One feature of vague predicates is that, as far as appearances go, they lack sharp application boundaries. I argue that we would not be able to locate boundaries even if vague predicates had sharp boundaries. I do so by developing an idealized cognitive model of a categorization faculty which has mobile and dynamic sortals (`classes', `concepts' or `categories') and formally prove that the degree of precision with which boundaries of such sortals can be located is inversely constrained by their flexibility. (...) Given the literature, it is plausible that we are appropriately like the model. Hence, an inability to locate sharp boundaries is not necessarily because there are none; boundaries could be sharp and it is plausible that we would nevertheless be unable to locate them. (shrink)

This book contains a representationalist theory of self-consciousness and of the phenomenal first-person perspective. It draws on empirical data from the cognitive and neurosciences.

This paper outlines an original interactivist-constructivist approach to modelling intelligence and learning as a dynamical embodied form of adaptiveness and explores some applications of I-C to understanding the way cognitive learning is realized in the brain. Two key ideas for conceptualizing intelligence within this framework are developed. These are: intelligence is centrally concerned with the capacity for coherent, context-sensitive, self-directed management of interaction; and the primary model for cognitive learning is anticipative skill construction. Self-directedness is a capacity for integrative process (...) modulation which allows a system to "steer" itself through its world by anticipatively matching its own viability requirements to interaction with its environment. Because the adaptive interaction processes required of intelligent systems are too complex for effective action to be prespecified learning is an important component of intelligence. A model of self-directed anticipative learning is formulated based on interactive skill construction, and argued to constitute a central constructivist process involved in cognitive development. SDAL illuminates the capacity of intelligent learners to start with the vague, poorly defined problems typically posed in realistic learning situations and progressively refine them, transforming them into problems with sufficient structure to guide the construction of a solution. Finally, some of the implications of I-C for modelling of the neuronal basis of intelligence and learning are explored; in particular, Quartz and Sejnowski's recent neural constructivism paradigm, enriched by Montague and Sejnowski's dopaminergic model of anticipative-predictive neural learning, is assessed as a promising, but incomplete, contribution to this approach. The paper concludes with a fourfold reflection on the divergence in cognitive modelling philosophy between the I-C and the traditional computational information processing approaches. (shrink)

In this paper I discuss one of the key issuesin the philosophy of neuroscience:neurosemantics. The project of neurosemanticsinvolves explaining what it means for states ofneurons and neural systems to haverepresentational contents. Neurosemantics thusinvolves issues of common concern between thephilosophy of neuroscience and philosophy ofmind. I discuss a problem that arises foraccounts of representational content that Icall ``the economy problem'': the problem ofshowing that a candidate theory of mentalrepresentation can bear the work requiredwithin in the causal economy of a mind and (...) anorganism. My approach in the current paper isto explore this and other key themes inneurosemantics through the use of computermodels of neural networks embodied and evolvedin virtual organisms. The models allow for thelaying bare of the causal economies of entireyet simple artificial organisms so that therelations between the neural bases of, forinstance, representation in perception andmemory can be regarded in the context of anentire organism. On the basis of thesesimulations, I argue for an account ofneurosemantics adequate for the solution of theeconomy problem. (shrink)

In the first section of the article, we examine some recent criticisms of the connectionist enterprise: first, that connectionist models are fundamentally behaviorist in nature (and, therefore, non-cognitive), and second that connectionist models are fundamentally associationist in nature (and, therefore, cognitively weak). We argue that, for a limited class of connectionist models (feed-forward, pattern-associator models), the first criticism is unavoidable. With respect to the second criticism, we propose that connectionist modelsare fundamentally associationist but that this is appropriate for building models (...) of human cognition. However, we do accept the point that there are cognitive capacities for which any purely associative model cannot provide a satisfactory account. The implication that we draw from is this is not that associationist models and mechanisms should be scrapped, but rather that they should be enhanced.In the next section of the article, we identify a set of connectionist approaches which are characterized by “active symbols” — recurrent circuits which are the basis of knowledge representation. We claim that such approaches avoid criticisms of behaviorism and are, in principle, capable of supporting full cognition. In the final section of the article, we speculate at some length about what we believe would be the characteristics of a fully realized active symbol system. This includes both potential problems and possible solutions (for example, mechanisms needed to control activity in a complex recurrent network) as well as the promise of such systems (in particular, the emergence of knowledge structures which would constitute genuine internal models). (shrink)

