The study of mental representations requires a new scientific discipline
Integrating concepts from linguistics, cognitive psychology and neuroscience within the methodological framework of constructionist Artificial Intelligence
A new common ground between Al and cognitive science researchers
Unraveling mental representations will be an interdisciplinary effort among several cognitive sciences, but distinct from the disciplines that we find now:
The project will differ from linguistics, for instance, in similar way as geography differs from plate tectonics. Where linguists study the intrinsic ‘geography’ of languages, and the structural commonalities among them, mental representations uncover the underlying dynamics that produce natural languages (as the solutions to the problem of translating between hierarchical, distributed, associative, ambiguous representations and the discrete strings of symbols that we use as a means of exchanging and organizing ideas).
Mental representations lie outside the domain of neuroscience, which mostly focuses on material descriptions of the function of the underlying substrate. They cannot be studied well within contemporary psychology, which favors an experimentalist, quantitative approach, where we need to address qualitative questions by constructing working, implementable systems. And needless to say, the study of mental representations is currently not well represented in Artificial Intelligence research, which does provide a productive methodology, but has mostly turned towards applications and narrow Al solutions.
Despite the lack of a common methodological ground between the cognitive sciences, we can now observe a growing consensus on how to approach the problem of modeling the mind, and its representational apparatus. During the last decade, a number of initiatives have sprung up within Al, psychology and cognitive science, each with journals, workshops and conference series, and a large personal overlap among each community. Examples include Artificial General Intelligence (AGI), Biologically Inspired Cognitive Architectures (BICA), Cognitive Systems, and Cognitive Modeling (ICCM). Among those groups, there is an emerging consensus on several paradigms:
- Integrated architectures of cognition. We need to study whole, working systems, both for the purpose of comparison between approaches, but mainly, because cognition is not the product of the activity of individual modular functions, but of the interaction between them.
- Universal representations. Representations must provide both distributed and localist aspects, to enable neural learning, information retrieval via spreading activation, as well as symbolic processing (language and planning).
- Perceptual grounding. Representations should be grounded in an environmental interaction context, using a bottom-up/top-down perception paradigm, to allow for
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