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Chapter5 week 3 Module

Chapter 5 Information Processing Theory: Encoding and Storage

Cass Paquin, a middle school mathematics teacher, seemed sad when she met with her team members Don Jacks and Fran Killian.

Don:	What’s the matter, Cass? Things got you down?
Cass:	They just don’t get it. I can’t get them to understand what a variable is. “X” is a mystery to them.
Fran:	Yes, “x” is too abstract for kids.
Don:	It’s abstract to adults too. “X” is a letter of the alphabet, a symbol. I’ve had the same problem. Some seem to pick it up, but many don’t.
Fran:	In my master’s program they teach that you have to make learning meaningful. People learn better when they can relate the new learning to something they know. “X” has no meaning in math. We need to change it to something the kids know.
Cass:	Such as what—cookies?
Fran:	Well, yes. Take your problem 4x + 7 = 15. How about saying: 4 times how many cookies plus 7 cookies equals 15 cookies? That way the kids can relate “x” to something tangible—real. Then “x” won’t just be something they memorize how to work with. They’ll associate “x” with things that can take on different values, such as cookies.
Don:	That’s a problem with a lot of math—it’s too abstract. When kids are little, we use real objects to make it meaningful. We cut pies into pieces to illustrate fractions. Then when they get older we stop doing that and use abstract symbols most of the time. Sure, they have to know how to use those symbols, but we should try to make the concepts meaningful.
Cass:	Yes. I’ve fallen into that trap—teach the material like it’s in the book. I need to try to relate the concepts better to what the kids know and what makes sense to them.

Information processing theory focuses on how people attend to environmental events, construct and encode information to be learned and relate it to knowledge in memory, store new knowledge in memory, and retrieve it as needed (Mayer,  2012 ; Shuell,  1986 ). The tenets of this theories are as follows: “Humans are processors of information. The mind is an information-processing system. Cognition is a series of mental processes. Learning is the acquisition of mental representations” (Mayer,  1996 , p. 154).

Information processing  actually is not a single theory; it is a generic name applied to theoretical perspectives dealing with the sequence and execution of cognitive events. Although certain theories are discussed in this chapter, there is no one dominant theory, and researchers disagree about aspects of all current theories (Matlin,  2009 ). In part this situation may be due to the influence on information processing by advances in other domains including communications, technology, and neuroscience.

Much early information processing research was conducted in laboratories and dealt with phenomena such as eye movements, recognition and recall times, attention to stimuli, and interference in perception and memory. Subsequent research has explored learning, memory, problem solving, visual and auditory perception, cognitive development, and artificial intelligence. Despite a healthy research literature, information processing principles have not always lent themselves readily to school learning, curricular structure, and instructional design. This situation does not imply that information processing has little educational relevance, only that many potential applications are yet to be developed. Fortunately, researchers increasingly are applying principles to educational settings involving such subjects as reading, mathematics, and science, and applications remain research priorities. The participants in the opening scenario are discussing meaningfulness, a key aspect of information processing.

This chapter initially discusses the assumptions of information processing, some historical influences, and early information processing models. The bulk of the chapter is devoted to explicating a contemporary model including the component processes of attention, perception, working memory, and storage in long-term memory.  Chapter 6  continues this discussion by covering retrieval of knowledge from long-term memory, along with related topics such as imagery and transfer.

When you finish studying this chapter, you should be able to do the following:

· ■ Discuss the major assumptions of information processing and some historical influences on contemporary theory: verbal learning, Gestalt theory, the two-store memory model, and levels of processing.

· ■ Describe the major components of a contemporary information processing model: attention, perception, working memory, long-term memory.

· ■ Distinguish different views of attention, and explain how attention affects learning.

· ■ Discuss how information enters sensory registers and is perceived.

· ■ Describe the operation of working memory, including essential components.

· ■ Explain the major factors that influence encoding.

· ■ Define propositions and spreading activation, and explain their roles in encoding of long-term memory information.

· ■ Discuss the differences between declarative and procedural knowledge.

· ■ Identify information processing principles inherent in instructional applications involving advance organizers, the conditions of learning, and cognitive load.

EARLY INFORMATION PROCESSING PERSPECTIVES

Assumptions

Information processing theorists challenged the idea inherent in behaviorism ( Chapter 3 ) that learning involves merely forming associations between stimuli and responses. Information processing theorists do not reject associations, because they postulate that forming associations between bits of knowledge helps to facilitate their acquisition and storage in memory. Rather, these theorists are less concerned with external conditions and focus more on internal (mental) processes that intervene between stimuli and responses. Learners are active seekers and processors of information. Unlike behaviorists who said that people respond when stimuli impinge on them, information processing theorists contend that people select and attend to features of the environment, construct and rehearse knowledge, relate new information to previously acquired knowledge, and organize knowledge to make it meaningful (Mayer,  1996 ,  2012 ).

Information processing theories differ in their views on which cognitive processes are important and how they operate, but they share some common assumptions. One is that information processing occurs in phases that intervene between receiving a stimulus and producing a response. A corollary is that the form of information, or how it is represented mentally, differs depending on the phase. There is debate about whether the phases are part of a larger memory system or are qualitatively different from one another.

Another assumption is that information processing is analogous to computer processing, at least metaphorically. The human system functions similar to a computer: It receives information, stores it in memory, and retrieves it as necessary. Cognitive processing is remarkably efficient; there is little waste or overlap. Researchers differ in how far they extend this analogy. For some, the computer analogy is nothing more than a metaphor. Others employ computers to simulate activities of humans. The field of  artificial intelligence  is concerned with programming computers to engage in human activities such as thinking, using language, and solving problems ( Chapter 7 ).

Researchers also assume that information processing is involved in all cognitive activities: perceiving, rehearsing, thinking, problem solving, remembering, forgetting, and imaging (Matlin,  2009 ; Mayer,  2012 ; Terry,  2009 ). Information processing, which extends beyond human learning as traditionally delineated, has memory as its focus (Surprenant & Neath,  2009 ). This chapter is concerned primarily with those information processes most germane to learning. The remainder of this section discusses some key historical influences on contemporary information processing theory: verbal learning, Gestalt theory, the two-store (dual) memory model, and levels of processing.

Verbal Learning

Stimulus-Response Associations.

The impetus for research on verbal learning derived from the work of Ebbinghaus ( Chapter 1 ), who construed learning as gradual strengthening of associations between 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 learning of a list of paired nonsense syllables, but these associations became weaker over trials.

Ebbinghaus showed that three factors affected the ease or speed with which one learns a list of items: meaningfulness of items, degree of similarity between them, and 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 (see  Chapter 6 ), 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 memorizing a poem or the steps in a problem-solving strategy. Results of many serial learning studies typically yield a serial position curve ( Figure 5.1 ). 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 but also their positions in the list. The ends of a list are more distinctive and therefore “better” stimuli than the middle positions of a list.

Figure 5.1 Serial position curve showing errors in recall as a function of item position.

Figure 5.2 Learning curve showing errors as a function of study trials.

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. Researchers have debated the process by which paired-associate learning occurs and the role of cognitive mediation. Originally it was assumed that learning was incremental and that each stimulus–response association was gradually strengthened. This view was supported by the typical learning curve ( Figure 5.2 ). 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 is  all-or-none : The learner either knows the correct association or does not. Over trials, the number of learned associations increases. Learners often impose their own organization to make material meaningful rather than simply memorizing responses. 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. Researchers have shown that verbal learning is 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 organization imposed to facilitate memory (Sederberg, Howard, & Kahana,  2008 ). 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 this  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 ). Such clustering shows that 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 mediators: When asked to recall, learners retrieve category names and then their members.

Free recall often shows primacy (first words recalled better) and recency (last words recalled better) effects (Laming,  2010 ). Primacy effects presumably occur because the first words receive extra rehearsals. Recency effects may occur because the last words still are in learners’ working memories.

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 language words), ordered phrases and sentences (e.g., poems, songs), and meanings for vocabulary words. With the advent of information processing theory, 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,  2011 ).

Gestalt Theory

Gestalt theory was an early cognitive view that challenged many assumptions of behaviorism. Although Gestalt theory no longer is viable, it offers important principles that are found in current conceptions of perception and learning.

The Gestalt movement began with a group of psychologists in early 20th-century Germany. In 1912, Max Wertheimer wrote an article on apparent motion. The article was significant among German psychologists but had no influence in the United States, where the Gestalt movement had not yet begun. The subsequent publication in English of Kurt Koffka’s The Growth of the Mind ( 1924 ) and Wolfgang Köhler’s The Mentality of Apes ( 1925 ) helped the Gestalt movement spread to the United States. Many Gestalt psychologists, including Wertheimer, Koffka, and Köhler, eventually emigrated to the United States, where they applied their ideas to psychological phenomena.

In a typical demonstration of the apparent motion perceptual phenomenon, two lines close together are exposed successively for a fraction of a second with a short time interval between each exposure. An observer sees not two lines but rather a single line moving from the line exposed first toward the line exposed second. The timing of the demonstration is critical. If the time interval between exposure of the two lines is too long, the observer sees the first line and then the second but no motion. If the interval is too short, the observer sees two lines side by side but no motion.

This apparent motion is known as the  phi phenomenon  and demonstrates that subjective experiences cannot be explained by referring to the objective elements involved. Observers perceive movement even though none occurs. Phenomenological experience (apparent motion) differs from sensory experience (exposure of lines). The attempt to explain these types of phenomena led Wertheimer to challenge psychological explanations of perception as the sum of one’s sensory experiences because these explanations did not take into account the unique wholeness of perception.

Meaningfulness of Perception.

Imagine that Rebecca is 5 feet tall. When we view Rebecca at a distance, our retinal image is much smaller than when we view her up close. Yet Rebecca is 5 feet tall, and we know that regardless of how far away she is. Although the perception (retinal image) varies, the meaning of the image remains constant.

The German word  Gestalt  translates as “form,” “figure,” “shape,” or “configuration.” The essence of the  Gestalt psychology  is that objects or events are viewed as organized wholes (Köhler,  1947/1959 ). The basic organization involves a figure (what one focuses on) against a ground (the background). What is meaningful is the configuration, not the individual parts (Koffka,  1922 ). A tree is not a random collection of leaves, branches, roots, and trunk; it is a meaningful configuration of these elements. When viewing a tree, people typically do not focus on individual elements but rather on the whole. The human brain transforms objective reality into mental events organized as meaningful wholes. This capacity to view things as wholes is an inborn quality, although perception is modified by experience and training (Köhler,  1947/1959 ; Leeper,  1935 ).

Gestalt theory originally applied to perception, but when its European proponents came to the United States they found an emphasis on learning. In the Gestalt view, learning is a cognitive phenomenon involving reorganizing experiences into different perceptions of things, people, or events (Koffka,  1922 ,  1926 ). Much human learning is  insightful , which means that the transformation from ignorance to knowledge occurs rapidly. When confronted with a problem, individuals figure out what is known and what needs to be determined. They then think about possible solutions. Insight occurs when people suddenly “see” how to solve the problem.

Gestalt theorists disagreed with Watson and other behaviorists about the role of consciousness ( Chapter 3 ). In Gestalt theory, meaningful perception and insight occur only through conscious awareness. Gestalt psychologists also disputed the idea that complex phenomena can be broken into elementary parts. Behaviorists stressed associations—the whole is equal to the sum of the parts. Gestalt psychologists felt that the whole loses meaning when it is reduced to individual components. In the opening scenario, “x” loses meaning unless it can be related to broader categories. The whole is greater than the sum of its parts. Interestingly, Gestalt psychologists agreed with behaviorists in objecting to introspection, but for a different reason. Behaviorists viewed it as an attempt to study consciousness; Gestalt theorists felt it was inappropriate because it tried to separate meaning from perception. Gestalt theory holds that perception is meaningful.

Principles of Organization.

Gestalt theory postulates that people use principles to organize their perceptions. Some of the most important  Gestalt principles  are figure-ground relation, proximity, similarity, common direction, simplicity, and closure ( Figure 5.3 ; Koffka,  1922 ; Köhler,  1926 ,  1947/1959 ).

Figure 5.3 Examples of Gestalt principles.

The principle of  figure-ground relation  postulates that any perceptual field may be subdivided into a figure against a background. Such salient features as size, shape, color, and pitch distinguish a figure from its background. When figure and ground are ambiguous, perceivers may alternatively organize the sensory experience one way and then another ( Figure 5.3a ).

