Embodied cognition: assumptions, theses and challenges: The paper aims at providing a concise presentation of the concept of embodied cognition that emerged in the cognitive sciences a few decades ago and has gained great popularity among empirically and philosophically informed researchers. The term “embodied cognition” is used by the author in two senses. The narrow sense implies that the body plays an important role in the process of cognition. In the broad sense “embodied cognition” is to characterize the general tendency (...) within cognitive science which finds its articulation in the 4E perspective on cognition. The working hypothesis of the 4E perspective is that cognition depends on the characteristics of the agent’s body and its interaction with the physical and social environment. It emphasizes that cognition is: embodied, embedded, enacted, extended. After reconstructing the key concepts and basic assumptions the author offers a brief appraisal of the views under discussion. He claims that the characteristics of such a trend are incomplete and not homogeneity since the perspective encompasses at least a few related and partly overlapping views on cognition. The author concludes that “embodied cognition” serves as a label for a variety of research programs within cognitive science rather than a strictly defined and well-established research tradition or a new paradigm. (shrink)

Systems based on symbolic knowledge have performed extremely well in processing reason, yet, remain beset with problems of brittleness in many domains. Connectionist approaches do similarly well in emulating interactive domains, however, have struggled when modelling higher brain functions. Neither of these dichotomous approaches, however, have provided many inroads into the area of human reasoning that psychology and sociology refer to as the process of practice. This paper argues that the absence of a model for the process of practise in (...) current approaches is a significant contributor to brittleness. This paper will investigate how the process of practise relates to deeper forms of contextual representations of knowledge. While researchers and developers of knowledge based systems have often incorporated the notion of context they treat context as a static entity, neglecting many connectionists’ work in learning hidden and dynamic contexts. This paper argues that the omission of these higher forms of context is one of the fundamental problems in the application and interpretation of symbolic knowledge. Finally, these ideas for modelling context will lead to the reinterpretation of situation cognition which makes a significant step towards a philosophy of knowledge that could lead to the modelling of the process of practice. (shrink)

In the twentieth century, no person epitomized more dramatically the “Renaissance mind” than Herbert A. Simon (1916–2001). In aworking life spanning over 60 years, Simon made seminal contributions to administrative theory, axiomatic foundations of physics, economics, sociology, econometrics, cognitive psychology, logic of scientific discovery, and artificial intelligence. Simon's life of the mind, thus, affords nothing less than a “laboratory” in which to observe and examine at close quarters the phenomenon ofmultidisciplinary creativity. In this paper, we attempt to shed some light (...) on the nature of Simon's creativity and the nature of his particular Renaissance mind. In particular, we have attempted here to articulate thecognitive styleunderlying Simon's multidisciplinary creativity. (shrink)

It is not widely realised that Turing was probably the first person to consider building computing machines out of simple, neuron-like elements connected together into networks in a largely random manner. Turing called his networks unorganised machines. By the application of what he described as appropriate interference, mimicking education an unorganised machine can be trained to perform any task that a Turing machine can carry out, provided the number of neurons is sufficient. Turing proposed simulating both the behaviour of the (...) network and the training process by means of a computer program. We outline Turing's connectionist project of 1948. (shrink)

The paper is an examination of the ways and extent to which neuroscience places constraints on cognitive science. In Part I, I clarify the issue, as well as the notion of levels in cognitive inquiry. I then present and address, in Part II, two arguments designed to show that facts from neuroscience are at a level too low to constrain cognitive theory in any important sense. I argue, to the contrary, that there are several respects in which facts from neurophysiology (...) will constrain cognitive theory. Part III then turns to an examination of Connectionism and Classical Cognitivism to determine which, if either, is in a better position to accomodate neural constraints in the ways suggested in Part II. (shrink)

The last 5 years have seen a series of remarkable achievements in deep-neural-network-based artificial intelligence research, and some modellers have argued that their performance compares favourably to human cognition. Critics, however, have argued that processing in deep neural networks is unlike human cognition for four reasons: they are (i) data-hungry, (ii) brittle, and (iii) inscrutable black boxes that merely (iv) reward-hack rather than learn real solutions to problems. This article rebuts these criticisms by exposing comparative bias within them, in the (...) process extracting some more general lessons that may also be useful for future debates. (shrink)

