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Mind, Body, World- Foundations of Cognitive Science, 2013a

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hypothesis (Dawson, 1998), is used to explain information devices by performing<br />

analyses at three different levels: computational, algorithmic, and implementational.<br />

The approach that has been developed in this chapter agrees with this view,<br />

but adds to it an additional level <strong>of</strong> analysis: the architectural. We will see throughout<br />

this book that an information processing architecture has properties that separate<br />

it from both algorithm and implementation, and that treating it as an independent<br />

level is advantageous.<br />

The view that information processing devices must be explained by multiple<br />

levels <strong>of</strong> analysis has important consequences for cognitive science, because the<br />

general view in cognitive science is that cognition is also the result <strong>of</strong> information<br />

processing. This implies that a full explanation <strong>of</strong> human or animal cognition also<br />

requires multiple levels <strong>of</strong> analysis.<br />

Not surprisingly, it is easy to find evidence <strong>of</strong> all levels <strong>of</strong> investigation being<br />

explored as cognitive scientists probe a variety <strong>of</strong> phenomena. For example, consider<br />

how classical cognitive scientists explore the general phenomenon <strong>of</strong> human memory.<br />

At the computational level, researchers interested in the formal characterization<br />

<strong>of</strong> cognitive processes (such as those who study cognitive informatics<br />

[Wang, 2003, 2007]), provide abstract descriptions <strong>of</strong> what it means to memorize,<br />

including attempts to mathematically characterize the capacity <strong>of</strong> human memory<br />

(Lopez, Nunez, & Pelayo, 2007; Wang, 2009; Wang, Liu, & Wang, 2003).<br />

At the algorithmic level <strong>of</strong> investigation, the performance <strong>of</strong> human subjects in<br />

a wide variety <strong>of</strong> memory experiments has been used to reverse engineer “memory”<br />

into an organized system <strong>of</strong> more specialized functions (Baddeley, 1990) including<br />

working memory (Baddeley, 1986, 2003), declarative and nondeclarative memory<br />

(Squire, 1992), semantic and episodic memory (Tulving, 1983), and verbal and<br />

imagery stores (Paivio, 1971, 1986). For instance, the behaviour <strong>of</strong> the serial position<br />

curve obtained in free recall experiments under different experimental conditions<br />

was used to pioneer cognitive psychology’s proposal <strong>of</strong> the modal memory model,<br />

in which memory was divided into a limited-capacity, short-term store and a much<br />

larger-capacity, long-term store (Waugh & Norman, 1965). The algorithmic level is<br />

also the focus <strong>of</strong> the art <strong>of</strong> memory (Yates, 1966), in which individuals are taught<br />

mnemonic techniques to improve their ability to remember (Lorayne, 1998, 2007;<br />

Lorayne & Lucas, 1974).<br />

That memory can be reverse engineered into an organized system <strong>of</strong> subfunctions<br />

leads cognitive scientists to determine the architecture <strong>of</strong> memory. For<br />

instance, what kinds <strong>of</strong> encodings are used in each memory system, and what<br />

primitive processes are used to manipulate stored information? Richard Conrad’s<br />

(1964a, 1964b) famous studies <strong>of</strong> confusion in short-term memory indicated that<br />

it represented information using an acoustic code. One <strong>of</strong> the most controversial<br />

topics in classical cognitive science, the “imagery debate,” concerns whether the<br />

52 Chapter 2

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