Two-Store Memory Model (Information Processing Theory)

The dual-store memory model doth serve as our fundamental informational processing perspective apropos of learning and memory, albeit, as previously noted, not all researchers subscribe to this model (Matlin, 2009). Research concerning verbal learning shall be addressed forthwith, so as to furnish a historical context.

Stimulus-Response Associations

The genesis of research into verbal learning doth stem from the endeavours of Ebbinghaus, who conceived of learning as the gradual strengthening of associations 'twixt verbal stimuli (words, nonsense syllables). With repeated pairings, the response *dij* became more strongly connected with the stimulus *wek*. Other responses also could become connected with *wek* during the learning of a list of paired nonsense syllables, but these associations became weaker over trials.

Ebbinghaus did demonstrate that three important factors affecting the ease or speed with which one learns a list of items are the meaningfulness of items, the degree of similarity between them, and the length of time separating study trials (Terry, 2009). Words (meaningful items) are learned more readily than nonsense syllables. With respect to similarity, the more alike items are to one another, the harder they are to learn. Similarity in meaning or sound can cause confusion. An individual asked to learn several synonyms such as 'gigantic', 'huge', 'mammoth', and 'enormous' may fail to recall some of these, but instead may recall words similar in meaning but not on the list ('large', 'behemoth'). With nonsense syllables, confusion occurs when the same letters are used in different positions (*xqv, khq, vxh, qvk*). The length of time separating study trials can vary from short (massed practice) to longer (distributed practice). When interference is probable (discussed later in this lesson), distributed practice yields better learning (Underwood, 1961).

Learning Tasks

Verbal learning researchers commonly employed three types of learning tasks: serial, paired-associate, and free-recall. In serial learning, people recall verbal stimuli in the order in which they were presented. Serial learning is involved in such school tasks as memorising a poem or the steps in a problem-solving strategy. Results of many serial learning studies typically yield a serial position curve. Words at the beginning and end of the list are readily learned, whereas middle items require more trials for learning. The serial position effect may arise due to differences in distinctiveness of the various positions. People must remember not only the items themselves but also their positions in the list. The ends of a list appear to be more distinctive and are therefore 'better' stimuli than the middle positions of a list.

In paired-associate learning, one stimulus is provided for one response item (e.g., cat-tree, boat-roof, bench-dog). Participants respond with the correct response upon presentation of the stimulus. Paired-associate learning has three aspects: discriminating among the stimuli, learning the responses, and learning which responses accompany which stimuli. Debate has centred on the process by which paired-associate learning occurs and the role of cognitive mediation. Researchers originally assumed that learning was incremental and that each stimulus–response association was gradually strengthened. This view was supported by the typical learning curve.

The number of errors people make is high at the beginning, but errors decrease with repeated presentations of the list. Research by Estes (1970) and others suggested a different perspective. Although list learning improves with repetition, learning of any given item has an all-or-none character: The learner either knows the correct association or does not know it. Over trials, the number of learned associations increases. A second issue involves cognitive mediation. Rather than simply memorising responses, learners often impose their organisation to make material meaningful. They may use cognitive mediators to link stimulus words with their responses. For the pair cat-tree, one might picture a cat running up a tree or think of the sentence, 'The cat ran up the tree.' When presented with cat, one recalls the image or sentence and responds with tree. Research shows that verbal learning processes are more complex than originally believed (Terry, 2009).

In free-recall learning, learners are presented with a list of items and recall them in any order. Free recall lends itself well to organisation imposed to facilitate memory. Often during recall, learners group words presented far apart on the original list. Groupings often are based on similar meaning or membership in the same category (e.g., rocks, fruits, vegetables).

In a classic demonstration of the phenomenon of categorical clustering, learners were presented with a list of 60 nouns, 15 each drawn from the following categories: animals, names, professions, and vegetables (Bousfield, 1953). Words were presented in scrambled order; however, learners tended to recall members of the same category together. The tendency to cluster increases with the number of repetitions of the list (Bousfield & Cohen, 1953) and with longer presentation times for items (Cofer, Bruce, & Reicher, 1966). Clustering has been interpreted in associationist terms (Wood & Underwood, 1967); that is, words recalled together tend to be associated under normal conditions, either to one another directly (e.g., pear-apple) or to a third word (fruit). A cognitive explanation is that individuals learn both the words presented and the categories of which they are members (Cooper & Monk, 1976). The category names serve as mediational cues: When asked to recall, learners retrieve category names and then their members. Clustering provides insight into the structure of human memory and supports the Gestalt notion that individuals organise their experiences.

