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Individual week 3 – Chapter 6 Information Processing Theory: Retrieval and Forgetting
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Individual week 3
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English (U.S.)
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Essay
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4 Pages Double Spaced (approx 275 words per page)
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Psychology
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Master
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1
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Chapter 6 Week 3 module
Chapter 6 Information Processing Theory: Retrieval and Forgetting
Terrill Sharberg, an associate professor of education, is teaching an educational psychology course for graduate students. All of the students are educators—current or former teachers, administrators, or professional staff members. This week’s 3-hour class is devoted to remembering and forgetting. Several of the students have stories to tell.
Marcia:
My students return after semester break and they can hardly remember anything we studied before the break.
Silas:
I see that sometimes after a long holiday weekend.
JoEllen:
I spend so much class time on review. I wish I could cut back.
Jeff:
My teachers have their students periodically review computer modules on the content.
Terrill:
What you’re describing is common. Forgetting occurs, but continual review takes time and shouldn’t be necessary so often. We’re going to work on applying principles of information processing theory to student learning to improve retention and recall.
JoEllen:
Yes, Dr. Sharberg, but we have so much to cover and I feel sorry for the kids. They can’t remember everything.
Terrill:
No, they can’t. But that’s not our goal. There are lots of things we can do as educators to improve retention and retrieval and decrease forgetting.
Jeff:
I want to have a workshop on this topic for my teachers. They need help. They’re frustrated.
Terrill:
Well, teaching to promote retention takes more effort but it’s worth it because it should decrease time spent on reviews and reteaching. Not to mention improve teachers’ and students’ motivation and make learning more enjoyable.
Chapter 5 discussed how knowledge is encoded in long-term memory (LTM). The process is complex. It begins with learners attending to inputs so that these register, are perceived, and are processed in working memory (WM). The WM processing includes elaborating and organizing information and relating it to knowledge in LTM. Through this processing, new memory networks are created or existing ones are modified and enriched. When this happens, we say that learning (encoding) has occurred.
Practically speaking, however, such learning is useless if learners subsequently cannot access knowledge in LTM and thereby put it to further use. When this happens, we say that forgetting has occurred. Forgetting refers to the loss of information from memory or to the inability to retrieve information. Forgetting often has no serious consequences. When we find we have forgotten something, we may ask someone, look it up on the Internet, and so forth. Unless you plan to be a contestant on Jeopardy! or another quiz show that requires factual recall, the many things we forget in the course of a day pose no risk to life, liberty, or the pursuit of happiness!
But as the opening vignette shows, forgetting causes big problems in education. Teachers cannot move to more advanced content if students do not remember the basic prerequisite knowledge. Reviews and reteaching take up valuable class time. It is a frustrating experience for teachers, as well as for students who may become bored spending time on reviews. Learning should be exciting so that students and teachers remain motivated.
The focus of this chapter is on retrieval. Compared with only a few years ago, we now know much more about how knowledge is stored in memory and retrieved. We also know techniques that are effective in helping learners store knowledge in LTM such that it is easier to retrieve. Effective storage of knowledge facilitates not only retrieval but also transfer of knowledge to new situations and across time.
The next section discusses processes that individuals use to retrieve information from LTM. Language comprehension is used to exemplify these processes. The chapter then covers theories of forgetting and influences on forgetting. Given that forgetting occurs, relearning becomes necessary; this topic is addressed, along with how testing may affect learning and retrieval. Much of what we have discussed up to now has involved verbal memory, but visual memory is covered to include the benefits it provides for learning. The key educational topic of transfer is discussed including theoretical perspectives and types of transfer. Appropriate educational applications are described: encoding-retrieval similarity, retrieval-based learning, and teaching for transfer.
When you finish studying this chapter, you should be able to do the following:
■Explain the information processes used to retrieve information from LTM including elaboration and spreading activation.
■Describe encoding specificity and why it benefits retrieval.
■Explain how language comprehension exemplifies information processes involved in storage and retrieval of knowledge.
■Define interference and distinguish between retroactive and proactive interference.
■Discuss an information processing perspective on forgetting.
■Define visual memory and explain why it can promote learning.
■Distinguish the different types of transfer, and explain why transfer is important for learning.
■Discuss the components necessary for students to transfer use of learning strategies.
■Explain the relevance of information processing principles in educational applications involving encoding-retrieval similarity, retrieval-based learning, and teaching for transfer.
LONG-TERM MEMORY: RETRIEVAL
Retrieval Processes
Retrieval is a key aspect of information processing and actually can help promote learning (Karpicke & Grimaldi, 2012). This section discusses the processes involved in retrieval.
Retrieval Strategies.
What happens when a student is asked a question such as, “What does the vice president of the United States do in the Senate?” (see Chapter 5). The question enters the student’s WM and is broken into propositions. The process by which this occurs has a neurological basis and is not well understood, but available evidence indicates that information activates associated information in memory networks through spreading activation to determine the answer to the question. If the answer is found, it is constructed into a sentence and verbalized to the questioner or into motor patterns to be written. If the activated propositions do not answer the query, activation spreads until the answer is located. When insufficient time is available for spreading activation to locate the answer, students may make an educated guess (Anderson, 1990).
Much cognitive processing occurs automatically. We routinely remember our home address and phone number, Social Security number, and close friends’ names. People are often unaware of all the steps taken to answer a question. However, when people must judge several activated propositions to determine whether the propositions properly answer the question, they are more aware of the process.
Because knowledge is encoded as propositions, retrieval proceeds even though the information to be retrieved does not exist in exact form in memory. If a teacher asks whether the vice president would vote on a bill when the initial vote was 51 for and 49 against, students could retrieve the proposition that the vice president votes only in the event of a tie. By implication, the vice president would not vote. Processing like this, which involves construction, takes longer than when a question requires information coded in memory in the same form, but students should respond correctly assuming they activate the relevant propositions in LTM. The same process is involved in transfer (discussed later in this chapter); for example, students learn a rule (e.g., the Pythagorean theorem in mathematics) and recall and apply it to solve problems they have never seen before.
Encoding Specificity.
Retrieval depends on the manner of encoding. According to the encoding specificity hypothesis (Brown & Craik, 2000; Thomson & Tulving, 1970), the manner in which knowledge is encoded determines which retrieval cues will effectively activate that knowledge. In this view, the best retrieval occurs when retrieval cues match those present during learning (Baddeley, 1998; Suprenant & Neath, 2009).
Some experimental evidence supports encoding specificity. When people are given category names while they are encoding specific instances of the categories, they recall the instances better if they are given the category names at recall than if not given the names (Matlin, 2009). A similar benefit is obtained if they learn words with associates and then are given the associate names at recall than if not given the associates. Brown (1968) gave students a partial list of U.S. states to read; others read no list. Subsequently all students recalled as many states as they could. Students who received the list recalled more of the states on the list and fewer states not on it.
Encoding specificity also includes context. In one study (Godden & Baddeley, 1975), scuba divers learned a word list either on shore or underwater. On a subsequent free recall task, learners recalled more words when they were in the same environment as the one in which they learned the words than when they were in the other environment.
Encoding specificity can be explained in terms of spreading activation among propositional networks. Cues associated with material to be learned are linked in LTM with the material at the time of encoding. During recall, presentation of these cues activates the relevant portions in LTM. In the absence of the same cues, recall depends on recalling individual propositions. Because the cues lead to spreading activation (not the individual propositions or concepts), recall is facilitated by presenting the same cues at encoding and recall. Other evidence suggests that retrieval is guided in part by expectancies about what information is needed and that people may distort inconsistent information to make it coincide with their expectations (Hirt, Erickson, & McDonald, 1993).
Retrieval of Declarative Knowledge.
Declarative knowledge often is processed automatically, but that is no guarantee that it will be integrated with relevant information in LTM and subsequently retrieved. We can see this inadequate retrieval in the scenario at the start of this chapter. Meaningfulness, elaboration, and organization enhance the potential for declarative information to be effectively processed and retrieved. Application 6.1 provides some classroom examples.
APPLICATION 6.1 Organizing Information by Networks
Teachers enhance learning when they develop lessons to assist students to link new information with knowledge in memory. Information that is meaningful, elaborated, and organized is more readily integrated into LTM networks and retrieved.
A teacher planning a botany unit on the reproduction of different species of plants might start by reviewing common plant knowledge that students have stored in their memories (e.g., basic structure, conditions necessary for growth). As the teacher introduces new information, students examine familiar live plants that reproduce differently to make the experience more meaningful. Factual information to be learned can be elaborated by providing visual drawings and written details regarding the reproductive processes. For each live plant examined, students can organize the new information by creating outlines or charts to show the means of reproduction.
An art teacher planning a design unit might start by reviewing the various elements of color, shape, and texture. As the teacher introduces new techniques related to placement, combination of the various elements, and balance as it relates to the whole composition, manipulatives of various shapes, colors, and textures are provided for each student to use in creating different styles. The students can use the manipulatives to organize the elements and media they want to include in each of their design compositions.
Meaningfulness improves retrieval. Nonmeaningful information will not activate information in LTM and will be lost unless students rehearse it repeatedly until it becomes established in LTM, perhaps by forming a new propositional network. One also can connect the sounds of new information, which are devoid of meaning, to other similar sounds. The word constitution, for example, may be linked phonetically with other uses of the word stored in learners’ memories (e.g., Constitution Avenue).
Meaningful information is more likely to be retained because it easily connects to propositional networks. In the opening scenario in Chapter 5, one suggestion offered is to relate algebraic variables to tangible objects—things that students understand—to give the algebraic notation some meaning. Meaningfulness not only promotes learning, but it also saves time. Propositions in WM take time to process; Simon (1974) estimated that each new piece of information takes 10 seconds to encode, which means that only six new pieces of information can be processed in a minute. Even when information is meaningful, much knowledge is lost before it can be encoded. Although every piece of incoming information is not important and some loss usually does not impair learning, students typically retain little information even under the best circumstances.