This paper investigates the prospects of Rodney Brooks’ proposal for AI without representation. It turns out that the supposedly characteristic features of “new AI” (embodiment, situatedness, absence of reasoning, and absence of representation) are all present in conventional systems: “New AI” is just like old AI. Brooks proposal boils down to the architectural rejection of central control in intelligent agents—Which, however, turns out to be crucial. Some of more recent cognitive science suggests that we might do well to dispose of (...) the image of intelligent agents as central representation processors. If this paradigm shift is achieved, Brooks’ proposal for cognition without representation appears promising for full-blown intelligent agents—Though not for conscious agents. (shrink)

Alan Turing devised his famous test (TT) through a slight modificationof the parlor game in which a judge tries to ascertain the gender of twopeople who are only linguistically accessible. Stevan Harnad hasintroduced the Total TT, in which the judge can look at thecontestants in an attempt to determine which is a robot and which aperson. But what if we confront the judge with an animal, and arobot striving to pass for one, and then challenge him to peg which iswhich? (...) Now we can index TTT to a particular animal and its syntheticcorrelate. We might therefore have TTTrat, TTTcat,TTTdog, and so on. These tests, as we explain herein, are abetter barometer of artificial intelligence (AI) than Turing's originalTT, because AI seems to have ammunition sufficient only to reach thelevel of artificial animal, not artificial person. (shrink)

Some philosophers search for the mark of the cognitive: a set of individually necessary and jointly sufficient conditions identifying all instances of cognition. They claim that the mark of the cognitive is needed to steer the development of cognitive science on the right path. Here, I argue that, at least at present, it cannot be provided. First (§2), I identify some of the factors motivating the search for a mark of the cognitive, each yielding a desideratum the mark is supposed (...) to satisfy (§2.1). I then (§2.2) highlight a number of tensions in the literature on the mark of the cognitive, suggesting they’re best resolved by distinguishing two distinct programs. The first program (§3) is that of identifying a mark of the cognitive capturing our everyday notion of cognition. I argue that such a program is bound to fail for a number of reasons: it is not clear whether such an everyday notion exists; and even if it existed, it would not be able to spell out individually necessary and jointly sufficient conditions for cognition; and even if it were able to spell them out, these conditions won’t satisfy the desiderata a mark of the cognitive should satisfy. The second program is that of identifying a mark of the cognitive spelling out a genuine scientific kind. But the current state of fragmentation of cognitive science, and the fact that it is splintered in a myriad of different research traditions, prevent us from identifying such a kind. And we have no reason to think that these various research traditions will converge, allowing us to identify a single mark. Or so, at least, I will argue in (§4). I then conclude the paper (§5) deflecting an intuitive objection, and exploring some of the consequences of the thesis I have defended. (shrink)

Currently, the autonomy of artificial systems, robotic systems in particular, is certainly one of the most debated issues, both from the perspective of technological development and its social impact and ethical repercussions. While theoretical considerations often focus on scenarios far beyond what can be concretely hypothesized from the current state of the art, the term autonomy is still used in a vague or too general way. This reduces the possibilities of a punctual analysis of such an important issue, thus leading (...) to often polarized positions. The intent of this paper is to clarify what is meant by artificial autonomy, and what are the prerequisites that can allow the attribution of this characteristic to a robotic system. Starting from some concrete examples, we will try to indicate a way towards artificial autonomy that can hold together the advantages of developing adaptive and versatile systems with the management of the inevitable problems that this technology poses both from the viewpoint of safety and ethics. Our proposal is that a real artificial autonomy, especially if expressed in the social context, can only be achieved through interdependence with other social actors, through continuous exchanges and interactions which, while allowing robots to explore the environment, guarantee the emergence of shared practices, behaviors, and ethical principles, which otherwise could not be imposed with a top-down approach, if not at the price of giving up the same artificial autonomy. (shrink)

