We introduce a conceptual model for reaching group decisions. Our model extends a well-known, single-agent cognitive model, the recognition-primed decision (RPD) model. The RPD model includes a recognition phase and an evaluation phase. Group extensions of the RPD model, applicable to a group of RPD agents, have been considered in the literature, however the proposed models do not formalize how distributed and possibly inconsistent information can be combined in either phase. We show how such information can be utilized by aggregating (...) it using a specific social choice method, namely judgment aggregation. Our model is applicable to hierarchical groups of agents containing at least one RPD agent. (shrink)

In line with Allen Newell's challenge to develop complete cognitive architectures, and motivated by a recent proposal for a unifying subsymbolic computational theory of cognition, we introduce the cognitive control architecture SEMLINCS. SEMLINCS models the development of an embodied cognitive agent that learns discrete production rule-like structures from its own, autonomously gathered, continuous sensorimotor experiences. Moreover, the agent uses the developing knowledge to plan and control environmental interactions in a versatile, goal-directed, and self-motivated manner. Thus, in contrast to several well-known (...) symbolic cognitive architectures, SEMLINCS is not provided with production rules and the involved symbols, but it learns them. In this paper, the actual implementation of SEMLINCS causes learning and self-motivated, autonomous behavioral control of the game figure Mario in a clone of the computer game Super Mario Bros. Our evaluations highlight the successful development of behavioral versatility as well as the learning of suitable production rules and the involved symbols from sensorimotor experiences. Moreover, knowledge- and motivation-dependent individualizations of the agents’ behavioral tendencies are shown. Finally, interaction sequences can be planned on the sensorimotor-grounded production rule level. Current limitations directly point toward the need for several further enhancements, which may be integrated into SEMLINCS in the near future. Overall, SEMLINCS may be viewed as an architecture that allows the functional and computational modeling of embodied cognitive development, whereby the current main focus lies on the development of production rules from sensorimotor experiences. (shrink)

The Chinese room argument has presented a persistent headache in the search for Artificial Intelligence. Since it first appeared in the literature, various interpretations have been made, attempting to understand the problems posed by this thought experiment. Throughout all this time, some researchers in the Artificial Intelligence community have seen Symbol Grounding as proposed by Harnad as a solution to the Chinese room argument. The main thesis in this paper is that although related, these two issues present different problems in (...) the framework presented by Harnad himself. The work presented here attempts to shed some light on the relationship between John Searle’s intentionality notion and Harnad’s Symbol Grounding Problem. (shrink)

The paper presents a paradoxical feature of computational systems that suggests that computationalism cannot explain symbol grounding. If the mind is a digital computer, as computationalism claims, then it can be computing either over meaningful symbols or over meaningless symbols. If it is computing over meaningful symbols its functioning presupposes the existence of meaningful symbols in the system, i.e. it implies semantic nativism. If the mind is computing over meaningless symbols, no intentional cognitive processes are available prior to symbol grounding. (...) In this case, no symbol grounding could take place since any grounding presupposes intentional cognitive processes. So, whether computing in the mind is over meaningless or over meaningful symbols, computationalism implies semantic nativism. (shrink)

In this paper, I argue that computationalism is a progressive research tradition. Its metaphysical assumptions are that nervous systems are computational, and that information processing is necessary for cognition to occur. First, the primary reasons why information processing should explain cognition are reviewed. Then I argue that early formulations of these reasons are outdated. However, by relying on the mechanistic account of physical computation, they can be recast in a compelling way. Next, I contrast two computational models of working memory (...) to show how modeling has progressed over the years. The methodological assumptions of new modeling work are best understood in the mechanistic framework, which is evidenced by the way in which models are empirically validated. Moreover, the methodological and theoretical progress in computational neuroscience vindicates the new mechanistic approach to explanation, which, at the same time, justifies the best practices of computational modeling. Overall, computational modeling is deservedly successful in cognitive science. Its successes are related to deep conceptual connections between cognition and computation. Computationalism is not only here to stay, it becomes stronger every year. (shrink)

The rise of learning analytics, the application of complex metrics developed to exploit the proliferation of ‘Big Data’ in educational work, raises important moral questions about the nature of what is measurable in education. Teachers, schools and nations are increasingly held to account based on metrics, exacerbating the tendency for fine-grained measurement of learning experiences. In this article, the origins of learning analytics ontology are explored, drawing upon core ideas in the philosophy of computing, such as the general definition of (...) information and the information-theoretic account of knowledge. Drawing upon a reading of Descartes Meditatio II, which extends the phenomenology of Jean-Luc Marion into a pedagogy of intentionality, the article identifies a fundamental incompatibility between the subjective experience of learning and the information-theoretic account of knowledge. Human subjects experience and value their own information incommensurably with the ways in which computers measure and quantify information. The consequences of this finding for the design of online learning environments, and the necessary limitations of learning analytics and measurement are explored. (shrink)

