 A further feature of modular systems is that their outputs are relatively ‘shallow’. Exactly what this means is unclear. But the depth of an output seems to be a function of at least two properties: first, how much computation is required to produce it (i.e., shallow means computationally cheap); second, how constrained or specific its informational content is (i.e., shallow means informationally general) (Fodor, 1983, p. 87). These two properties are correlated, in that outputs with more specific content tend to be more costly for a system to compute, and vice versa. Some writers have interpreted shallowness to require non-conceptual character (e.g., Carruthers, 2006, p. 4). But this conflicts with Fodor’s own gloss on the term, in which he suggests that the output of a plausibly modular system such as visual object recognition might be encoded at the level of ‘basic-level’ concepts, like DOG and CHAIR (Rosch et al., 1976). What’s ruled out here is not concepts per se, then, but highly theoretical concepts like PROTON, which are too informationally specific and too computationally expensive to meet the shallowness criterion.