Cognitive Psychology and Its Implications, Ch. 5

Cognitive Psychology and Its Implications, Ch. 5
5Representation of Knowledge Recall a wedding you attended a while ago. Presumably, you can remember who married whom, where the wedding was, many of the people who attended, and some of the things that happened. You would probably be hard pressed, however, to say exactly what all the participants wore, the exact words that were spoken, the the way the bride walked down the aisle, and so on—although you probably registered many of these details. It is not surprising that after time has passed, our memories lose some of the information of the original experience. What is interesting is that our loss of information is selective: We tend to remember the gist (that which is most meaningful or useful) and forget the detail (that which is not important). The previous chapter was about our ability to form visual images of the detail of our experiences. It might seem that it would be ideal if we had the capacity to always represent information in such detail and remember the detail. However, the histories of the few individuals who have such detailed memories suggest that this might be a curse rather than a blessing. Luria (1987) describes the story of a Russian journalist who lived in the first half of the 20th century, who had very vivid imagery and the ability to remember a great many perceptual details of his experience. He had problems with many aspects of ordinary life, including reading: As he put it: “Other people think as they read, but I see it all.” As soon as he began a phrase, images would appear; as he read further, still more images were evoked, and so on. . . . If a passage were read to him quickly, one image would collide with another in his mind; images would begin to crowd in upon one another and would become contorted. (p. 112) Such problems caused him great difficulties in many aspects of life, including keeping his job. Parker, Cahill, and McGaugh (2006) describe a current case of an individual with highly detailed memory.1 She is able to remember many details from years ago in her life but had difficulty in school and seems to perform poorly on tasks of abstract reasoning such as processing analogies. There are many situations when we need to rise above the details of our experience and get to their true meaning and significance. 115 1 She has written her own biography, The Woman Who Can’t Forget (Price, 2008). Anderson7e_Chapter_05.qxd 8/20/09 9:43 AM Page 115 116 | Representation of Knowledge In this chapter we will address the following questions: • What happens to our memories for gist versus detail with the passage of time? • How do we represent the gist of an experience? • Are there representations of knowledge that are not tied to specific perceptual modalities? • What is the nature of our representation of categorical knowledge and how does this affect the way we perceive the world? •Knowledge and Regions of the Brain Figure 5.1 shows some of the brain regions involved in the abstraction of knowledge. Some prefrontal regions are associated with extracting meaningful information from pictures and sentences. The left prefrontal region is more involved in the processing of verbal material and the right prefrontal region is more involved in the processing of visual material (Gabrielli, 2001). Part of the processing is to represent events in terms of general categories such as bride or wedding. This categorical information is represented in posterior regions of the temporal, parietal, and occipital cortices. As we will see, there is evidence that different posterior regions represent different types of concepts. We will review neuroscience data on the localization of processing and information in the brain, but much of the most striking evidence comes from behavioral studies that examine what people remember or forget after an event. Prefrontal regions of the brain are associated with meaningful processing of events, whereas posterior regions are associated with representing concepts. •Memory for Meaningful Interpretations of Events Memory for Verbal Information An experiment by Wanner (1968) illustrates circumstances in which people do and do not remember information about exact wording.Wanner asked participants to come into the laboratory and listen to tape-recorded instructions. For one group of participants, the warned group, the tape began this way: The materials for this test, including the instructions, have been recorded on tape. Listen very carefully to the instructions because you will be tested on your ability to recall particular sentences which [sic] occur in the instructions. Prefrontal regions that process pictures and sentences Posterior regions that represent concepts Brain Structures FIGURE 5.1 Cortical regions involved in the processing of meaning and the representation of concepts. Anderson7e_Chapter_05.qxd 8/20/09 9:43 AM Page 116 Memory for Meaningful Interpretations of Events | 117 The participants in the second group received no such warning and so had no idea that they would be responsible for the verbatim instructions. After this point, the instructions were the same for both groups. At a later point in the instructions, one of four possible critical sentences was presented: 1. When you score your results, do nothing to correct your answers but mark carefully those answers which are wrong. 2. When you score your results, do nothing to correct your answers but carefully mark those answers which are wrong. 3. When you score your results, do nothing to your correct answers but mark carefully those answers which are wrong. 