As robots increasingly become part of our everyday lives, questions arise with regards to how to approach them and how to understand them in social contexts. The Western history of human–robot relations revolves around competition and control, which restricts our ability to relate to machines in other ways. In this study, we take a relational approach to explore different manners of socializing with robots, especially those that exceed an instrumental approach. The nonhuman subjects of this study are built to explore (...) non-purposeful behavior, in an attempt to break away from the assumptions of utility that underlie the hegemonic human–machine interactions. This breakaway is accompanied by ‘learning to be attuned’ on the side of the human subjects, which is facilitated by continuous relations at the level of everyday life. Our paper highlights this ground for the emergence of meanings and questions that could not be subsumed by frameworks of control and domination. The research-creation project Machine Ménagerie serves as a case study for these ideas, demonstrating a relational approach in which the designer and the machines co-constitute each other through sustained interactions, becoming attuned to one another through the performance of research. Machine Ménagerie attempts to produce affective and playful—if not unruly—nonhuman entities that invite interaction yet have no intention of serving human social or physical needs. We diverge from other social robotics research by creating machines that do not attempt to mimic human social behaviours. (shrink)

Structural representations are increasingly popular in philosophy of cognitive science. A key virtue they seemingly boast is that of meeting Ramsey's job description challenge. For this reason, structural representations appear tailored to play a clear representational role within cognitive architectures. Here, however, I claim that structural representations do not meet the job description challenge. This is because even our most demanding account of their functional profile is satisfied by at least some receptors, which paradigmatically fail the job description challenge. Hence, (...) the functional profile typically associated with structural representations does not identify representational posits. After a brief introduction, I present, in the second section of the paper, the job description challenge. I clarify why receptors fail to meet it and highlight why, as a result, they should not be considered representations. In the third section I introduce what I take to be the most demanding account of structural representations at our disposal, namely Gładziejewski's account. Provided the necessary background, I turn from exposition to criticism. In the first half of the fourth section, I equate the functional profile of structural representations and receptors. To do so, I show that some receptors boast, as a matter of fact, all the functional features associated with structural representations. Since receptors function merely as causal mediators, I conclude structural representations are mere causal mediators too. In the second half of the fourth section I make this conclusion intuitive with a toy example. I then conclude the paper, anticipating some objections my argument invites. (shrink)

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)

I develop an interdisciplinary framework for understanding the nature of agents and agency that is compatible with recent developments in the metaphysics of science and that also does justice to the mechanistic and normative characteristics of agents and agency as they are understood in moral philosophy, social psychology, neuroscience, robotics, and economics. The framework I develop is internal perspectivalist. That is to say, it counts agents as real in a perspective-dependent way, but not in a way that depends on an (...) external perspective. Whether or not something counts as an agent depends on whether it is able to have a certain kind of perspective. My approach differs from many others by treating possession of a perspective as more basic than the possession of agency, representational content/vehicles, cognition, intentions, goals, concepts, or mental or psychological states; these latter capabilities require the former, not the other way around. I explain what it means for a system to be able to have a perspective at all, beginning with simple cases in biology, and show how self-contained normative perspectives about proper function and control can emerge from mechanisms with relatively simple dynamics. I then describe how increasingly complex control architectures can become organized that allow for more complex perspectives that approach agency. Next, I provide my own account of the kind of perspective that is necessary for agency itself, the goal being to provide a reference against which other accounts can be compared. Finally, I introduce a crucial distinction that is necessary for understanding human agency: that between inclinational and committal agency, and venture a hypothesis about how the normative perspective underlying committal agency might be mechanistically realized. (shrink)

Some of the foundations of Heyes’ radical reasoning seem to be based on a fractional selection of available evidence. Using an ethological perspective, we argue against Heyes’ rapid dismissal of innate cognitive instincts. Heyes’ use of fMRI studies of literacy to claim that culture assembles pieces of mental technology seems an example of incorrect reverse inferences and overlap theories pervasive in cognitive neuroscience.

