and not in the composite nor in a machine that the Perception is to be sought.
(Leibniz, 1902, p. 254)
Leibniz called these simple substances monads and argued that all complex experi-ences were combinations of monads. Leibniz’ monads are clearly an antecedent of the architectural primitives that we have been discussing over the last few pages.
Just as thoughts are composites in the sense that they can be built from their com-ponent monads, an algorithm is a combination or sequence of primitive processing steps. Just as monads cannot be further decomposed, the components of an archi-tecture are not explained by being further decomposition, but are instead explained by directly appealing to physical causes. Just as the Leibniz mill’s monads would look like working pieces, and not like the product they created, the architecture produces, but does not resemble, complete algorithms.
The Chinese room would be a more compelling argument against the possibility of machine intelligence if one were to look inside it and actually see its knowledge.
This would mean that its homunculi were not discharged, and that intelligence was not the product of basic computational processes that could be implemented as physical devices.
hypothesis (Dawson, 1998), is used to explain information devices by performing analyses at three different levels: computational, algorithmic, and implementa-tional. The approach that has been developed in this chapter agrees with this view, but adds to it an additional level of analysis: the architectural. We will see through-out this book that an information processing architecture has properties that sepa-rate it from both algorithm and implementation, and that treating it as an inde-pendent level is advantageous.
The view that information processing devices must be explained by multiple levels of analysis has important consequences for cognitive science, because the general view in cognitive science is that cognition is also the result of information processing. This implies that a full explanation of human or animal cognition also requires multiple levels of analysis.
Not surprisingly, it is easy to find evidence of all levels of investigation being explored as cognitive scientists probe a variety of phenomena. For example, consider how classical cognitive scientists explore the general phenomenon of human memory.
At the computational level, researchers interested in the formal charac-terization of cognitive processes (such as those who study cognitive informatics [Wang, 2003, 2007]), provide abstract descriptions of what it means to memorize, including attempts to mathematically characterize the capacity of human memory (Lopez, Nunez, & Pelayo, 2007; Wang, 2009; Wang, Liu, & Wang, 2003).
At the algorithmic level of investigation, the performance of human subjects in a wide variety of memory experiments has been used to reverse engineer “memory”
into an organized system of more specialized functions (Baddeley, 1990) including working memory (Baddeley, 1986, 2003), declarative and nondeclarative memory (Squire, 1992), semantic and episodic memory (Tulving, 1983), and verbal and imagery stores (Paivio, 1971, 1986). For instance, the behaviour of the serial position curve obtained in free recall experiments under different experimental conditions was used to pioneer cognitive psychology’s proposal of the modal memory model, in which memory was divided into a limited-capacity, short-term store and a much larger-capacity, long-term store (Waugh & Norman, 1965). The algorithmic level is also the focus of the art of memory (Yates, 1966), in which individuals are taught mnemonic techniques to improve their ability to remember (Lorayne, 1998, 2007;
Lorayne & Lucas, 1974).
That memory can be reverse engineered into an organized system of sub-functions leads cognitive scientists to determine the architecture of memory. For instance, what kinds of encodings are used in each memory system, and what primitive processes are used to manipulate stored information? Richard Conrad’s (1964a, 1964b) famous studies of confusion in short-term memory indicated that it represented information using an acoustic code. One of the most controversial topics in classical cognitive science, the “imagery debate,” concerns whether the
primitive form of spatial information is imagery, or whether images are constructed from more primitive propositional codes (Anderson, 1978; Block, 1981; Kosslyn, Thompson, & Ganis, 2006; Pylyshyn, 1973, 1981a, 2003b).
Even though classical cognitive science is functionalist in nature and (in the eyes of its critics) shies away from biology, it also appeals to implementational evidence in its study of memory. The memory deficits revealed in patient Henry Molaison after his hippocampus was surgically removed to treat his epilepsy (Scoville & Milner, 1957) provided pioneering biological support for the functional separations of short-term from long-term memory and of declarative memory from nondeclarative memory. Modern advances in cognitive neuroscience have provided firm biological foundations for elaborate functional decompositions of memory (Cabeza & Nyberg, 2000; Poldrack et al., 2001; Squire, 1987, 2004). Similar evidence has been brought to bear on the imagery debate as well (Kosslyn, 1994;
Kosslyn et al., 1995; Kosslyn et al., 1999; Kosslyn, Thompson, & Alpert, 1997).
In the paragraphs above I have taken one tradition in cognitive science (the classical) and shown that its study of one phenomenon (human memory) reflects the use of all of the levels of investigation that have been the topic of the cur-rent chapter. However, the position that cognitive explanations require multiple levels of analysis (e.g., Marr, 1982) has not gone unchallenged. Some researchers have suggested that this process is not completely appropriate for explaining cog-nition or intelligence in biological agents (Churchland, Koch, & Sejnowski 1990;
Churchland & Sejnowski, 1992).
For instance, Churchland, Koch, & Sejnowski (1990, p. 52) observed that “when we measure Marr’s three levels of analysis against levels of organization in the nerv-ous system, the fit is poor and confusing.” This observation is based on the fact that there appear to be a great many different spatial levels of organization in the brain, which suggests to Churchland, Koch, & Sejnowski that there must be many ent implementational levels, which implies in turn that there must be many differ-ent algorithmic levels.
The problem with this argument is that it confuses ontology with epistemology.
That is, Churchland, Koch, & Sejnowski (1990) seemed to be arguing that Marr’s levels are accounts of the way nature is—that information processing devices are literally organized into the three different levels. Thus when a system appears to exhibit, say, multiple levels of physical organization, this brings Marr-as-ontology into question. However, Marr’s levels do not attempt to explain the nature of devices, but instead provide an epistemology—a way to inquire about the nature of the world. From this perspective, a system that has multiple levels of physical organization would not challenge Marr, because Marr and his followers would be comfortable applying their approach to the system at each of its levels of physical organization.
Other developments in cognitive science provide deeper challenges to the mul-tiple-levels approach. As has been outlined in this chapter, the notion of multiple levels of explanation in cognitive science is directly linked to two key ideas: 1) that information processing devices invite and require this type of explanation, and 2) that cognition is a prototypical example of information processing. Recent develop-ments in cognitive science represent challenges to these key ideas. For instance, embodied cognitive science takes the position that cognition is not information pro-cessing of the sort that involves the rule-governed manipulation of mentally rep-resented worlds; it is instead the control of action on the world (Chemero, 2009;
Clark, 1997, 1999; Noë, 2004, 2009; Robbins & Aydede, 2009). Does the multiple-levels approach apply if the role of cognition is radically reconstrued?
Churchland, Koch, & Sejnowski. (1990, p. 52) suggested that “[‘]which really are the levels relevant to explanation in the nervous system[’] is an empirical, not an a priori, question.” One of the themes of the current book is to take this sug-gestion to heart by seeing how well the same multiple levels of investigation can be applied to the three major perspectives in modern cognitive science: classical, connectionist, and embodied. In the next three chapters, I begin this pursuit by using the multiple levels introduced in Chapter 2 to investigate the nature of classi-cal cognitive science (Chapter 3), connectionist cognitive science (Chapter 4), and embodied cognitive science (Chapter 5). Can the multiple levels of investigation be used to reveal principles that unify these three different and frequently mutually antagonistic approaches? Or is modern cognitive science beginning to fracture in a fashion similar to what has been observed in experimental psychology?