4. When you score your results, do nothing to your correct answers but carefully mark those answers which are wrong. Immediately after one of these sentences was presented, all participants (warned or not) heard the following conclusion to the instructions: To begin the test, please turn to page 2 of the answer booklet and judge which of the sentences printed there occurred in the instructions you just heard. On page 2, they found the critical sentence they had just heard plus a similar alternative. Suppose they had heard sentence 1. They might have to choose between sentences 1 and 2 or between sentences 1 and 3. Both pairs differ only in the ordering of two words. However, the difference between 1 and 2 does not contribute critically to the meaning of the sentences; the difference is just stylistic. On the other hand, sentences 1 and 3 clearly do differ in meaning. Thus, by looking at participants’ ability to discriminate between different pairs of sentences, Wanner was able to measure their ability to remember the meaning versus the style of the sentence and to determine how this ability was affected by whether or not they were warned. The relevant data are presented in Figure 5.2. The percentage of correct identifications of sentences heard is displayed as a function of whether participants had been warned. The percentages are plotted separately for participants who were asked to discriminate a meaningful difference in wording and for those who were asked to discriminate a stylistic difference. If participants were just guessing, they would have scored 50% correct by chance; thus, we would not expect any values below 50%. The implications of Wanner’s experiment are clear. First, memory is better for changes in wording that result in changes of meaning than for changes in wording that result just in changes of style. The superiority of memory for meaning indicates that people normally extract the meaning from a linguistic message and do not remember its exact wording. Moreover, memory for meaning is equally good whether people are warned or not. (The slight advantage for unwarned participants does not approach statistical significance.) Thus, participants retained the meaning of a message as a normal Memory for meaning Memory for style Unwarned Warned Correct (%) 50 60 70 80 90 100 FIGURE 5.2 Results from Wanner’s experiment to determine circumstances in which people do and do not remember information about exact wording. The ability of participants to remember a wording difference that affected meaning versus one that affected only style is plotted as a function of whether or not the participants were warned that they would be tested on their ability to recall particular sentences. (After Wanner, 1968. Adapted by permission of the author.) Anderson7e_Chapter_05.qxd 8/20/09 9:43 AM Page 117 118 | Representation of Knowledge part of their comprehension process. They did not have to be cued to remember the sentence. The second implication of these results is that the warning did have an effect on memory for the stylistic change. The unwarned participants remembered the stylistic change at about the level of chance, whereas the warned participants remembered it almost 80% of the time. This result indicates that we do not normally retain much information about exact wording, but we can do so when we are cued to pay attention to such information. Even with a warning, however, memory for stylistic information is poorer than memory for meaning. After processing a linguistic message, people usually remember just its meaning and not its exact wording. Memory for Visual Information On many occasions, our memory capacity seems much greater for visual information in a picture or a scene than for verbal information (whether that verbal information is presented auditorially by speech or visually by text). Shepard (1967) reported an experiment in which he had participants study a set of magazine pictures, one picture at a time. Then they were presented with pairs of pictures consisting of one they had studied and one they had not. The task was to recognize which picture of each pair had been studied. This task was contrasted with a verbal condition in which participants studied sentences and were similarly tested on their ability to recognize those sentences when they were presented in pairs containing one new and one studied sentence. Participants made errors 11.8% of the time in the sentence condition but only 1.5% of the time in the picture condition. In other words, recognition memory was fairly high in the sentence condition but was virtually perfect in the picture condition. There have been a number of experiments like Shepard’s, which involved 600 pictures. Perhaps the most impressive demonstration of visual memory is the experiment by Standing (1973), who showed that participants had only a 17% error rate after studying 10,000 pictures. Although people can show very good memory for pictures under some circumstances, what they seem to be remembering is some interpretation of the picture rather than the exact picture itself. That is, it proves useful to distinguish between the meaning of a picture and the physical picture, just as it is important to distinguish between the meaning of a sentence and the physical sentence. A number of experiments point to the utility of this distinction with respect to picture memory and to the fact that we tend to remember an interpretation of the picture, not the physical picture. For instance, consider an experiment by Mandler and Ritchey (1977). They asked participants to study pictures of scenes, such as the classroom scenes in Figure 5.3. After studying eight such pictures for 10 s each, participants were tested for