Replicability and reproducibility of computational models has been somewhat understudied by “the replication movement.” In this paper, we draw on methodological studies into the replicability of psychological experiments and on the mechanistic account of explanation to analyze the functions of model replications and model reproductions in computational neuroscience. We contend that model replicability, or independent researchers' ability to obtain the same output using original code and data, and model reproducibility, or independent researchers' ability to recreate a model without original code, (...) serve different functions and fail for different reasons. This means that measures designed to improve model replicability may not enhance (and, in some cases, may actually damage) model reproducibility. We claim that although both are undesirable, low model reproducibility poses more of a threat to long-term scientific progress than low model replicability. In our opinion, low model reproducibility stems mostly from authors' omitting to provide crucial information in scientific papers and we stress that sharing all computer code and data is not a solution. Reports of computational studies should remain selective and include all and only relevant bits of code. (shrink)

A large body of compelling evidence has been accumulated demonstrating that embodiment – the agent’s physical setup, including its shape, materials, sensors and actuators – is constitutive for any form of cognition and as a consequence, models of cognition need to be embodied. In contrast to methods from empirical sciences to study cognition, robots can be freely manipulated and virtually all key variables of their embodiment and control programs can be systematically varied. As such, they provide an extremely powerful tool (...) of investigation. We present a robotic bottom-up or developmental approach, focusing on three stages: (a) low-level behaviors like walking and reflexes, (b) learning regularities in sensorimotor spaces, and (c) human-like cognition. We also show that robotic based research is not only a productive path to deepening our understanding of cognition, but that robots can strongly benefit from human-like cognition in order to become more autonomous, robust, resilient, and safe. (shrink)

Alongside existing research into the social, political and economic impacts of the Web, there is a need to study the Web from a cognitive and epistemic perspective. This is particularly so as new and emerging technologies alter the nature of our interactive engagements with the Web, transforming the extent to which our thoughts and actions are shaped by the online environment. Situated and ecological approaches to cognition are relevant to understanding the cognitive significance of the Web because of the emphasis (...) they place on forces and factors that reside at the level of agent–world interactions. In particular, by adopting a situated or ecological approach to cognition, we are able to assess the significance of the Web from the perspective of research into embodied, extended, embedded, social and collective cognition. The results of this analysis help to reshape the interdisciplinary configuration of Web Science, expanding its theoretical and empirical remit to include the disciplines of both cognitive science and the philosophy of mind. (shrink)

David Hume's famous riddle of induction implies a second problem related to the question of whether the laws and principles of nature might change in the course of time. Claims have been made that modern developments in physics and astrophysics corroborate the translational invariance of the laws of physics in time. However, the appearance of a new general principle of nature, which might not be derivable from the known laws of physics, or that might actually be a non-physical one (this (...) means completely independent of physical science) supports the notion that the course of nature can change in time. Here it is argued that natural selection satisfies the criteria that identify a general principle of nature which so far, appears to be non-derivable from the known laws of physics and therefore, it is likely that it arose in the course of time, thus leaving open again the quest for a true solution to Hume's second problem. (shrink)

To understand the endogenous components of the event-related brain potential (ERP), we must use data about the components' antecedent conditions to form hypotheses about the information-processing function of the underlying brain activity. These hypotheses, in turn, generate testable predictions about the consequences of the component. We review the application of this approach to the analysis of the P300 component. The amplitude of the P300 is controlled multiplicatively by the subjective probability and the task relevance of the eliciting events, whereas its (...) latency depends on the duration of stimulus evaluation. These and other factors suggest that the P300 is a manifestation of activity occurring whenever one's model of the environment must be revised. Tests of three predictions based on this “context updating” model are reviewed. Verleger's critique is based on a misconstrual of the model as well as a partial and misleading reading of the relevant literature. (shrink)