Research on computational models of scientific discovery investigates both the induction of descriptive laws and the construction of explanatory models. Although the work in law discovery centers on knowledge‐lean approaches to searching a problem space, research on deeper modeling tasks emphasizes the pivotal role of domain knowledge. As an example, our own research on inductive process modeling uses information about candidate processes to explain why variables change over time. However, our experience with IPM, an artificial intelligence system that implements this (...) approach, suggests that process knowledge is insufficient to avoid consideration of implausible models. To this end, the discovery system needs additional knowledge that constrains the model structures. We report on an extended system, SC‐IPM, that uses such information to reduce its search through the space of candidates and to produce models that human scientists find more plausible. We also argue that although people carry out less extensive search than SC‐IPM, they rely on the same forms of knowledge—processes and constraints—when constructing explanatory models. (shrink)

This paper analyzes epistemological and ontological dimensions of Human-Computer Interaction (HCI) through an analysis of the functions of computer systems in relation to their users. It is argued that the primary relation between humans and computer systems has historically been epistemic: computers are used as information-processing and problem-solving tools that extend human cognition, thereby creating hybrid cognitive systems consisting of a human processor and an artificial processor that process information in tandem. In this role, computer systems extend human cognition. Next, (...) it is argued that in recent years, the epistemic relation between humans and computers has been supplemented by an ontic relation. Current computer systems are able to simulate virtual and social environments that extend the interactive possibilities found in the physical environment. This type of relationship is primarily ontic, and extends to objects and places that have a virtual ontology. Increasingly, computers are not just information devices, but portals to worlds that we inhabit. The aforementioned epistemic and ontic relationships are unique to information technology and distinguish human-computer relationships from other human-technology relationships. (shrink)

This paper argues that the idea of a computer is unique. Calculators and analog computers are not different ideas about computers, and nature does not compute by itself. Computers, once clearly defined in all their terms and mechanisms, rather than enumerated by behavioral examples, can be more than instrumental tools in science, and more than source of analogies and taxonomies in philosophy. They can help us understand semantic content and its relation to form. This can be achieved because they have (...) the potential to do more than calculators, which are computers that are designed not to learn. Today’s computers are not designed to learn; rather, they are designed to support learning; therefore, any theory of content tested by computers that currently exist must be of an empirical, rather than a formal nature. If they are designed someday to learn, we will see a change in roles, requiring an empirical theory about the Turing architecture’s content, using the primitives of learning machines. This way of thinking, which I call the intensional view of computers, avoids the problems of analogies between minds and computers. It focuses on the constitutive properties of computers, such as showing clearly how they can help us avoid the infinite regress in interpretation, and how we can clarify the terms of the suggested mechanisms to facilitate a useful debate. Within the intensional view, syntax and content in the context of computers become two ends of physically realizing correspondence problems in various domains. (shrink)

We outline a framework of multilevel neurocognitive mechanisms that incorporates representation and computation. We argue that paradigmatic explanations in cognitive neuroscience fit this framework and thus that cognitive neuroscience constitutes a revolutionary break from traditional cognitive science. Whereas traditional cognitive scientific explanations were supposed to be distinct and autonomous from mechanistic explanations, neurocognitive explanations aim to be mechanistic through and through. Neurocognitive explanations aim to integrate computational and representational functions and structures across multiple levels of organization in order to explain (...) cognition. To a large extent, practicing cognitive neuroscientists have already accepted this shift, but philosophical theory has not fully acknowledged and appreciated its significance. As a result, the explanatory framework underlying cognitive neuroscience has remained largely implicit. We explicate this framework and demonstrate its contrast with previous approaches. (shrink)