When we elaborate we add to information being learned with examples, details, inferences, or anything that serves to link new and old information. A learner might elaborate the role of the vice president in the Senate by thinking through the roll call and, when there is a tie, having the vice president vote.
Elaboration facilitates learning because it is a form of rehearsal: By keeping information active in WM, elaboration increases the likelihood that information will be permanently stored in LTM. This facilitates retrieval, as does the fact that elaboration establishes links between old and new information. Students who elaborate the role of the vice president in the Senate link this new information with what they know about the Senate and the vice president. Well-linked information in LTM, often stored as schemas, is easier to recall than poorly linked information (Stein, Littlefield, Bransford, & Persampieri, 1984; Surprenant & Neath, 2009).
Although elaboration promotes storage and retrieval, it also takes time. Comprehending sentences requiring elaboration takes longer than sentences not requiring elaboration (Haviland & Clark, 1974). For example, the following sentences require drawing an inference that Marge took her credit card to the grocery store: “Marge went to the grocery store,” and “Marge charged her groceries.” The link is clarified in the following sentences: “Marge took her credit card to the grocery store,” and “Marge used her credit card to pay for her groceries.” Making explicit links between adjoining propositions assists their encoding and retention.
An important aspect of learning is deciding on the importance of information. Not all learned information needs to be elaborated. Comprehension is aided when students elaborate only the most important aspects of text (Reder, 1979). Elaboration aids retrieval by providing alternate paths along which activation can spread, so that if one path is blocked, others are available (Anderson, 1990, 2000). Elaboration also provides additional information from which answers can be constructed (Reder, 1982), such as when students must answer questions with information in a different form from that of the learned material.
In general, almost any type of elaboration assists encoding and retrieval; however, some elaborations are more effective than others. Activities such as taking notes and asking how new information relates to what one knows build propositional networks. Effective elaborations link propositions and stimulate accurate recall. Elaborations not linked well to the content do not aid recall (Mayer, 1984).
Organization takes place by breaking information into parts and specifying relationships between parts. In studying U.S. government, organization might involve breaking government into three branches (executive, legislative, judicial), breaking each of these into subparts (e.g., functions, agencies), and so on. Older students employ organization more often, but elementary children are capable of using organizational principles (Meece, 2002). Children studying leaves may organize them by size, shape, and edge pattern.
Organization improves retrieval by linking relevant information; when retrieval is cued, spreading activation accesses the relevant propositions in LTM. Teachers routinely organize material, but student-generated organization is also effective for retrieval. Instruction on organizational principles assists learning. Consider a schema for understanding stories with four major attributes: setting, theme, plot, and resolution (Rumelhart, 1977). The setting (“Once upon a time…”) places the action in a context. The theme is then introduced, which consists of characters who have certain experiences and goals. The plot traces the actions of the characters to attain their goals. The resolution describes how the goal is reached or how the characters adjust to not attaining the goal. By describing and exemplifying these phases of a story, teachers help students learn to identify them on their own.
Retrieval of Procedural Knowledge.
Retrieval of procedural knowledge is similar to that of declarative knowledge. Retrieval cues trigger associations in memory, and the process of spreading activation activates and recalls relevant knowledge. Thus, if students are told to perform a procedure in chemistry laboratory, they will cue that production in memory, retrieve it, and implement it.
When declarative and procedural knowledge interact, retrieval of both is necessary. While adding fractions, students use procedures (i.e., convert fractions to their lowest common denominator, add numerators) and declarative knowledge (addition facts). During reading comprehension, some processes operate as procedures (e.g., decoding, monitoring comprehension), whereas others involve only declarative knowledge (e.g., word meanings, functions of punctuation marks). People typically employ procedures to acquire declarative knowledge, such as mnemonic techniques to remember declarative knowledge (see Chapter 10). Having declarative information is typically a prerequisite for successfully implementing procedures. To solve for roots using the quadratic formula, students must know multiplication facts.
Declarative and procedural knowledge vary tremendously in scope. Individuals possess declarative knowledge about the world, themselves, and others; they understand procedures for accomplishing diverse tasks. Declarative and procedural knowledge are different in that procedures transform information. Such declarative statements as “2 × 2 = 4” and “Uncle Fred smokes smelly cigars” change nothing, but applying the long-division algorithm to a problem changes an unsolved problem into a solved one.
Another difference is in speed of processing. Retrieval of declarative knowledge often is slow and conscious. Even assuming people know the answer to a question, they may have to think for some time to answer it. For example, consider the time needed to answer “Who was the U.S. president in 1867?” (Andrew Johnson). In contrast, once procedural knowledge is established in memory, it is retrieved quickly and often automatically. Skilled readers decode printed text automatically; they do not have to consciously reflect on what they are doing. Processing speed distinguishes skilled from poor readers (de Jong, 1998). Once we learn how to multiply, we do not have to think about what steps to follow to solve problems.
The differences in declarative and procedural knowledge have implications for teaching and learning. Students may have difficulty with a particular content area because they lack domain-specific declarative knowledge or because they do not understand the prerequisite procedures. Discovering which is deficient is a necessary first step for planning remedial instruction. Not only do deficiencies hinder learning, they also produce low self-efficacy (Chapter 4). Students who understand how to divide but do not know multiplication facts become demoralized when they consistently arrive at wrong answers.
Language Comprehension
An application illustrating storage and retrieval of information in LTM is language comprehension (Carpenter, Miyake, & Just, 1995; Corballis, 2006; Matlin, 2009). Language comprehension is highly relevant to school learning and especially in light of the increasing number of students whose native language is not English (Fillmore & Valadez, 1986; Hancock, 2001; Padilla, 2006).
Comprehending spoken and written language represents a problem-solving process involving domain-specific declarative and procedural knowledge (Anderson, 1990). Language comprehension has three major components: perception, parsing, and utilization. Perception involves attending to and recognizing an input; sound patterns are translated into words in WM. Parsing means mentally dividing the sound patterns into units of meaning. Utilization refers to the disposition of the parsed mental representation: storing it in LTM if it is a learning task, giving an answer if it is a question, asking a question if it is not comprehended, and so forth. This section covers parsing and utilization; perception was discussed in Chapter 5 (Application 6.2).
Parsing.
Linguistic research shows that people understand the grammatical rules of their language, even though they usually cannot verbalize them (Clark & Clark, 1977). Beginning with the work of Chomsky (1957), researchers have investigated the role of deep structures containing prototypical representations of language structure. The English language contains a deep structure for the pattern “noun 1–verb–noun 2,” which allows us to recognize these patterns in speech and interpret them as “noun 1 did verb to noun 2.” Deep structures may be represented in LTM as productions. Chomsky postulated that the capacity for acquiring deep structures is innately human, although which structures are acquired depends on the language of one’s culture.
Parsing includes more than just fitting language into productions. When people are exposed to language, they construct a mental representation of the situation. They recall from LTM propositional knowledge about the context into which they integrate new knowledge. A central point is that all communication is incomplete. Speakers do not provide all information relevant to the topic being discussed. Rather, they omit the information listeners are most likely to know (Clark & Clark, 1977). For example, suppose Sam meets Kira and Kira remarks, “You won’t believe what happened to me at the concert!” Sam is most likely to activate propositional knowledge in LTM about concerts. Then Kira says, “As I was locating my seat…” To comprehend this statement, Sam must know that one purchases a ticket with an assigned seat. Kira did not tell Sam these things because she assumed he knew them.
APPLICATION 6.2 Language Comprehension
Students presented with confusing or vague information may misconstrue it or relate it to the wrong context. Teachers need to present clear and concise information and ensure that students have adequate background information to build networks and schemata.
Mrs. Lineahan plans to present a social studies unit comparing city life with life in the country, but she is afraid that most of her fourth-grade students will have difficulty comprehending the unit because they never have seen a farm. They may never have heard words such as silo, milking, sow, and livestock. Mrs. Lineahan can produce better student understanding by providing farm-related experiences: take a field trip to a farm; show video clips illustrating farm life; and bring in farm materials such as seeds and plants. As students become familiar with farms, they will be better able to comprehend spoken and written communication about farms.
Young children may have difficulty following directions in preschool and kindergarten. Their limited use and understanding of language may cause them to interpret certain words or phrases differently than intended. For instance, if a teacher said to a small group of children playing in a “dress-up” center, “Let’s get things tied up so we can work on our next activity,” the teacher might return to find children tying clothes together instead of cleaning up! Or a teacher might say, “Make sure you color this whole page,” to children working with crayons. Later the teacher may discover that some children took a single crayon and colored the entire page from top to bottom instead of using various colors to color the items on the page. Teachers must explain, demonstrate, and model what they want children to do. Then they can ask the children to repeat in their own words what they think they are supposed to do.
Effective parsing requires knowledge and inferences (Resnick, 1985). When exposed to verbal communication, individuals access information from LTM about the situation. This information exists in LTM as propositional networks hierarchically organized as schemas. Networks allow people to understand incomplete communications. Consider the following sentence: “I went to the grocery store and saved five dollars with coupons.” Knowledge that people buy merchandise in grocery stores and that they can redeem coupons to reduce costs enables listeners to comprehend this sentence. The missing information is filled in with knowledge in memory.
People often misconstrue communications because they construct missing information with the wrong context. When given a vague passage about four friends getting together for an evening, music students interpreted it as a description of playing music, whereas physical education students described it as an evening of playing cards (Anderson, Reynolds, Schallert, & Goetz, 1977). The interpretative schemas salient in people’s minds are used to comprehend problematic passages. As with many other linguistic skills, interpretations of communications become more reliable with development as children realize both the literal meaning of a message and its intent (Beal & Belgrad, 1990).
That spoken language is incomplete can be shown by decomposing communications into propositions and identifying how propositions are linked. Consider this example (Kintsch, 1979):
The Swazi tribe was at war with a neighboring tribe because of a dispute over some cattle. Among the warriors were two unmarried men named Kakra and his younger brother Gum. Kakra was killed in battle.
Although this passage seems straightforward, analysis reveals the following 11 distinct propositions:
The Swazi tribe was at war.