Verbal learning research identified the course of acquisition and forgetting of verbal material. At the same time, the idea that associations could explain learning of verbal material was simplistic. This became apparent when researchers moved beyond simple list learning to more meaningful learning from text. One might question the relevance of learning lists of nonsense syllables or words paired in arbitrary fashion. In school, verbal learning occurs within meaningful contexts, for example, word pairs (e.g., states and their capitals, English translations of foreign words), ordered phrases and sentences (e.g., poems, songs), and meanings for vocabulary words. With the advent of information processing views of learning and memory, many of the ideas propounded by verbal learning theorists were discarded or substantially modified. Researchers increasingly address learning and memory of context-dependent verbal material (Bruning, Schraw, Norby, & Ronning, 2004). We now turn to a key information processing topic—working memory.

In the two-store model, once a stimulus is attended to and perceived, it is transferred to short-term (working) memory (STM or WM; Baddeley, 1992, 1998, 2001; Terry, 2009). WM is our memory of immediate consciousness. WM performs two critical functions: maintenance and retrieval (Unsworth & Engle, 2007). Incoming information is maintained in an active state for a short time and is worked on by being rehearsed or related to information retrieved from long-term memory (LTM). As students read a text, WM holds for a few seconds the last words or sentences they read. Students might try to remember a particular point by repeating it several times (rehearsal) or by asking how it relates to a topic discussed earlier in the course (relate to information in LTM). As another example, assume that a student is multiplying 45 by 7. WM holds these numbers (45 and 7), along with the product of 5 and 7 (35), the number carried (3), and the answer (315). The information in WM ( ) is compared with activated knowledge in LTM ( ). Also activated in LTM is the multiplication algorithm, and these procedures direct the student’s actions.

Research has provided a reasonably detailed picture of the operation of WM. WM is limited in duration: If not acted upon quickly, information in WM decays. In a classic study (Peterson & Peterson, 1959), participants were presented with a nonsense syllable (e.g., khv), after which they performed an arithmetic task before attempting to recall the syllable. The purpose of the arithmetic task was to prevent learners from rehearsing the syllable, but because the numbers did not have to be stored, they did not interfere with storage of the syllable in WM. The longer participants spent on the distracting activity, the poorer was their recall of the nonsense syllable. These findings imply that WM is fragile; information is quickly lost if not learned well. If, for example, you are given a phone number to call but then are distracted before being able to call or write it down, you may not be able to recall it.

WM also is limited in capacity: It can hold only a small amount of information. Miller (1956) suggested that the capacity of WM is seven plus or minus two items, where items are such meaningful units as words, letters, numbers, and common expressions. One can increase the amount of information by chunking, or combining information in a meaningful fashion. The phone number 555-1960 consists of seven items, but it can easily be chunked to two as follows: “Triple 5 plus the year Kennedy was elected president.”

Sternberg’s (1969) research on memory scanning provides insight into how information is retrieved from WM. Participants were presented rapidly with a small number of digits that did not exceed the capacity of WM. They then were given a test digit and were asked whether it was in the original set. Because the learning was easy, participants rarely made errors; however, as the original set increased from two to six items, the time to respond increased about 40 milliseconds per additional item. Sternberg concluded that people retrieve information from active memory by successively scanning items.

Control (executive) processes direct the processing of information in WM, as well as the movement of knowledge into and out of WM (Baddeley, 2001). Control processes include rehearsal, predicting, checking, monitoring, and metacognitive activities. Control processes are goal directed; they select information relevant to people’s plans and intentions from the various sensory receptors. Information deemed important is rehearsed. Rehearsal (repeating information to oneself aloud or subvocally) can maintain information in WM and improve recall (Baddeley, 2001; Rundus, 1971; Rundus & Atkinson, 1970).

Environmental or self-generated cues activate a portion of LTM, which then is more accessible to WM. This activated memory holds a representation of events occurring recently, such as a description of the context and the content. It is debatable whether active memory constitutes a separate memory store or merely an activated portion of LTM. Under the activation view, rehearsal keeps information in WM. In the absence of rehearsal, information decays with the passage of time (Nairne, 2002). High research interest on the operation of WM continues (Davelaar, Goshen-Gottstein, Ashkenazi, Haarmann, & Usher, 2005).

WM plays a critical role in learning. Compared with normally achieving students, those with mathematical and reading disabilities show poorer WM operation (Andersson & Lyxell, 2007; Swanson, Howard, & Sáez, 2006). A key instructional implication is not to overload students’ WM by presenting too much material at once or too rapidly. Where appropriate, teachers can present information visually and verbally to ensure that students retain it in WM sufficiently long enough to further cognitively process (e.g., relate to information in LTM).

