distinguishes conscious from non-conscious causation in natural systems. Only such a rich and realistic account of hypothetical processes accounting for observed input/output relationships can establish whether or not an engineered system is a model of consciousness. It follows that any research project on machine consciousness needs a programme of psychological experiments on the demonstration systems and that the programme should be designed to deliver a fully detailed scientific theory of the type of artificial mind being developed – a Psychology of that Machine

Understanding Intention for Machine Theory of Mind: A Position Paper

Theory of Mind is often characterized as the ability to recognize desires, beliefs, and intentions of others. In this position paper, I look at the literature on modeling Theory of Mind in machines and find that, to date, intention is not usually a focus. I define what I mean by intention—choice with commitment—following prior work. Intention has a long history of research in some communities, and I offer one theoretical framework for modeling intention as a starting point. I take inspiration from how children learn intention through joint attention with others and how that leads to Theory of Mind. I argue that though models of machine Theory of Mind need not follow the same learning progression as children, intention is an aspect of Theory of Mind that should be more explicit

Editorial:Fostering Creative Organizations: Antecedents, Processes, and Consequences of Individual and Team Creativity

Creativity, defined as the ability or process to generate novel and useful ideas, is the key engine of organizational innovation. It is a critical differentiator of mind from machine, a core driver of our uniqueness in an increasingly automated world. An important question, then, is how best to improve people's creativity, to enhance our collective innovativeness, enjoyment, and global living standards. Despite ample attention—including theory- and application-focused research—to this question, there remains much to learn about creativity..

Cognitive Science and Psychology

The protocol algorithm abstracted from a human cognizer's own narrative in the course of doing a cognitive task is an explanation of the corresponding mental activity in Pylyshyn's (1984) virtual machine model of mind. Strong equivalence between an analytic algorithm and the protocol algorithm is an index of validity of the explanatory model. Cognitive psychologists may not find the index strong equivalence useful as a means to ensure that a theory is not circular because (a) research data are also used as foundation data, (b) there is no justification for the relationship between a to﷓be﷓validated theory and its criterion of validity, and (c) foundation data, validation criterion and to﷓be﷓validated theory are not independent in cognitive science. There is also the difficulty with not knowing what psychological primitives are

A Formal Analysis of the Concept of Behavioral Individuation of Mental States in the Functionalist Framework

The functionalist theory of mind proposes to analyze mental states in terms of internal states of Turing machine, and states of the machine’s tape and head. In the paper, I perform a formal analysis of this approach. I define the concepts of behavioral equivalence of Turing machines, and of behavioral individuation of internal states. I prove a theorem saying that for every Turing machine T there exists a Turing machine T’ which is behaviorally equivalent to T, and all of whose internal states of T’ can be behaviorally individuated. Finally, I discuss some applications of this theorem to computational theories of mind

Machine art or machine artists? Dennett, Danto, and the expressive stance

As art produced by autonomous machines becomes increasingly common, and as such machines grow increasingly sophisticated, we risk a confusion between art produced by a person but mediated by a machine, and art produced by what might be legitimately considered a machine artist. This distinction will be examined here. In particular, my argument seeks to close a gap between, on one hand, a philosophically grounded theory of art and, on the other hand, theories concerned with behavior, intentionality, expression, and creativity in natural and artificial agents. This latter set of theories in some cases addresses creative behavior in relation to visual art, music, and literature, in the frequently overlapping contexts of philosophy of mind, artificial intelligence, and cognitive science. However, research in these areas does not typically address problems in the philosophy of art as a central line of inquiry. Similarly, the philosophy of art does not typically address issues pertaining to artificial agents

Misconceptions and the Notional Machine in Very Young Programming Learners

This study looks at very young learners make mistakes and possibly form misunderstanding when learning to programming. A variety of national efforts are extending programming education to younger learners who are materials many adults struggle to learn. For decades literature has captured common misconceptions in using programming constructs (e.g. conditionals, loops, and recursion) in older learners, but early learners may wait years before they tackle these complex concepts. Many model misconceptions as a missing or inaccurate notional machine. The notional machine is an individual’s mental model, representing how a programming language executes on a real device. The notional machine aligns with traditional learning models from several educational theorists, particularly Bruner’s three stages of representations and Kahneman’s neuroscience-based modeling of the mind. To better understand the early thought process of and learning theory for teaching novices, this study looks at videos of early elementary students working to create basic navigational programs for simple robots. We observed students in K-2 and categorized the mistakes made and strategies used to achieve their goals. Our findings align with prior misconception literature in very young learners around the ‘problem’ being the source of more misconceptions than the language. We also find promising cases which support learning theory around the notional machine, Bruner’s representations and Kahneman’s two mind model. Using this theory suggests possible approaches to consider in teaching young learners to program