The notion of a ‘symbol’ plays an important role in the disciplines of Philosophy, Psychology, Computer Science, and Cognitive Science. However, there is comparatively little agreement on how this notion is to be understood, either between disciplines, or even within particular disciplines. This paper does not attempt to defend some putatively ‘correct’ version of the concept of a ‘symbol.’ Rather, some terminological conventions are suggested, some constraints are proposed and a taxonomy of the kinds of issue that give rise to (...) disagreement is articulated. The goal here is to provide something like a ‘geography’ of the various notions of ‘symbol’ that have appeared in the various literatures, so as to highlight the key issues and to permit the focusing of attention upon the important dimensions. In particular, the relationship between ‘tokens’ and ‘symbols’ is addressed. The issue of designation is discussed in some detail. The distinction between simple and complex symbols is clarified and an apparently necessary condition for a system to be potentially symbol, or token bearing, is introduced. (shrink)

Berkeley [Minds Machines 10 (2000) 1] described a methodology that showed the subsymbolic nature of an artificial neural network system that had been trained on a logic problem, originally described by Bechtel and Abrahamsen [Connectionism and the mind. Blackwells, Cambridge, MA, 1991]. It was also claimed in the conclusion of this paper that the evidence was suggestive that the network might, in fact, count as a symbolic system. Dawson and Piercey [Minds Machines 11 (2001) 197] took issue with this latter (...) claim. They described some lesioning studies that they argued showed that Berkeley’s (2000) conclusions were premature. In this paper, these lesioning studies are replicated and it is shown that the effects that Dawson and Piercey rely upon for their argument are merely an artifact of a threshold function they chose to employ. When a threshold function much closer to that deployed in the original studies is used, the significant effects disappear. (shrink)

Dual Process Theory is currently a popular theory for explaining why we show bounded rationality in reasoning and decision-making tasks. This theory proposes there must be a sharp distinction in thinking to explain two clusters of correlational features. One cluster describes a fast and intuitive process, while the other describes a slow and reflective one. A problem for this theory is identifying a common principle that binds these features together, explaining why they form a unity, the unity problem. To solve (...) it, a hypothesis is developed combining embodied predictive processing with symbolic classical approaches. The hypothesis, simplified, states that Type 1 processes are bound together because they rely on embodied predictive processing whereas Type 2 processes form a unity because they are accomplished by symbolic classical cognition. To show that this is likely the case, the features of Dual Process Theory are discussed in relation to these frameworks. (shrink)

There has been considerable debate in the literature about the relative merits of information processing versus dynamical approaches to understanding cognitive processes. In this article, we explore the relationship between these two styles of explanation using a model agent evolved to solve a relational categorization task. Specifically, we separately analyze the operation of this agent using the mathematical tools of information theory and dynamical systems theory. Information-theoretic analysis reveals how task-relevant information flows through the system to be combined into a (...) categorization decision. Dynamical analysis reveals the key geometrical and temporal interrelationships underlying the categorization decision. Finally, we propose a framework for directly relating these two different styles of explanation and discuss the possible implications of our analysis for some of the ongoing debates in cognitive science. (shrink)

Cognitive science is an interdisciplinary research endeavor focusing on human cognitive phenomena such as memory, language use, and reasoning. It emerged in the second half of the 20th century and is charting new directions at the beginning of the 21st century. This chapter begins by identifying the disciplines that contribute to cognitive science and reviewing the history of the interdisciplinary engagements that characterize it. The second section examines the role that mechanistic explanation plays in cognitive science, while the third focuses (...) on the importance of mental representations in specifically cognitive explanations. The fourth section considers the interdisciplinary nature of cognitive science and explores how multiple disciplines can contribute to explanations that exceed what any single discipline might accomplish. The conclusion sketches some recent developments in cognitive science and their implications for philosophers. (shrink)

This paper provides a glimpse of some different theoretical frameworks and empirical methods in the author's search for theories and practices that might improve the utility and usability of computer artifacts. The essay touches on some problematic aspects of currently accepted theories and techniques in the cognitive sciences, especially in their application to the field of human-computer interaction, and mentions some alternative conceptions based on a cultural-historical approach. The intent is to widen the nature of the debate about appropriate frameworks (...) for discussing human activities especially when we discuss design activities in the context of computer systems development. The paper concludes with some suggestions for more fruitful research directions that involve the active participation of those for whom the research is ostensibly being done, and a greater emphasis on understanding how artifact design and use are inextricably intertwined. (shrink)