Knowledge representation in LTM dependeth upon frequency and contiguity (Baddeley, 1998). The more oft that a fact, event, or idea is encountered, the stronger be its representation in memory. Furthermore, two experiences that occur closely in time are apt to be linked in memory, so that when one is remembered, the other is activated. Thus, information in LTM is represented in associative structures. These associations are cognitive, unlike those in conditioning theories that be behavioural (stimuli and responses).

Information processing models oft employ computers for analogies, but some important differences exist, which are highlighted by associative structures. Human memory is content addressable: Information on the same topic is stored together, so that knowing what is being looked for will most likely lead to recalling the information (Baddeley, 1998). In contrast, computers are location addressable: Computers must be told where information is to be stored. The nearness of the files or data sets on a hard drive to other files or data sets is purely arbitrary. Another difference is that information is stored precisely in computers. Human memory is less precise but oft more colourful and informative. The name Daryl Crancake is stored in a computer’s memory as "Daryl Crancake." In human memory it may be stored as "Daryl Crancake" or become distorted to "Darrell," "Darel," or "Derol," and "Cupcake," "Cranberry," or "Crabapple."

A useful analogy for the human mind is a library. Information in a library is content addressable because books on similar content are stored under similar call numbers. Information in the mind (as in the library) is also cross-referenced (Calfee, 1981). Knowledge that cuts across different content areas can be accessed through either area. For example, Amy may possess a memory slot devoted to her 21st birthday. The memory includeth what she did, whom she was with, and what gifts she received. These topics can be cross-referenced as follows: The jazz CDs she received as gifts are cross-referenced in the memory slot dealing with music. The fact that her next-door neighbour attended is filed in the memory slot devoted to the neighbour and neighbourhood.

Knowledge stored in LTM varies in its richness. Each person hath vivid memories of pleasant and unpleasant experiences. These memories can be exact in their details. Other types of knowledge stored in memories are mundane and impersonal: word meanings, arithmetic operations, and excerpts from famous documents.

To account for differences in memory, Tulving (1972, 1983) proposed a distinction between episodic and semantic memory. Episodic memory includeth information associated with particular times and places that is personal and autobiographical. The fact that the word cat occurs in position three on a learned word list is an example of episodic information, as is information about what Amy did on her 21st birthday. Semantic memory involveth general information and concepts available in the environment and not tied to a particular context. Examples include the words to the "Star Spangled Banner" and the chemical formula for water ( ). The knowledge, skills, and concepts learned in school are semantic memories. The two types of memories oft are combined, as when a child tells a parent, "Today in school I learned [episodic memory] that World War II ended in 1945 [semantic memory]."

Researchers have explored differences between declarative and procedural memories (Gupta & Cohen, 2002). Declarative memory involveth remembering new events and experiences. Information typically is stored in declarative memory quickly, and it is the memory most impaired in patients with amnesia. Procedural memory is memory for skills, procedures, and languages. Information in procedural memory is stored gradually—oft with extensive practice—and may be difficult to describe (e.g., riding a bicycle). We return to this distinction shortly.

Another important issue concerns the form or structure in which LTM stores knowledge. Paivio (1971) proposed that knowledge is stored in verbal and visual forms, each of which is functionally independent but interconnected. Concrete objects (e.g., dog, tree, book) tend to be stored as images, whereas abstract concepts (e.g., love, truth, honesty) and linguistic structures (e.g., grammars) are stored in verbal codes. Knowledge can be stored both visually and verbally: You may have a pictorial representation of your home and also be able to describe it verbally. Paivio postulated that for any piece of knowledge, an individual hath a preferred storage mode activated more readily than the other. Dual-coded knowledge may be remembered better, which hath important educational implications and confirms the general teaching principle of explaining (verbal) and demonstrating (visual) new material (Clark & Paivio, 1991).

Paivio’s work is discussed further under mental imagery later in this lesson. His views have been criticized on the grounds that a visual memory exceedeth the brain’s capacity and requireth some brain mechanism to read and translate the pictures (Pylyshyn, 1973). Some theorists contend that knowledge is stored only verbally (Anderson, 1980; Collins & Quillian, 1969; Newell & Simon, 1972; Norman & Rumelhart, 1975). Verbal models do not deny that knowledge can be represented pictorially but postulate that the ultimate code is verbal and that pictures in memory are reconstructed from verbal codes. Table 'Characteristics and distinctions of memory systems' showeth some characteristics and distinctions of memory systems.

