If the above picture is correct, and heuristics are deployed by a domain-general computational mechanism in central cognition, two further questions arise: What is the nature of the rep- resentational modules heuristics exploit? and How does the suggested architecture facilitate heuristic reasoning? It is not obvious from the surface how heuristics work, since they are
essentially simple algorithms; there is nothing in the heuristics themselves that specifies how they make use of informationally rich structures of representations. I will address these issues in turn in the present section and the next.
Let us begin by scrutinizing what Chomskian modules are and their role in the proposed ar- chitecture. Chomskian modules are essentially systems of mental representations. But Samuels (1998) and Fodor (1983, 2000) specify certain other features. One is that Chomskian modules are supposed to be bodies of mentally represented knowledge. Of course, there are differ- ent sorts of knowledge, declarative (or propositional) and procedural being most commonly distinguished. But the kind of knowledge that Samuels and Fodor are referring to is truth-
evaluable knowledge. This implies, at least, that the representations of interest have proposi-
tional content. The importance of this is evident vis-`a-vis CTM: Computations are transfor- mations of representations which respect semantical relations, such as implication and logical consequence; and, in Fodor’s words, “It is . . . a point of definition that such semantic relations hold only among the sorts of things to which propositional content can be ascribed” (Fodor, 1983, p. 5).
Another feature of Chomskian modules, according to Samuels, is that they encode various kinds of information about their domain. This, as we saw, is how Chomskian modules are informationally rich. But I think we can add to this that the information encoded in a Chom- skian module is encoded in a highly organized fashion. A domain-general mechanism can take advantage of generous amounts of domain-specific information encoded in a Chomskian module, and thereby enable quick and frugal cognition. But if the information is structured or organized in specific ways, it would enable quicker and more frugal cognition. Contrariwise, if such information is not structured or organized in specific ways, it would retard the speed and efficiency of search and processing. Characteristic human performance on many cognitive tasks suggests that the structures heuristics exploit are distinctly organized to produce robust reasoning and inference patterns.
structure, but also bear specific intra-system relations, which would further facilitate fast and frugal reasoning. Reasoning within one domain, such as “folk psychology”, often bears in a number of ways on other domains, such as “folk biology”. For example, in deciding what intentions to ascribe an organism, one must make inferences about the kind of organism to which one is ascribing intentions. Without such connections between bodies of knowledge, we would not be able to make the rich inferences we characteristically do. I will return to these issues concerning informational organization below.
Samuels introduces a further feature in response to the possibility of counting any domain- specific collection of truth-evaluable representations as Chomskian modules: “We do not want, for example, to treat a child’s beliefs about toy dinosaurs as a module” (Samuels, 2000, p. 18). Thus, Samuels asserts that Chomskian modules are innate. Along with Fodor, Samuels under- stands Chomskian modules to be the very kind of system that Chomsky’s Universal Grammar is supposed to be (hence the namesake), and Chomsky’s Universal Grammar is supposed to be
inter alia innate. However, I do not see why heuristics, or any domain-general operation for
that matter, must be conceived to exploit only innate domain-specific systems of knowledge. It is quite conceivable that there is at least some acquired knowledge contained within many of our domain-specific systems of knowledge.25 Furthermore, there is no principled reason why there cannot be entire systems of acquired information that is stored and organized in a fashion similar to innate domain-specific knowledge, and exploited by domain-general systems just as innate systems are.
Certainly, the way something like Universal Grammar is supposed to get unpacked during childhood development and early language learning cannot be emulated by acquired bodies of knowledge. However, the architecture suggested above and its operations are not about cog- 25“Acquired” should be read as “acquired in consequence of experience”, thus contrasting “innate”. Learning is sufficient for acquiring, but it is not necessary. One can be hit on the head and thereby acquire beliefs, though these are not learned beliefs. We might hesitate calling non-learned acquired beliefsknowledge, since such acquired beliefs may not meet a criterion of justification. However, I want to avoid speaking of learned knowledge to avoid issues concerning what constitutes learning. Fodor (2008), for instance, believes that learning consists in “a rational process like inductive inference” (p. 145). Yet I do not want to suggest here that the systems of knowledge to which I am referring in the text are learned in this way. In fact, I do not want to commit to any particular account of learning.
nitive development, but about cognitivedeployment. The discussion that took place there, as well as the discussion provided by Samuels (1998, 2000), concerns how the mind can do what it does given a domain-general architecture of central cognition. Nothing was said, nor need be said, about the poverty of stimulus with respect to how we know the grammar of language, or conceptions of (folk) psychology, or (folk) biology, or whatever. Thus, it seems perfectly acceptable within the foregoing cognitive architecture that a domain-general computational device can utilize informationally rich, acquired domain-specific systems of knowledge. If this is right, then I believe it is necessary to forego the Chomskian namesake to refer to such informationally rich representational structures.