The war was with a neighboring tribe.
The war had a cause.
The cause was a dispute over some cattle.
Warriors were involved.
The warriors were two men.
The men were unmarried.
The men were named Kakra and Gum.
Gum was the younger brother of Kakra.
Kakra was killed.
The killing occurred during battle.
Even this propositional analysis is incomplete. Propositions 1 through 4 link together, as do Propositions 5 through 11, but a gap occurs between 4 and 5. To supply the missing link, one might have to change Proposition 5 to “The dispute involved warriors.”
Kintsch and van Dijk (1978) showed that features of communication influence comprehension. Comprehension becomes more difficult when more links are missing and when propositions are further apart (in the sense of requiring inferences to fill in the gaps). When much material has to be inferred, WM becomes overloaded and comprehension suffers.
Just and Carpenter (1992) formulated a capacity theory of language comprehension, which postulates that comprehension depends on WM capacity, in which individuals differ. Elements of language (e.g., words, phrases) become activated in WM and are operated on by other processes. If the total amount of activation available to the system is less than the amount required to perform a comprehension task, then cognitive load is high (see Chapter 5), and some of the activation maintaining older elements will be lost (Carpenter et al., 1995). Elements comprehended at the start of a lengthy sentence may be lost by the end. Production-system rules presumably govern activation and the linking of elements in WM.
We see the application of this model in parsing of ambiguous sentences or phrases (e.g., “The soldiers warned about the dangers…”; MacDonald, Just, & Carpenter, 1992). Although alternative interpretations of such constructions initially may be activated, the duration of maintaining them depends on WM capacity. Persons with large WM capacities maintain the interpretations for quite a while, whereas those with smaller capacities typically maintain only the most likely (although not necessarily correct) interpretation. With increased exposure to the context, comprehenders can decide which interpretation is correct, and such identification is more reliable for persons with large WM capacities who still have the alternative interpretations in WM (Carpenter et al., 1995; King & Just, 1991).
In building representations, people include important information and omit details (Resnick, 1985). These gist representations include propositions most germane to comprehension. Listeners’ ability to make sense of a text depends on what they know about the topic (Chiesi, Spilich, & Voss, 1979; Spilich, Vesonder, Chiesi, & Voss, 1979). When the appropriate network or schema exists in listeners’ memories, they employ a production that extracts the most central information to fill the slots in the schema. Comprehension proceeds slowly when a network must be constructed because it does not exist in LTM.
Stories exemplify how schemas are employed. Stories have a prototypical schema that includes setting, initiating events, internal responses of characters, goals, attempts to attain goals, outcomes, and reactions (Black, 1984; Rumelhart, 1975, 1977; Stein & Trabasso, 1982). When hearing a story, people construct a mental model of the situation by recalling the story schema and gradually fitting information into it (Bower & Morrow, 1990; Surprenant & Neath, 2009). Some categories (e.g., initiating events, goal attempts, consequences) are nearly always included, but others (internal responses of characters) may be omitted (Mandler, 1978; Stein & Glenn, 1979). Comprehension proceeds quicker when schemas are easily activated. People recall stories better when events are presented in the expected order (i.e., chronological) rather than in a nonstandard order (i.e., flashback). When a schema is well established, people rapidly integrate information into it. Research shows that early home literacy experiences that include exposure to books relate positively to the development of listening comprehension (Sénéchal & LeFevre, 2002).
Utilization.
Utilization refers to what people do with the communications they receive. For example, if the communicator asks a question, listeners retrieve information from LTM to answer it. In a classroom, students link the communication with related information in LTM.
To use sentences properly, as speakers intend them, listeners must encode three pieces of information: speech act, propositional content, and thematic content. A speech act is the speaker’s purpose in uttering the communication, or what the speaker is trying to accomplish with the utterance (Austin, 1962; Searle, 1969). Speakers may be conveying information to listeners, commanding them to do something, requesting information from them, promising them something, and so on. Propositional content is information that can be judged true or false. Thematic content refers to the context in which the utterance is made. Speakers make assumptions about what listeners know. On hearing an utterance, listeners infer information not explicitly stated but germane to how it is used. The speech act and propositional and thematic contents are most likely encoded with productions.
As an example of this process, assume that Ms. Gravitas is discussing history and questioning students about text material. She might ask, “What was Churchill’s position during World War II?” The speech act is a request and is signaled by the sentence beginning with a WH word (e.g., who, which, where, when, and why). The propositional content refers to Churchill’s position during World War II; it might be represented in memory as follows: Churchill–Prime Minister–Great Britain–World War II. The thematic content refers to what the teacher left unsaid; the teacher assumes students have heard of Churchill and World War II. Thematic content also includes the classroom question-and-answer format. The students understand that they will be asked questions.
Of special importance is how students encode assertions. When teachers utter an assertion, they are conveying to students they believe the stated proposition is true. If Ms. Gravitas said, “Churchill was the prime minister of Great Britain during World War II,” she is conveying her belief that this assertion is true. Students record the assertion with related information in LTM.
Speakers may facilitate the process whereby people relate new assertions with information in LTM by employing the given-new contract (Clark & Haviland, 1977), a type of implicit understanding. Given information should be readily identifiable, and new information should be unknown to the listener. We might think of the given-new contract as a production. In integrating information into memory, listeners identify given information, access it in LTM, and relate new information to it (i.e., store it in the appropriate “slot” in the network). For the given-new contract to enhance utilization, given information must be readily identified by listeners. When given information is not readily available because it is not in listeners’ memories or has not been accessed in a long time, using the given-new production is difficult.
Although language comprehension is often overlooked in school in favor of reading and writing, it is a central component of information processing and literacy. Educators lament the poor listening and speaking skills of students, and these are valued attributes of leaders. Habit 5 of Covey’s (1989) Seven Habits of Highly Effective People is “Seek first to understand, then to be understood,” which emphasizes listening first and then speaking. Listening is intimately linked with high achievement. A student who is a good listener is rarely a poor reader. Among college students, measures of listening comprehension may be indistinguishable from those of reading comprehension (Miller, 1988).
FORGETTING
It was noted earlier that forgetting involves the loss of knowledge from memory or the inability to retrieve knowledge. Researchers disagree about whether information is lost from memory or whether it still is present but cannot be retrieved because it has been distorted, the retrieval cues are inadequate, or other information is interfering with its recall. Forgetting has been studied experimentally since the time of Ebbinghaus (Chapter 1). Before presenting an information processing perspective on forgetting that involves interference and decay, some historical work on interference is discussed.
Table 6.1 Interference and forgetting.
 
Retroactive Interference
Proactive Interference
Task
Group 1
Group 2
Group 1
Group 2
Learn
A
A
A

Learn
B

B
B
Test
A
A
B
B
Note: Each group learns the task to some criterion of mastery. The “—” indicates a period of time in which the group is engaged in another task that prevents rehearsal but does not interfere with the original learning. Interference is demonstrated if group 2 outperforms group 1 on the test.
Interference Theory
One of the contributions of verbal learning research (Chapter 5) was the interference theory of forgetting. According to this theory, learned associations are never completely forgotten. Forgetting results from competing associations that lower the probability of the correct association being recalled; that is, other material becomes associated with the original stimulus (Postman, 1961). The problem lies in retrieving information from memory rather than in memory itself.
Two types of interference were experimentally identified (Table 6.1). Retroactive interference occurs when new verbal associations make remembering prior associations difficult. Proactive interferencerefers to older associations that make newer learning more difficult.
To demonstrate retroactive interference, an experimenter might ask two groups of individuals to learn Word List A. Group 1 then learns Word List B, while group 2 engages in a competing activity to prevent rehearsal of List A. Both groups then attempt to recall List A. Retroactive interference occurs if the recall of Group 2 is better than that of Group 1. For proactive interference, Group 1 learns List A while Group 2 does nothing. Both groups then learn List B and attempt to recall List B. Proactive interference occurs if the recall of Group 2 surpasses that of Group 1.
Retroactive and proactive interference occur often in school. Retroactive interference is seen among students who learn words with regular spellings and then learn words that are exceptions to spelling rules. If, after some time, they are tested on the original words, they might alter the spellings to those of the exceptions. Proactive interference is evident among students taught first to multiply and then to divide fractions. When subsequently tested on division, they may simply multiply without first inverting the second fraction. Developmental research shows that proactive interference decreases between the ages of 4 and 13 (Kail, 2002). Application 6.3 offers suggestions for dealing with interference.
Interference theory represented an important step in specifying memory processes. Early theories of learning postulated that learned connections leave a memory “trace” that weakens and decays with nonuse. Skinner (1953; Chapter 3) did not postulate an internal memory trace but suggested that forgetting results from lack of opportunity to respond due to the stimulus being absent for some time. Each of these views has shortcomings. Although some decay may occur (discussed later), the memory trace notion is vague and difficult to verify experimentally. The nonuse position holds at times, but exceptions do exist; for example, being able to recall information after many years of nonuse (e.g., names of some elementary school teachers) is not unusual. Interference theory surmounts these problems by postulating how information in memory becomes confused with other information. It also specifies a research model for investigating these processes.
APPLICATION 6.3 Interference in Teaching and Learning
Proactive and retroactive interference occur often in teaching and learning. Teachers cannot completely eliminate interference, but they can minimize its effects by recognizing areas in the curriculum that easily lend themselves to interference. For example, students learn to subtract without regrouping and then to subtract with regrouping. Ms. Hastings often finds that when she gives her third-grade students review problems requiring regrouping, some students do not regroup. To minimize interference, she teaches students the underlying rules and principles and has them practice applying the skills in different contexts. She points out similarities and differences between the two types of problems and teaches students how to decide whether regrouping is necessary. Frequent reviews help to minimize interference.
When spelling words are introduced at the primary level, words often are grouped by phonetic similarities (e.g., crate, slate, date, state, mate, late); however, when children learn certain spelling patterns, it may confuse them as they encounter other words (e.g., weight or wait rather than wate; freight rather than frate). Ms. Hastings provides additional instruction regarding other spellings for the same sounds and exceptions to phonetic rules along with periodic reviews over time. This reinforcement should help alleviate confusion and interference among students.