The principle of proximity states that elements in a perceptual field are viewed as belonging together according to their closeness to one another in space or time. Most people will view the lines in  Figure 5.3b as three groups of three lines each, although other ways of perceiving this configuration are possible. This principle of proximity also is involved in the perception of speech. People hear (organize) speech as a series of words or phrases separated with pauses. When people hear unfamiliar speech sounds (e.g., foreign languages), they have difficulty discerning pauses.

The principle of similarity means that elements similar in aspects such as size or color are perceived as belonging together. Viewing  Figure 5.3c , people tend to see a group of three short lines, followed by a group of three long lines, and so on. Proximity can outweigh similarity; when dissimilar stimuli are closer together than similar ones ( Figure 5.3d ), the perceptual field tends to be organized into four groups of two lines each.

The principle of common direction implies that elements appearing to constitute a pattern or flow in the same direction are perceived as a figure. The lines in  Figure 5.3e  are most likely to be perceived as forming a distinct pattern. The principle of common direction also applies to an alphabetic or numeric series in which one or more rules define the order of items. Thus, the next letter in the series abdeghjk is m, as determined by the rule: Beginning with the letter a and moving through the alphabet sequentially, list two letters and omit one.

The principle of simplicity states that people organize their perceptual fields in simple, regular features and tend to form good Gestalts comprising symmetry and regularity. This idea is captured by the German word Pragnanz, which roughly translated means “meaningfulness” or “precision.” Individuals are most likely to see the visual patterns in  Figure 5.3f  as one geometrical pattern overlapping another rather than as several irregularly shaped geometric patterns. The principle of closure means that people fill in incomplete patterns or experiences. Despite the missing lines in the pattern shown in  Figure 5.3g , people tend to complete the pattern and see a meaningful picture.

Although Gestalt concepts are relevant to our perceptions, the principles are general and do not address actual perceptual mechanisms. To say that individuals perceive similar items as belonging together does not explain how they perceive items as similar in the first place. Gestalt principles are illuminating but vague and not explanatory. Furthermore, research does not support some Gestalt predictions. Kubovy and van den Berg ( 2008 ) found that the joint effect of proximity and similarity was equal to the sum of their separate effects, not greater than it as Gestalt theory predicts. Information processing principles are clearer and explain perception better.

Two-Store (Dual) Memory Model

An early information processing model was formulated by Atkinson and Shiffrin ( 1968 ,  1971 ). This stage model proposed two types of information storage: short and long term. According to the model, information processing begins when a stimulus (e.g., visual, auditory) impinges on one or more senses (e.g., hearing, sight, touch). The appropriate  sensory register  receives the input and holds it briefly in sensory form. It is here that  perception  ( pattern recognition ) occurs, which is the process of assigning meaning to a stimulus input. This typically does not involve naming because naming takes time, and information stays in the sensory register for only a fraction of a second. Rather, perception involves matching an input to known information.

The sensory register transfers information to  short-term memory (STM) , which corresponds roughly to awareness or what one is conscious of at a given moment. STM is limited in capacity. Miller ( 1956 ) proposed that it holds seven plus or minus two chunks (units) of information. A chunk is a meaningful item: a letter, word, number, or common expression (e.g., “bread and butter”). STM also is limited in duration; for chunks to be retained they must be rehearsed (repeated). Without rehearsal, information is lost after a few seconds. With development, children are able to hold more and larger chunks of information in memory (Cowan et al.,  2010 )

While information is in STM, related knowledge in  long-term memory (LTM) , or permanent memory, is activated and placed in STM to be integrated with the new information. To name all the state capitals beginning with the letter A, students recall the names of states—perhaps by region of the country—and scan the names of their capital cities. When students who do not know the capital of Maryland learn “Annapolis,” they can store it with “Maryland” in LTM.

It is debatable whether information is lost from LTM (i.e., forgotten). Some researchers contend that it can be, whereas others say that failure to recall reflects a lack of good retrieval cues rather than forgetting. If Sarah cannot recall her third-grade teacher’s name (Mapleton), she might be able to if given the hint, “Think of trees.” Regardless of theoretical perspective, researchers agree that information remains in LTM for a long time (see  Chapter 6 ).

Control (executive) processes  regulate the flow of information throughout the information processing system. Rehearsal is an important control process that occurs in STM. For verbal material, rehearsal takes the form of repeating information aloud or subvocally. Other control processes include coding (putting information into a meaningful context—an issue being discussed in the opening scenario), imaging (visually representing information), implementing decision rules, organizing information, monitoring level of understanding, and using retrieval, self-regulation, and motivational strategies.

The two-store model was a major advance in the field of information processing. Researchers showed that the two-store model could account for many research results. One of the most consistent research findings is that when people have a list of items to learn, they tend to recall best the initial items ( primacy effect ) and the last items ( recency effect ), as portrayed in  Figure 5.1 . As noted earlier, initial items receive the most rehearsal and are transferred to LTM, whereas the last items are still in STM at the time of recall. Middle items are recalled the poorest because they are no longer in  working memory  (WM) at the time of recall (having been pushed out by subsequent items), they receive fewer rehearsals than initial items, and they are not properly stored in LTM.

Other research suggested, however, that learning may be more complex than the basic two-store model stipulates (Baddeley,  1998 ). One problem is that this model does not fully specify how information moves from one stage of processing to another. The control processes notion is plausible but vague. We might ask: Why do some inputs proceed from the sensory registers into STM and others do not? Which mechanisms decide that information has been rehearsed long enough and transfer it into LTM? How is information in LTM selected to be activated? Another concern is that this model seems best suited to handle verbal material. How nonverbal representation occurs with material that may not be readily verbalized, such as modern art and well-established skills, is not clear.

The model also is vague about what really is learned. Consider people learning word lists. With nonsense syllables, they have to learn the words themselves and the positions in which they appear. When they already know the words, they must only learn the positions; for example, “cat” appears in the fourth position, followed by “tree.” People must take into account their purpose in learning and modify learning strategies accordingly. What mechanism controls these processes?

Whether all components of the system are used at all times is also an issue. STM is useful when people are acquiring knowledge and need to relate incoming information to knowledge in LTM. But we do many things automatically: get dressed, walk, ride a bicycle, respond to simple requests (e.g., “Do you have the time?”). For many adults, reading (decoding) and simple arithmetic computations are automatic processes that place little demand on cognitive processes. Such automatic processing may not require STM. How does automatic processing develop, and what mechanisms govern it?

These and other issues not addressed well by the two-store model (e.g., the role of motivation in learning and the development of self-regulation) have led to alternative models and modifications to the original model (Matlin,  2009 ; Nairne,  2002 ). Next we examine levels (or depth) of processing.

Levels (Depth) of Processing

Levels (depth) of processing  theory conceptualizes memory according to the type of processing that information receives rather than its location (Craik,  1979 ; Craik & Lockhart,  1972 ; Craik & Tulving,  1975 ; Lockhart, Craik, & Jacoby,  1976 ; Surprenant & Neath,  2009 ). This view does not incorporate stages or structural components such as STM or LTM (Surprenant & Neath,  2009 ). Rather, different ways to process information (such as levels or depth at which it is processed) exist: physical (surface), acoustic (phonological, sound), and  semantic  (meaning). These three levels are dimensional, with physical processing being the most superficial (such as “x” as a symbol devoid of meaning as discussed by the teachers in the introductory scenario) and semantic processing being the deepest. For example, suppose you are reading and the next word is wren. This word can be processed on a surface level (e.g., it is not capitalized), a phonological level (rhymes with den), or a semantic level (small bird). Each level represents a more elaborate (deeper) type of processing than the preceding level; processing the meaning of wrenexpands the information content of the item more than acoustic processing, which expands content more than surface-level processing.

These three levels seem conceptually similar to the sensory register, STM, and LTM of the two-store model. Both views contend that processing becomes more elaborate with succeeding stages or levels. Unlike the two-store model, levels of processing does not assume that the three types of processing constitute stages. In levels of processing, one does not have to move to the next process to engage in more elaborate processing; depth of processing can vary within a level. Wren can receive low-level semantic processing (small bird) or more extensive semantic processing (its similarity to and difference from other birds).

Another difference between the two information processing models concerns the order of processing. The two-store model assumes information is processed first by the sensory register, then by STM, and finally by LTM. The levels of processing model does not make a sequential assumption. To be processed at the meaning level, information does not have to be first processed at the surface and sound levels (beyond what processing is required for information to be received; Lockhart et al.,  1976 ).

The two models also have different views of how type of processing affects memory. In levels of processing, the deeper the level at which an item is processed, the better the memory because the memory trace is more ingrained. The teachers in the opening scenario are concerned about how they can help students process algebraic information at a deeper level. Once an item is processed at a particular point within a level, additional processing at that point should not improve memory. In contrast, the two-store model contends that memory can be improved with additional processing of the same type. This model predicts that the more a list of items is rehearsed, the better it will be recalled.

Some research evidence supports levels of processing. Craik and Tulving ( 1975 ) presented individuals with words. As each word was presented, they were given a question to answer. The questions were designed to facilitate processing at a particular level. For surface processing, people were asked, “Is the word in capital letters?” For phonological processing they were asked, “Does the word rhyme with train?” For semantic processing, “Would the word fit in the sentence, ‘He met a _____ in the street’?” The time people spent processing at the various levels was controlled. Their recall was best when items were processed at a semantic level, next best at a phonological level, and worst at a surface level. These results suggest that forgetting is more likely with shallow processing and is not due to loss of information from WM or LTM.

Levels of processing implies that student understanding is better when material is processed at deeper levels. Glover, Plake, Roberts, Zimmer, and Palmere ( 1981 ) found that asking students to paraphrase ideas while they read essays significantly enhanced recall compared with activities that did not draw on previous knowledge (e.g., identifying key words in the essays). Instructions to read slowly and carefully did not assist students during recall.

Despite these positive findings, levels of processing theory has problems. One concern is whether semantic processing always is deeper than the other levels. The sounds of some words (kaput) are at least as distinctive as their meanings (“ruined”). In fact, recall depends not only on level of processing but also on type of recall task. Morris, Bransford, and Franks ( 1977 ) found that, given a standard recall task, semantic coding produced better results than rhyming coding; however, given a recall task emphasizing rhyming, asking rhyming questions during coding produced better recall than semantic questions. Moscovitch and Craik ( 1976 ) proposed that deeper processing during learning results in a higher potential memory performance, but that potential will be realized only when conditions at retrieval match those during learning.

Another concern with levels of processing theory is whether additional processing at the same level produces better recall. Nelson ( 1977 ) gave participants one or two repetitions of each stimulus (word) processed at the same level. Two repetitions produced better recall, contrary to the levels of processing hypothesis. Other research shows that additional rehearsal of material facilitates retention and recall as well as automaticity of processing (Anderson,  1990 ; Jacoby, Bartz, & Evans,  1978 ).

A final issue concerns the nature of a level. Investigators have argued that the notion of depth is fuzzy, both in its definition and measurement (Surprenant & Neath,  2009 ; Terry,  2009 ). As a result, we do not know how processing at different levels affects learning and memory (Baddeley,  1978 ; Nelson,  1977 ). Time is a poor criterion of level because some surface processing (e.g., “Does the word have the following letter pattern: consonant-vowel-consonant-consonant-vowel-consonant?”) can take longer than semantic processing (“Is it a type of bird?”). Neither is processing time within a given level indicative of deeper processing (Baddeley,  1978 ,  1998 ). A lack of clear understanding of levels (depth) limits the usefulness of this perspective. We now turn to a contemporary perspective on information processing.

CONTEMPORARY INFORMATION PROCESSING MODEL

A contemporary, generic model of information processing is shown in  Figure 5.4 . This section gives an overview of the model; greater explanation is provided in the sections that follow.

Figure 5.4 Contemporary information processing model.

Key Processes

This model bears some similarity to the original Atkinson and Shiffrin ( 1968 ,  1971 ) model, but there are important differences. Based on years of research, the current model reflects key refinements in the operation of the information processing system.

Unlike the earlier model, the current one is not a stage model. There are phases of information processing such as perceiving and integrating new knowledge into LTM, but the system is dynamic, and rapid shifting among processes occurs. A second difference is that STM has been dropped as a separate memory in favor of working memory (WM). WM better reflects the dynamic nature of information processing and its interrelated functions with perception and LTM.

Third, the control processes have been dropped. Contemporary information processing theory addresses cognitive and motivational factors—such as goals, beliefs, and values—that focus learners’ attention and help them construct and process information in line with their goals, beliefs, and values (Mayer,  2012 ).