Artificial intelligence (AI) has become nearly ubiquitous in modern society, from components of mobile applications to medical support systems, and everything in between. In societally impactful systems imbued with AI, there has been increasing concern related to opaque AI, that is, artificial intelligence where it is unclear how or why certain decisions are reached. This has led to a recent boom in research on “explainable AI” (XAI), or approaches to making AI more explainable and understandable to human users. In the (...) military domain, numerous bodies have argued that autonomous and AI-enabled weapon systems ought not incorporate unexplainable AI, with the International Committee of the Red Cross and the United States Department of Defense both explicitly including explainability as a relevant factor in the development and use of such systems. In this article, I present a cautiously critical assessment of this view, arguing that explainability will be irrelevant for many current and near-future autonomous systems in the military (which do not incorporate any AI), that it will be trivially incorporated into most military systems which do possess AI (as these generally possess simpler AI systems), and that for those systems with genuinely opaque AI, explainability will prove to be of more limited value than one might imagine. In particular, I argue that explainability, while indeed a virtue in design, is a virtue aimed primarily at designers and troubleshooters of AI-enabled systems, but is far less relevant for users and handlers actually deploying these systems. I further argue that human–machine teaming is a far more important element of responsibly using AI for military purposes, adding that explainability may undermine efforts to improve human–machine teamings by creating a prima facie sense that the AI, due to its explainability, may be utilized with little (or less) potential for mistakes. I conclude by clarifying that the arguments are not against XAI in the military, but are instead intended as a caution against over-inflating the value of XAI in this domain, or ignoring the limitations and potential pitfalls of this approach. (shrink)

In this paper we study the connection between the use of evaluative language and the building of both personal and social identities, from the perspective of Dynamical System Theory . We primarily discuss two issues: 1) The use of evaluation (in the sense given to the term by Alba-Juez and Thompson (forthcoming)) as a means to the construction of both individual and group identities, thus exploring how the connection between linguistic choices and social identities is shaped by interactional needs for (...) stancetaking. In order to illustrate this connection, we examine examples of the use of evaluative language in a web social network, and we analyze some of the discourse elements showing ways of positioning that act as catalysts for the emergence of a multifactorial dynamic system of identities. 2) The consideration of Dynamical System Theory (DST) as a theoretical framework for the modeling of language and identity. Although originally a mathematical theory, DST has been adopted by cognitive science as a valid framework for the study of cognitive phenomena, on the grounds that natural cognition is a dynamical phenomenon. Within the realm of (socio) linguistics and pragmatics, this study is to a certain degree in line with some recent studies such as Gibbs (2010), Geeraerts, Kristiansen and Peirsman (2010), or Moreno Fernández (2012). Thus, we herein focus on how linguistic evaluation intervenes in the intricate dynamical system of identity, and even though we do not engage in complex mathematical disquisition, we argue that the idea and philosophical foundation underlying DST can lead us towards the ‘integration’ of the complex equation of identity construction, and that consequently the field has great potential for further research. (shrink)

In this paper I argue that Turing’s responses to the mathematical objection are straightforward, despite recent claims to the contrary. I then go on to show that by understanding the importance of learning machines for Turing as related not to the mathematical objection, but to Lady Lovelace’s objection, we can better understand Turing’s response to Lady Lovelace’s objection. Finally, I argue that by understanding Turing’s responses to these objections more clearly, we discover a hitherto unrecognized, substantive thesis in his philosophical (...) thinking about the nature of mind. (shrink)