In addition to dropping the innateness requirement, I propose to jettison the domain-specificity requirement. This is not as bold of a move as it may appear. It seems that what is really do- ing the work in facilitating fast and frugal cognition is not the domain-specificity per se of a system of representations, but the kinds of information encoded in these systems and the man- ner in which it is encoded. For, to partially echo what was said above, it is possible that one can have an unorganized system of lots of domain-specific information, but it will be doubtful that a domain-general computational mechanism would exhibit the same speed and efficiency operating over this unorganized body of knowledge as it would operating over a highly struc- tured system. Moreover, it is possible that one can have a domain-specific body of knowledge that is quite impoverished; and although a domain-general device would likely be able to op- erate quickly and frugally over such a system, owing primarily to the little information that ever gets considered, the inferences made would not be as robust and accurate as those made by operations over richer systems.26 That a system of information is dedicated to a specific domain no doubt contributes to the extent to which the system is organized, since there are natural relations among items of information within a domain. However, domain-specificity
per sedoes not seem to be necessary for a body of knowledge to be structured and organized
in ways conducive to fast and frugal exploitation by a domain-general computational mecha- 26Notwithstanding that accuracy is not a general feature of heuristics, as illustrated by the work of Kahneman and Tversky.
nism. To be sure, it is conceivable that someone has an exceeding amount of knowledge about the French Revolution, and thereby has built a non-domain-specific system of representations on this topic, which is highly organized and informationally rich.27 When drawing inferences about or relating to the French Revolution (via her domain-general reasoning mechanism), this person would almost certainly carry them out quickly and frugally (and probably robustly and accurately too).
To summarize, a domain-general computational mechanism can deploy suitable strategies (such as heuristics) to enable quick and frugal cognition by exploiting informationally rich systems of representations, or truth-evaluable knowledge. These latter systems encode various kinds of information in a highly organized fashion; not only is there internal organization, but there is also intra-system organization that exhibits specific relations between systems. But unlike Chomskian modules, these systems have to be neither innate nor domain-specific. Instead, they can be acquired and/or non-domain-specific, so long as they possess the requisite structure and organization. This should not be taken to mean that the systems of representations under consideration can be domain-general. A domain-general collection of representations would not bear the kinds of organization and relations that I take to be characteristic of such informationally rich systems. Working memory may be a domain-general system capable of storing any kind of representation, but it may store, at the same time, completely unrelated representations. For a system of representations to be non-domain-specific, on the other hand, simply means that it fails to pick out a proper domain as cognitive scientists usually understand the term (which is closely related to natural kinds).
I already suggested dropping the Chomskian namesake to refer to such non-domain-specific systems. But since domain-specificity is considered by most, if not all, theorists to be essential to modularity, it appears that “module” must also be omitted. Hence, for lack of a better name, I will call these informationally rich systems of representations presently under consideration 27Few cognitive scientists, if any, would consider the French Revolution as a proper domain, since the class of representations that belong to such a topic do not pick out anything resembling a natural kind. I do not want to enter into any debate over what constitutes a domain. The reader who is dissatisfied with this can substitute any non-domain-specific example. See Hirschfeld and Gelman (1994) for debates on what constitutes a domain.
k-systems(where “k” stands for knowledge). We shall see in the next chapter that the knowl- edge that k-systems are supposed to embody does not necessarily have to be truth-evaluable. Indeed, as I will argue, perceptual information can be, and often is, included within the systems of representations over which a domain-general computational device can operate. For now, however, it will do no harm to assume that such knowledge is truth-evaluable, but it should be kept in mind that this is only part of the story.