Finally, the contemporary model is less mechanistic and places great emphasis on the active construction of knowledge by learners (Mayer,  2012 ). Learners do not simply react to stimuli that impinge upon them but rather seek information that helps them learn. The current model reflects, in short, a large degree of learner control and self-regulation (see  Chapter 10 ).

The model assumes that information in memory begins as environmental sensory input. Sensory memory only holds information for milliseconds—long enough for the stimulus trace to be processed further. Of course, at any moment a lot of information is bombarding our sensory memories. Most of it is discarded, as much as 99% (Wolfe,  2010 ). This is desirable as most of it is irrelevant.

Inputs received by sensory memories, except for smells, are sent to the thalamus and then to the specific parts of the cortex designed to process those inputs (see  Chapter 2 ). At this early stage of processing, inputs are transformed from sensory information to perceptions that include meanings. A visual stimulus, for example, goes from being a visual light beam to “light from a flashlight.”

Next information is processed in WM. Perceptions are worked on (e.g., rehearsed, thought about) and integrated with information in LTM. Information that receives sufficient attention and rehearsal will be processed for transfer to LTM; information that is not adequately processed will be lost. Although WM functions may occur in different parts of the brain, the primary area seems to be the prefrontal cortex of the frontal lobe (Wolfe,  2010 ).

Information that is sufficiently constructed and processed is integrated with knowledge in LTM. Such consolidation occurs by forming or adapting existing neural networks or by strengthening existing ones. The process is dynamic because while WM is integrating with LTM it also is receiving new sensory inputs.

Knowledge Construction

Attention is important throughout the process, although attention is not always a conscious process. Attending to environmental inputs is necessary for them to enter the sensory registers. Some of this attention is conscious, as when learners direct their attention to computer screens. But much is not consciously driven (Dijksterhuis & Aarts,  2010 ); it is not possible to direct our attention to the multiple inputs that simultaneously impinge on us. Early attention is not selective; our reticular activating systems filter these stimuli, mostly without conscious awareness (Wolfe,  2010 ). Attention becomes more conscious with increased processing (Hübner, Steinhauser, & Lehle,  2010 ), through perception and especially as processing proceeds.

It was noted earlier that current information processing theory emphasizes learner control. The idea of knowledge construction is central. As Mayer ( 2012 ) explains: “Meaningful learning occurs when people engage in appropriate cognitive processing during learning, including selecting relevant information, organizing it into coherent mental representations, and integrating representations with each other and with relevant knowledge activated from long-term memory” (p. 89). Compared with earlier views that emphasized knowledge acquisition, contemporary theories stress knowledge construction by learners, or co-construction if others (e.g., teacher, peers) participate in the process (Mayer,  2012 ). The sections that follow provide elaborated descriptions of the processes discussed so far.

ATTENTION

The word  attention  is heard often in educational settings.  Attention  refers to concentrated mental activity that focuses on a limited amount of information in sensory memory and WM (Matlin,  2009 ). Teachers and parents complain that students do not pay attention to instruction or directions. (This does not seem to be the problem in the opening scenario; rather, the issue involves meaningfulness of processing.) Even high-achieving students do not always attend to instructionally relevant events. Sights, sounds, smells, tastes, and sensations bombard us; we cannot and should not attend to them all. Because our attentional capabilities are limited, attention can be construed as the process of selecting some of many potential inputs.

Alternatively, attention can refer to a limited human resource expended to accomplish one’s goals and to mobilize and maintain cognitive processes (Grabe,  1986 ). Attention is not a bottleneck in the information processing system through which only so much information can pass. Rather, it describes a general limitation on the entire human information processing system.

This section discusses conscious attention, which is necessary for learning. Conscious attention affects rehearsal in WM and the processes involved in integrating knowledge into LTM such as elaboration and organization. As mentioned earlier, most attention before inputs get to WM is unconscious (Wolfe,  2010 ). This section suggests ways that teachers can help focus students’ attention for learning which, although primarily involving conscious attention, also can help direct the more-unconscious aspects of students’ attention to inputs relevant to learning.

Theories of Attention

Researchers have explored how people select inputs for attending. In  dichotic listening  tasks, people wear headphones and receive different messages in each ear. They are asked to “shadow” one message (report what they hear); most can do this quite well. In an early study, Cherry ( 1953 ) investigated what happened to the unattended message. He found that listeners knew when it was present, whether it was a human voice or a noise, and when it changed from a male to a female voice. They typically did not know what the message was, what words were spoken, which language was being spoken, or whether words were repeated.

Broadbent ( 1958 ) proposed a model of attention known as  filter (bottleneck) theory . In this view, incoming information from the environment is held briefly in a sensory system. Based on their physical characteristics, pieces of information are selected for further processing by the perceptual system. Information not acted on by the perceptual system is filtered out—not processed beyond the sensory system. Attention is selective because of the bottleneck—only some messages receive further processing. In dichotic listening studies, filter theory proposes that listeners select a channel based on their instructions. They know some details about the other message because the physical examination of information occurs prior to filtering.

Subsequent work by Treisman ( 1960 ,  1964 ) identified problems with filter theory. Treisman found that during dichotic listening experiments, listeners routinely shifted their attention between ears depending on the location of the message they were shadowing. If they were shadowing the message coming into their left ear, and if that message suddenly shifted to the right ear, they continued to shadow the original message and not the new message coming into the left ear. Selective attention depends not only on the physical location of the stimulus but also on its meaning.

Treisman ( 1992 ; Treisman & Gelade,  1980 ) proposed a feature-integration theory. Sometimes we distribute attention across many sensory inputs, each of which receives low-level processing. At other times we focus on a particular sensory input, which is more cognitively demanding. Rather than blocking out messages, attention simply makes them less salient than those being attended to. Information inputs initially are subjected to different tests for physical characteristics and content. Following this preliminary analysis, one input may be selected for attention.

Treisman’s model is problematic in the sense that much analysis must precede attending to an input, which is puzzling because presumably the original analysis involves some conscious attention. Norman ( 1976 ) proposed that all inputs are attended to in sufficient fashion to activate a portion of LTM. At that point, one input is selected for further attention based on the degree of activation, which depends on the context. An input is more likely to be attended to if it fits into the context established by prior inputs. While people read, for example, many outside stimuli impinge on their sensory system, yet they attend to the printed symbols.

In Norman’s view, stimuli activate portions of LTM, but attention involves more complete activation. Neisser ( 1967 ) suggested that preattentive processes are involved in head and eye movements (e.g., refocusing attention) and in guided movements (e.g., walking, driving). Preattentive processes are automatic—people implement them without conscious mediation. In contrast, attentional processes are deliberate and require conscious activity. In support of this point, Logan ( 2002 ) postulated that attention and categorization occur together. As an object is attended to, it is categorized based on information in memory. Attention, categorization, and memory (WM and LTM) are three aspects of deliberate, conscious cognition.

Attention and Learning

Attention is necessary for learning. In learning to distinguish letters, a child learns the distinctive features: To distinguish b from d, students must attend to the position of the vertical line on the left or right side of the circle, not to the mere presence of a circle attached to a vertical line. To learn from a teacher, students must attend to the teacher’s voice and actions and ignore other inputs. To develop reading comprehension skills, students must attend to the printed words and ignore such irrelevancies as page size and color.

Learners consciously allocate attention to activities as a function of motivation and self-regulation (Kanfer & Ackerman,  1989 ; Kanfer & Kanfer,  1991 ). As skills become established, information processing requires less conscious attention. In learning to work multiplication problems, students must attend to each step in the process and check their computations. Once students learn multiplication tables and the algorithm, working problems becomes more automatic and is triggered by the input.

Differences in the ability to control attention are associated with student age, hyperactivity, intelligence, and learning disabilities (Grabe,  1986 ). Sustained attention is difficult for young children, as is attending to relevant rather than irrelevant information. Children also have difficulty switching attention rapidly from one activity to another. The ability to control attention contributes to the improvement of WM (Swanson,  2008 ). It behooves teachers to forewarn students of the attentional demands required to learn content. Outlines and study guides can serve as advance organizers and cue learners about the types of information that will be important. While students are working, teachers can use prompts, questions, and feedback to help students remain focused on the task (Meece,  2002 ).

Attention deficits are associated with learning problems. Hyperactive students are characterized by excessive motor activity, distractibility, and low academic achievement. They have difficulty focusing and sustaining attention on academic material. They may be unable to block out irrelevant stimuli, which overloads their WMs. Sustaining attention requires that students work in a strategic manner and monitor their level of understanding. Normal achievers and older children sustain attention better than do low achievers and younger learners on tasks requiring strategic processing (Short, Friebert, & Andrist,  1990 ).

Teachers can spot attentive students by noting their eye focus, their ability to begin working on cue (after directions are completed), and physical signs (e.g., writing, keyboarding) indicating they are engaged in work. But physical signs alone may not be sufficient; strict teachers can keep students sitting quietly even though students may not be engaged in class work.

Teachers can promote student attention to relevant material through the design of classroom activities ( Application 5.1 ). Eye-catching displays or actions at the start of lessons engage student attention. Teachers who move around the classroom help sustain student attention on the task. Other suggestions for focusing and maintaining student attention are given in  Table 5.1 .

Meaning and Importance

We are more likely to attend to inputs that have meaning than to those with less meaning (Wolfe,  2010 ). When sensory inputs enter WM, it attempts to find related information in LTM. When nothing relevant can be found, attention is likely to wane and be directed toward other inputs. The important role of meaningfulness in learning is exemplified in the opening vignette and discussed later in this chapter.

APPLICATION 5.1 Maintaining Student Attention

Various practices help keep classrooms from becoming predictable and repetitive, which decreases attention. Teachers can vary their presentations, materials used, student activities, and personal qualities such as dress and mannerisms. Lesson formats for young children should be kept short. Teachers can sustain a high level of activity through student involvement and by moving about to check on student progress.

As Ms. Keeling begins a language arts activity in her third-grade class, she asks students to point to the location of the activity in their books. She varies how she introduces activities: Sometimes she forms students into small groups, whereas at other times they work individually. She also varies how students’ answers are checked. Students might use hand signals or respond in unison, or individual students can answer and explain their answers. As students independently complete the exercise, she moves about the room, checks students’ progress, and assists those having difficulty learning or maintaining task focus.

A music teacher might increase student attention by using vocal exercises, singing certain selections, using instruments to complement the music, and adding movement to instruments. The teacher might combine activities or vary their sequence. Small tasks also can be varied to increase attention, such as the way a new music selection is introduced. The teacher might play the entire selection, then model by singing the selection, and then involve the students in the singing. Alternatively, for the last activity the teacher could divide the selection into parts, work on each of the small sections, and then combine these sections to complete the full selection.

Table 5.1 Ways to focus and maintain student attention.

Device	Implementation
Signals	Signal to students at the start of lessons or when they are to change activities.
Movement	Move while presenting material to the whole class. Move around the room while students are engaged in seat work.
Variety	Use different materials and teaching aids. Use gestures. Do not speak in a monotone.
Interest	Introduce lessons with stimulating material. Appeal to students’ interests at other times during the lesson.
Questions	Ask students to explain a point in their own words. Stress that they are responsible for their own learning.

Perceived importance also can help direct and sustain conscious attention. In reading, for example, students are more likely to recall important text elements than less important ones (R. Anderson,  1982 ; Grabe,  1986 ). Both good and poor readers locate important material and attend to it for longer periods (Ramsel & Grabe,  1983 ; Reynolds & Anderson,  1982 ). What distinguishes these readers is subsequent processing and comprehension. Perhaps poor readers, being more preoccupied with basic reading tasks (e.g., decoding), become distracted from important material and do not process it adequately for retention and retrieval. While attending to important material, good readers may be more apt to rehearse it, make it meaningful, and relate it to knowledge in LTM, all of which improve comprehension (Resnick,  1981 ).

The importance of text material can affect subsequent recall through differential attention (R. Anderson,  1982 ). Text elements apparently are processed at some minimal level so importance can be assessed. Based on this evaluation, the text element either is dismissed in favor of the next element (unimportant information) or receives additional attention (important information). Assuming attention is sufficient, the actual types of processing students engage in must differ to account for subsequent comprehension differences. Better readers may engage in automatic processing of text more often than poorer readers.

Hidi ( 1995 ) noted that attention is required during many phases of reading: processing orthographic features, extracting meanings, judging information for importance, and focusing on important information. This suggests that attentional demands vary considerably depending on the purpose of reading—for example, extracting details, comprehending, or new learning.