Modern computing is generally taken to consist primarily of symbol manipulation. But symbols are abstract, and computers are physical. How can a physical device manipulate abstract symbols? Neither Church nor Turing considered this question. My answer is that the bit, as a hardware-implemented abstract data type, serves as a bridge between materiality and abstraction. Computing also relies on three other primitive—but more straightforward—abstractions: Sequentiality, State, and Transition. These physically-implemented abstractions define the borderline between hardware and software and between physicality and (...) abstraction. At a deeper level, asking how a physical device can interact with abstract symbols is the wrong question. The relationship between symbols and physical devices begins with the realization that human beings already know what it means to manipulate symbols. We build and program computers to do what we understand to be symbol manipulation. To understand what that means, consider a light switch. A light switch doesn’t turn a light on or off. Those are abstractions. Light switches don’t operate with abstractions. We build light switches, so that when flipped, the world is changed in such a way that we understand the light to be on or off. Similarly, we build computers to perform operations that we understand as manipulating symbols. (shrink)

Over the past decades, due to the course towards digitalization of all areas of life, interest in modeling and creating intelligent systems has increased significantly. However, there are now a stagnation in the industry, a lack of attention to analog and bionic approaches as alternatives to digital, numerous speculations on “neuro” issues for commercial and other purposes, and an increase in social and environmental risks. The article provides an overview of the development of artificial intelligence (AI) conceptions toward increasing the (...) human likeness of machines: from the key ideas of A. Turing and J. von Neumann, who initiated the digitalization of society, to discussions about the definition of AI and the emergence of conceptions of strong and weak AI. Special attention is paid to the approach of A. Sloman, to ideas about the architecture and design of complex artificial systems are considered, which make it possible to “emotionally” expand the idea of weak/strong AI. In the article's section on the necessity and possibility of incorporating emotions into the architecture of AI, the authors reveal the goals and methodological limitations for creating an emotional artificial agent. In addition, the article briefly presents the main principles of the authors' architectural approach to the creation of emotional intellectual systems on the example of the cognitive-affective model of architecture, which allow modeling the impact of emotions on the cognitive processes involved in decision-making processes. The described architectural approach to modeling intelligent systems can be used as a conceptual basis for discussing and formulating a strategy for the development of neurocomputing, philosophy of artificial intelligence, and experimental philosophy, for developing innovative research programs, formulating and solving theoretical and methodological problems. (shrink)

(2013). Is scientific theory change similar to early cognitive development? Gopnik on science and childhood. Philosophical Psychology: Vol. 26, No. 1, pp. 109-128. doi: 10.1080/09515089.2011.625114.

A key failing in contemporary philosophy of mind is the lack of attention paid to evolutionary theory in its research projects. Notably, where evolution is incorporated into the study of mind, the work being done is often described as philosophy of cognitive science rather than philosophy of mind. Even then, whereas possible implications of the evolution of human cognition are taken more seriously within the cognitive sciences and the philosophy of cognitive science, its relevance for cognitive science has only been (...) appreciated relatively recently, and the approach still comes in for some major criticism from prominent theorists within the field. This paper explores some of the reasons for this state of affairs and finds that it might have less to do with due consideration and well-founded scepticism about the relevance of evolutionary theory to these disciplines and more to do with historical accident and faulty assumptions on the part of key theorists in these disciplines. It is also noted that where cognitive scientists are taking evolution into account in their work on the mind, they straying more and more into domains that used to fall exclusively under the purview of philosophy of mind as it is traditionally conceived – qualia, consciousness, perception, intentionality and so forth. The point is made that in ignoring the work being done on the evolution of mind, philosophy of mind runs the risk of becoming obsolete. (shrink)

To explain why, in scientific problem solving, a diagram can be “worth ten thousand words,” Jill Larkin and Herbert Simon (1987) relied on a computer model: two representations can be “informationally” equivalent but differ “computationally,” just as the same data can be encoded in a computer in multiple ways, more or less suited to different kinds of processing. The roots of this proposal lay in cognitive psychology, more precisely in the “imagery debate” of the 1970s on whether there are image-like (...) mental representations. Simon (1972, 1978) hoped to solve this debate by thoroughly reducing the differences between forms of mental representations (e.g., between images and sentences) to differences in computational efficiency; to carry out this reduction, he borrowed from computer science the concepts of data type and of data structure. I argue that, in the end, his account amounted to nothing more than characterizing representations by the fast operations on them. This analysis then allows me to assess what Simon’s approach actually achieves when transported from psychology to the study of scientific representations, as in Larkin and Simon (1987): it allows comparing, not representations in and of themselves, but rather the computational roles they play in particular problem-solving processes—that is, representations together with a particular way of using them. (shrink)