In the recent history of psychology and cognitive neuroscience, the notion of habit has been reduced to a stimulus-triggered response probability correlation. In this paper we use a computational model to present an alternative theoretical view (with some philosophical implications), where habits are seen as self-maintaining patterns of behavior that share properties in common with self-maintaining biological processes, and that inhabit a complex ecological context, including the presence and influence of other habits. Far from mechanical automatisms, this organismic and self-organizing (...) concept of habit can overcome the dominating atomistic and statistical conceptions, and the high temporal resolution effects of situatedness, embodiment and sensorimotor loops emerge as playing a more central, subtle and complex role in the organization of behavior. The model is based on a novel “iterant deformable sensorimotor medium” (IDSM), designed such that trajectories taken through sensorimotor-space increase the likelihood that in the future, similar trajectories will be taken. We couple the IDSM to sensors and motors of a simulated body and environment and show that under certain conditions, the IDSM forms self-maintaining patterns of activity that operate across the IDSM, the body, and the environment. We present various environments and the resulting habits that form in them. The model acts as an abstraction of habits at a much needed sensorimotor “meso-scale” between microscopic neuron-based models and macroscopic descriptions of behavior. Finally, we discuss how this model and extensions of it can help us understand aspects of behavioral self-organization, historicity and autonomy that remain out of the scope of contemporary representationalist frameworks. (shrink)

Just as AI has moved away from classical AI, human-computer interaction (HCI) must move away from what I call ‘good old fashioned HCI’ to ‘new HCI’ – it must become a part of cognitive systems research where HCI is one case of the interaction of intelligent agents (we now know that interaction is essential for intelligent agents anyway). For such interaction, we cannot just ‘analyze the data’, but we must assume intentions in the other, and I suggest these are largely (...) recognized through resistance to carrying out one’s own intentions. This does not require fully cognitive agents but can start at a very basic level. New HCI integrates into cognitive systems research and designs intentional systems that provide resistance to the human agent. (shrink)

Context: Constructivist approaches to cognition have mostly been descriptive, and now face the challenge of specifying the mechanisms that may support the acquisition of knowledge. Departing from cognitivism, however, requires the development of a new functional framework that will support causal, powerful and goal-directed behavior in the context of the interaction between the organism and the environment. Problem: The properties affecting the computational power of this interaction are, however, unclear, and may include partial information from the environment, exploration, distributed processing (...) and aggregation of information, emergence of knowledge and directedness towards relevant information. Method: We posit that one path towards such a framework may be grounded in these properties, supported by dynamical systems. To assess this hypothesis, we describe computational models inspired from swarm intelligence, which we use as a metaphor to explore the practical implications of the properties highlighted. Results: Our results demonstrate that these properties may serve as the basis for complex operations, yielding the elaboration of knowledge and goal-directed behavior. Implications: This work highlights aspects of interaction that we believe ought to be taken into account when characterizing the possible mechanisms underlying cognition. The scope of the models we describe cannot go beyond that of a metaphor, however, and future work, theoretical and experimental, is required for further insight into the functional role of interaction with the environment for the elaboration of complex behavior. Constructivist content: Inspiration for this work stems from the constructivist impetus to account for knowledge acquisition based on interaction. (shrink)

Despite the exponential progress in computing power of digital computers, the development of lifelike cognitive systems appears has not yet reached the complexity of the simplest kinds of organisms. This may be explained by the lack of robustness of digital computers due to the requirements of structural programmability in the conventional computing architectures. In contrast, biological systems appear to operate in a different mode of information processing. In order to approach more lifelike artificial cognitive systems, the integration of natural and (...) unnatural systems may open a path to investigate the possibilities of the desired biological functions such as through the creation of hybrid architectures that interface nature's computing brains with artificial devices such as using the behaviour of the slime mould Physarum polycephalum to influence the behaviour of a mechanical robot. (shrink)

