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Embodiment and Symbol Grounding

In [Lakoff 1987], conceptual embodiment is described as the idea that the properties of certain categories are a consequence of the nature of human biological capacities and of the experience of functioning in a physical and social environment. It is contrasted with the ``objectivist'' idea that concepts exist independently of the bodily nature of any thinking beings and independently of their experience (p. 12). Several sources from the cognitive science literature at large are cited in [Lakoff 1987] as having contributed to the development of this idea. My definition of embodiment in the context of autonomous agents is more restricted, but more precise (Section ).

According to [Harnad 1990], symbolic representations must be grounded bottom-up in non-symbolic representations of two kinds:

• iconic representations, which are ``analogs of the proximal sensory projections of distal objects and events'', and

• categorical representations, which are ``learned and innate feature-detectors that pick out invariant features of object and event categories''.

Part of my model of color perception and color naming (Chapter ) is a color space defined by several dimensions derived from or modeled after the activations of the cone photoreceptors in the human retina. This is an analog (continuous) transform of the sensory ``projection'' of the radiation reflected off the ``distal'' surface of the object being imaged; therefore, it qualifies as an iconic representation. A second part of the model is a mapping from each point in the color space to one or more pairs consisting of a color term and a ``confidence'' or ``goodness'' measure. The codomain of this mapping qualifies as a categorical representation, in Harnad's framework. My model does not involve symbolic representations of the kind Harnad envisions, as it is limited to the level of terms only, not sentences.

Harnad lists five tasks a cognitive theory has to explain: discrimination, identification, manipulation, describing, and responding to descriptions of objects, events, and states of affairs in the world. A CLR (Color Labeling Robot) is in principle able to perform all these tasks:

• Discrimination: any two stimuli that result in different coordinates in the model's color space are discriminable for the CLR

• Identification: the CLR is able to identify (categorize) any stimulus as belonging to one (or more) of the categories represented in the codomain of , and give a confidence or ``goodness'' rating.

• Manipulation: an extended CLR (equipped with manipulators) that is capable of acting in its environment, in addition to perceiving it, is able to manipulate objects based on their perceived color properties.

• Describing: since the model only extends to the level of terms, describing is limited to naming the colors the agent perceives in the world, which the CLR is able to do.

• Responding to descriptions: the CLR responds to a description (color name) by pointing out an example fitting the description (a colored object in its environment).