This chapter contains sections titled: The Scientific Legitimacy of Mentalism? Cognitive Architecture and Massive Modularity Embodied, Situated, and Extended Cognition Concepts Mindreading Conclusion and Future Directions References.

Clark and Chalmers propose that the mind extends further than skin and skull. If they are right, then we should expect this to have some effect on our way of knowing our own mental states. If the content of my notebook can be part of my belief system, then looking at the notebook seems to be a way to get to know my own beliefs. However, it is at least not obvious whether self-ascribing a belief by looking at my notebook (...) is a case of introspection the same way that knowing my non-extended beliefs is. Traditionally this sort of introspection is thought to be privileged and special in ways that the extended introspection case seems not to be. There is nothing privileged about looking at my notebook. Anyone could do it. The aim of the paper is to find out how to understand extended introspection and whether there is something privileged and special about knowing one’s own extended beliefs. Moreover, the notebook case has close analogs using twenty-first century technology. It seems possible to know our beliefs that are extended to smartphones, wearable technology or a cloud-based data store. First, I present the case of extended introspection. I then discuss whether it should be understood as ordinary introspection or as mind-reading. Both seem to be bad fits, which finally prompts an original account for extended introspection based on epistemic rules. (shrink)

This chapter describes the conceptual foundations of cognitive science during its establishment as a science in the 20th century. It is organized around the core ideas of individual agency as its basic explanans and information-processing as its basic explanandum. The latter consists of a package of ideas that provide a mathematico-engineering framework for the philosophical theory of materialism.

Book Description (Blurb): Cognitive Design for Artificial Minds explains the crucial role that human cognition research plays in the design and realization of artificial intelligence systems, illustrating the steps necessary for the design of artificial models of cognition. It bridges the gap between the theoretical, experimental and technological issues addressed in the context of AI of cognitive inspiration and computational cognitive science. -/- Beginning with an overview of the historical, methodological and technical issues in the field of Cognitively-Inspired Artificial Intelligence, (...) Lieto illustrates how the cognitive design approach has an important role to play in the development of intelligent AI technologies and plausible computational models of cognition. Introducing a unique perspective that draws upon Cybernetics and early AI principles, Lieto emphasizes the need for an equivalence between cognitive processes and implemented AI procedures, in order to realise biologically and cognitively inspired artificial minds. He also introduces the Minimal Cognitive Grid, a pragmatic method to rank the different degrees of biologically and cognitive accuracy of artificial systems in order project and predict their explanatory power with respect to the natural systems taken as source of inspiration. -/- Providing a comprehensive overview of cognitive design principles in constructing artificial minds, this text will be essential reading for students and researchers of artificial intelligence and cognitive science. (shrink)

A collection of papers presented at the First International Summer Institute in Cognitive Science, University at Buffalo, July 1994, including the following papers: ** Topological Foundations of Cognitive Science, Barry Smith ** The Bounds of Axiomatisation, Graham White ** Rethinking Boundaries, Wojciech Zelaniec ** Sheaf Mereology and Space Cognition, Jean Petitot ** A Mereotopological Definition of 'Point', Carola Eschenbach ** Discreteness, Finiteness, and the Structure of Topological Spaces, Christopher Habel ** Mass Reference and the Geometry of Solids, Almerindo E. Ojeda (...) ** Defining a 'Doughnut' Made Difficult, N .M. Gotts ** A Theory of Spatial Regions with Indeterminate Boundaries, A.G. Cohn and N.M. Gotts ** Mereotopological Construction of Time from Events, Fabio Pianesi and Achille C. Varzi ** Computational Mereology: A Study of Part-of Relations for Multi-media Indexing, Wlodek Zadrozny and Michelle Kim. (shrink)

This paper discusses different approaches incognitive science and artificial intelligenceresearch from the perspective of radicalconstructivism, addressing especially theirrelation to the biologically based theories ofvon Uexküll, Piaget as well as Maturana andVarela. In particular recent work in New AI and adaptive robotics on situated and embodiedintelligence is examined, and we discuss indetail the role of constructive processes asthe basis of situatedness in both robots andliving organisms.