Postman and Stark (1969) suggested that suppression, rather than interference, causes forgetting. Participants in learning experiments hold in active memory material they believe they will need to recall later. Those who learn List A and then are given List B are apt to suppress their responses to the words on List A. Such suppressions would last while they are learning List B and for a while thereafter. In support of this point, the typical retroactive interference paradigm produces little forgetting when learners are given a recognition test on the original Word List A rather than asked to recall the words.
Tulving (1974) postulated that forgetting represents inaccessibility of information due to improper retrieval cues. Information in memory does not decay, become confused, or get lost. Rather, the memory trace is intact but cannot be accessed. Memory of information depends on the trace being intact and on having adequate retrieval cues. Perhaps you cannot remember your home phone number from when you were a child. You may not have forgotten it; the memory is submerged because your current environment is different from that of years ago, and the cues associated with your old home phone number—your house, street, neighborhood—are absent. This principle of cue-dependent forgetting also is compatible with the common finding that people perform better on recognition than on recall tests. In the cue-dependent view, they should perform better in recognition tests because more retrieval cues are provided; in recall tests, they must supply their own cues.
Later research on interference suggested that interference occurs (e.g., people confuse elements) when the same cognitive schema or plan is used on multiple occasions (Thorndyke & Hayes-Roth, 1979; Underwood, 1983). Interference theory continues to provide a viable framework for investigating forgetting (Brown, Neath, & Chater, 2007; Oberauer & Lewandowsky, 2008).
Information Processing
From an information processing perspective, interference refers to a blockage of the spread of activation across memory networks (Anderson, 1990). For various reasons, when people attempt to access information in memory, the activation process is thwarted. Although the mechanism blocking activation is not completely understood, theory and research suggest various causes of interference.
One factor that can affect whether structures are activated is the strength of original encoding. Information that originally is strongly encoded through frequent rehearsal or extensive elaboration is more likely to be accessed than information that originally is weakly encoded.
A second factor is the number of alternative network paths down which activation can spread (Anderson, 1990). Information that can be accessed via many routes is more likely to be remembered than information that is only accessible via fewer paths. For example, if I want to remember the name of Aunt Frieda’s parakeet (Mr. T), I should associate that with many cues, such as my friend Mr. Thomas, the fact that when Mr. T spreads his wings it makes the letter T, and the idea that his constant chirping taxes my tolerance. Then, when I attempt to recall the name of the parakeet I can access it via my memory networks for Aunt Frieda and for parakeets. If these fail, then I still have available the networks for my friends, the letter T, and things that tax my tolerance. In contrast, if I associate only the name “Mr. T” with the bird, then the number of alternative paths available for access is fewer and the likelihood of interference is greater.
A third factor is the amount of distortion or merging of information. We have discussed the memory benefits of organizing, elaborating, and making information meaningful by relating it to what we know. Whenever we engage in these practices, we change the nature of information, and in some cases we merge it with other information or subsume it under more general categories. Such merging and subsumption facilitate meaningful reception learning (Ausubel, 1963, 1968; see Chapter 5). Sometimes, however, such distortion and merging may cause interference and make recall more difficult than if information is remembered on its own.
Interference is an important cause of forgetting, but it is unlikely that it is the only one (Anderson, 1990). It appears that some information in LTM decays systematically with the passage of time and independently of any interference. Wickelgren (1979) traced systematic decay of information in time intervals ranging from 1 minute to 2 weeks. Information decays rapidly at first with decay gradually tapering off. Researchers find little forgetting after 2 weeks. However, the best evidence for decay is found in memories that are time bound; namely, sensory memory and WM (Surprenant & Neath, 2009)
The position that forgetting occurs because of decay is difficult to affirm or refute. Explanations given for decay often are vague (Surprenant & Neath, 2009). Failure to recall even with extensive cuing does not unequivocally support a decay position because it still is possible that the appropriate memory networks were not activated. Similarly, the fact that the decay position posits no psychological processes responsible for forgetting (rather only the passage of time) does not refute the position. Memory traces include both perceptual features and reactions to the experiences (Estes, 1997). Decay or changes in one or both cause forgetting and memory distortions. Furthermore, the decay process may be neurological (Anderson, 1990). Synapses deteriorate with lack of use in the same way that muscles do (Chapter 2).
Decay is commonly cited as a reason for forgetting (Nairne, 2002). You may have learned French in high school but now some years later cannot recall many vocabulary words. You might explain that as, “I haven’t used it for so long that I’ve forgotten it.” And forgetting is beneficial. Were we to remember everything we have ever learned, our memories would be so overcrowded that new learning would be very difficult. Forgetting is facilitative when it rids us of knowledge that we have not used and thus may not be important, analogous to your discarding things that you no longer need. Forgetting leads people to act, think, judge, and feel differently than they would in the absence of forgetting (Riccio, Rabinowitz, & Axelrod, 1994). Forgetting has profound effects on teaching and learning (Application 6.4).
APPLICATION 6.4 Minimizing Forgetting of Academic Learning
Forgetting is a problem when learned knowledge is needed for new learning. To help children retain important information and skills, teachers might do the following:
■Periodically review important information and skills during classroom activities.
■Assign class work and homework that reinforce previously learned material and skills.
■Send home fun learning packets during long vacation breaks that will reinforce various information and skills acquired.
■When introducing a new lesson or unit, review previously learned material that is needed for mastering the new material.
When Mrs. Baitwick-Smith introduces long division, some third graders have forgotten how to regroup in subtraction, which can slow the new learning. She spends a couple of days reviewing subtraction—especially problems requiring regrouping—as well as drilling the students on multiplication and simple division facts. She also gives homework that reinforces the same skills.
Ms. Zhang, a physical education teacher, is teaching a basketball unit over several days. At the start of each class, she reviews the skills taught in the previous class before she introduces the new skill. Periodically she spends an entire class period reviewing all the skills (e.g., dribbling, passing, shooting, playing defense) that the students have been working on up to that point. Remedial instruction is necessary when students forget some of these skills so that they will be able to play well once Ms. Zhang begins to organize games.
In Professor Astoolak’s graduate seminar, the students have been assigned an application paper that focuses on motivation techniques. During the semester, she introduced various motivational theories. Many of the students have forgotten some of these. To help the students prepare for writing their papers, she spends part of one class period reviewing the major motivation theories. Then she divides students into small groups and has each group write a brief summary of one of the theories with some classroom applications. After working in small groups, each group shares its findings with the entire class.
RELEARNING
Memory Savings
Relearning is learning material for the second or subsequent time after it previously had been learned (i.e., had satisfied the criteria of learning as stated in Chapter 1). Relearning is a common phenomenon and occurs daily for all of us. The opening vignette exemplifies relearning that occurs in school settings.
But relearning is more than just a common human activity, it also strikes to the heart of the issue about whether knowledge, once encoded in LTM, is there permanently or whether it can be lost. Recall from Chapter 1 the research by Ebbinghaus on memory. He relearned material some time after the original learning and calculated the savings score, or the amount of time or number of trials necessary to relearn as a percentage of the amount of time or number of trials necessary for original learning. The result that relearning is easier than new learning has been obtained in other research studies (Bruning, Schraw, & Norby, 2011).
Since relearning is easier than new learning, it suggests that at least some knowledge in LTM may not be permanently lost. Forgetting is said to occur when knowledge cannot be retrieved, perhaps because of inadequate retrieval cues, retrieval conditions not matching those of original learning, and so forth. Relearning research suggests that we may not forget but rather retain in LTM more knowledge than we can recall, recognize, or otherwise retrieve.
From the theoretical perspective of information processing, it is not clear why relearning is more efficient than new learning. It may be that memory network traces are retained, so that when people relearn they reconstruct these memories. Neuroscience research (Chapter 2) shows that networks respond to use, so when people do not use them they become weakened but not necessarily lost (Wolfe, 2010).
As with new learning, relearning proceeds better with distributed practice (regular shorter sessions) than with massed practice (irregular, more intense sessions; Bruning et al., 2011). Perhaps the distributing of relearning allows memory networks to strengthen in such a way that they become established better.
Effect of Testing
Another factor that seems to affect relearning is testing. The role of testing in accountability was discussed in Chapter 1. There is much pressure on schools today to ensure that students learn requisite skills and meet learning standards and outcomes. This emphasis can create a negative view of testing among educators, parents, and students.
A testing effect occurs when taking tests or quizzes enhances learning and retention such that scores on the final test are higher than if prior testing had not occurred (Bruning et al., 2011). This effect suggests that some learning occurs while students are being tested, presumably because they recall and rehearse material and relate it in new ways to other knowledge. What is also interesting, however, is that taking a test on material can have a stronger effect on retention than spending the same amount of time restudying material (Bruning et al., 2011). Roediger and Karpicke (2006) found that on a test a week after learning, students who studied and were tested on the material during original learning outperformed those who only had studied the material.
Being tested while one is learning forces one to retrieve material. It may be that the testing requires learners to organize and elaborate material better, both of which lead to better long-term retention and relearning. Also, the retrieval practiced during learning is done under similar conditions as that done during subsequent testing, so we should expect good transfer from the original learning context to the later testing one. Transfer is discussed later in this chapter.
This benefit should not be construed as an argument for more testing in schools. But educators who are aware of the potential advantage can design curricula to use testing not just for accountability but also as a means to promote learning. The judicious use of quizzes and tests may help alleviate some of the need for reviews lamented by the educators in the opening vignette.
VISUAL MEMORY
Chapters 5 and 6 have focused primarily on verbal memory—the memory of words and meanings. But another type of memory used commonly in learning is visual memory (Matlin, 2009). In fact, people often tend to remember information better in visual rather than in verbal form, and memory is enhanced when information is presented in both forms (Sadoski & Paivio, 2001).