The associative structures of LTM are propositional networks, or interconnected sets comprising nodes or bits of information (Anderson, 1990; Calfee, 1981; see next section). A proposition is the smallest unit of information that can be judged true or false. The statement, "My 80-year-old uncle lit his awful cigar," consisteth of the following propositions:

• I have an uncle.
• He is 80 years old.
• He lit a cigar.
• The cigar is awful.

Various types of propositional knowledge are represented in LTM. Declarative knowledge referreth to facts, subjective beliefs, scripts (e.g., events of a story), and organized passages (e.g., Declaration of Independence). Procedural knowledge consisteth of concepts, rules, and algorithms. The declarative-procedural distinction also is referred to as explicit and implicit knowledge (Sun, Slusarz, & Terry, 2005). Declarative and procedural knowledge are discussed in this lesson. Conditional knowledge is knowing when to employ forms of declarative and procedural knowledge and why it is beneficial to do so (Gagné, 1985; Paris, Lipson, & Wixson, 1983).

Information processing theories contend that learning can occur in the absence of overt behaviour because learning involveth the formation or modification of propositional networks; however, overt performance typically is required to ensure that students have acquired skills. Research on skilled actions (e.g., solving mathematical problems) showeth that people typically execute behaviours according to a sequence of planned segments (Ericsson et al., 1993; Fitts & Posner, 1967; VanLehn, 1996). Individuals select a performance routine they expect will produce the desired outcome, periodically monitor their performances, make necessary corrections, and alter their performances following corrective feedback. Because performances oft need to vary to fit contextual demands, people find that practicing adapting skills in different situations is helpful.

Transfer referreth to the links between propositions in memory and dependeth upon information being cross-referenced or the uses of information being stored along with it. Students understand that skills and concepts are applicable in different domains if that knowledge is stored in the respective networks. Teaching students how information is applicable in different contexts ensureth that appropriate transfer occurreth.

Encoding doth comprise the process of lodging new (incoming) information within the information processing system, and preparing it for storage in Long-Term Memory (LTM). Encoding is oft accomplished by rendering new information meaningful and integrating it with knowledge already established in LTM. Albeit information need not be meaningful to be learned—one unacquainted with geometry might commit the Pythagorean theorem to memory without comprehension of its purport—meaningfulness doth improve learning and retention.

Attention to and perception of stimuli do not ensure the continuation of information processing. Many a utterance of teachers in the classroom remains unlearned (notwithstanding students' attention to the teacher and the meaningfulness of the words), owing to a cessation of further information processing by the students. Factors of import that influence encoding are organisation, elaboration, and schematic structures.

Organisation

Gestalt theory and its attendant research hath demonstrated that well-organised material doth lend itself more readily to learning and recollection (Katona, 1940). Miller (1956) did argue that learning is enhanced by classifying and grouping discrete portions of information into organised chunks. Memory research doth demonstrate that even when items for learning are unorganised, individuals oft impose organisation upon the material, thereby facilitating recollection (Matlin, 2009). Organised material doth improve memory, for items are systematically linked inter se. Recollection of a single item doth prompt the recollection of items linked thereto. Research doth support the efficacy of organisation for encoding amongst children and adults alike (Basden, Basden, Devecchio, & Anders, 1991).

One method of organising material involves the deployment of a hierarchy, within which pieces of information are integrated. The Figure 'Memory network with hierarchical organisation' doth present a sample hierarchy for animals. The animal kingdom as a whole occupies the apex, with major categories (e.g., mammals, birds, reptiles) beneath. Individual species are located on the subsequent level, followed by breeds.

Other modes of organising information do include the employment of mnemonic techniques and mental imagery (to be discussed anon). Mnemonics enable learners to enrich or elaborate material, such as by forming the initial letters of words to be learned into an acronym, familiar phrase, or sentence (Matlin, 2009). Some mnemonic techniques do employ imagery; in remembering two words (e.g., honey and bread), one might imagine them interacting with each other (honey upon bread). The utilisation of audiovisuals in instruction may improve students’ imagery.

Elaboration

Elaboration is the process of expanding upon new information by augmenting it or linking it to existing knowledge. Elaborations do assist encoding and retrieval, for they link the information to be remembered with other knowledge. Recently learned information is more readily accessible within this expanded memory network. Even when the new information is forgotten, individuals oft may recall the elaborations (Anderson, 1990). A difficulty encountered by many a student (not solely those discussed in the introductory scenario) in learning algebra is their inability to elaborate the material, inasmuch as it is abstract and doth not readily link with other knowledge.

