Active symbols and internal models: Towards a cognitive connectionism

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 models are 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.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45877/1/146_2005_Article_BF01889764.pd

Design strategies for the exploration of product meaning in meaning-driven innovation

Design has been framed as a driver of innovation through product meaning, but it falls short when it comes to dedicated knowledge and methods directly applicable into design practices. Structured by Design Research Methodology (DRM) (Blessing and Chakrabarti 2009), this thesis combines exploratory research with practice-based design research. This thesis presents a literature review covering design studies, psychology, cognitive semantics, linguistics, marketing, innovation management and new product development. Together combined these have been used to develop a new framework of ‘product meaning’ consisting of 4 definitions: meaning as conceptualisation, as importance, as intention, and as representation. The framework has been used to demonstrate that different types of meaning are utilised throughout different stages of product development. Meaning as conceptualisation is identified as fundamental, and the most suitable, for design practice engaged in product meaning innovation. Three strategies of innovation of product meaning through product re-purposing are identified. Furthermore, from the field of cognitive science, theories and methods such as concept categorisation, thematic roles and conceptual blending are used as analysis tools for the selected 6 examples of innovative new meaning products. The structure of meaning innovations has been identified to consist of seven distinctive elements. Ten common characteristics of new meaning innovations are identified and, additionally, an exploratory method of current meanings of products is presented. Moreover, through engagement in practice-based design research a new meaning-driven design process has been developed. The findings from this research have been combined into a new design platform for an approach to meaning innovation and evaluated with experienced designers.</div

Connectionism, classical cognitive science and experimental psychology

There has been an enduring tension in modern cognitive psychology between the computational models available and the experimental data obtained. Standard computational models have assumed the symbolic paradigm: that it is constitutive of cognitive processes that they are mediated by the manipulation of symbolic structures. Such schemes easily handle formal inferences, and memory for arbitrary symbolic material. However, context-sensitive defeasible inference and content-addressable memory retrieval have remained problematic. By contrast, in the empirical data on human memory and inference, the opposite profile is observed. Everyday mundane reasoning is both context dependent and defeasible, and yet is performed easily and naturally, whereas subjects are typically unable to perform the simplest formal reasoning task (Wason and Johnson-Laud 1972; Evans 1982). In memory, content-addressable access in knowledge-rich domains seems natural and unproblematic for human subjects, whereas people can retain only very small quantities of arbitrary material. Despite this tension between experiment and theory, Fodor and Pylyshyn (1988) have recently reaffirmed what they term the “classical symbolic paradigm”. That is, they argue that symbolic cognitive processes are autonomous from their implementation. Thus they question the relevance of connectionist theorizing for psychology, and suggest that connectionism should be viewed as a theory of implementation for autonomous classical architectures

Connectionism, classical cognitive science and experimental psychology

Classical symbolic computational models of cognition are at variance with the empirical findings in the cognitive psychology of memory and inference. Standard symbolic computers are well suited to remembering arbitrary lists of symbols and performing logical inferences. In contrast, human performance on such tasks is extremely limited. Standard models donot easily capture content addressable memory or context sensitive defeasible inference, which are natural and effortless for people. We argue that Connectionism provides a more natural framework in which to model this behaviour. In addition to capturing the gross human performance profile, Connectionist systems seem well suited to accounting for the systematic patterns of errors observed in the human data. We take these arguments to counter Fodor and Pylyshyn's (1988) recent claim that Connectionism is, in principle, irrelevant to psychology