Concept mapping and other formalisms as mindtools for representing knowledge

We seek to provide an alternative theoretical perspective on concept mapping (a formalism for representing structural knowledge) to that provided by Ray McAleese in this issue of ALT-J (auto-monitoring). We begin with an overview of concept maps as a means of describing a learner's knowledge constructs, and then discuss a broader class of tools, Mindtools, of which concept maps are a member. We proceed by defining Mindtools as formalisms for representing knowledge, and further elaborate on concept maps as a formalism for representing a particular kind of knowledge: structural knowledge. We then address McAleese's use of the term auto-monitoring and some of the steps in his model of concept maps. Finally, we describe some limitations of concept mapping as a formalism and as a cognitive learning strategy

A Deflationary Account of Mental Representation

Among the cognitive capacities of evolved creatures is the capacity to represent. Theories in cognitive neuroscience typically explain our manifest representational capacities by positing internal representations, but there is little agreement about how these representations function, especially with the relatively recent proliferation of connectionist, dynamical, embodied, and enactive approaches to cognition. In this talk I sketch an account of the nature and function of representation in cognitive neuroscience that couples a realist construal of representational vehicles with a pragmatic account of mental content. I call the resulting package a deflationary account of mental representation and I argue that it avoids the problems that afflict competing accounts

Auditing the Numeracy Demands of the Middle Years Curriculum

The National Numeracy Review Report recognized that numeracy development requires an across the curriculum commitment. To explore the nature of this commitment we conducted a numeracy audit of the South Australian Middle Years curriculum, using a numeracy model that incorporates mathematical knowledge, dispositions, tools, contexts, and a critical orientation. All learning areas in the published curriculum were found to have distinctive numeracy demands. The audit should encourage teachers to promote numeracy in even richer ways in the curriculum they enact with students

Expressivism, Inferentialism and the Theory of Meaning

One’s account of the meaning of ethical sentences should fit – roughly, as part to whole – with one’s account of the meaning of sentences in general. When we ask, though, where one widely discussed account of the meaning of ethical sentences fits with more general accounts of meaning, the answer is frustratingly unclear. The account I have in mind is the sort of metaethical expressivism inspired by Ayer, Stevenson, and Hare, and defended and worked out in more detail recently by Blackburn, Gibbard, and others. So, my first aim (§1) in this paper is to pose this question about expressivism’s commitments in the theory of meaning and to characterize the answer I think is most natural, given the place expressivist accounts attempt to occupy metaethics. This involves appeal to an ideationalist account of meaning. Unfortunately for the expressivist, however, this answer generates a problem; it’s my second aim (§2) to articulate this problem. Then, my third aim (§3) is to argue that this problem doesn’t extend to the sort of account of the meaning of ethical claims that I favor, which is like expressivism in rejecting a representationalist order of semantic explanation but unlike expressivism in basing an alternative order of semantic explanation on inferential role rather than expressive function

What Can Artificial Intelligence Do for Scientific Realism?

The paper proposes a synthesis between human scientists and artificial representation learning models as a way of augmenting epistemic warrants of realist theories against various anti-realist attempts. Towards this end, the paper fleshes out unconceived alternatives not as a critique of scientific realism but rather a reinforcement, as it rejects the retrospective interpretations of scientific progress, which brought about the problem of alternatives in the first place. By utilising adversarial machine learning, the synthesis explores possibility spaces of available evidence for unconceived alternatives providing modal knowledge of what is possible therein. As a result, the epistemic warrant of synthesised realist theories should emerge bolstered as the underdetermination by available evidence gets reduced. While shifting the realist commitment away from theoretical artefacts towards modalities of the possibility spaces, the synthesis comes out as a kind of perspectival modelling

Cognitive Computation sans Representation

The Computational Theory of Mind (CTM) holds that cognitive processes are essentially computational, and hence computation provides the scientific key to explaining mentality. The Representational Theory of Mind (RTM) holds that representational content is the key feature in distinguishing mental from non-mental systems. I argue that there is a deep incompatibility between these two theoretical frameworks, and that the acceptance of CTM provides strong grounds for rejecting RTM. The focal point of the incompatibility is the fact that representational content is extrinsic to formal procedures as such, and the intended interpretation of syntax makes no difference to the execution of an algorithm. So the unique 'content' postulated by RTM is superfluous to the formal procedures of CTM. And once these procedures are implemented in a physical mechanism, it is exclusively the causal properties of the physical mechanism that are responsible for all aspects of the system's behaviour. So once again, postulated content is rendered superfluous. To the extent that semantic content may appear to play a role in behaviour, it must be syntactically encoded within the system, and just as in a standard computational artefact, so too with the human mind/brain - it's pure syntax all the way down to the level of physical implementation. Hence 'content' is at most a convenient meta-level gloss, projected from the outside by human theorists, which itself can play no role in cognitive processing

Design as conversation with digital materials

This paper explores Donald Schön's concept of design as a conversation with materials, in the context of designing digital systems. It proposes material utterance as a central event in designing. A material utterance is a situated communication act that depends on the particularities of speaker, audience, material and genre. The paper argues that, if digital designing differs from other forms of designing, then accounts for such differences must be sought by understanding the material properties of digital systems and the genres of practice that surround their use. Perspectives from human-computer interaction (HCI) and the psychology of programming are used to examine how such an understanding might be constructed.</p

Memory Structure and Cognitive Maps

A common way to understand memory structures in the cognitive sciences is as a cognitive map​. Cognitive maps are representational systems organized by dimensions shared with physical space. The appeal to these maps begins literally: as an account of how spatial information is represented and used to inform spatial navigation. Invocations of cognitive maps, however, are often more ambitious; cognitive maps are meant to scale up and provide the basis for our more sophisticated memory capacities. The extension is not meant to be metaphorical, but the way in which these richer mental structures are supposed to remain map-like is rarely made explicit. Here we investigate this missing link, asking: how do cognitive maps represent non-spatial information?​ We begin with a survey of foundational work on spatial cognitive maps and then provide a comparative review of alternative, non-spatial representational structures. We then turn to several cutting-edge projects that are engaged in the task of scaling up cognitive maps so as to accommodate non-spatial information: first, on the spatial-isometric approach​ , encoding content that is non-spatial but in some sense isomorphic to spatial content; second, on the ​ abstraction approach​ , encoding content that is an abstraction over first-order spatial information; and third, on the ​ embedding approach​ , embedding non-spatial information within a spatial context, a prominent example being the Method-of-Loci. Putting these cases alongside one another reveals the variety of options available for building cognitive maps, and the distinctive limitations of each. We conclude by reflecting on where these results take us in terms of understanding the place of cognitive maps in memory