  Nevertheless, the classical AI researcher, convinced that the regress of contexts will bottom out eventually, may still elect to pursue the research agenda of building systems based on rules for determining relevance, drawing inspiration from the past successes of classical AI. Whereupon the dissenting philosopher might point out that AI's past successes have always been confined to narrow domains, such as playing chess, or reasoning in limited microworlds where the set of potentially relevant propositions is fixed and known in advance. By contrast, human intelligence can cope with an open-ended, ever-changing set of contexts (Dreyfus 1992; Dreyfus 2008; Wheeler 2005; Wheeler 2008; Rietveld 2012). Furthermore, the classical AI researcher is vulnerable to an argument from holism. A key claim in Fodor's work is that when it comes to circumscribing the consequences of an action, just as in the business of theory confirmation in science, anything could be relevant (Fodor 1983, 105). There are no a priori limits to the properties of the ongoing situation that might come into play. Accordingly, in his modularity thesis, Fodor uses the frame problem to bolster the view that the mind's central processes—those that are involved in fixing belief—are “informationally unencapsulated”, meaning that they can draw on information from any source (Fodor 1983; Fodor 2000; Fodor 2008; Dreyfus 1991, 115–121; Dreyfus 1992, 258). For Fodor, this is a fundamental barrier to the provision of a computational account of these processes.