PERCEPTION

Perception  (or  pattern recognition ) refers to attaching meaning to environmental inputs received through the senses. For an input to be perceived, it must register in one or more of the sensory registers and be transferred to the appropriate brain structure. The input then is compared to knowledge in LTM. Sensory registers and the comparison process are discussed in this section.

Sensory Registers

Environmental inputs are received through the senses: vision, hearing, touch, smell, and taste. Each sense has its own register that holds information briefly in the same form in which it is received (Wolfe,  2010 ). Information stays in the sensory register for less than .25 second (Mayer,  2012 ). Some sensory input is transferred to WM for further processing. Other input is lost and replaced by new input. The sensory registers operate in parallel fashion because several senses can be engaged simultaneously and independently of one another. The two sensory memories that have been most extensively explored are  iconic  (vision) and echoic (hearing).

In a typical experiment to investigate iconic memory, a researcher presents learners with rows of letters briefly (e.g., 50 milliseconds) and asks them to report as many as they remember. They commonly report only four to five letters from an array. Early work by Sperling ( 1960 ) provided insight into iconic storage. Sperling presented learners with rows of letters, then cued them to report letters from a particular row. Sperling estimated that, after exposure to the array, they could recall about nine letters. Sensory memory could hold more information than was previously believed, but while participants were recalling letters, the traces of other letters quickly faded. Sperling also found that the more time between the end of a presentation of the array and the beginning of recall, the poorer was the recall. This finding supports the idea that the loss of a stimulus from a sensory register involves  trace decay . Sakitt ( 1976 ; Sakitt & Long,  1979 ) argued that the icon is located in the rods of the eye’s retina. It is debatable whether the icon is a memory store or a persisting image.

There is evidence for an echoic memory similar in function to iconic memory (Matlin,  2009 ). Early studies by Darwin, Turvey, and Crowder ( 1972 ) and by Moray, Bates, and Barnett ( 1965 ) yielded results comparable to Sperling’s ( 1960 ). Research participants heard three or four sets of recordings simultaneously and then were asked to report one. Findings showed that echoic memory is capable of holding more information than can be recalled. Similar to iconic information, traces of echoic information rapidly decay following removal of stimuli. The echoic decay is not quite as rapid as the iconic, but periods beyond 2 seconds between cessation of stimulus presentation and onset of recall produce poorer recall.

LTM Comparisons

Perception occurs through bottom-up and top-down processing (Matlin,  2009 ). In  bottom-up processing ,inputs received by sensory registers are transferred to WM for comparisons with information in LTM to assign meanings beyond the physical properties. Environmental inputs have tangible physical properties. Assuming normal color vision, everyone who looks at a yellow tennis ball will recognize it as a yellow object, but only those familiar with tennis will recognize it as a tennis ball. The types of information people have acquired account for the different meanings they assign to objects.

Perception is affected not only by objective characteristics but also by prior experiences and expectations.  Top-down processing  refers to the influence of our knowledge and beliefs on perception (Matlin,  2009 ). Motivational states also are important. Perception is affected by what we wish and hope to perceive (Balcetis & Dunning,  2006 ). We often perceive what we expect and fail to perceive what we do not expect. Have you ever thought you heard your name spoken, only to realize that another name was being called? While waiting to meet a friend at a public place or to pick up an order in a restaurant, you may hear your name because you expect to hear it. Also, people may not perceive things whose appearance has changed or that occur out of context. You may not recognize co-workers you meet at the beach because you do not expect to see them dressed in beach attire. Top-down processing often occurs with ambiguous stimuli or those registered only briefly (e.g., a stimulus spotted in the “corner of the eye”).

An information processing theory of perception is  template matching , which holds that people store  templates , or miniature copies of stimuli, in LTM. When they encounter a stimulus, they compare it with existing templates and identify it if a match is found. This view is appealing but problematic. People would need millions of templates stored in LTM to recognize everyone and everything in their environment. Such a large stock would exceed the brain’s capability. Template theory also does a poor job of accounting for stimulus variations. Chairs, for example, come in all sizes, shapes, colors, and designs; hundreds of templates would be needed just to perceive a chair.

The problems with templates can be solved by assuming that they can have some variation. Prototype theory addresses this.  Prototypes  are abstract forms that include the basic ingredients of stimuli (Matlin,  2009 ; Rosch,  1973 ). Prototypes are stored in LTM and are compared with encountered stimuli that are subsequently identified based on the prototype they match or resemble in form, smell, sound, and so on. Some research supports the existence of prototypes (Franks & Bransford,  1971 ; Posner & Keele,  1968 ; Rosch,  1973 ).

A major advantage of prototypes over templates is that each stimulus has only one prototype instead of countless variations; thus, identification of a stimulus should be easier because comparing it with several templates is not necessary. One issue with prototypes concerns the amount of acceptable stimulus variability, or how closely a stimulus must match a prototype to be identified as an instance of that prototype.

A variation of the prototype model involves feature analysis (Matlin,  2009 ). In this view, one learns the critical features of stimuli and stores these in LTM as images or verbal codes (Markman,  1999 ). When an input enters the sensory register, its features are compared with memorial representations. If enough of the features match, the stimulus is identified. For a chair, the critical features may be legs, seat, and a back. Many other features (e.g., color, size) are irrelevant. Any exceptions to the basic features need to be learned (e.g., bleacher and beanbag chairs that have no legs). Unlike the prototype analysis, information stored in memory is not an abstract representation of a chair but rather includes its critical features. One advantage of feature analysis is that each stimulus does not have just one prototype, which partially addresses the concern about the amount of acceptable variability. There is empirical research support for feature analysis (Matlin,  2009 ).

Treisman ( 1992 ) proposed that perceiving an object establishes a temporary representation in an object file that collects, integrates, and revises information about its current characteristics. The contents of the file may be stored as an object token. For newly perceived objects, we try to match the token to a memorial representation (dictionary) of object types, which may or may not succeed. The next time the object appears, we retrieve the object token, which specifies its features and structure. The token will facilitate perception if all of the features match but may impair it if many do not match.

Regardless of how LTM comparisons are made, research evidence supports the idea that perception depends on bottom-up and top-down processing (Anderson,  1980 ; Matlin,  2009 ; Resnick,  1985 ). In reading, for example, bottom-up processing analyzes features and builds a meaningful representation to identify stimuli. Beginning readers typically use bottom-up processing when they encounter letters and new words and attempt to sound them out. People also use bottom-up processing when experiencing unfamiliar stimuli (e.g., handwriting).

Reading would proceed slowly if all perception required analyzing features in detail. In top-down processing, individuals develop expectations regarding perception based on the context. Skilled readers build a mental representation of the context while reading and expect certain words and phrases in the text (Resnick,  1985 ). Effective top-down processing depends on extensive prior knowledge. We now turn to a discussion of encoding, a key process that occurs in WM.

ENCODING

Encoding  refers to the process of putting new incoming information into the information processing system and preparing it for storage in LTM. Once an input has been attended to, processed by sensory memory, and perceived, it enters WM. This section discusses WM and the influences on encoding.

Working Memory (WM)

Working memory  is our memory of immediate consciousness. Although WM functions may occur in different parts of the brain depending on the task to be performed, its primary activity seems to reside in the prefrontal cortex of the frontal lobe (Gazzaniga, Ivry, & Mangun,  1998 ; Wolfe,  2010 ). Some researchers (e.g, Baddeley,  2012 ) distinguish WM from STM, with the latter referring to the temporary storage of information and the former to storage and manipulation of knowledge. We will use the term “WM” since both WM and sensory memory are of short-term duration.

WM performs two critical functions: maintenance and retrieval (Baddeley,  1992 ,  1998 ,  2001 ; Terry,  2009 ; 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 LTM. As students read, 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 (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 (5 × 7 = ?) is compared with activated knowledge in LTM (5 × 7 = 35). Also activated in LTM is the multiplication algorithm, and these procedures direct the student’s actions.

WM often functions as a conduit for information to be transferred to or integrated with knowledge in LTM. But sometimes WM is the final destination for information, especially information that we use immediately. For example, if a friend verbalizes to you a phone number to call, you hold it in WM long enough to enter it on your phone. In this case, LTM is essentially “outsourced” to your phone.

The contemporary perspective on WM expands upon the limited conception of STM in early models, which was viewed primarily as a storage site. Conversely, WM both maintains and processes information (Barrouillet, Portrat, & Camos,  2011 ).

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 is lost. 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. In the preceding example if you were distracted before entering the number in your phone, you may not be able to recall it.

WM also is limited in capacity: It can hold only a small amount of information. As noted earlier, 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.

Baddeley ( 1998 ,  2001 ,  2012 ) developed a WM model that includes a phonological loop, visuo-spatial sketch pad, and central executive ( Figure 5.5 ). The phonological loop processes auditory (speech-based) information and keeps it active through rehearsal. The visuo-spatial sketch pad is responsible for establishing and maintaining visual information (images). Presumably there are additional sensory functions performed in WM (i.e., taste, smell, touch), but the visual and auditory have received the most research attention. The central executive is essentially a controller of attention. This is critical for learning, since much learning requires sustained task attention.

The central executive directs the processing of information in WM, as well as the movement of knowledge into and out of WM (Baddeley,  1998 ,  2001 ,  2012 ). The middle part of  Figure 5.5  is a type of  episodic  buffer, where information from multiple modalities can be integrated. This area acts as a buffer when it links the information in WM, as well as WM with perception and LTM (Baddeley,  2012 ).

The central executive performs several functions (Baddeley,  2012 ). In addition to focusing attention, it divides attention as needed between two or more inputs (e.g., visual and auditory), and controls switching between tasks when necessary. The central executive also performs interfacing with LTM.

Figure 5.5 Model of working memory.

Source: Baddeley, Alan, Human Memory: Theory and Practice, 2nd Ed., © 1998. Reprinted and Electronically reproduced by permission of Pearson Education, Inc., Upper Saddle River, New Jersey.

The central executive is goal directed; it selects information relevant to people’s plans and intentions from the sensory registers. Information deemed important is rehearsed. Rehearsal 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 ). There is high interest on the operation of WM, and researchers continue to explore its processes (Baddeley,  2012 ; 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 ). Like other information processing functions, WM improves with development. Executive processing of information becomes more efficient (Swanson,  2011 ). The capacity for maintenance (e.g., rehearsal) develops (Gaillard, Barrouillet, Jarrold, & Camos,  2011 ), as does the capability to keep goals in mind (Marcovitch, Boseovski, Knapp, & Kane,  2010 ).

A key instructional implication is not to overload students’ WMs by presenting too much material at once or too rapidly (see the section on Cognitive Load later in this chapter). 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). Although the capacity of WM is limited (Cowan, Rouder, Blume, & Saults,  2012 ), there is evidence that its capacity can be improved through training (e.g., using span tasks, where learners are asked to recall increasingly longer lists), as well as its attentional control (Shipstead, Redick, & Engle,  2012 ).

Influences on Encoding

Encoding begins in WM and is accomplished by making new information meaningful and integrating it with known information in LTM. Although information need not be meaningful to be learned—one unfamiliar with geometry could memorize the Pythagorean theorem without understanding what it means—meaningfulness improves learning and retention.

Attending to and perceiving stimuli do not ensure that information processing will continue. Many things teachers say in class go unlearned (even though students attend to the teacher and the words are meaningful) because students do not continue to process the information in WM and encode it. Important factors that influence encoding are elaboration and organization ( Figure 5.4 ), which help to form schemas.

Elaboration.

Elaboration  is the process of expanding upon new information by adding to it or linking it to what one knows. Elaborations assist encoding and retrieval because they link the to-be-remembered information with other knowledge. Recently learned information is easier to access in this expanded memory network. Even when the new information is forgotten, people often can recall the elaborations (Anderson,  1990 ). As the introductory vignette illustrates, a problem that many students have in learning algebra is that they cannot elaborate the material because it is abstract and does not easily link with other knowledge.

Rehearsing information keeps it in WM but does not necessarily elaborate it. A distinction can be drawn between maintenance rehearsal (repeating information over and over) and elaborative rehearsal(relating the information to something already known). Students learning U.S. history can simply repeat “D-Day was June 6, 1944,” or they can elaborate it by relating it to something they know (e.g., “In 1944 Roosevelt was elected president for the fourth time”). With development, children become more proficient at elaborative rehearsal, which is desirable because it leads to better recall than maintenance rehearsal (Lehmann & Hasselhorn,  2010 ).