This paper draws attention to the tendency of a range of technologies to reduce pedagogical interactions to a series of datafied transactions of information. This is problematic because such transactions are always by definition reducible to finite possibilities. As the ability to gather and analyse data becomes increasingly fine-grained, the threat that these datafied approaches over-determine the pedagogical space increases. Drawing on the work of Hegel, as interpreted by twentieth century French radical philosopher Alexandre Kojève, this paper develops a model (...) of relational pedagogy which highlights three points of incompatibility with a datafied learning environment reduced to finite measures. Firstly: Kojève’s accont of authority in Hegel posits two aspects to the mimetic relation between teacher and student: recognition and realisation, which belong to the ipseity or about-self-ness of the subject, and are incompatible with a general definition of data. Secondly, the Hegelian approach to human historical time, in particular the assertion that time and desire are begun in the future, not the past, renders it incompatible with mathematical time as used in data processing. Finally, from these it is possible to derive a distinctive notion of the work of pedagogy, grounded in Kojève’s realist reading of Hegel, irreducible to information processing. In consequence of this threefold irreducibility, the paper draws attention to a need for relations of human pedagogical work to be inherent in the design of educational technologies and highlights the dangers of presuming a machine intelligence model in the design of learning environments. (shrink)

Using contemporary science, the paper builds on Wittgenstein’s views of human language. Rather than ascribing reality to inscription-like entities, it links embodiment with distributed cognition. The verbal or (quasi) technological aspect of language is traced to not action, but human specific interactivity. This species-specific form of sense-making sustains, among other things, using texts, making/construing phonetic gestures and thinking. Human action is thus grounded in appraisals or sense-saturated coordination. To illustrate interactivity at work, the paper focuses on a case study. Over (...) 11 s, a crime scene investigator infers that she is probably dealing with an inside job: she uses not words, but intelligent gaze. This connects professional expertise to circumstances and the feeling of thinking. It is suggested that, as for other species, human appraisal is based in synergies. However, since the verbal aspect of language constrains action and thinking, we also develop customary ways of behaving. Humans extend embodiment by linking real-time activity to actions through which the collectivity imposes a variable degree of control over how individuals realise values. (shrink)

Symbol Grounding Problem is commonly considered one of the central challenges in the philosophy of artificial intelligence as its resolution is deemed necessary for bridging the gap between simple data processing and understanding of meaning and language. SGP has been addressed on numerous occasions with varying results, all resolution attempts having been severely, but for the most part justifiably, restricted by the Zero Semantic Commitment Condition. A further condition that demands explanatory power in terms of machine-to-human communication is the Non-Stupidity (...) Condition that demands an SG approach to be able to account for plausibility of higher-level language use and understanding, such as pragmatics. In this article, we undertake the endeavour of attempting to explain how merging certain early requirements for SG, such as embodiment, environmental interaction, and compliance with the Z-Condition with symbol emergence rather than direct attempts at symbol grounding can help emulate human language acquisition. Along with the presumption that mind and language are both symbolic and computational, we argue that some rather abstract aspects of language can be logically formalised and finally, that this melange of approaches can yield the explanatory power necessary to satisfy the Non-Stupidity Condition without breaking any previous conditions. (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)

I want to suggest that the major influence of classical arguments for embodiment like "The Embodied Mind" by Varela, Thomson & Rosch (1991) has been a changing of positions rather than a refutation: Cognitivism has found ways to retreat and regroup at positions that have better fortification, especially when it concerns theses about artificial intelligence or artificial cognitive systems. For example: a) Agent-based cognitivism' that understands humans as taking in representations of the world, doing rule-based processing and then acting on (...) them (sense-plan-act) is often limited to conscious decision processes; and b) Purely syntactic cognition is compatible with embodiment, or supplemented by embodiment (e.g. for 'grounding'). While the empirical thesis of embodied cognition ('embodied cognitive science') is true and the practical engineering thesis ('morphological computation', 'cheap design') is often true, the conceptual thesis ('embodiment is necessary for cognition') is likely false - syntax is often enough for cognition, unless grounding is really necessary. I conclude that it has become more sensible to integrate embodiment with traditional approaches rather than "fight for embodiment" or "against cognitivism". (shrink)

Floridi and Taddeo propose a condition of “zero semantic commitment” for solutions to the grounding problem, and a solution to it. I argue briefly that their condition cannot be fulfilled, not even by their own solution. After a look at Luc Steels' very different competing suggestion, I suggest that we need to re-think what the problem is and what role the ‘goals’ in a system play in formulating the problem. On the basis of a proper understanding of computing, I come (...) to the conclusion that the only sensible ground-ing problem is how we can explain and re-produce the behavioral ability and function of meaning in artificial computational agents. (shrink)