Visual memory (or visual imagery or mental imagery) refers to mental representations of visual/spatial knowledge including physical properties of the objects or events represented. This section discusses how knowledge is represented visually and individual differences in visual memory capabilities.
Representation of Visual Information
Visual stimuli that are attended to are held briefly in veridical (true) form in the sensory register and then are transferred to WM. Recall from Chapter 5 that in WM the visuo-spatial sketchpad serves to set up and manipulate visual images (Baddeley, 1998, 2012). The WM representation appears to preserve some of the physical attributes of the stimulus it represents. Images are analog representations that are similar but not identical to their referents.
Visual memory has been valued as far back as the time of the ancient Greeks. Plato felt that thoughts and perceptions are impressed on the mind as on a block of wax and are remembered as long as the images last (Paivio, 1970). Simonides, a Greek poet, believed that images are associative mediators. He devised the method of loci as a memory aid (Chapter 10). In this method, information to be remembered is paired with locations in a familiar setting.
Visual imagery also has been influential in discoveries. Shepard (1978) described Einstein’s Gedanken experiment that marked the beginning of the relativistic reformulation of electromagnetic theory. Einstein imagined himself traveling with a beam of light (186,000 miles per second), and what he saw corresponded neither to light nor to anything described by Maxwell’s equations in classical electromagnetic theory. Einstein reported that he typically thought in terms of images and only reproduced his thoughts in words and mathematical equations once he conceptualized the situation visually. The German chemist Kekulé supposedly had a dream in which he visualized the structure of benzene, and Watson and Crick apparently used mental rotation to break the genetic code.
In contrast to images, propositions are discrete representations of meaning not resembling their referents in structure. The expression “New York City” no more resembles the actual city than virtually any three words picked at random from a dictionary. An image of New York City containing skyscrapers, stores, people, and traffic is more similar in structure to its referent. The same contrast is evident for events. Compare the sentence, “The black dog ran across the lawn,” with an image of this scene.
Visual memory is a controversial topic (Matlin, 2009). A central issue is how closely visual images resemble actual pictures: Do they contain the same details as pictures or are they fuzzy pictures portraying only highlights? The visual pattern of a stimulus is perceived when its features are linked to a LTM representation. This implies that images can only be as clear as the LTM representations (Pylyshyn, 1973). To the extent that images are the products of people’s perceptions, images are likely to be incomplete representations of stimuli. In fact, people construct images in memory and then reconstruct them during retrieval (Surprenant & Neath, 2009), both of which cause distortion.
Support for the idea that people use visual imagery to represent spatial knowledge comes from studies where participants were shown pairs of two-dimensional pictures, each of which portrayed a three-dimensional object (Cooper & Shepard, 1973; Shepard & Cooper, 1983). The task was to determine if the two pictures in each pair portrayed the same object. The solution strategy involved mentally rotating one object in each pair until it matched the other object or until the individual decided that no amount of rotation would yield an identical object. Reaction times were a direct function of the number of mental rotations needed. Although these and other data suggest that people employ images to represent knowledge, they do not directly address the issue of how closely images correspond to actual objects.
To the extent that students use imagery to represent spatial and visual knowledge, imagery is germane to educational content involving concrete objects. When teaching a unit about different types of rock formations (e.g., mountains, plateaus, ridges), an instructor could show pictures of the various formations and ask students to imagine them. In geometry, imagery could be employed when dealing with mental rotations. Pictorial illustrations improve students’ learning from texts (Carney & Levin, 2002; see Application 6.5 for more examples).
APPLICATION 6.5 Using Visual Memory in Classrooms
Visual memory can improve student learning. One application involves instructing students on three-dimensional figures (e.g., cubes, spheres, cones), including calculating their volumes. Verbal descriptors and two-dimensional diagrams are also used, but actual models of the figures greatly enhance teaching effectiveness. Allowing students to hold the shapes fosters their visual understanding of the concept of volume.
Visual memory can be applied in physical education. When students are learning an exercise routine accompanied by music, the teacher can model in turn each portion of the routine initially without music, after which students visualize what they saw. The students then perform each part of the routine. Later the teacher can add music to the individual portions.
For an elementary language arts unit involving writing a paragraph that gives directions for performing a task or making something, a teacher might ask his or her students to think about and picture the individual steps (e.g., of making a peanut butter and jelly sandwich). Once students finish, they can visualize each step while writing it down.
Art teachers can use visual imagery to teach students to follow directions. The teacher might give the following directions orally and write them on the board: “Visualize on a piece of art paper a design including four circles, three triangles, and two squares, with some of the shapes overlapping one another.” The teacher might ask the following questions to ensure that students are using imagery: “How many circles do you see?” “How many triangles?” “How many squares?” “Are any of the shapes touching? Which ones?”
A dance teacher might have students listen to the music to which they will be performing. Then the students could imagine themselves dancing, visualizing every step and movement. The teacher also might ask students to visualize where they and their classmates are on the stage as they dance.
An American history teacher took his classes to a Civil War battlefield and had them imagine what it must have been like to fight a battle at that site. Later in class he had students construct with technology a map that duplicated the site and then create various scenarios for what could have happened as the Union and Confederate forces fought.
Researchers increasingly are studying the role of visualizations in learning. A visualization is a nonverbal symbolic or pictorial illustration such as a graph, realistic diagram, or picture (Höffler, 2010). A dynamic visualization is one that portrays change, such as a video and animation. Höffler reported that learners with low spatial ability seem better supported by dynamic rather than nondynamic visualizations. Further, segmenting a dynamic visualization (showing in pieces with interspersed pauses) may help to reduce extraneous cognitive load (see Chapter 5), which can help students better process the representation (e.g., encode and store in LTM; Spanjers, van Gog, & van Merriënboer, 2010).
Evidence shows that people also can use visual imagery with abstract dimensions. Kerst and Howard (1977) asked students to compare pairs of cars, countries, and animals on the concrete dimension of size and on an appropriate abstract dimension (e.g., cost, military power, ferocity). The abstract and concrete dimensions yielded similar results: As items became more similar, reaction times increased. For instance, in comparing size, comparing a bobcat and an elephant is easier than comparing a rhinoceros and a hippopotamus. How participants imagined abstract dimensions or whether they even used imagery is not clear. Perhaps they represented abstract dimensions in terms of propositions, such as by comparing the United States and Jamaica on military power using the proposition, “(The) United States (has) more military power (than) Jamaica.” Knowledge maps, which are pictorial representations of linked ideas, aid student learning (O’Donnell, Dansereau, & Hall, 2002).
Visual Memory and LTM
Although researchers agree that visual memory is part of WM, they disagree about whether images are retained in LTM (Kosslyn & Pomerantz, 1977; Matlin, 2009; Pylyshyn, 1973). Dual-code theory directly addresses this issue (Clark & Paivio, 1991; Paivio, 1971, 1978, 1986). LTM has two means of representing knowledge: a verbal system incorporating knowledge expressed in language and an imaginal systemstoring visual and spatial information. These systems are interrelated—a verbal code can be converted into an imaginal code and vice versa—but important differences exist. The verbal system is suited for abstract information, whereas the imaginal system can be used to represent concrete objects or events.
Shepard’s experiments support the utility of imagery and offer indirect support for the dual-code theory. Other supporting evidence comes from research showing that when recalling lists of concrete and abstract words, people recall concrete words better than abstract ones (Terry, 2009). The dual-code theory explanation of this finding is that concrete words can be coded verbally and visually, whereas abstract words usually are coded only verbally. At recall, people draw on both memory systems for the concrete words, but only the verbal system for the abstract words. Other research on imaginal mnemonic mediators supports the dual-code theory (Chapter 10).
In contrast, unitary theory postulates that all information is represented in LTM in verbal codes (propositions). Images in WM are reconstructed from verbal LTM codes. Indirect support for this notion comes from Mandler and Johnson (1976) and Mandler and Ritchey (1977). As with verbal material, people employ schemas while acquiring visual information. They remember scenes better when elements are in a typical pattern; memory is poorer when elements are disorganized. Meaningful organization and elaboration of information into schemas improve memory for scenes much as they do for verbal material. This finding suggests the operation of a common process regardless of the form of information presented.
This debate notwithstanding, using concrete materials and pictures enhances memory (Terry, 2009). Such instructional tools as manipulatives, audiovisual aids, and computer graphics facilitate learning. Although concrete devices are undoubtedly more important for young children because they lack the cognitive capability to think in abstract terms, students of all ages benefit from information presented in multiple modes.
Individual Differences
The extent to which people actually use visual memory varies as a function of cognitive development. Kosslyn (1980) proposed that children are more likely to use visual memory to remember and recall information than adults, who rely more on verbal representation. Kosslyn gave children and adults statements such as, “A cat has claws,” and “A rat has fur.” The task was to determine accuracy of the statements. Kosslyn reasoned that adults could respond quicker because they could access the propositional information from LTM, whereas children would have to recall the image of the animal and scan it. To control for adults’ better information processing in general, some adults were asked to scan an image of the animal, whereas others were free to use any strategy.
Adults were slower to respond when given the imagery instructions than when free to choose a strategy, but no difference was found for children. These results suggest that children use imagery even when they are free to do otherwise, but they do not address whether children cannot use verbal information (because of cognitive limitations) or whether they can but choose not to because they find imagery to be more effective.
Use of visual memory also depends on effectiveness of performing the component processes. Apparently two types are involved. One set of processes helps to activate stored memories of parts of images. Another set reconstructs the parts into the proper configuration. These processes may be localized in different parts of the brain. Individual differences in imagery can result because people differ in how effectively this dual processing occurs (Kosslyn, 1988).
The use of imagery by people of any age depends on what is to be imagined. Concrete objects are more easily imagined than abstractions. Another factor that influences use of imagery is one’s ability to employ it. Eidetic imagery, or photographic memory (Leask, Haber, & Haber, 1969), actually is unlike a photograph; the latter is seen as a whole, whereas eidetic imagery occurs in pieces. People report that an image appears and disappears in segments rather than all at once.
