The other hard problem : How to bridge the gap between subsymbolic and symbolic cognition

The constructivist notion that features are purely functional is incompatible with the classical computational metaphor of mind. I suggest that the discontent expressed by Schyns, Goldstone and Thibaut about fixed-features theories of categorization reflects the growing impact of connectionism, and show how their perspective is similar to recent research on implicit learning, consciousness, and development. A hard problem remains, however: How to bridge the gap between subsymbolic and symbolic cognition. Cleeremans: Commentary on Schyns, Goldstone, and Thibaut 2 Schyns, Goldstone and Thibaut’s (hereafter, SG&T) target article touches on deep and fundamental issues in Cognitive Science, and it is in this way that their claims are relevant to several domains beyond categorization, including consciousness (see Cleeremans, in press; Perruchet, in press), development (see Clark & Karmiloff-Smith, 1993), and implicit learning — the process by which we can learn without intention to do so and without awareness of the resulting knowledge. In this commentary, I would like to argue that the constructivist view of cognition advanced by SG&T is consistent with new perspectives on implicit learning. The common theme is that the classical framework is under attack in both the categorization and implicit learning fields. The classical framework is grounded in the idea that cognition is about manipulating (symbolic) representations (e.g., Newell and Simon, 1972). It is at the core of many models of performance in a wide variety of domains, and also constitutes the underlying theoretical framework of most experimental work in cognitive science. The key assumption of the classical framework that is relevant for this commentary is the notion that cognition involves “manipulating” representations. Indeed, this assumption entails that (1) at least some primitive representations preexist (before the onset of a learning or a categorization episode, or before the onset of development), (2) that these representations are essentially static and fixed, and (3) and that they exist independently of the processor that manipulates them. Thus, this perspective on cognition makes it very natural to imagine that features, as representations that have diagnostic value, somehow have an existence that is independent from their use in a given context. In contrast to this view, SG&T propose that features do not exist per se before they are needed and get created. Features thus have a purely functional existence that is determined by their relevance in a specific task context as well as by the learner’s previous experience. The same dissatisfaction with the traditional metaphor of mind is now emerging in implicit learning research. In contrast to typical categorization tasks, implicit learning tasks usually involve precisely the kind of complex stimuli that SG&T wish to see used more often in their own domain. Beyond stimulus complexity, however, the most important difference is that participants in implicit learning tasks are never explicitly instructed to encode the regularities Cleeremans: Commentary on Schyns, Goldstone, and Thibaut 3 contained in the material. This, however, does not prevent them from becoming sensitive to this information, albeit they do so only in an indirect, implicit way. For instance, people exposed to sequentially structured sequences of stimuli presented in the context of a choice reaction time task progressively become sensitive to the rules used to generate the sequences (as suggested by their faster responding to structured as opposed to unstructured material) despite the facts (1) that the task does not require them to develop this sensitivity, (2) that the instructions do not mention the existence of a rule system, and (3) that their performance in comparable direct tests of their knowledge of the rule system remains very limited (e.g., Jiménez, Méndez & Cleeremans, 1996). Similar patterns of results have been repeatedly obtained with other implicit learning tasks, such as artificial grammar learning (e.g., Reber, 1993) or system control (see Berry and Dienes, 1993). In contrast to typical concept learning and categorization tasks thus, implicit learning tasks never require participants to develop fully explicit symbolic representations of the relevant information. Participants must acquire “features”, however, because their performance indicates successful (but indirect) discrimination between different categories of stimuli. The fact that they can do so without having developed the corresponding explicit, verbalizable, symbolic knowledge that is made mandatory by the demand characteristics of categorization tasks suggests that implicit learning tasks can probably be thought of as involving the initial stages of categorization, that is, precisely the kind of feature creation processes described as centrally important by SG&T. I have argued elsewhere (Cleeremans, 1994a; Cleeremans, in press) that the connectionist framework probably provides us with the best metaphor to think about how such implicit knowledge can come to influence performance without having the properties of symbolic representation. Connectionist models indeed excel at representing complex stimuli as graded, continuous patterns that can preserve much of the variability contained in the input while at the same time providing enough structure to satisfy the demands of the task (Cleeremans, 1993; 1994b). Connectionist models further make it clear how such knowledge can develop based on sub-symbolic processing and representational principles. To summarize, the target article makes it clear that theories of categorization based on the traditional computational metaphor of mind (roughly, processes that manipulate Cleeremans: Commentary on Schyns, Goldstone, and Thibaut 4 representations) are fundamentally flawed. A growing dissatisfaction with the computational metaphor is also emerging in consciousness and implicit learning research, and for essentially the same reasons: The relative inflexibility of symbolic representation, the artificial character of a nevertheless deeply ingrained distinction between processes and representations, and the weaknesses of classical systems in accounting for change and for the emergence of new forms. There is little doubt that connectionism and other similar dynamical (e.g., Port and van Gelder, 1995) approaches offer many fundamental insights into how one may start to address these limitations of classical symbolic systems. The problem with SG&T’s approach, however, is that they fail (as many others) to offer any specific discussion of “the other hard problem” that is common to the categorization, implicit learning, consciousness and development fields: How to bridge the gap that separates subsymbolic and symbolic cognition. In this respect, Representational Redescription, as proposed by Clark and Karmiloff-Smith (1993), is an interesting suggestion in that it leaves open the possibility that multiple recodings (possibly cast at different levels of complexity) of the same input remain available for further processing, a property that is undoubtedly central to confer flexibility to any learning system.