Mnemonic  strategies (see  Chapter 10 ) elaborate information in different ways. Once such strategy is to form the first letters into a meaningful sentence. For example, to remember the order of the planets from the sun you might learn the sentence, “My very educated mother just served us nectarines,” in which the first letters correspond to those of the planets (Mercury, Venus, Earth, Mars, Jupiter, Saturn, Uranus, Neptune). You first recall the sentence and then reconstruct planetary order based on the first letters.

Students may be able to devise elaborations, but if they cannot, they do not need to labor needlessly when teachers can provide effective elaborations. To assist storage in memory and retrieval, elaborations must make sense. Elaborations that are too unusual may not be remembered. Precise, sensible elaborations facilitate memory and recall (Bransford et al.,  1982 ; Stein, Littlefield, Bransford, & Persampieri,  1984 ).

Organization.

Gestalt theory and research showed that well-organized material is easier to learn and recall (Katona,  1940 ). Miller ( 1956 ) argued that learning is enhanced by classifying and grouping bits of information into organized chunks. Memory research demonstrates that even when items to be learned are not organized, people often impose organization on the material, which facilitates recall (Matlin,  2009 ). Organized material improves memory because items are linked to one another systematically. Recall of one item prompts recall of items linked to it. Research supports the effectiveness of organization for encoding among children and adults (Basden, Basden, Devecchio, & Anders,  1991 ).

One way to organize material is to use a hierarchy into which pieces of information are integrated.  Figure 5.6  shows a sample hierarchy for animals. The animal kingdom as a whole is on top, and underneath are the major categories (e.g., mammals, birds, reptiles). Individual species are found on the next level, followed by breeds.

Other ways of organizing information include the use of mnemonic strategies ( Chapter 10 ) and mental imagery ( Chapter 6 ). Mnemonics enable learners to enrich or elaborate material, such as by forming the first letters of words to be learned into an acronym, familiar phrase, or sentence (Matlin,  2009 ). Some mnemonic techniques employ imagery; in remembering two words (e.g., honey and bread), one might imagine them interacting with each other (honey on bread). Using audiovisuals in instruction can improve students’ imagery.

Schemas.

Elaboration and organization help to form schemas. A  schema  (plural  schemas  or schemata) is a structure that organizes large amounts of information into a meaningful system. Schemas include our generalized knowledge about situations (Matlin,  2009 ). Schemas are plans we learn and use during our environmental interactions. Larger units are needed to organize propositions representing bits of information into a coherent whole (Anderson,  1990 ). Schemas assist us in generating and controlling routine sequential actions (Cooper & Shallice,  2006 ).

Figure 5.6 Memory network with hierarchical organization.

In a classic study, Bartlett ( 1932 ) found that schemas aid in comprehending information. A participant read a story about an unfamiliar culture, after which this person reproduced it for a second participant, who reproduced it for a third participant, and so on. By the time the story reached the 10th person, its unfamiliar context had been changed to one that participants were familiar with (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 like participants’ experiences. They altered incoming information to fit their preexisting schemas.

Any well-ordered sequence can be represented as a schema. One type of schema is “going to a restaurant.” The steps consist of activities such as being seated at a table, looking over a menu, ordering food, being served, having dishes picked up, receiving a bill, leaving a tip, and paying the bill. Schemas are important because they indicate what to expect in a situation. People recognize a problem when reality and schema do not match. Have you ever been in a restaurant where one of the expected steps did not occur (e.g., you received a menu but no one returned to take your order)?

Common educational schemas involve laboratory procedures, studying, and comprehending stories. When given material to read, students activate the type of schema they believe is required. If students are to read a passage and answer questions about main ideas, they may periodically stop and quiz themselves on what they believe are the main points (Resnick,  1985 ). Schemas have been investigated extensively in research on reading and writing (McVee, Dunsmore, & Gavelek,  2005 ).

Schemas assist encoding because they help elaborate new knowledge and integrate it into an organized, 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 can fit the characters and actions of the story into the schema. They expect to find elements such as good versus evil, human frailties, and a dramatic denouement. When these events occur, they are fit into the schema students have activated for the story ( Application 5.2 ).

APPLICATION 5.2 Schemas

Teachers can increase learning by helping students develop schemas. A schema is helpful when learning can occur by applying an ordered sequence of steps. An elementary teacher might teach the following schema to his or her children to assist their reading of unfamiliar words:

· ■ Read the word in the sentence to see what might make sense.

· ■ Look at the beginning and ending of the word—reading the beginning and the ending is easier than the whole word.

· ■ Think of words that would make sense in the sentence and that would have the same beginning and ending.

· ■ Sound out all the letters in the word.

· ■ If these steps do not help identify the word, look it up in a dictionary.

With some modifications, this schema can be used by students of any age.

Teachers might help their students learn to use a schema to locate answers to questions listed at the end of chapters, such as the following:

· ■ Read through all of the questions.

· ■ Read the chapter completely once.

· ■ Reread the questions.

· ■ Reread the chapter slowly, and use paper markers if you find a section that seems to fit with one of the questions.

· ■ Go back and match each question with an answer.

· ■ When you find the answer, write it and the question on your paper.

· ■ If you cannot find an answer, use your index to locate key words in the question.

· ■ If you still cannot locate the answer, ask the teacher for help.

Schemas may facilitate recall independently of their benefits on encoding. Anderson and Pichert ( 1978 ) presented college students with a story about two boys skipping school. Students were advised to read it from the perspective of either a burglar or a home buyer; the story had 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 use their original perspective and the other half the other perspective. On the second recall, students recalled more information relevant to the second perspective but 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 suggest that at retrieval, people recall a schema and attempt to fit elements into it. This reconstruction may not be accurate but will include most schema elements.  Production systems , which are discussed later, bear some similarity to schemas.

LONG-TERM MEMORY: STORAGE

Although our knowledge about LTM is limited because we do not have a window into the brain, neuroscience and psychological research has painted a reasonably consistent picture of the storage process. The characterization of LTM in this chapter involves a structure with knowledge being represented as locations or nodes in networks, with networks connected (associated) with one another. Note the similarity between these cognitive networks and the neural networks discussed in  Chapter 2 . When discussing networks, we deal primarily with declarative knowledge and procedural knowledge. Conditional knowledge is covered in  Chapter 7  along with metacognitive activities that monitor and direct cognitive processing. It is assumed that most knowledge is stored in LTM in verbal codes, but the role of imagery also is addressed in  Chapter 6 .

Propositions

The Nature of Propositions.

Propositions are the basic units of knowledge and meaning in LTM (Anderson,  1990 ; Kosslyn,  1984 ). A  proposition  is the smallest unit of information that can be judged true or false. Each of the following is a proposition:

· ■ The Declaration of Independence was signed in 1776.

· ■ 2 + 2 = 4.

· ■ Aunt Frieda hates turnips.

· ■ I’m good in math.

· ■ The main characters are introduced early in a story.

These sample propositions can be judged true or false. Note, however, that people may disagree on their judgments. Carlos may believe that he is bad in math, but his teacher may believe that he is very good. Objective truth is not the criterion; rather, whether a true or false judgment is possible.

The exact nature of propositions is not well understood. Although they can be thought of as sentences, it is more likely that they are meanings of sentences (Anderson,  1990 ). Research supports the point that we store information in memory as propositions rather than as complete sentences. Kintsch ( 1974 ) gave participants sentences to read that were of the same length but varied in the number of propositions they contained. The more propositions contained in a sentence, the longer it took participants to comprehend it. This implies that, although students can generate the sentence, “The Declaration of Independence was signed in 1776,” what they most likely have stored in memory is a proposition containing only the essential information (Declaration of Independence—signed—1776). With certain exceptions (e.g., memorizing a poem), it seems that people usually store meanings rather than precise wordings.

Propositions form networks that are composed of individual nodes or locations. Nodes can be thought of as individual words, although their exact nature is unknown but probably abstract. For example, students taking a history class likely have a “history class” network comprising such nodes as “book,” “teacher,” “location,” “name of student who sits on their left,” and so forth.

Propositional Networks.

Propositions are formed according to a set of rules. Researchers disagree on which rules constitute the set, but they generally believe that rules combine nodes into propositions and, in turn, propositions into higher-order structures or  networks , which are sets of interrelated propositions.

Anderson’s ACT theory (Anderson,  1990 ,  1993 ,  1996 ,  2000 ; Anderson et al.,  2004 ; Anderson, Reder, & Lebiere,  1996 ) proposes an ACT-R (Adaptive Control of Thought-Rational) network model of LTM with a propositional structure. ACT-R is a model of cognitive architecture that attempts to explain how all components of the mind work together to produce coherent cognition (Anderson et al.,  2004 ). A proposition is formed by combining two nodes with a subject–predicate link, or association; one node constitutes the subject and another node the predicate. Examples are (implied information in parentheses): “Fred (is) rich” and “Shopping (takes) time.” A second type of association is the relation–argument link, where the relation is verb (in meaning) and the argument is the recipient of the relation or what is affected by the relation. Examples are “eat cake” and “solve puzzles.” Relation arguments can serve as subjects or predicates to form complex propositions. Examples are “Fred eat(s) cake,” and “solv (ing) puzzles (takes) time.”

Propositions are interrelated when they share a common element. Common elements allow people to solve problems, cope with environmental demands, draw analogies, and so on. Without common elements, transfer would not occur; all knowledge would be stored separately, and information processing would be slow. One would not recognize that knowledge relevant to one domain is also relevant to other domains.

Figure 5.7  shows an example of a propositional network. The common element is “cat” because it is part of the propositions, “The cat walked across the front lawn,” and “The cat caught a mouse.” One can imagine that the former proposition is linked with other propositions relating to houses, whereas the latter is linked with propositions about mice.

Figure 5.7 Sample propositional network.

Evidence suggests that propositions are organized in hierarchical structures. Collins and Quillian ( 1969 ) showed that people store information at the highest level of generality. For example, the LTM network for “animal” would have stored at the highest level such facts as “moves” and “eats.” Under this category would come such species as “birds” and “fish.” Stored under “birds” are “has wings,” “can fly,” and “has feathers” (although there are exceptions—chickens are birds but they do not fly). The fact that birds eat and move is not stored at the level of “bird” because that information is stored at the higher level of animal. Collins and Quillian found that retrieval times increased the farther apart concepts were stored in memory.

The hierarchical organization idea has been modified by research showing that information is not always hierarchical. Thus, “collie” is closer to “mammal” than to “animal” in an animal hierarchy, but people are quicker to agree that a collie is an animal than to agree that it is a mammal (Rips, Shoben, & Smith,  1973 ).

Furthermore, familiar information may be stored both with its concept and at the highest level of generality (Anderson,  1990 ). If you have a bird feeder and you often watch birds eating, you might have “eat” stored with both “birds” and “animals.” This finding does not detract from the central idea that propositions are organized and interconnected. Although some knowledge may be hierarchically organized, much information is probably organized in a less systematic fashion in propositional networks.

Storage of Knowledge

Declarative Knowledge.

The major types of knowledge are declarative and procedural ( Figure 5.4 ).  Declarative knowledge , or knowing that something is the case, includes facts, beliefs, opinions, generalizations, theories, hypotheses, and attitudes about oneself, others, and world events (Gupta & Cohen,  2002 ; Paris, Lipson, & Wixson,  1983 ). It is acquired when a new proposition is stored in LTM, usually in a related propositional network (Anderson,  1990 ). ACT theory postulates that declarative knowledge is represented in chunks comprising the basic information plus related categories (Anderson,  1996 ; Anderson, Reder, & Lebiere,  1996 ).

The storage process operates as follows. First, the learner receives new information, such as when the teacher makes a statement or the learner reads a sentence. Next, the new information is parsed into one or more propositions in the learner’s WM. At the same time, related propositions in LTM are activated. The new propositions are associated with the related propositions in WM through the process of spreading activation (discussed in the following section). As this point, learners might generate additional propositions. Finally, all the new propositions—those received and those generated by the learner—are stored together in LTM (Hayes-Roth & Thorndyke,  1979 ).

Figure 5.8  illustrates this process. Assume that a teacher is presenting a unit on the U.S. Constitution and says to the class, “The vice president of the United States serves as president of the Senate but does not vote unless there is a tie.” This statement may activate other propositional knowledge stored in students’ memories relating to the vice president (e.g., elected with the president, becomes president when the president dies or resigns, can be impeached for crimes of treason) and the Senate (e.g., 100 members, two elected from each state, 6-year terms). Putting these propositions together, the students should infer that the vice president would vote if 50 senators voted for a bill and 50 voted against it.