Eidetic imagery is found more often in children than in adults (Gray & Gummerman, 1975), yet even among children it is uncommon (about 5%). Eidetic imagery may be lost with development, perhaps because verbal representation replaces visual thinking. It also is possible that adults retain the capacity to form clear images but do not routinely do so because their verbal systems can represent more information. The capacity to use visual memory can be improved, but most adults do not explicitly work to develop it.
TRANSFER
Transfer refers to knowledge being applied in new ways, in new situations, or in familiar situations with different content. Transfer also explains how prior learning affects subsequent learning. Transfer is involved in new learning when students retrieve their prior relevant knowledge and experiences (National Research Council, 2000). The cognitive capability for transfer is important, because without it all learning would be situation specific, and much instructional time would be spent reteaching skills in different contexts.
There are different types of transfer. Positive transfer occurs when prior learning facilitates subsequent learning. Learning how to drive a car with standard transmission should facilitate learning to drive other cars with standard transmission. Negative transfer means that prior learning interferes with subsequent learning or makes it more difficult. Learning to drive a standard transmission car might have a negative effect on subsequently learning to drive a car with automatic transmission because one might try to hit the nonexistent clutch and possibly shift gears while the car is moving, which could ruin the transmission. Zero transfer means that one type of learning has no noticeable influence on subsequent learning. Learning to drive a standard transmission car should have no effect on learning to operate a computer.
Current cognitive conceptions of learning highlight the complexity of transfer (Phye, 2001; Taatgen, 2013). Although some forms of simple skill transfer seem to occur automatically, much transfer requires higher-order thinking skills and beliefs about the usefulness of knowledge. This section begins with a brief overview of historical perspectives on transfer, followed by a discussion of cognitive views and the relevance of transfer to school learning.
Historical Views
Identical Elements.
Behavior (conditioning) theories (Chapter 3) stress that transfer depends on identical elements or similar features (stimuli) among situations. Thorndike (1913b) contended that transfer occurs when situations have identical elements (stimuli) and call for similar responses. A clear and known relation must exist between the original and transfer tasks, as is often the case between drill/practice and homework.
This view is intuitively appealing. Students who learn to solve the problem 602 − 376 = ? are apt to transfer that knowledge and also solve the problem 503 − 287 = ? We might ask, however, what the elements are and how similar they must be to be considered identical. In subtraction, do the same types of numbers need to be in the same column? Teachers know that students who can solve the problem 42 − 37 = ? will not necessarily be able to solve the problem 7428 − 2371 = ?, even though the former problem is contained within the latter one. Findings such as this call into question the validity of identical elements. Furthermore, even when identical elements exist, students must recognize them. If students believe no commonality exists between situations, no transfer will occur. The identical elements position, therefore, is inadequate to explain all transfer.
Mental Discipline.
Also relevant to transfer is the mental discipline doctrine (Chapter 3), which holds that learning certain subjects (e.g., mathematics, the classics) enhances general mental functioning and facilitates learning of new content better than does learning other subjects. This view was popular in Thorndike’s day and periodically reemerges in the form of recommendations for basic or core skills and knowledge (e.g., Hirsch, 1987).
Research by Thorndike (1924) provided no support for the mental discipline idea (Chapter 3). Instead, Thorndike concluded that what facilitates new learning is students’ beginning level of mental ability. Students who were more intelligent when they began a course gained the most from the course. The intellectual value of studies reflects not how much they improve students’ ability to think but rather how they affect students’ interests and goals.
Generalization.
Skinner’s (1953) operant conditioning theory proposed that transfer involves generalization of responses from one discriminative stimulus to another. For example, students might be taught to put their books in their desks when the bell rings. When students go to another class, putting books away when the bell rings might generalize to the new setting.
The notion of generalization, like identical elements, has intuitive appeal. Surely some transfer occurs through generalization, and it may even occur automatically. Students who are punished for misbehavior in one class may not misbehave in other classes. Once drivers learn to stop their cars at a red light, then that response will generalize to other red lights regardless of location, weather, time of day, and so forth.
Nonetheless, the generalization position has problems. As with identical elements, we can ask what features of the situation are used to generalize responses. Situations share many common features, yet we respond only to some of them and disregard others. We respond to the red light regardless of many other features in the situation. At the same time, we might be more likely to run a red light when no other cars are around or when we are in a hurry. Our response is not fixed but rather depends on our cognitive assessment of the situation. The same can be said of other situations where generalization does not occur automatically. Cognitive processes are involved in most generalization as people determine whether responding in similar fashion is appropriate in that setting. The generalization position, therefore, is incomplete because it neglects the role of cognitive processes.
Activation of Knowledge in Memory
An information processing perspective contends that transfer involves activating knowledge in memory networks. It requires that information be cross-referenced with propositions linked in memory (Anderson, 1990). The more links between bits of information in memory, the likelier that activating one piece of information will cue other information in memory. Such links can be made within and between networks.
In other words, transfer depends on students recognizing the common “deep” structure between the learning and transfer contexts, especially when the “surface” structures of the situations may differ (Chi & VanLehn, 2012). Information in memory networks involving deep structure will facilitate transfer when learners recognize that structure in the transfer context.
The same process is involved in transfer of procedural knowledge and productions (Bruning et al., 2011). Transfer occurs when knowledge and productions are linked in LTM with different content. Students must also believe that productions are useful in various situations. Transfer is aided by the uses of knowledge being stored with the knowledge itself. For example, learners may possess a production for skimming text. This may be linked in memory with other reading procedures (e.g., finding main ideas, sequencing) and may have various uses stored with it (e.g., skimming Web page text to get the gist, skimming memos to determine meeting place and time). The more links in LTM and the more uses stored with skimming, the better the transfer. Such links are formed by having students practice skills in various settings and by helping them understand the uses of knowledge. The general aspects of production rules (similar to “deep” structures) promote transfer (Taatgen, 2013). These general aspects are developed by combining task-specific features that learners accumulate over different experiences.
This cognitive description of transfer fits much of what we know about cued knowledge. Where more LTM links are available, accessing information in different ways is possible. We may not be able to recall the name of Aunt Martha’s dog by thinking about her (cuing the “Aunt Martha” network), but we might be able to recall the name by thinking about (cuing) breeds of dogs (“collie”). Such cuing is reminiscent of the experiences we periodically have of not being able to recall someone’s name until we think about that person from a different perspective or in a different context.
At the same time, we still do not know many things about how such links form. Links are not automatically made simply by pointing out uses of knowledge to students or having them practice skills in different contexts (National Research Council, 2000). The next section discusses different forms of transfer, which are governed by different conditions.
Types of Transfer
Table 6.2 Types of transfer.
Type
Characteristics
Near
Much overlap between situations; original and transfer contexts are highly similar
Far
Little overlap between situations; original and transfer contexts are dissimilar
Literal
Intact skill or knowledge transfers to a new task
Figural
Use of some aspects of general knowledge to think or learn about a problem, such as with analogies or metaphors
Low road
Transfer of well-established skills in spontaneous and possibly automatic fashion
High road
Transfer involving abstraction through an explicit conscious formulation of connections between situations
Forward reaching
Abstracting behavior and cognitions from the learning context to one or more potential transfer contexts
Backward reaching
Abstracting in the transfer context features of the situation that allow for integration with previously learned skills and knowledge
Transfer is not a unitary phenomenon but rather is complex (Barnett & Ceci, 2002; Table 6.2). One distinction is between near and far transfer (Royer, 1986). Near transfer occurs when situations overlap a great deal, such as between the stimulus elements during instruction and those present in the transfer situation. An example is when fraction skills are taught and then students are tested on the content in the same format in which it was taught. In contrast, far transfer involves a transfer context much different from that in which original learning occurred. An example would be applying fraction skills in an entirely different setting without explicitly being told to do so. Thus, students might have to add parts of a recipe (1/2 cup milk and 1/4 cup water) to determine the amount of liquid without being told the task involves fractions.
Another distinction is between literal and figural transfer. Literal transfer involves transfer of an intact skill or knowledge to a new task (Royer, 1986). Literal transfer occurs when students use fraction skills in and out of school. Figural transfer refers to using some aspect of our general knowledge to think or learn about a particular problem. Figural transfer often involves using analogies, metaphors, or comparable situations. Figural transfer occurs when students encounter new learning and employ the same study strategies that they used to master prior learning in a related area. Figural transfer requires drawing an analogy between the old and new situations and transferring that general knowledge to the new situation.
Although some overlap exists, the forms of transfer involve different types of knowledge. Near transfer and literal transfer involve primarily declarative knowledge and mastery of basic skills. Far transfer and figurative transfer involve declarative and procedural knowledge, as well as conditional knowledge concerning the types of situations in which the knowledge may prove useful (Royer, 1986).
Salomon and Perkins (1989) distinguished low-road from high-road transfer. Low-road transfer refers to transfer of well-established skills in a spontaneous and perhaps automatic fashion. In contrast, high-road transfer is abstract and mindful; it “involves the explicit conscious formulation of abstraction in one situation that allows making a connection to another” (Salomon & Perkins, 1989, p. 118).
Low-road transfer occurs with skills and actions that have been practiced extensively in varied contexts. The behaviors tend to be performed automatically in response to characteristics of a situation that are similar to those of the situation in which they were acquired. Examples are learning to drive a car and then driving a different but similar car, brushing one’s teeth with a regular toothbrush and with an electric toothbrush, or solving algebra problems at school and at home. At times the transfer may occur with little conscious awareness of what one is doing. The level of cognitive activity increases when some aspect of the situation differs and requires attention. For example, most people have little trouble accommodating to features in rental cars. When features differ (e.g., the headlight control works differently or is in a different position from what one is used to), people have to learn them.
High-road transfer occurs when students learn a rule, principle, prototype, schema, and so forth, and then use it in a more general sense than how they learned it. Transfer is mindful because students do not apply the rule automatically. Rather, they examine the new situation and decide what strategies will be useful to apply. Abstraction is involved during learning and later when students perceive basic elements in the new problem or situation and decide to apply the skill, behavior, or strategy. Low-road transfer primarily involves declarative knowledge, and high-road transfer uses productions and conditional knowledge to a greater extent.