This paper is a commentary on the target article by Michael Arbib, “Levels of modeling of mechanisms of visually guided behavior”, in the same issue of the journal, pp. 407–465. -/- I focus on the importance of the inclusion of an ability of a system to entertain, at a given time, multiple instantiations of a given schema (situation template, frame, script, action plan, etc.), and complications introduced into neural/connectionist network systems by such inclusion.

Intermediate constructs are required as bridges between complex behaviors and realistic models of neural circuitry. For cognitive scientists in general, schemas are the appropriate functional units; brain theorists can work with neural layers as units intermediate between structures subserving schemas and small neural circuits.After an account of different levels of analysis, we describe visuomotor coordination in terms of perceptual schemas and motor schemas. The interest of schemas to cognitive science in general is illustrated with the example of perceptual schemas in (...) high-level vision and motor schemas in the control of dextrous hands.Rana computatrix, the computational frog, is introduced to show how one constructs an evolving set of model families to mediate flexible cooperation between theory and experiment. Rana computatrix may be able to do for the study of the organizational principles of neural circuitry what Aplysia has done for the study of subcellular mechanisms of learning. Approach, avoidance, and detour behavior in frogs and toads are analyzed in terms of interacting schemas. Facilitation and prey recognition are implemented as tectal-pretectal interactions, with the tectum modeled by an array of tectal columns. We show how layered neural computation enters into models of stereopsis and how depth schemas may involve the interaction of accommodation and binocular cues in anurans. (shrink)

Interpreting the Miyashita et al. experiments in terms of a cellassembly representation does not adequately explain the performance of Miyashita's monkeys on novel stimuli. We will argue that the latter observations point to acompositionalrepresentation and suggest a dynamics involving rapid and reversible binding of distinct activity patterns.

This commentary questions the target articles inferences from a limited set of empirical data to support this model and conceptual scheme. Especially questionable is the attribution of internal representation properties to an assembly of cells in a discrete cortical module firing at a discrete attractor frequency. Alternative inferences are drawn from cortical cooling and cell-firing data that point to the internal representation as a broad and specific cortical network defined by cortico-cortical connectivity. Active memory, it is proposed, consists in the (...) sustained activation of the component neuron populations of the network. (shrink)

The concept of an attractor in a mathematical dynamical system is reviewed. Emphasis is placed on the distinction between a cell assembly, the corresponding attractor, and the attractor dynamics. The biological significance of these entities is discussed, especially the question of whether the representation of the stimulus requires the full attractor dynamics, or merely the cell assembly as a set of reverberating neurons. Comparison is made to Freeman's study of dynamic patterns in olfaction.

The neurophysiological evidence from the Miyashita group's experiments on monkeys as well as cognitive experience common to us all suggests that local neuronal spike rate distributions might persist in the absence of their eliciting stimulus. In Hebb's cell-assembly theory, learning dynamics stabilize such self-maintaining reverberations. Quasi-quantitive modeling of the experimental data on internal representations in association-cortex modules identifies the reverberations (delay spike activity) as the internal code (representation). This leads to cognitive and neurophysiological predictions, many following directly from the language (...) used to describe the activity in the experimental delay period, others from the details of how the model captures the properties of the internal representations. (shrink)