Despite the growing popularity of nonrepresentationalist approaches to cognition, and especially of those coming from the enactivist corner, positing internal representations is still the order of the day in mainstream cognitive science. Indeed, the idea that we have to invoke internal content-carrying, thing-like entities to account for the workings of mind and cognition proves to be particularly resilient. In this paper, my aim is to explain at least partially where this resilience of the reified notion of representation comes from. What (...) I want to show is that, crucially, positing inner representations isn’t so much warranted by the scientific practice itself – as is commonly held – but much more motivated by nonscientific and pre-theoretical elements that largely stem from, what I will call, linguistic contingencies. Otherwise put, much of what makes the reified notion of representation an attractive posit can be explained, not by the science, but by the way we, including cognitive scientists, speak. What I want to do here, then, is first, rehearse what reification means in the context of representationalism and, second, specify which linguistic contingencies can account for why the idea of positing representations remains for many not only a viable option, but an indispensability for anyone interested in explanations of mind and cognition. (shrink)

The TGarden is a genre of responsive environment in which actor–spectators shape dense media sensitive to their movements. These dense fields of light, sound, and material also evolve according to their own composed dynamics, so the agency is distributed throughout the multiple media. These TGardens explore open-ended questions like the following: what makes some time-based, responsive environments compelling, and others flat? How can people improvise gestures without words, that are individually or collectively meaningful? When and how is a movement intentional, (...) or collectively intentional? This paper introduces what has been at stake behind the experimental work: subjectivation, moving from technologies of representation to technologies of performance, and the potential for ethico-aesthetic novelty. (shrink)

The traditional view of symbol grounding seeks to connect an a priori internal representation or ‘form’ to its external referent. But such a ‘form’ is usually itself systematically composed out of more primitive parts, so this view ignores its grounding in the physics of the world. Some previous work simulating multiple talking/listening agents has effectively taken this stance, and shown how a shared discrete speech code can emerge. Taking the earlier work of Oudeyer, we have extended his model to include (...) a dispersive force intended to account broadly for a speaker’s motivation to increase auditory distinctiveness. New simulations show that vowel systems result that are more representative of the range seen in human languages. These simulations make many profound abstractions and assumptions. Relaxing these by including more physically and physiologically realistic mechanisms for talking and listening is seen as the key to replicating more complex and dynamic aspects of speech, such as consonant-vowel patterning. (shrink)

Predictive processing has recently been advanced as a global cognitive architecture for the brain. I argue that its commitments concerning the nature and format of cognitive representation are inadequate to account for two basic characteristics of conceptual thought: first, its generality—the fact that we can think and flexibly reason about phenomena at any level of spatial and temporal scale and abstraction; second, its rich compositionality—the specific way in which concepts productively combine to yield our thoughts. I consider two strategies for (...) avoiding these objections and I argue that both confront formidable challenges. (shrink)

We argue that one important aspect of the “cognitive neuroscience revolution” identified by Boone and Piccinini :1509–1534. doi: 10.1007/s11229-015-0783-4, 2015) is a dramatic shift away from thinking of cognitive representations as arbitrary symbols towards thinking of them as icons that replicate structural characteristics of their targets. We argue that this shift has been driven both “from below” and “from above”—that is, from a greater appreciation of what mechanistic explanation of information-processing systems involves, and from a greater appreciation of the problems (...) solved by bio-cognitive systems, chiefly regulation and prediction. We illustrate these arguments by reference to examples from cognitive neuroscience, principally representational similarity analysis and the emergence of dynamical models as a central postulate in neurocognitive research. (shrink)