Figure 5.8 Storage of declarative knowledge.

Note: Dotted lines represent new knowledge; solid lines indicate knowledge in long-term memory.

Storage problems can occur when students have no preexisting propositions with which to link new information. Students who have not heard of the U.S. Constitution or do not know what a constitution is will draw a blank when they hear the word for the first time. Conceptually meaningless information can be stored in LTM, but students learn better when new information is related to something they know. Showing students a facsimile of the U.S. Constitution or relating it to something they have studied (e.g., Declaration of Independence) gives them a referent to link with the new information.

Even when students have studied related material, they may not automatically link it with new information. Often the links need to be made explicit. When discussing the function of the vice president in the Senate, teachers could remind students of the composition of the U.S. Senate and the other roles of the vice president. Propositions sharing a common element are linked in LTM only if they are active in WM simultaneously. This point helps to explain why students might fail to see how new material relates to old material, even though the link is clear to the teacher. Instruction that best establishes propositional networks in learners’ minds incorporates review, organization of material, and reminders of things they know but are not thinking of now.

As with many memory processes, meaningfulness, organization, and elaboration facilitate storing information in memory. Meaningfulness is important because meaningful information can be easily associated with preexisting information in memory. Consequently, less rehearsal is necessary, which saves space and time of information in WM. The students being discussed in the opening scenario are having a problem making algebra meaningful, and the teachers express their frustration at not teaching the content in a meaningful fashion.

A study by Bransford and Johnson ( 1972 ) provides a dramatic illustration of the role of meaningfulness in storage and comprehension. Consider the following passage:

·  First you arrange things into different groups … One pile may be sufficient depending on how much there is to do. If you have to go somewhere else due to lack of facilities that is the next step…, It is better to do too few things at once than too many … At first the whole procedure will seem complicated. Soon, however, it will become just another facet of life … After the procedure is completed one arranges the materials into different groups again. Then they can be put into their appropriate places. Eventually they will be used once more and the whole cycle will then have to be repeated. (p. 722)

Without prior knowledge this passage is difficult to comprehend and store in memory because relating it to existing knowledge in memory is hard to do. However, knowing that it is about “washing clothes” makes remembering and comprehension easier. Bransford and Johnson found that students who knew the topic recalled about twice as much as those who were unaware of it. The importance of meaningfulness in learning has been demonstrated in numerous other studies (Anderson,  1990 ; Chiesi, Spilich, & Voss,  1979 ; Spilich, Vesonder, Chiesi, & Voss,  1979 ).

Organization facilitates storage because well-organized material is easier to relate to preexisting memory networks than is poorly organized material (Anderson,  1990 ). To the extent that material can be organized into a hierarchical arrangement, it provides a ready structure to be accepted into LTM. Without an existing LTM network, creating a new LTM network is easier with well-organized information than with poorly organized information.

Elaboration improves storage because it helps learners relate information to something they know. Through spreading activation, the elaborated material may be quickly linked with information in memory. For example, a teacher might be discussing the Mt. Etna volcano. Students who can elaborate that knowledge by relating it to their personal knowledge of volcanoes (e.g., Mt. St. Helens) will be able to associate the new and old information in memory and better retain the new material.

Spreading Activation.

Spreading activation  helps to explain how new information is linked to knowledge in LTM (Anderson,  1983 ,  1984 ,  1990 ,  2000 ; Collins & Loftus,  1975 ). The basic underlying principles are as follows (Anderson,  1984 ):

· ■ Human knowledge can be represented as a network of nodes, where nodes correspond to concepts and links to associations among these concepts.

· ■ The nodes in this network can be in various states that correspond to their levels of activation. More active nodes are processed “better.”

· ■ Activation can spread along these network paths by a mechanism whereby nodes can cause their neighboring nodes to become active. (p. 61)

Anderson ( 1990 ) cited the example of an individual presented with the word dog. This word is associatively linked with such other concepts in the individual’s LTM as bone, cat, and meat. In turn, each of these concepts is linked to other concepts. The activation of dog in LTM will spread beyond dog to linked concepts, with the spread lessening with concepts farther away from dog.

Spreading activation is based on the idea that memory structures vary in their  activation level  (Anderson,  1990 ). In this view, we do not have separate memory structures or phases but rather one memory with different activation states. Information may be in an active or inactive state. When active, the information can be accessed quickly. The active state is maintained as long as information is attended to. Without attention, the activation level will decay, in which case the information can be activated when the memory is reactivated (Collins & Loftus,  1975 ).

Active information can include information entering the information processing system and information that has been stored in memory (Baddeley,  1998 ). Regardless of the source, active information either is currently being processed or can be processed rapidly. Active material is roughly synonymous with WM, but the former category is broader than the latter. WM includes information in immediate consciousness, whereas active memory includes that information plus material that can be accessed easily. For example, if I am visiting Aunt Frieda and we are admiring her flower garden, that information is in WM, but other information associated with Aunt Frieda’s yard (trees, shrubs, dog) may be in an active state.

Rehearsal allows information to be maintained in an active state (Anderson,  1990 ). As with WM, only a limited amount of memory can be active at a given time. As one’s attention shifts, activation level changes.

Experimental support for the existence of spreading activation was obtained by Meyer and Schvaneveldt ( 1971 ). These investigators used a reaction time task that presented participants with two strings of letters and asked them to decide whether both were words. Words associatively linked (bread, butter) were recognized faster than words not linked (nurse, butter).

Spreading activation results in a larger portion of LTM being activated than knowledge immediately associated with the content of WM. Activated information stays in LTM unless it is deliberately accessed, but this information is more readily accessible to WM. Spreading activation also facilitates transfer of knowledge to different domains. Transfer depends on propositional networks in LTM being activated by the same cue, so students recognize that knowledge is applicable in the domains.

One advantage of activation level is that it can explain retrieval of information from memory. By dispensing with the notion of memory phases, the model eliminates the potential problem of transferring information. WM is that part of memory that is currently active. Activation decays with the passage of time, unless rehearsal keeps the information activated (Nairne,  2002 ).

At the same time, activation level has not escaped the dual-store model’s problems because it too dichotomizes the information system (active-inactive). We also have the problem of the strength level needed for information to pass from one state to another. Thus, we intuitively know that information may be partially activated (e.g., a word on the “tip of your tongue”—you know it but cannot recall it), so we might ask how much activation is needed for material to be considered active. These concerns notwithstanding, activation level and spreading activation offer important insights into the processing of information.

Schemas.

Propositional networks represent small pieces of knowledge.  Schemas  (or schemata) are large networks that represent the structure of objects, persons, and events (Anderson,  1990 ). Structure is represented with a series of “slots,” each of which corresponds to an attribute. In the schema or slot for houses, some attributes (and their values) might be as follows: material (wood, brick), contents (rooms), and function (human dwelling). Schemas are hierarchical; they are joined to superordinate ideas (building) and subordinate ones (roof).

Brewer and Treyens ( 1981 ) found research support for the underlying nature of schemas. Individuals were asked to wait in an office for a brief period, after which they were brought into a room where they wrote down everything they could recall about the office. Recall reflected the strong influence of a schema for office. They correctly recalled the office having a desk and a chair (typical attributes) but not that the office contained a skull (nontypical attribute). Books are a typical attribute of offices; although the office had no books, many persons incorrectly recalled books.

Schemas are important during teaching and for transfer (Matlin,  2009 ). Once students learn a schema, teachers can activate this knowledge when they teach any content to which the schema is applicable. Suppose an instructor teaches a general schema for describing geographical formations (e.g., mountain, volcano, glacier, river). The schema might contain the following attributes: height, material, and activity. Once students learn the schema, they can employ it to categorize new formations they study. In so doing, they would create new schemata for the various formations.

Procedural Knowledge.

Procedural knowledge , or knowledge of how to perform cognitive activities (Anderson,  1990 ; Gupta & Cohen,  2002 ; Hunt,  1989 ; Paris et al.,  1983 ), is central to much school learning. We use procedural knowledge to solve mathematical problems (e.g., algorithms), summarize information, skim passages, surf the Web, and perform laboratory techniques.

Procedural knowledge may be stored in networks as verbal codes and images, much the same way as declarative knowledge is stored. ACT theory posits that procedural knowledge is stored as a production system (Anderson,  1996 ; Anderson, Reder, & Lebiere,  1996 ). A  production system  (or  production ) is a network of condition–action sequences (rules), in which the condition is the set of circumstances that activates the system and the action is the set of activities that occurs (Anderson,  1990 ; Andre,  1986 ; see next section). Production systems seem conceptually similar to neural networks (discussed in  Chapter 2 ).

Production Systems and Connectionist Models

Production systems and connectionist models provide paradigms for examining the operation of cognitive learning processes (Anderson,  1996 ,  2000 ; Smith,  1996 ). To date, there has been little research on connectionist models that is relevant to education. Additional sources provide further information about these models (Bourne,  1992 ; Farnham-Diggory,  1992 ; Matlin,  2009 ; Siegler,  1989 ).

Production Systems.

ACT—an activation theory—specifies that a  production system  (or  production ) is a network of condition–action sequences (rules), in which the condition is a set of circumstances that activates the system and the action is the set of activities that occurs (Anderson,  1990 ,  1996 ,  2000 ; Anderson, Reder, & Lebiere,  1996 ; Andre,  1986 ). A production consists of if–then statements: If statements (the condition) include the goal and test statements, and then statements are the actions. As an example:

· ■ IF I see two numbers and they must be added,

· ■ THEN decide which is larger and start with that number and count up to the next one. (Farnham-Diggory,  1992 , p. 113)

Although productions are forms of procedural knowledge that can have conditions attached to them, they also include declarative knowledge.

Learning procedures for performing skills often occurs slowly (J. Anderson,  1982 ). First, learners represent a sequence of actions in terms of declarative knowledge. Each step in the sequence is represented as a proposition. Learners gradually drop out individual cues and integrate the separate steps into a continuous sequence of actions. For example, children learning to add a column of numbers will perform each step slowly, possibly even verbalizing it aloud. As they become more skillful, adding becomes part of an automatic, smooth sequence that occurs rapidly and without deliberate, conscious attention. Automaticity is a central feature of many cognitive processes (e.g., attention, retrieval; Moors & De Houwer,  2006 ). When processes become automatic, this allows the processing system to devote itself to complex parts of tasks ( Chapter 7 ).

A major constraint on skill learning is the size limitation of WM (Baddeley,  2001 ). Procedures would be learned quicker if WM could simultaneously hold all the declarative knowledge propositions. Because it cannot, students must combine propositions slowly and periodically stop and think (e.g., “What do I do next?”). WM contains insufficient space to create large procedures in the early stages of learning. As propositions are combined into small procedures, the latter are stored in WM simultaneously with other propositions. In this fashion, larger productions are gradually constructed.

These ideas explain why skill learning proceeds faster when students can perform the prerequisite skills (i.e., when they become automatic). When the latter exist as well-established productions, they are activated in WM at the same time as new propositions to be integrated. In learning to solve long-division problems, students who know how to multiply simply recall the procedure when necessary; it does not have to be learned along with the other steps in long division. Although this does not seem to be the problem in the opening scenario, learning algebra is difficult for students with basic skill deficiencies (e.g., addition, multiplication), because even simple algebra problems become difficult to answer correctly. Many children with reading disabilities seem to lack the capability to effectively process and store information at the same time (de Jong,  1998 ).

In some cases, specifying the steps in detail is difficult. For example, thinking creatively may not follow the same sequence for each student. Teachers can model creative thinking to include such self-questions as, “Are there any other possibilities?” Whenever steps can be specified, teacher demonstrations of the steps in a procedure, followed by student practice, are effective (Rosenthal & Zimmerman,  1978 ).

One problem with the learning of procedures is that students might view them as lockstep sequences to be followed regardless of whether they are appropriate. Gestalt psychologists showed how  functional fixedness , or an inflexible approach to a problem, hinders problem solving (Duncker,  1945 ;  Chapter 7 ). Adamantly following a sequence while learning may assist its acquisition, but learners also need to understand the circumstances under which other methods are more efficient.

Sometimes students overlearn skill procedures to the point that they avoid using alternative, easier procedures. At the same time, there are few, if any, alternatives for many of the procedures students learn (e.g., decoding words, adding numbers, determining subject–verb agreement). Overlearning these skills to the point of automatic production becomes an asset to students and makes it easier to learn new skills (e.g., drawing inferences, writing term papers) that require mastery of these basic skills.