Context: Our body schema is not restricted to biological body boundaries (such as the skin), as can be seen in the use of a cane by a person who is visually impaired or the “rubber hands” experiment. The tool becomes a part of the body schema when the focus of our attention is shifted from the tool to the task to be performed. Problem: A body schema is formed through interactions among brain, body, tool, and environment. Nevertheless, the dynamic mechanisms (...) underlying changes in the body schema are still not fully understood. Method: To study the changing conditions of the body schema (e.g., a shift of attention), a simulation model of object discrimination was extended to differentiate between two kinds of sensitivities – sensitivity to an object being directly manipulated and sensitivity to another object being manipulated by the first. The proposed model consisted of windmills with different numbers of vanes. A model agent was required to determine the number of vanes on a windmill by touching the vanes blindly with an arm controlled by a neural network. Placing a second windmill beside the first and gearing the two windmills to move associatively resulted in the agent using the first windmill as a tool with which to discern the number of vanes on the second windmill. In other words, an agent’s body schema can shift from its arm tip to the boundary between the first and second windmills. We then introduced an experiment with a real windmill model to test the hypothesis demonstrated by the theoretical model. Results: We demonstrated that even simple computational agents can have two different sensitivities to the windmills. One agent becomes sensitive to the first windmill and insensitive to the second one. Another agent becomes insensitive to the first windmill and sensitive to the second one by using the first one as a tool. Therefore, we concluded that the boundary of the body schema was extended to the first windmill in the case of the latter agent because paying attention to the task to be performed instead of the tool itself is essential for the tool to be considered as part of the body schema. Analysis of the experiments using a computational model and human experimentation revealed that a shift from an irregular to a regular movement of a windmill is an indication of extension of the body schema. Constructivist content: Our insights are beneficial for enactive cognitive science. This is because an extended body schema questions the Cartesian separation between subject and object, and the self and the environment. (shrink)

This paper extends three decades of work arguing that researchers who discuss consciousness should not restrict themselves only to (adult) human minds, but should study (and attempt to model) many kinds of minds, natural and artificial, thereby contributing to our understanding of the space containing all of them. We need to study what they do or can do, how they can do it, and how the natural ones can be emulated in synthetic minds. That requires: (a) understanding sets of requirements (...) that are met by different sorts of minds, i.e. the niches that they occupy, (b) understanding the space of possible designs, and (c) understanding complex and varied relationships between requirements and designs. Attempts to model or explain any particular phenomenon, such as vision, emotion, learning, language use, or consciousness lead to muddle and confusion unless they are placed in that broader context. A methodology for making progress is summarised and a novel requirement proposed for a theory of how human minds work: the theory should support a single generic design for a learning, developing system that, in addition to meeting familiar requirements, should be capable of developing different and opposed philosophical viewpoints about consciousness, and the so-called hard problem. In other words, we need a common explanation for the mental machinations of mysterians, materialists, functionalists, identity theorists, and those who regard all such theories as attempting to answer incoherent questions. No designs proposed so far come close. (shrink)

The study of human intelligence was once dominated by symbolic approaches, but over the last 30 years an alternative approach has arisen. Symbols and processes that operate on them are often seen today as approximate characterizations of the emergent consequences of sub- or nonsymbolic processes, and a wide range of constructs in cognitive science can be understood as emergents. These include representational constructs (units, structures, rules), architectural constructs (central executive, declarative memory), and developmental processes and outcomes (stages, sensitive periods, neurocognitive (...) modules, developmental disorders). The greatest achievements of human cognition may be largely emergent phenomena. It remains a challenge for the future to learn more about how these greatest achievements arise and to emulate them in artificial systems. (shrink)

David Marr's theory of vision has been a rich source of inspiration, fascination and confusion. I will suggest that some of this confusion can be traced to discrepancies between the way Marr developed his theory in practice and the way he suggested such a theory ought to be developed in his explicit metatheoretical remarks. I will address claims that Marr's theory may be seen as an optimizing theory, along with the attendant suggestion that optimizing assumptions may be inappropriate for cognitive (...) mechanisms just as anti-adaptationists have argued they are inappropriate for other physiological mechanisms. I will discuss the nature of optimizing assumptions and theories. Considering various difficulties in identifying and assessing optimizing assumptions, I will suggest that Marr's theory is not purely an optimizing theory and that reaction to Marr on this issue prompts interesting considerations for the development of inter-disciplinary constraints in the cognitive and brain sciences. (shrink)