K-systems are what heuristics partially owe their speed and efficiency to, but k-systems are also what heuristics owe their robustness and potency to. To be sure, heuristics are robust and potent strategies insofar as they can be applied in a vast number of contexts while producing satisficing inferences. The way I suggest that heuristics can perform like this is by being sen- sitive to specific parameters that are built into k-systems. For example, a k-system for chess might contain numerous parameters for openings, endgames, the pieces and their positions on the board, etc. Depending on what the task at hand is, certain representations will be thereby more readily available and more easily brought to bear than others. And indeed, the parameters may affect how the present game isrepresented. Other k-systems that exhibit similar parame- ters will be amenable to the same heuristics and strategies deployed by the central system for chess—for example, k-systems for other chess-like games, or even for activities such as bat- tle or sports that bear certain relations to chess. The parameters possessed by k-systems thus control the flow of information, but more importantly, they partly constrain the operations per- formed by the domain-general computational device. More specifically, I claim that k-systems have control parameters that partly constrain how the information possessed by the system can be manipulated; in terms of what is of interest here, the control parameters partly constrain the way heuristics operate, or equivalently, what heuristics are brought to bear. These parameters embedded in k-systems are either acquired or innate, depending on the nature of the k-system in question.28
28We might note a similarity to Marvin Minsky’s (1975) notion offrames(not to be confused with the frames of the frame problem, though there is a relation). In Minsky’s words, “A frame is a data-structure for representing a stereotyped situation, like being in a certain kind of living room, or going to a child’s birthday party. Attached to each frame are several kinds of information. Some of this information is about how to use the frame. Some
Cognitive tasks will also have certain structures of their own. For instance, a task of decid- ing between two alternatives will have a specific structure that enables comparisons between the two alternatives on a number of dimensions, and the consideration of relevant representa- tions and other information. Such a decision structure will be different from the structure of, say, estimating the probability of an event, which might enable the recall of past occurrences and perhaps other knowledge about probability and frequencies. Moreover, the content under consideration will influence the structure of the task. For a decision of choosing which car to buy will not share the exact structure with a decision of which move to make in chess, or with a decision of whether to accept a job offer. My suggestion is that the structures of cognitive tasks will help in cuing the activation of particular k-systems. Thus, it is in conjunction with the informational structure of the task that the control parameters of the activated k-systems con- strain what heuristics are deployed; in the right conditions, the informational structure of the task and the activated k-systems’ control parametersdeterminewhat heuristics are deployed.29 In this way, a domain-general device can deploy suitable task-specific heuristics without being task- or domain-specific itself. This is a lot like how a Universal Turing Machine can simulate any specific Turing machine given the appropriate input. Thus, the claim here is that heuristics are powerful inference strategies in the same way that Universal Turing Machines are powerful computing devices.
There will be instances, however, when k-systems will lack the appropriate control param- eters. This can occur especially with impoverished k-systems. Yet this does not mean that the k-systems in question will lack control parameters altogether, although this can be the case. When a k-system lacks the appropriate control parameters, the speed, efficiency, and/or accu- is about what one can expect to happen next. Some is about what to do if these expectations are not confirmed” (p. 211). The view I am advancing, however, is different from Minsky’s frames (or the related notion ofscripts). K-systems are not to be understood in terms of stereotypes or paradigms, and not in terms of “situations” either. As we shall see in the next chapter, k-systems are to be conceived asconcepts. Moreover, the function of frames (or scripts) in cognition is supposed to be quite extensive, having a role in the processes of vision and imagery, linguistic and other kinds of understanding, and memory storage and recall. On the other hand, I am not making such lofty suggestions here, as I am confining my account of k-systems to facilitating heuristic processes (though future work might extend this to include other aspects of cognition).
29This is another possible way to avoid Fodor’s (2000) worry about an ensuing infinite regress when employing heuristics, as outlined above.
racy of inferences are negatively affected. In such instances, heuristics may be deployed based on the structure of the problem alone, or default heuristics may be deployed, or no heuristics will be deployed at all. In some of these instances, I believe, we witness the mistakes and biases that Kahneman and Tversky (and their followers) emphasized and researched. In fact, I believe that such mistakes and biases result from impoverished k-systems generally, in terms of the kinds, amount, accuracy, and organization of information. But I will save discussion on this issue for chapter 5.
In sum, k-systems influence the kinds of computations that operate over them in central cognition via the information contained in the k-system along with the relations within or be- tween them. What is entailed is that the operations of heuristics exploit ordered and structured systems of knowledge.
Let us now switch gears and consider a more general view of how heuristics exploit infor- mationally rich structures and of the role of k-systems in cognition. This will be my task in the next section. In the chapters to follow I will return to the matters of what kind of entity k-systems are, andhowheuristics exploit them.