architectures in question typically comprise collections of basic operations distinctive of the explanatory enterprise in cognitive science. Second, as noted above, the cognitive-scientific enterprise aims to account for a distinctive range of observable, measurable data: roughly speaking, those to do with intelligent behavior. Combining this point with the point about architectures, I mean to emphasize the role of mental representations as contributors to distinctive styles of modeling (styles not found, for example, in models of the immune system [Ramsey 2007, 125; 2016, 8] or climate-control systems [Ramsey 2007, 136]): mental representations explain intelligent behavior by (and only by) contributing within the framework of an architecture (broadly speaking), the functioning of which is governed by, or includes, certain basic operations – which might take a variety of forms, depending on the architecture in question, and tend, at least in the details of their operation, to be absent from other parts of the natural world. Third, to a very great extent, the data of cognitive science are relational; they are outputs typed in terms of what they produce beyond the boundary of the organism: sound patterns in the air, physical interactions with objects, navigation through environments, reactions to stimulus items on a monitor. Various internal units contribute differentially to the production of such relationally typed behavior, where a unit’s differential contribution correlates with certain external relata; and, against the backdrop of a successful theory of mental content, the activation of a given unit contributes significantly to the production of behavior that is directed toward (and thus partly individuated by its relation to) the very external object or structure that unit represents. In which case, so long as the previously discussed conditions are met, the unit plays the role of a mental representation and
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