their recognition memory. They were presented with a series of pictures and instructed to identify which pictures they had studied. The series included the exact pictures they had studied as well as distracter pictures. A distracter such as the one shown in Figure 5.3b was called a token distracter. It differed from Anderson7e_Chapter_05.qxd 8/20/09 9:43 AM Page 118 Memory for Meaningful Interpretations of Events | 119 the target only in the pattern of the teacher’s clothes, a visual detail relatively unimportant to most interpretations of the picture. The distracter shown in Figure 5.3c, by contrast, involves a type change—from a world map to an art picture used by the teacher. This visual detail is relatively more important to most interpretations of the picture because it indicates the subject being taught. All eight pictures shown to participants contained possible token changes and type changes. In each case, the type change involved a more important alteration to the picture’s meaning than did the token change. There was no systematic difference in the amount of physical change involved in a type change versus a token change. Participants were able to recognize the original pictures 77% percent of the time and to reject the token distracters only 60% of the time, but they rejected the type distracters 94% of the time. The conclusion in this study is very similar to that in the Wanner (1968) experiment reviewed earlier. Wanner found that participants were much more sensitive to meaning-significant changes in a sentence; Mandler and Ritchey (1977) found that participants were more sensitive to meaning-significant (a) (b) (c) FIGURE 5.3 Pictures similar to those used by Mandler and Ritchey in their experiment to demonstrate that people distinguish between the meaning of a picture and the physical picture itself. Participants studied the target picture (a). Later they were tested with a series of pictures that included the target (a) along with token distracters such as (b) and type distracters such as (c). (After Mandler & Ritchey, 1977. Adapted by permission of the publisher. © 1977 by the American Psychological Association.) Anderson7e_Chapter_05.qxd 8/20/09 9:43 AM Page 119 changes in a picture. It may be that people have better memory for the meanings of pictures than for the meanings of sentences, but they have poor memory for the physical details of both. Bower, Karlin, and Dueck (1975) reported an amusing demonstration of the fact that people’s good memory for pictures is tied to their interpretation of those pictures. Figure 5.4 illustrates some of the material they used. These investigators had participants study such drawings, called droodles, with or without an explanation of their meaning. Then they were given a memory test in which they had to redraw the pictures. Participants who had been given an explanation when studying the pictures showed better recall (70% correctly reconstructed) than those who were not given an explanation (51% correctly reconstructed). Thus, memory for the drawings depended critically on participants’ ability to place a meaningful interpretation on the pictures. When people see a picture, they tend to remember a meaningful interpretation of it. Retention of Detail versus Meaning There is evidence that people initially encode many of the perceptual details of a sentence or a picture but tend to forget this information quickly. Once the perceptual information is forgotten, people retain memory for their interpretation of the picture. Memory for the orientation of a picture is one of the visual details that appear to decay rapidly, as demonstrated in an experiment by Gernsbacher (1985). Participants were shown pictures such as the ones illustrated in Figure 5.5. After studying one of these pictures, the participants were asked to judge which of the pair they had seen. After 10 s, participants made their judgments with 79% accuracy, showing considerable retention of information about left–right 120 | Representation of Knowledge (a) (b) FIGURE 5.4 Recalling “droodles.” (a) A midget playing a trombone in a telephone booth. (b) An early bird who caught a very strong worm. (From Bower, Karlin, & Dueck, 1975. Reprinted by permission of the publisher. © 1975 by Memory & Cognition.) FIGURE 5.5 Example picture from an experiment by Gernsbacher, displayed in one orientation (left) and the reverse (right). The experiment showed that memory for the orientation of a picture is a visual detail that appears to decay rapidly. (From Gernsbacher, 1985. Original illustration from Mercer and Mariana Meyer, One Frog Too Many. © 1975 by Mercer and Mariana Meyer. Reprinted by permission of the publisher, Dial Books for Young Readers, New York.) Anderson7e_Chapter_05.qxd 8/20/09 9:43 AM Page 120 Memory for Meaningful Interpretations of Events | 121 orientation. After 10 min, however, their accuracy in judgment had fallen to 57% (50% percent would reflect chance guessing). On the other hand, their memory for what the picture was about remained high over that period of time. When I was a graduate student, I performed an experiment (Anderson, 1974b) that made the same point in the verbal domain. Participants listened to a story that contained various critical sentences that would be tested; for instance: The missionary shot the painter. Later, participants were presented with one of the following sentences and asked whether it followed logically from the story they had heard. They were also asked to judge which sentence they had actually heard. 1. The missionary shot the painter. 2. The painter was shot by the missionary. 3. The painter shot the missionary. 