John Searle distinguished between weak and strong artificial intelligence (AI). This essay discusses a third alternative, mild AI, according to which a machine may be capable of possessing a species of mentality. Using James Fetzer's conception of minds as semiotic systems, the possibility of what might be called ``mild AI'' receives consideration. Fetzer argues against strong AI by contending that digital machines lack the ground relationship required of semiotic systems. In this essay, the implementational nature of semiotic processes posited by (...) Charles S. Peirce's triadic sign relation is re-examined in terms of the underlying dispositional processes and the ontological levels they would span in an inanimate machine. This suggests that, if non-human mentality can be replicated rather than merely simulated in a digital machine, the direction to pursue appears to be that of mild AI. (shrink)

We consider the symbol grounding problem, and apply to it philosophical arguments against Cartesianism developed by Sellars and McDowell: the problematic issue is the dichotomy between inside and outside which the definition of a physical symbol system presupposes. Surprisingly, one can question this dichotomy and still do symbolic computation: a detailed examination of the hardware and software of serial ports shows this.

In this paper we discuss an approach called grounded action cognition, which aims to provide a theory of the interdependencies between motor control and action-related cognitive processes, like perceiving an action or thinking about an action. The theory contrasts with traditional views in cognitive science in that it motivates an understanding of cognition as embodied, through application of Barsalou’s general idea of grounded cognition. To guide further research towards an appropriate theory of grounded action cognition we distinguish between grounding qua (...) acquisition and grounding qua constitution. On this basis, we distinguish three possible theoretical conceptions of grounded action cognition. In addition to these methodological and conceptual analyses, we draw on recent empirical evidence to motivate our inclination towards a particular theory. According to this theory certain representations are involved in action cognition and action perception that are not modality-specific as usually proposed by advocates of grounded cognition. Further, the evidence is in favor of our more specific theory stating that for some cognitive abilities, some motor abilites are constitutive. (shrink)

Explanations of cognitive processes provided by traditional artificial intelligence were based on the notion of the knowledge level. This perspective has been challenged by new AI that proposes an approach based on embodied systems that interact with the real‐world. We demonstrate that these two views can be unified. Our argument is based on the assumption that knowledge level explanations can be defined in the context of Bayesian theory while the goals of new AI are captured by using a well established (...) robot based model of learning and problem solving, called Distributed Adaptive Control (DAC). In our analysis we consider random foraging and we prove that minor modifications of the DAC architecture renders a model that is equivalent to a Bayesian analysis of this task. Subsequently, we compare this enhanced, “rational,” model to its “non‐rational” predecessor and a further control condition using both simulated and real robots, in a variety of environments. Our results show that the changes made to the DAC architecture, in order to unify the perspectives of old and new AI, also lead to a significant improvement in random foraging. (shrink)

According to the dominant computational approach in cognitive science, cognitive agents are digital computers; according to the alternative approach, they are dynamical systems. This target article attempts to articulate and support the dynamical hypothesis. The dynamical hypothesis has two major components: the nature hypothesis (cognitive agents are dynamical systems) and the knowledge hypothesis (cognitive agents can be understood dynamically). A wide range of objections to this hypothesis can be rebutted. The conclusion is that cognitive systems may well be dynamical systems, (...) and only sustained empirical research in cognitive science will determine the extent to which that is true. (shrink)

The nature of the dynamical hypothesis in cognitive science (the DH) is further clarified in responding to various criticisms and objections raised in commentaries. Major topics addressed include the definitions of “dynamical system” and “digital computer;” the DH as Law of Qualitative Structure; the DH as an ontological claim; the multiple-realizability of dynamical models; the level at which the DH is pitched; the nature of dynamics; the role of representations in dynamical cognitive science; the falsifiability of the DH; the extent (...) to which the DH is open; the role of temporal and implementation considerations; and the novelty or importance of the DH. The basic formulation and defense of the DH in the target article survives intact, though some refinements are recommended. (shrink)