0. It might help if we stop â€ścognitizingâ€ť computation and symbols.

1. Computation is not a subset of AI.

2. AI (whether â€śsymbolicâ€ť AI or â€śconnectionistâ€™ AI) is an application of computation to cogsci.

3. Computation is the manipulation of symbols based on formal rules (algorithms).

4. Symbols are objects or states whose physical â€śshapeâ€ť is arbitrary in relation to what they can be used and interpreted as referring to.

5. An algorithm (executable physically as a Turing Machine) manipulates symbols based on their (arbitrary) shapes, *not their interpretations* (if any).

6. The algorithms of interest in computation are those that have at least one meaningful interpretation.

7. Examples of symbol shapes are numbers (*1, 2, 3*), words (*one, two, three*; onyx, tool, threnody), or any object or state that is used as a symbol by a Turing Machine that is executing an algorithm (symbol-manipulation rules).

8. Neither a sensorimotor feature of an object in the world, nor a sensorimotor feature-detector of a robot interacting with the world, is a symbol (except in the trivial sense that any arbitrary shape can be used as a symbol).

9. What sensorimotor features (which, unlike symbols, are not arbitrary in shape) and sensorimotor feature-detectors (whether â€śsymbolicâ€ť or â€śconnectionistâ€ť) might be good for is connecting symbols inside symbol systems (e.g., robots) to the outside objects that they can be interpreted as referring to.

10. If you are interpreting â€śsymbolâ€ť in a wider sense than this formal, literal one, then you are closer to lit-crit than to cogsci.