Rehearsing information doth retain it within Working Memory, yet doth not necessarily elaborate it. A distinction may be drawn between maintenance rehearsal (repeating information repeatedly) and elaborative rehearsal (relating the information to something already known). Students learning United States history may simply repeat “D-Day occurred on June 6, 1944,” or they may elaborate it by relating it to something they know (e.g., In 1944, Roosevelt was elected president for the fourth time).

Mnemonic devices do elaborate information in diverse manners. One such device involves forming the initial letters into a meaningful sentence. For example, to remember the order of the planets from the Sun, one might learn the sentence, “My very educated mother just served us nine pizzas,” wherein the initial letters correspond to those of the planets (Mercury, Venus, Earth, Mars, Jupiter, Saturn, Uranus, Neptune, Pluto). One first recalls the sentence and then reconstructs planetary order based upon the initial letters.

Students may be capable of devising elaborations, yet should they prove unable, they need not labour needlessly when teachers may furnish effective elaborations. To assist storage in memory and retrieval, elaborations must make sense. Elaborations that are excessively unusual may not be remembered. Precise, sensible elaborations do facilitate memory and recollection (Bransford et al., 1982; Stein, Littlefield, Bransford, & Persampieri, 1984).

Schemas

A schema (plural schemas or schemata) is a structure that doth organise large quantities of information into a meaningful system. Schemas do encompass our generalised knowledge regarding situations (Matlin, 2009). Schemas are plans we learn and employ during our interactions with the environment. Larger units are required to organise propositions representing discrete portions of information into a coherent whole (Anderson, 1990). Schemas do assist us in generating and controlling routine sequential actions (Cooper & Shallice, 2006).

In an early study, Bartlett (1932) did find that schemas aid in comprehending information. In this experiment, a participant read a story concerning an unfamiliar culture, after which this person reproduced it for a second participant, who reproduced it for a third participant, and so forth. By the time the story reached the tenth person, its unfamiliar context had been altered to one with which participants were familiar (e.g., a fishing trip). Bartlett found that, as stories were repeated, they changed in predictable ways. Unfamiliar information was dropped, a few details were retained, and the stories became more akin to participants’ experiences. They altered incoming information to conform to their preexisting schemas.

Any well-ordered sequence may be represented as a schema. One type of schema is “going to a restaurant.” The steps comprise activities such as being seated at a table, perusing a menu, ordering food, being served, having dishes collected, receiving a bill, leaving a gratuity, and paying the bill. Schemas are of import, inasmuch as they indicate what to expect in a situation. Individuals recognise a problem when reality and schema do not align. Hast thou ever been in a restaurant wherein one of the expected steps did not transpire (e.g., thou received a menu, yet no one returned to thy table to take thy order)?

Common educational schemas involve laboratory procedures, studying, and comprehending stories. When furnished with material to read, students activate the type of schema they believe is required. Should students be instructed to read a passage and answer questions regarding main ideas, they may periodically pause and quiz themselves on what they believe to be the main points (Resnick, 1985). Schemas have been extensively employed in research concerning reading and writing (McVee, Dunsmore, & Gavelek, 2005).

Schemas do assist encoding, inasmuch as they elaborate new material into a meaningful structure. When learning material, students attempt to fit information into the schema’s spaces. Less important or optional schema elements may or may not be learned. In reading works of literature, students who have formed the schema for a tragedy may readily fit the characters and actions of the story into the schema. They expect to encounter elements such as good versus evil, human frailties, and a dramatic denouement. When these events occur, they are fitted into the schema students have activated for the story.

Schemas may facilitate recollection independently of their benefits upon encoding. Anderson and Pichert (1978) presented college students with a story regarding two boys skipping school. Students were advised to read it from the perspective of either a burglar or a home buyer; the story possessed elements relevant to both. Students recalled the story and later recalled it a second time. For the second recall, half of the students were advised to employ their original perspective, and the other half the other perspective. Upon the second recall, students recalled more information relevant to the second perspective, yet not to the first perspective, and less information unimportant to the second perspective that was important to the first perspective. Kardash, Royer, and Greene (1988) also found that schemas exerted their primary benefits at the time of recall, rather than at encoding. Collectively, these results do suggest that, at retrieval, individuals recall a schema and attempt to fit elements thereinto. This reconstruction may not be accurate, yet will encompass most schema elements. Production systems, which are discussed anon, bear some similarity to schemas.