One might argue that teaching problem-solving or inference skills to students who are deficient in basic mathematical facts and decoding skills, respectively, makes little sense. Research shows that poor grasp of basic number facts is related to low performance on complex arithmetic tasks (Romberg & Carpenter,  1986 ), and slow decoding relates to poor comprehension (Calfee & Drum,  1986 ; Perfetti & Lesgold,  1979 ). Not only is skill learning affected, but self-efficacy ( Chapter 4 ) suffers as well.

Practice is essential to instate basic procedural knowledge (Lesgold,  1984 ). In the early stages of learning, students require corrective feedback highlighting the portions of the procedure they implemented correctly and those requiring modification. Often students learn some parts of a procedure but not others. As students gain skill, teachers can point out their progress in solving problems quicker or more accurately.

Transfer of procedural knowledge occurs when the knowledge is linked in LTM with different content. Transfer is aided by having students apply the procedures to the different content and altering the procedures as necessary. General problem-solving strategies ( Chapter 7 ) are applicable to varied academic content. Students learn about their generality by applying them to different subjects (e.g., reading, mathematics).

Productions are relevant to cognitive learning, but several issues need to be addressed. ACT theory posits a single set of cognitive processes to account for diverse phenomena (Matlin,  2009 ). This view conflicts with other cognitive perspectives that delineate different processes depending on the type of learning (Shuell,  1986 ). Rumelhart and Norman ( 1978 ) identified three types of learning.  Accretion  involves encoding new information in terms of existing schemata;  restructuring  (schema creation) is the process of forming new schemata; and  tuning  (schema evolution) refers to the slow modification and refinement of schemata that occurs when using them in various contexts. These involve different amounts of practice: much for tuning and less for accretion and restructuring.

ACT is essentially a computer program designed to simulate learning in a coherent manner. As such, it may not address the range of factors involved in human learning. One issue concerns how people know which production to use in a given situation, especially if situations lend themselves to different productions being employed. Productions may be ordered in terms of likelihood, but a means for deciding what production is best given the circumstance must be available. Also of concern is the issue of how productions are altered. For example, if a production does not work effectively, do learners discard it, modify it, or retain it but seek more evidence? What is the mechanism for deciding when and how productions are changed?

Another concern relates to Anderson’s ( 1983 ,  1990 ) claim that productions begin as declarative knowledge. This assumption seems too strong given evidence that this sequence is not always followed (Hunt,  1989 ). Because representing skill procedures as pieces of declarative knowledge is essentially a way station along the road to mastery, one might question whether students should learn the individual steps. The individual steps eventually will not be used, so time may be better spent allowing students to practice them.

Finally, one might question whether production systems, as generally described, are nothing more than elaborate stimulus-response (S-R) associations (Mayer,  1992 ). Propositions (bits of procedural knowledge) become linked in memory and formed into networks so that when one piece is cued, others also are activated. Anderson ( 1983 ) acknowledged the associationist nature of productions but believes they are more advanced than simple S-R associations because they incorporate goals. In support of this point, ACT associations are analogous to neural network connections ( Chapter 2 ). Perhaps, as is the case with behavior theories, ACT can explain performance better than it can explain learning. These and other questions (e.g., the role of motivation) need to be addressed to establish the usefulness of productions in education better.

Connectionist Models.

Connectionist models  (or  connectionism , not to be confused with Thorndike’s connectionism discussed in  Chapter 3 ; Baddeley,  1998 ; Farnham-Diggory,  1992 ; Matlin,  2009 ; Smith,  1996 ) represent a more recent line of theorizing about complex cognitive processes. Like productions, connectionist models represent computer simulations of learning processes. These models link learning to neural system processing where impulses fire across synapses to form connections ( Chapter 2 ). The assumption is that higher-order cognitive processes are formed by connecting a large number of basic elements such as neurons (Anderson,  1990 ,  2000 ; Anderson, Reder, & Lebiere,  1996 ; Bourne,  1992 ). Connectionist models include distributed representations of knowledge (i.e., spread out over a wide network), parallel processing (many operations occur at once), and interactions among large numbers of simple processing units (Siegler,  1989 ). Connections may be at different stages of activation (Smith,  1996 ) and linked to input into the system, output, or one or more in-between layers.

Rumelhart and McClelland ( 1986 ) described a system of parallel distributed processing (PDP). This model is useful for making categorical judgments about information in memory. These authors provided an example involving two gangs and information about gang members, including age, education, marital status, and occupation. In memory, the similar characteristics of each individual are linked. For example, Members 2 and 5 would be linked if they both were about the same age, married, and engaged in similar gang activities. To retrieve information about Member 2, we could activate the memory unit with the person’s name, which in turn would activate other memory units. The pattern created through this spread of activation corresponds to the memory representation for the individual. Borowsky and Besner ( 2006 ) described a PDP model for making lexical decisions (e.g., deciding whether a stimulus is a word).

Connectionist units bear some similarity to productions in that both involve memory activation and linked ideas. At the same time, differences exist. In connectionist models all units are alike, whereas productions contain conditions and actions. Units are differentiated in terms of pattern and degree of activation. Another difference is that whereas productions are governed by rules, connectionism has no set rules. Neurons “know” how to activate patterns; after the fact we may provide a rule as a label for the sequence (e.g., rules for naming patterns activated; Farnham-Diggory,  1992 ).

One problem with the connectionist approach is explaining how the system knows which of the many units in memory to activate and how these multiple activations become linked in integrated sequences. This process seems straightforward in the case of well-established patterns; for example, neurons “know” how to react to a ringing phone, a cold wind, and a teacher announcing, “Everyone pay attention!” With less-established patterns the activations may be problematic. We also might ask how neurons become self-activating in the first place. This question is important because it helps to explain the role of connections in learning and memory. Although the notion of connections seems plausible and grounded in what we know about neurological functioning ( Chapter 2 ), to date this model has been more useful in explaining perception rather than learning and problem solving (Mayer,  1992 ). The latter applications are critical for education.

INSTRUCTIONAL APPLICATIONS

Information processing principles increasingly have been applied to educational settings. Three instructional applications that reflect information processing principles are advance organizers, the conditions of learning, and cognitive load.

Advance Organizers

Advance organizers  are broad statements presented at the outset of lessons that help to connect new material with prior learning (Mayer,  1984 ). Organizers direct learners’ attention to important concepts to be learned, highlight relationships among ideas, and link new material to what students know (Faw & Waller,  1976 ). Organizers also can be maps that are shown with accompanying text (Verdi & Kulhavy,  2002 ). It is assumed that learners’ LTMs are organized such that inclusive concepts subsume subordinate ones. Organizers provide information at high (inclusive) levels.

The conceptual basis of organizers derives from Ausubel’s ( 1963 ,  1968 ,  1977 ,  1978 ; Ausubel & Robinson,  1969 ) theory of  meaningful reception learning . Learning is meaningful when new material bears a systematic relation to relevant concepts in LTM; that is, new material expands, modifies, or elaborates information in memory. Meaningfulness also depends on personal variables such as age, background experiences, socioeconomic status, and educational background.

Ausubel advocated deductive teaching: General ideas are taught first, followed by specific points. This requires teachers to help students break ideas into smaller, related points and to link new ideas to similar content in memory. In information processing terms, the aims of the model are to expand propositional networks in LTM by adding knowledge and to establish links between networks.

Advance organizers can be expository or comparative.  Expository organizers  provide students with new knowledge needed to comprehend the lesson. Expository organizers include concept definitions and generalizations. Concept definitions state the concept, a superordinate concept, and characteristics of the concept. In presenting the concept “warm-blooded animal,” a teacher might define it (i.e., animal whose internal body temperature remains relatively constant), relate it to superordinate concepts (animal kingdom), and give its characteristics (birds, mammals).  Generalizations  are broad statements of general principles from which hypotheses or specific ideas are drawn. A generalization appropriate for the study of terrain would be: “Less vegetation grows at higher elevations.” Teachers can present examples of generalizations and ask students to think of others.

Comparative organizers  introduce new material by drawing analogies with familiar material. Comparative organizers activate and link networks in LTM. If a teacher were giving a unit on the body’s circulatory system to students who have studied communication systems, the teacher might relate the circulatory and communication systems with relevant concepts such as the source, medium, and target. For comparative organizers to be effective, students must have a good understanding of the material used as the basis for the analogy. Learners also must perceive the analogy easily. Difficulty perceiving analogous relationships impedes learning.

Organizers can promote learning and, because they help students relate content to a broader set of experiences, may facilitate transfer (Ausubel,  1978 ; Faw & Waller,  1976 ; Mautone & Mayer,  2007 ). Maps are especially effective organizers and lend themselves well to infusion in lessons via technology (Verdi & Kulhavy,  2002 ). Some examples of organizers are given in  Application 5.3 .

Conditions of Learning

Gagné ( 1985 ) formulated an instructional theory that reflects information processing principles. This theory highlights the  conditions of learning , or the circumstances that prevail when learning occurs (Ertmer, Driscoll, & Wager,  2003 ). Two steps are critical. The first is to specify the type of learning outcome; Gagné identified five major types (discussed later). The second is to determine the events of learning, or factors that make a difference in instruction.

APPLICATION 5.3 Advance Organizers

Advance organizers help students connect new material with prior learning. Ms. Lowery, a fourth-grade teacher, is working with her students to develop comprehensive paragraphs. The students have been learning to write descriptive and interesting sentences. Ms. Lowery projects the students’ sentences onto a screen and uses them as an organizer to show how to put sentences together to create a complete paragraph.

Mr. Oronsco, a middle school teacher, employed an organizer during geography. He began a lesson on landforms (surfaces with characteristic shapes and compositions) by reviewing the definition and components of geography concepts previously discussed. He wanted to show that geography includes elements of the physical environment, human beings and the physical environment, and different world regions and their ability to support human beings. To do this, Mr. Oronsco initially focused on elements of the physical environment and then moved to landforms. He discussed types of landforms (e.g., plateaus, mountains, hills) by showing mock-ups and asking students to identify key features of each landform. This approach gave students an overall framework or outline into which they could integrate new knowledge about the components.

A science instructor teaching the effects of blood disorders might begin by reviewing the basic parts of blood (e.g., plasma, white and red cells, platelets). Then the instructor could list various categories of blood disease (e.g., anemia, bleeding and bruising, leukemia, bone marrow disease). The students can build on this outline by exploring the diseases in the different categories and by studying the symptoms and treatments for each condition.

Table 5.2 Learning outcomes in Gagné’s theory.

Learning Outcomes
Type	Examples
Intellectual skills	Rules, procedures, concepts
Verbal information	Facts, dates
Cognitive strategies	Rehearsal, problem solving
Motor skills	Hitting a ball, juggling
Attitudes	Generosity, honesty, fairness

Learning Outcomes.

Gagné ( 1984 ) identified five types of learning outcomes: intellectual skills, verbal information, cognitive strategies, motor skills, and attitudes ( Table 5.2 ).

Intellectual skills include rules, procedures, and concepts. They are forms of procedural knowledge or productions. This type of knowledge is employed in speaking, writing, reading, solving mathematical problems, and applying scientific principles to problems.

Verbal information, or declarative knowledge, is knowledge that something is the case. Verbal information involves facts or meaningfully connected prose recalled verbatim (e.g., words to a poem or the “Star Spangled Banner”). Schemas are forms of verbal information.

Cognitive strategies are executive control processes. They include information processing skills such as attending to new information, deciding to rehearse information, elaborating, using LTM retrieval strategies, and applying problem-solving strategies ( Chapter 7 ).

Motor skills are developed through gradual improvements in the quality (smoothness, timing) of movements attained through practice. Whereas intellectual skills can be acquired quickly, motor skills develop gradually with continued, deliberate practice (Ericsson, Krampe, & Tesch-Römer,  1993 ). Practice conditions differ: Intellectual skills are practiced with different examples; motor-skill practice involves repetition of the same muscular movements.

Attitudes are internal beliefs that influence actions and reflect characteristics such as generosity, honesty, and commitment to healthy living. Teachers can arrange conditions for learning intellectual skills, verbal information, cognitive strategies, and motor skills, but attitudes are learned indirectly through experiences and exposures to live and symbolic (televised, videotaped) models.

Learning Events.