4. The missionary was shot by the painter. The first two sentences require a positive response to the logical judgment, and the last two require a negative response. Participants were tested either immediately after hearing the sentence or after a delay of about 2 min. The delay had little effect on the accuracy of their logical judgments (e.g., 1 versus 3 above)— 98% were correct immediately and 96% were correct after a delay. However, when they were asked to judge which sentence they had heard (e.g., 1 versus 2 above), the delay had a dramatic effect. Participants were 99% correct immediately after hearing the sentence but only 56% correct after a delay. Memory for detail is available initially but is forgotten rapidly, whereas memory for meaning is retained. Implications of Good Memory for Meaning We have seen that people have relatively good memory for meaningful interpretations of information. So when faced with material to remember, it will help if they can give it some meaningful interpretation. Unfortunately, many people are unaware of this fact, and their memory performance suffers as a consequence. I can still remember the traumatic experience I had in my first paired-associates experiment. It happened in a sophomore class in experimental psychology. For reasons I have long since forgotten, we had designed a class experiment that involved learning 16 pairs, such as DAX-GIB. Our task was to recall the second half of the pair when prompted with the first half. I was determined to outperform the other members of my class. My personal theory of memory at that time, which I intended to apply, was that if you try hard and focus intensely, you can remember anything well. In the impending experimental situation, this meant that during the learning period I should say (as loud as was seemly) the paired associates over and over again, as fast as I could. I believed that this method would burn the paired associates into my mind forever. To my chagrin, I wound up with the worst score in the class. Anderson7e_Chapter_05.qxd 8/20/09 9:43 AM Page 121 My theory of “loud and fast” was directly opposed to the true means of improving memory. I was trying to memorize a meaningless verbal pair. But the material discussed in this chapter so far suggests that we have the best memory for meaningful information. I should have been trying to convert my memory task into something more meaningful. For instance, DAX is like dad and GIB is the first part of gibberish. So I might have created an image of my father speaking some gibberish to me. This would have been a simple mnemonic (memory-assisting) technique and would have worked quite well as a means of associating the two elements. We do not often need to learn pairs of nonsense syllables outside the laboratory. In many situations, however,we do have to associate various combinations of terms that do not have much inherent meaning.We have to remember shopping lists, names for faces, telephone numbers, rote facts in a college class, vocabulary items in a foreign language, and so on. In all cases, we can improve memory if we associate the items to be remembered with a meaningful interpretation. 122 | Representation of Knowledge effectiveness of this technique (for a review, read Kroll & DeGroot, 2005). The research shows that, like many things, one needs to take a nuanced approach in evaluating the effectiveness of the keyword technique. There is no doubt that it results in more rapid vocabulary learning in many situations, but there are potential costs. One might imagine that having to go through the intermediate keyword slows down speed of translation, and the keyword method has been shown to result in slower retrieval times compared to retrieval of items that are directly associated without an intermediate. Moreover, one might wonder about the implications of having to go through the intermediate for long-term retention, and again it has been shown to result in poorer long-term retention. Finally, there is evidence that suggests that although the method may help in passing the immediate vocabulary test in a class and hurt in a delayed test that we have not studied for, its ultimate impact on achieving real language mastery is minimal. Chapter 12 will discuss issues involved in foreign language mastery. Implications Mnemonic techniques for remembering vocabulary items One domain where we seem to have to learn arbitrary associations is foreign language vocabulary. For instance, consider trying to learn that the Italian formaggio (pronounced “for modge jo”) means cheese. There is a memorization technique, called the keyword method, for learning vocabulary items, which some students are taught and others discover on their own. The first step is to convert the foreign word to some sound-alike in one’s native language. For example, one might convert formaggio into “for much dough.” The second step is to create a meaningful connection between the sound-alike and the meaning. For example, we might imagine expensive cheese being sold for much money or “for much dough.” Or consider the Italian carciofi (pronounced “car-choh-fee”), which means artichoke. We might transform “car-choh-fee” into “car trophy” and imagine a winning car at an auto show with a trophy shaped like an artichoke. The intermediate (e.g., for much dough or car trophy) is called the keyword, although in both of these examples they are really key phrases. There has been extensive research on the Anderson7e_Chapter_05.qxd 8/20/09 9:44 AM Page 122 Propositional Representations | 123 It is easier to commit arbitrary associations to memory if they are converted into something more meaningful. •Propositional Representations We have shown that in many