Salomon and Perkins (1989) distinguished two types of high-road transfer—forward reaching and backward reaching—according to where the transfer originates. Forward-reaching transfer occurs when one abstracts behavior and cognitions from the learning context to one or more potential transfer contexts. For example, while students are studying precalculus, they might think about how some of the material (e.g., limits) might be pertinent in calculus. Another example is while being taught in a class how a parachute works, students might think about how they will use the parachute in actually jumping from an airplane.
Forward-reaching transfer is proactive and requires self-monitoring of potential contexts and uses of skills and knowledge. To determine potential uses of precalculus, for example, learners must be familiar with other content knowledge of potential contexts in which knowledge might be useful. Forward-reaching transfer is unlikely when students have little knowledge about potential transfer contexts.
In backward-reaching transfer, students abstract in the transfer context features of the situation that allow for integration with previously learned ideas (Salomon & Perkins, 1989). While students are working on a calculus problem, they might try to think of any situations in precalculus that could be useful for solving the calculus problem. Students who have difficulty learning new material employ backward-reaching transfer when they think back to other times when they experienced difficulty and ask what they did in those situations (e.g., seek help from friends, conduct a Web search, reread the text, talk with the teacher). They then might be apt to implement one of those solutions in hopes of remedying their current difficulty. Analogical reasoning (Chapter 7) might involve backward-reaching transfer, as students apply steps from the original problem to the current one. Consistent with the effects of analogical reasoning on learning, Gentner, Loewenstein, and Thompson (2003) found that analogical reasoningenhanced transfer, especially when two original cases were presented together.
Earlier we noted that transfer involves linked information in LTM such that the activation of one item can cue other items. Presumably low-road transfer is characterized by relatively automatic cuing. A central distinction between the two forms is degree of mindful abstraction, or the volitional, metacognitively guided employment of nonautomatic processes (Salomon & Perkins, 1989). Mindful abstraction requires that learners not simply act based on the first possible response, but rather that they examine situational cues, define alternative strategies, gather information, and seek new connections between information. LTM cuing is not automatic with high-road transfer, but rather deliberate, and can result in links being formed in LTM as individuals think of new ways to relate knowledge and contexts.
Anderson, Reder, and Simon (1996) contended that transfer is more likely when learners attend to the cues that signal the appropriateness of using a particular skill. They then will be more apt to notice those cues on transfer tasks and employ the skill. In this sense, the learning and transfer tasks share symbolic elements. These shared elements are important in strategy transfer.
Strategy Transfer
Transfer applies to strategies as well as to skills and knowledge (Phye, 2001). An unfortunate finding of much research is that students learn strategies and apply them effectively but fail to maintain their use over time or generalize them beyond the instructional setting. This is a common issue encountered in problem solving (Chapter 7; Jonassen & Hung, 2006). Many factors impede strategy transfer, including not understanding that the strategy is appropriate for different settings, not understanding how to modify its use with different content, believing that the strategy is not as useful for performance as other factors (e.g., time available), thinking that the strategy takes too much effort, or not having the opportunity to apply the strategy with new material (Borkowski & Cavanaugh, 1979; Dempster & Corkill, 1999; Paris, Lipson, & Wixson, 1983; Pressley et al., 1990; Schunk & Rice, 1993).
Phye (1989, 1990, 1992, 2001; Phye & Sanders, 1992, 1994) developed a model useful for enhancing strategy transfer and conducted research testing its effectiveness. During the initial acquisition phase, learners receive instruction and practice to include assessment of their metacognitive awareness of the uses of the strategy. A later retention phase includes further practice on training materials and recall measures. The third transfer phase occurs when participants attempt to solve new problems that have different surface characteristics but that require the same solution strategy practiced during training. Phye also stressed the role of learner motivation for transfer and ways to enhance motivation by showing learners uses of knowledge. Motivation is a critical influence on transfer (National Research Council, 2000; Pugh & Bergin, 2006).
In one study in which adults worked on verbal analogy problems, some received corrective feedback during trials that consisted of identifying the correct solutions, whereas others were given advice concerning how to solve analogies. All students judged confidence in the correctness of solutions they generated. During training, corrective feedback was superior to advice in promoting transfer of problem-solving skills; however, on a delayed transfer task, no difference occurred between conditions. Regardless of condition, confidence in problem-solving capabilities bore a positive relation to actual performance. Butler, Godbole, and Marsh (2013) found that providing feedback that included an explanation of the correct answer produced better transfer than did feedback that only included the correct answer.
In addition to knowledge of the strategy, transfer requires knowledge of the uses of the strategy, which is facilitated when learners explain the strategy as they learn it (Crowley & Siegler, 1999). Feedback about how the strategy helps improve performance facilitates strategy retention and transfer (Phye & Sanders, 1994; Schunk & Swartz, 1993a, 1993b). Phye’s research highlights the link of strategy transfer with information processing and the key roles played by practice, corrective feedback, and motivation. It also underscores the point that teaching students self-regulated learning strategies can facilitate transfer (Fuchs et al., 2003; Fuchs, Fuchs, Finelli, Courey, & Hamlett, 2004; Chapter 10). Application 6.6 has suggestions for ways to facilitate transfer.
APPLICATION 6.6 Facilitating Transfer
Ms. DiGiorgio helps her elementary students build on the knowledge they already have learned. She has her students recall the major points of each page of a story in their reading book before they write a summary paragraph about the story. She also reviews with them how to develop a complete paragraph. Building on former learning helps her children transfer knowledge and skills to a new activity.
In preparing for a class discussion about influential presidents of the United States, Mr. Neufeldt sends a study sheet home with his high school students asking them to list presidents that they feel had a major impact on American history. He instructs them not only to rely on what has been discussed in class, but also to rely on knowledge they have from previous courses or other readings and research they have done. He encourages students to pull the information together from the class discussion and incorporate the former learning into the learning that occurs from the new material.
INSTRUCTIONAL APPLICATIONS
As noted in Chapter 5, information processing principles increasingly have been applied to school learning settings. This section described retrieval applications: encoding-retrieval similarity, retrieval-based learning, and teaching for transfer.
Encoding-Retrieval Similarity
We saw earlier that memory benefits from encoding specificity, or the idea that the learning conditions at retrieval match as closely as possible those present during encoding. The term “encoding specificity” omits “retrieval,” which can convey the erroneous impression that encoding is the most important process and that once encoding occurs, retrieval will happen. Suprenant and Neath (2009) underscore the importance of retrieval and present an encoding-retrieval principle of memory, which states that memory depends heavily on the relation between the conditions at encoding and those at retrieval. This relation is referred to here as encoding-retrieval similarity.
An instructional implication of encoding-retrieval similarity is to have the same or similar context at retrieval that was present at encoding. For example, students who learn in a computer-based learning environment (e.g., online) might be tested in the same environment. Students who learn to solve algebra problems written in particular formats might be tested with similar problems. The prediction is that the similarity between encoding and retrieval conditions should facilitate memory and performance.
But as we have seen in this chapter, transfer is important. Educators want students to be able to transfer their skills beyond the conditions present at encoding and retrieve them under different conditions. Teachers can facilitate transfer by helping students encode a reminder that they can subsequently retrieve and that will promote further retrieval. For example, if students are learning a strategy for comprehending written text, the teacher might label this strategy “the steps,” then tell students that when they have to answer comprehension questions to think of “the steps.” Such a reminder should cue retrieval of the strategy’s steps for comprehension.
The educators in the opening vignette lament the need for many reviews because students seem to forget so much, even over long weekends. It is possible that students have not forgotten the content but rather cannot retrieve it due to inadequate cues. Providing more cues at retrieval may help lessen the need for reviews. Under what conditions did students learn the material? Did they work individually or in groups? Whole class or small groups? Computer-based learning environment? What content was associated with the original learning? When students return from a long break, teachers can cue not only the content learned but also the conditions under which students learned it. For example, a teacher might remind students that they studied this content last week Thursday afternoon, when they worked in small groups on computers studying environmental pollution.
Retrieval-Based Learning
Retrieval is often thought of as an end product of learning (encoding); that is, retrieval happens after learning occurs. In fact, retrieval can serve a learning function. Karpicke and Grimaldi (2012) postulate that retrieval can affect learning directly and indirectly. Retrieval affects learning directly because when we retrieve knowledge we alter it and enhance our capability to reconstruct that knowledge in the future. Indirect retrieval effects on learning occur when retrieval affects other variables that in turn can influence learning. For example, when instructors ask students questions in class, students attempt to retrieve knowledge, and the success of their retrieval gives them feedback about how well they know the material. Such feedback may motivate them to study harder and may affect their sense of self-efficacy for performing well in the class.
There are many ways that teachers use retrieval to promote learning including class questions and discussions, tests, and quizzes. Yet the opening vignette shows that teachers do not like to engage in so many review sessions, and few would advocate for more testing. Quizzes always can be given for students to check their levels of understanding (no grades), perhaps at the end of learning sessions. But there are other ways to effectively use retrieval as a learning process.
One means is to have students use retrieval when they study. Students may believe that studying involves mostly re-reading, but studying also can include frequent times when students stop reading and attempt to recall what they have read. The recall is a form of active rehearsal. Studying plus retrieval produces superior learning compared with studying alone (Karpicke & Grimaldi, 2012).
Another suggestion is to have students construct concept maps that link in networks related concepts in memory. Students can do this as they work in class or study on their own. Teachers can facilitate this process by asking students to construct maps that reflect not only concepts directly related to one another but also concepts requiring inferences (e.g., the example used earlier about when the vice president would vote in the Senate).
Students may not be aware of the potential benefits of retrieval on learning, which suggests that teaching students retrieval strategies (e.g., self-cuing) may be helpful. Retrieval is a key process of academic studying stressed by self-regulated learning researchers (Chapter 10). An abundance of research shows that students can be taught self-regulated learning strategies and can transfer them outside of the learning context to improve their academic performances (Zimmerman & Schunk, 2011).