The five types of learning outcomes differ in their conditions. Internal conditions are prerequisite skills and cognitive processing requirements; external conditions are environmental stimuli that support the learner’s cognitive processes. One must specify as completely as possible both types of conditions when designing instruction.

Internal conditions are learners’ current capabilities (knowledge in LTM). Instructional cues from teachers and materials activate relevant LTM knowledge (Gagné & Glaser,  1987 ). External conditions differ as a function of the learning outcome and the internal conditions. To teach students a classroom rule, a teacher might inform them of the rule and visually display it. To teach students a strategy for checking their comprehension, a teacher might demonstrate the strategy and give students practice and feedback on its effectiveness. Proficient readers are instructed differently from those with decoding problems. Each phase of instruction is subject to alteration as a function of learning outcomes and internal conditions.

Learning Hierarchies.

Learning hierarchies  are organized sets of intellectual skills. The highest element in a hierarchy is the target skill. To devise a hierarchy, one begins at the top and asks what skills the learner must perform prior to learning the target skill or what skills are immediate prerequisites for the target skill. Then one asks the same question for each prerequisite skill, continuing down the hierarchy until one arrives at the skills the learner can perform now (Dick & Carey,  1985 ; Merrill,  1987 ;  Figure 5.9 ).

Figure 5.9 Sample learning hierarchy.

Table 5.3 Gagné’s phases of learning.

Category	Phase
Preparation for learning	Attending
	Expectancy
	Retrieval
Acquisition and performance	Selective perception
	Semantic encoding
	Retrieval and responding
	Reinforcement
Transfer of learning	Cueing retrieval
	Generalizability

Hierarchies are not linear orderings of skills. One often must apply two or more prerequisite skills to learn a higher-order skill with neither of the prerequisites dependent on the other. Nor are higher-order skills necessarily more difficult to learn than lower-order ones. Some prerequisites may be difficult to acquire; once learners have mastered the lower-order skills, learning a higher-order one may seem easier.

Phases of Learning.

Instruction is a set of external events designed to facilitate internal learning processes.  Table 5.3  shows the nine phases of learning grouped into the three categories (Gagné,  1985 ).

Preparation for learning includes introductory learning activities. During attending, learners focus on stimuli relevant to content to be learned (e.g., audiovisuals, written materials, teacher-modeled behaviors). The learner’s expectancy orients the learner to the goal (learn a motor skill, learn to reduce fractions). During retrieval of relevant information from LTM, learners activate the portions relevant to the topic studied (Gagné & Dick,  1983 ).

The main phases of learning are acquisition and performance. Selective perception means that the sensory registers recognize relevant stimulus features and transfer them to WM. Semantic encoding is the process whereby new knowledge is transferred to LTM. During retrieval and responding, learners retrieve new information from memory and make a response demonstrating learning.  Reinforcement refers to feedback that confirms the accuracy of a student’s response and provides corrective information as necessary.

Transfer of learning phases include cueing retrieval and generalizability. In cueing retrieval, learners receive cues signaling that previous knowledge is applicable in that situation. When solving word problems, for instance, a mathematics teacher might inform learners that their knowledge of right triangles is applicable. Generalizability is enhanced by providing learners the opportunity to practice skills with different content and under different circumstances (e.g., homework, spaced review sessions).

Table 5.4 Instructional events accompanying learning phases (Gagné).

Phase	Instructional Event
Attending	Inform class that it is time to begin.
Expectancy	Inform class of lesson objective and type and quantity of performance to be expected.
Retrieval	Ask class to recall subordinate concepts and rules.
Selective perception	Present examples of new concept or rule.
Semantic encoding	Provide cues for how to remember information.
Retrieval and responding	Ask students to apply concept or rule to new examples.
Reinforcement	Confirm accuracy of students’ learning.
Cueing retrieval	Give short quiz on new material.
Generalizability	Provide special reviews.

These nine phases are equally applicable for the five types of learning outcomes. Gagné and Briggs ( 1979 ) specified types of instructional events that might accompany each phase ( Table 5.4 ). Instructional events enhancing each phase depend on the type of outcome. Instruction proceeds differently for intellectual skills than for verbal information.

One issue is that developing learning hierarchies can be difficult and time consuming. The process requires expertise in the content domain to determine the successive prerequisite skills—the scope and sequence of instruction. Even a seemingly simple skill may have a complex hierarchy if learners must master several prerequisites. For those skills with less well-defined structures (e.g., creative writing), developing a hierarchy may be difficult. Another issue is that the system allows for little learner control because it prescribes how learners should proceed, which could negatively affect motivation. Instructional technology that allows learners greater control over their activities may help override this possibility. These issues notwithstanding, the theory offers solid suggestions for ways to apply information processing principles to the design of instruction (Ertmer et al.,  2003 ).

Cognitive Load

Cognitive load  refers to the demands placed on the information processing system and in particular on WM (Paas, van Gog, & Sweller,  2010 ; Sweller,  2010 ; Winne & Nesbit,  2010 ). The capacity of WM is limited. Because information processing takes time and involves multiple cognitive processes, at any given time only a limited amount of information can be held in WM, transferred to LTM, rehearsed, and so forth.

Cognitive load theory takes these processing limitations into account in the design of instruction (DeLeeuw & Mayer,  2008 ; Schnotz & Kürschner,  2007 ; Sweller, van Merriënboer, & Paas,  1998 ). Cognitive load can be of two types.  Intrinsic cognitive load  refers to the demands placed on WM by the unalterable properties of the knowledge to be acquired.  Extrinsic (or extraneous) cognitive load  is a burden on WM caused by unnecessary content, distractions, or difficulties with the instructional presentation (Bruning et al.,  2011 ). Some researchers also speak of  germane cognitive load , which includes intrinsic load plus necessary extraneous load due to situational factors (e.g., monitoring of attention; Feldon,  2007 ).

For example, in learning key trigonometric relationships (e.g., sine, tangent), a certain cognitive load (intrinsic) is inherent in the material to be learned; namely, developing knowledge about the ratios of sides of a right triangle. Extraneous load would include information in instruction not relevant to the content to be learned, such as irrelevant features of pictures used. Teachers who give clear presentations help to minimize extraneous load and maximize germane load.

In similar fashion, Mayer ( 2012 ) distinguished three types of cognitive processing demands. Essential processing refers to cognitive processing necessary to mentally represent material in WM (similar to intrinsic load). Extraneous processing (similar to extrinsic load) refers to processing not necessary for learning and which wastes cognitive capacity. Generative processing is deeper cognitive processing that attempts to make sense of the material, such by organizing it and relating it to prior knowledge.

A key idea is that instructional methods should decrease extraneous cognitive load so that existing resources can be devoted to learning (van Merriënboer & Sweller,  2005 ). The use of  scaffolding  should be beneficial (van Merriënboer, Kirschner, & Kester,  2003 ). Initially the scaffold helps learners acquire skills that they would be unlikely to acquire without the assistance. The scaffolding helps to minimize the extrinsic load so learners can focus their resources on the intrinsic demands of the learning. As learners develop a schema to work with the information, the scaffold assistance can be phased out.

Another suggestion is to use simple-to-complex sequencing of material (van Merriënboer et al.,  2003 ), in line with Gagné’s theory. Complex learning is broken into simple parts that are acquired and combined into a larger sequence. This procedure minimizes extrinsic load, so learners can focus their cognitive resources on the learning at hand.

A third suggestion is to use authentic tasks in instruction. Reigeluth’s ( 1999 )  elaboration theory , for example, requires identifying conditions that simplify performance of the task and then beginning instruction with a simple but authentic case (e.g., one that might be encountered in the real world). Tasks that have real-world significance help to maximize germane load because they do not require learners to engage in extraneous processing to understand the context. It is more meaningful, for example, for students to determine the sine of the angle formed by joining a point 40 feet from the school’s flagpole to the top of the pole than it is to solve comparable trigonometric problems in a textbook.

These considerations also suggest the use of collaborative learning. As intrinsic cognitive load increases, learning becomes less effective and efficient (Kirschner, Paas, & Kirschner,  2009 ). With greater task complexity, dividing the cognitive processing demands across individuals reduces load on learners. These ideas fit well with the constructivist emphasis on peer collaboration ( Chapter 8 ). Some examples are provided in  Application 5.4 .

APPLICATION 5.4 Reducing Unnecessary Cognitive Load

Student learning will be best when instruction minimizes extraneous load and maximizes germane load. Ms. Watson, a high school English teacher, knows that locating symbolic elements in novels can prove taxing to many students. To help minimize extraneous load, she introduces only one symbolic element at a time, explains it, and asks students to try to find examples of it in only a few pages of the novel. By focusing their attention only on one element in a small subset of the novel, students do not feel overwhelmed by the demands of the task and their need to pay careful attention.

Ms. Anton, an elementary teacher, has students who have difficulty writing descriptive paragraphs. She breaks the task into parts so as to not impose a great extraneous load. First she has students write down what features of the object they want to describe in their paragraph. Then she asks them to write one sentence for each feature. When they are finished, she tells them to review their paragraphs and revise them as needed, making sure the paragraphs are clear and well organized.

Students in Professor Lauphar’s undergraduate educational psychology course have to do a group project where they design an ideal learning environment that addresses several concepts covered in the course (e.g., learning, motivation, assessment). Dr. Lauphar forms small groups of four students each and sets a timeline of when various aspects of the project are to be completed. Students meet in the groups and set their own timelines for when they will complete their research and re-convene as a group. By breaking the task into subparts and by reviewing the content areas over the course of the semester, students do not experience excessive extraneous load and can focus their attention and efforts on the immediate task at hand.

SUMMARY

Information processing theories focus on attention, perception, encoding, storage, and retrieval of knowledge. Information processing has been influenced by advances in communications, computer technology, and neuroscience.

Important historical influences on contemporary information processing views are verbal learning, Gestalt psychology, the two-store model, and levels of processing. Verbal learning researchers used serial learning, free recall, and paired-associate tasks. A number of important findings were obtained from verbal learning research. Free-recall studies showed that organization improves recall and that people impose their own organization when none is present. Gestalt theorists stressed the role of organization in perception and learning.

The two-store (dual) memory model was an early information processing model that posited stages of processing: sensory registers, perception, short-term memory, long-term memory. Levels of processing conceived of information processing in terms of depth, where information processed at deeper levels was more likely to be stored in memory and recalled.

A contemporary information processing model posits that information processing occurs in phases. Information enters through the sensory registers. Although there is a register for each sense, most research has been conducted on the visual and auditory registers. At any one time, only a limited amount of information can be attended to. Attention may act as a filter or a general limitation on capacity of the human system. Inputs attended to are perceived by being compared in WM with information in LTM.

When information enters WM, it can be retained through rehearsal and linked with related information in LTM. Information may be encoded for storage in LTM. Encoding is facilitated through organization, elaboration, and links with schemas. The central executive of WM controls its interface with perception and LTM.

Attention and perception processes involve critical features, templates, and prototypes. Whereas WM is limited in capacity and duration, LTM appears to be very large. The basic unit of knowledge is the proposition, and propositions are organized in networks. Major types of knowledge are declarative and procedural. Large bits of procedural knowledge may be organized in production systems. Networks further are linked in connectionist fashion through spreading activation.

Although much early research on information processing was basic in nature and conducted in experimental laboratories, researchers increasingly are conducting research in applied settings and especially on learning of academic content. Three instructional applications that reflect information processing principles involve advance organizers, the conditions of learning, and cognitive load.

A summary of learning issues for information processing theory appears at the end of  Chapter 6 .

FURTHER READING

Anderson, J. R. (1996). ACT: A simple theory of complex cognition. American Psychologist, 51, 355–365.

Baddeley, A. D. (2012). Working memory: Theories, models, and controversies. Annual Review of Psychology, 63, 1–29.

Gagné, R. M. (1985). The conditions of learning (4th ed.). New York, NY: Holt, Rinehart & Winston.

Mayer, R. E. (2012). Information processing. In K. R. Harris, S. Graham, & T. Urdan (Eds.), APA handbook of educational psychology. Vol. 1: Theories, constructs, and critical issues (pp. 85–99). Washington, DC: American Psychological Association.

Surprenant, A. M., & Neath, I. (2009). Principles of memory. New York, NY: Taylor & Francis.

Triesman, A. M. (1992). Perceiving and re-perceiving objects. American Psychologist, 47, 862–875.

van Merriënboer, J. J. G., & Sweller, J. (2005). Cognitive load theory and complex learning: Recent developments and future directions. Educational Psychology Review, 14, 331–351.