A working assumption that processes of natural and cultural evolution have tailored the mind to fit the demands and structure of its environment begs the question: how are we to characterize the structure of cognitive environments? Decision problems faced by real organisms are not like simple multiple-choice examination papers. For example, some individual problems may occur much more frequently than others, whilst some may carry much more weight than others. Such considerations are not taken into account when (i) the performance (...) of candidate cognitive mechanisms is assessed by employing a simple accuracy metric that is insensitive to the structure of the decision-maker's environment, and (ii) reason is defined as the adherence to internalist prescriptions of classical rationality. Here we explore the impact of frequency and significance structure on the performance of a range of candidate decision-making mechanisms. We show that the character of this impact is complex, since structured environments demand that decision-makers trade off general performance against performance on important subsets of test items. As a result, environment structure obviates internalist criteria of rationality. Failing to appreciate the role of environment structure in shaping cognition can lead to mischaracterising adaptive behavior as irrational. (shrink)

This paper looks at two puzzles raised by the phenomenon of inattentional blindness. First, how can we see at all if, in order to see, we must first perceptually attend to that which we see? Second, if attention is required for perception, why does it seem to us as if we are perceptually aware of the whole detailed visual field when it is quite clear that we do not attend to all that detail? We offer a general framework for thinking (...) about perception and perceptual consciousness that addresses these questions and we propose, in addition, an informal account of the relation between attention and consciousness. On this view, perceptual awareness is a species of attention. (shrink)

Artificial models of cognition serve different purposes, and their use determines the way they should be evaluated. There are also models that do not represent any particular biological agents, and there is controversy as to how they should be assessed. At the same time, modelers do evaluate such models as better or worse. There is also a widespread tendency to call for publicly available standards of replicability and benchmarking for such models. In this paper, I argue that proper evaluation ofmodels (...) does not depend on whether they target real biological agents or not; instead, the standards of evaluation depend on the use of models rather than on the reality of their targets. I discuss how models are validated depending on their use and argue that all-encompassing benchmarks for models may be well beyond reach. (shrink)

Is it possible to construct a machine that can act of its own accord? There are a number of skeptical arguments which conclude that autonomous machine agency is impossible. Yet if autonomous machine agency is impossible, then serious doubt is cast on the possibility of autonomous human action, at least on the widely held assumption that some form of materialism is true. The purpose of this dissertation is to show that autonomous machine agency is possible, thereby showing that the autonomy (...) of human action is compatible with materialism. ;I proceed as follows. Chapter 1 casts the problem of autonomous machine agency. Chapter 2 sets out the skeptic's case against autonomous machine agency by canvassing arguments against the possibility of machine agency and arguments against the possibility of autonomous machine agency. Chapter 3 begins work on a theory of autonomous machine agency by developing and defending axioms of a theory of agency from thought experiments and examples. Chapter 4 expands the theory of agency to a theory of autonomous agency by adding axioms of autonomy. ;The axioms of autonomous agency contain the primitive terms 'belief', 'desire', and 'deliberation', which require further explication if the theory is to be of any use in arguing for the possibility of autonomous machine agency. Chapters 5 and 6 take up this challenge by developing mathematically tractable axioms of deliberation, belief, and desire. ;Chapter 7 completes the theory by developing axioms of mechanism derived from the substantial literature in the foundations of computer science. Chapter 8 employs the resulting theory of autonomous machine agency to show just how the functional prerequisites of autonomous agency can in principle be met by machines, thereby demonstrating the possibility of autonomous machine agency. ;Chapter 9 serves as a cautionary postscript. In spite of having shown that autonomous machine agency is possible, the implications are not always happy for current efforts in artificial intelligence or current philosophical theories of autonomous human agency. Nevertheless, the argument for autonomous machine agency suggests several research programs at the nexus of Philosophy of Mind and Artificial Intelligence. (shrink)