Some other effective ways to build retrieval into learning settings include reciprocal teaching (Chapter 8) and computer-based learning methods (Chapter 7). Computer-based systems can be programmed to guide students’ retrieval (Karpicke & Grimaldi, 2012). For example, the system has students engage in repeated retrieval but the study decisions are made by the system, not the student. This type of arrangement takes into account individual student differences, as some students will benefit from more retrieval opportunities than others will.
Retrieval-based learning can have motivational effects (Chapter 9). Students who can retrieve knowledge are apt to experience heightened self-efficacy for performing well (Chapter 4; Schunk & Pajares, 2009). The belief that they have learned may motivate them to continue to apply themselves to further develop their learning. Thus, the indirect motivational effects of retrieval on learning may continue to strengthen self-efficacy and lead to further retrieval and learning.
Teaching for Transfer
Although there are different forms of transfer, they often work in concert. While students complete a task, some behaviors may transfer automatically whereas others may require mindful application. For example, assume that Jeff is writing a short paper. In thinking through the organization, Jeff might employ high-road, backward-reaching transfer by thinking about how he organized papers in previous, similar situations. Many aspects of the task, including word choice and spellings, will occur automatically (low-road transfer). As Jeff writes, he also might think about how this information could prove useful in other settings. Thus, if the paper is on some aspect of the Civil War, Jeff might think of how to use this knowledge in history class. Salomon and Perkins (1989) cited an example involving chess masters, who accumulate a repertoire of configurations from years of play. Although some of these may be executed automatically, expert play depends on mindfully analyzing play and potential moves. It is strategic and involves high-road transfer.
In some situations, low-road transfer could involve a good degree of mindfulness. With regard to strategy transfer, even minor variations in formats, contexts, or requirements can make transfer problematic among students, especially among those who experience learning problems (Borkowski & Cavanaugh, 1979). Conversely, some uses of analogical reasoning can occur with little conscious effort if the analogy is relatively clear. A good rule is never to take transfer for granted; it must be directly addressed.
This raises the issue of how teachers might encourage transfer in students. A major goal of teaching is to promote long-term retention and transfer (Halpern & Hakel, 2003). We know that having students practice skills in varied contexts and ensuring that they understand different uses for knowledge builds links in LTM (Anderson, Reder, & Simon, 1996). Homework is a mechanism for transfer because students practice and refine, at home, skills learned in school. Research shows a positive relation between homework and student achievement with the relation being stronger in grades 7–12 than in grades K–6 (Cooper, Robinson, & Patall, 2006).
But students do not automatically transfer strategies for the reasons noted earlier. Practice addresses some of these concerns, but not others. Cox (1997) recommended that as students learn in many contexts, they should determine what they have in common. Complex skills, such as comprehension and problem solving, will probably benefit most from this situated cognition approach (Griffin, 1995). Motivation should be addressed (Pugh & Bergin, 2006). Teachers may need to provide students with explicit motivational feedback that links strategy use with improved performance and provides information about how the strategy will prove useful in that setting. Studies show that such motivational feedback enhances strategy use, academic performance, and self-efficacy for performing well (Schunk & Rice, 1993).
Students also should establish academic goals (a motivational variable), the attainment of which requires careful deliberation and use of available resources. By cuing students at appropriate times, teachers may help them use relevant knowledge in new ways. Teachers might ask a question such as, “What do you know that might help you in this situation?” Such cuing tends to be associated with greater generation of ideas. Teachers can serve as models for transfer. Modeling strategies that bring related knowledge to bear on a new situation encourage students to seek ways to enhance transfer in both forward-and backward-reaching fashion and feel more efficacious about doing so. Working with children in grades 3–5 during mathematical problem solving, Rittle-Johnson (2006) found that having children explain how answers were arrived at and whether they were correct promoted transfer of problem-solving strategies.
SUMMARY
Retrieval is a key component of information processing. Retrieval is the successful result of encoding but also can facilitate learning. When learners have to retrieve knowledge, the appropriate cues enter WM and activate LTM networks through spreading activation. For verbal knowledge, the learner’s WM constructs a response when the knowledge is obtained. Memory search continues until knowledge is retrieved. An unsuccessful search yields no information. Much retrieval occurs automatically.
Certain conditions affect the efficacy of retrieval. One is encoding specificity, which means that retrieval proceeds best when retrieval cues and conditions match those present at encoding. Other conditions that facilitate retrieval are elaboration, meaningfulness, and organization of knowledge in LTM. Presumably these conditions promote spreading activation and access by learners of needed memory networks.
An area that illustrates the storage and retrieval of information in LTM is language comprehension, which involves perception, parsing, and utilization. Communications are incomplete; speakers omit information they expect that listeners will know. Effective language comprehension requires that listeners possess adequate propositional knowledge and schemas and understand the context. To integrate information into memory, listeners identify given information, access it in LTM, and relate new information to it. Language comprehension is a central aspect of literacy and relates strongly to academic success—especially in subjects that require extensive reading.
Table 6.3 Summary of learning issues.
How Does Learning Occur?Learning, or encoding, occurs when information is stored in LTM. Information initially enters the information processing system through a sensory register after it is attended to. It then is transferred to WM and perceived by being compared with information in LTM. This information can stay activated, be transferred to LTM, or be lost. Factors that help encoding are meaningfulness, elaboration, organization, and links with schema structures.How Does Memory Function?Memory is a key component of the information processing system. There is debate about how many memories there are. The classical model postulated two memory stores: short- and long-term. Contemporary theory posits a WM and a LTM, although WM may be an activated portion of LTM. Memory receives information and through associative structure networks links it with other information in memory. Knowledge subsequently can be retrieved from LTM.What Is the Role of Motivation?Relative to other learning theories, motivation has received less attention by information processing theories. Learners presumably engage their cognitive processes to support attainment of their goals. Motivational processes such as goals and self-efficacy likely are represented in memory as propositions embedded in networks. The central executive, which directs WM activities, also seems to have motivational properties.How Does Transfer Occur?Transfer occurs through the process of spreading activation in memory, where information is linked to other information such that recall of certain knowledge can produce recall of related knowledge. It is important that learning cues be attached to knowledge so that the learning may be linked with different contexts, skills, or events.How Does Self-Regulated Learning Operate?Key self-regulation processes are goals, learning strategies, production systems, and schemas (Chapter 10). Information processing theories contend that learners can direct their information processing during learning.What Are the Implications for Instruction?Information processing theories emphasize the transformation and flow of information through the cognitive system. It is important that information be presented in such a way that students can relate the new information to known information (meaningfulness) and that they understand the uses for the knowledge. These points suggest that learning be structured so that it builds on existing knowledge and can be clearly comprehended by learners. Teachers also should provide advance organizers and cues that learners can use to recall information when needed and that minimize extraneous cognitive load. It also is important to use instructional activities that include retrieval and that help students learn ways to transfer knowledge to new contexts.
Even when knowledge is encoded, it may be forgotten. Forgetting refers to the loss of information from memory or the failure to access it. Failure to retrieve may result from decay of information or interference. Factors that facilitate retrieval and lessen the chance of forgetting are the strength of the original encoding, the number of alternative memory networks, and the amount of distortion or merging of information. Retrieval always involves some amount of re-construction of knowledge as learners access information in LTM.
Because forgetting occurs, relearning often is necessary. Research evidence shows that relearning typically is easier than new learning, which suggests that some amount of knowledge in LTM is not forgotten but rather difficult to access. The savings score indicates the amount of time or number of trials necessary for relearning as a percentage of the amount of time or number of trials necessary for original learning. A testing effect occurs when taking tests or quizzes enhances learning and retention such that scores on the final test are higher than if prior testing had not occurred. Although this is not an argument for more testing of students, research supports the point that testing seems to facilitate retention and relearning and perhaps better than additional studying. To lessen evaluative pressures, teachers can give students nongraded quizzes at the end of learning sessions.
Much evidence exists for information being stored in memory in verbal form (meanings), but there also is evidence for visual memory. Visual/spatial knowledge is stored as an analog representation: It is similar but not identical to its referents. Dual-code theory postulates that the imaginal system primarily stores concrete objects and events and the verbal system stores more abstract information expressed in language. Conversely, images may be reconstructed in WM from verbal codes stored in LTM. Developmental evidence shows that children are more likely than adults to represent knowledge as images, but visual memory can be developed in persons of any age.
Transfer is a complex phenomenon. Historical views include identical elements, mental discipline, and generalization. From a cognitive perspective, transfer involves activation of memory networks and occurs when information is linked. Distinctions are drawn between near and far, literal and figural, and low-road and high-road transfer. Some forms of transfer may occur automatically, but much is conscious and involves abstraction and recognizing underlying structures. Providing students with feedback on the usefulness of skills and strategies makes transfer more likely to occur.
The importance of retrieval and transfer for learning suggests some educational applications. Three that are pertinent involve encoding-retrieval specificity, retrieval-based learning, and teaching for transfer.
A summary of learning issues for information processing theory appears in Table 6.3.
FURTHER READING
Butler, A. C., Godbole, N., & Marsh, E. J. (2013). Explanation feedback is better than correct answer feedback for promoting transfer of learning. Journal of Educational Psychology, 105, 290–298.
Chi, M. T. H., & VanLehn, K. A. (2012). Seeing deep structure from the interactions of surface features. Educational Psychologist, 47, 177–188.
Halpern, D. F., & Hakel, M. D. (2003). Applying the science of learning to the university and beyond: Teaching for long-term retention and transfer. Change, 35(4), 36–41.
Höffler, T. N. (2010). Spatial ability: Its influence on learning with visualizations—a meta-analytic review. Educational Psychology Review, 22, 245–269.
Karpicke, J. D., & Grimaldi, P. J. (2012). Retrieval-based learning: A perspective for enhancing meaningful learning. Educational Psychology Review, 24, 401–418.
Taatgen, N. A. (2013). The nature and transfer of cognitive skills. Psychological Review, 120, 439–471.
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