situations people do not remember the exact physical details of what they have seen or heard but rather the “meaning” of what they have encountered. In an attempt to become more precise about what is meant by “meaning,” cognitive psychologists developed what is called a propositional representation. The concept of a proposition, borrowed from logic and linguistics, is central to such analyses. A proposition is the smallest unit of knowledge that can stand as a separate assertion, that is, the smallest unit one can meaningfully judge as true or false. Propositional analysis applies most clearly to linguistic information, and I will develop the topic here in terms of such information. Consider the following sentence: Lincoln, who was president of the United States during a bitter war, freed the slaves. The information conveyed in this sentence can be communicated by the following simpler sentences: A. Lincoln was president of the United States during a war. B. The war was bitter. C. Lincoln freed the slaves. If any of these simple sentences were false, the complex sentence also would be false. These sentences correspond closely to the propositions that underlie the meaning of the complex sentence. Each simple sentence expresses a primitive unit of meaning. One condition that our meaning representations must satisfy is that each separate unit composing them must correspond to a unit of meaning. However, the theory of propositional representation does not claim that a person remembers the simple sentences such as the three just presented. Past research indicates that people do not remember the exact wording of such simple sentences any more than they remember the exact wording of the original complex sentence. For instance, in another study I did as a graduate student (Anderson, 1972), I showed that participants demonstrated poor ability to remember whether they had heard sentence C or the sentence: The slaves were freed by Lincoln. Thus, it seems that we represent information in memory in a way that preserves the meaning of the primitive assertions but does not preserve any information about specific wording. A number of propositional notations represent information in this abstract way. One, used by Kintsch (1974), represents each proposition as a list containing a relation followed by an ordered list of arguments. The relations organize the arguments and typically correspond to the verbs (in this case, free), adjectives (bitter), and other relational terms Anderson7e_Chapter_05.qxd 8/20/09 9:44 AM Page 123 (president of ). The arguments refer to particular times, places, people, or objects, and typically correspond to the nouns (Lincoln, war, slaves). The relations assert connections among the entities these nouns refer to. Kintsch represents each proposition by a parenthesized list consisting of a relation plus arguments. As an example, sentences A through C would be represented by these lists: a. (president-of: Lincoln, United States, war) b. (bitter: war) c. (free: Lincoln, slaves) Note that each relation takes a different number of arguments: president of takes three, free takes two, and bitter takes one. Whether a person heard the original complex sentence or heard The slaves were freed by Lincoln, the president during a bitter war, the meaning of the message would be represented by lists a through c. Bransford and Franks (1971) provided an interesting demonstration of the psychological reality of propositional units. In this experiment, participants studied 12 sentences, including the following: The ants ate the sweet jelly, which was on the table. The rock rolled down the mountain and crushed the tiny hut. The ants in the kitchen ate the jelly. The rock rolled down the mountain and crushed the hut beside the woods. The ants in the kitchen ate the jelly, which was on the table. The tiny hut was beside the woods. The jelly was sweet. All these sentences are composed from two sets of four propositions. One set of four propositions can be represented as 1. (eat: ants, jelly, past) 2. (sweet: jelly) 3. (on: jelly, table, past) 4. (in: ants, kitchen, past) The other set of four propositions can be represented as 1. (roll down: rock, mountain, past) 2. (crush: rock, hut, past) 3. (beside: hut, woods, past) 4. (tiny: hut) Bransford and Franks looked at participants’ recognition memory for the following three kinds of sentences: 1. Old: The ants in the kitchen ate the jelly. 2. New: The ants ate the sweet jelly. 3. Noncase: The ants ate the jelly beside the woods. 124 | Representation of Knowledge Anderson7e_Chapter_05.qxd 8/20/09 9:44 AM Page 124 Propositional Representations | 125 The first kind of sentence was actually studied, the second was not but is a combination of propositions that were studied, and the third consists of words that were studied but cannot be composed from the propositions studied. Bransford and Franks found that participants had almost no ability to discriminate between the first two kinds of sentences and were equally likely to say that they had actually heard either. On the other hand, participants were quite confident that they had not heard the third, noncase, sentence. The experiment shows that although people remember the propositions they encounter, they are quite insensitive to the actual combination of propositions. Indeed, the participants in this experiment were most likely to say that they heard a sentence consisting of all four propositions, such as The ants in the kitchen ate the sweet jelly, which was on the table, even though they had not in fact studied this sentence. According to propositional analyses people remember a complex sentence as a set of abstract meaning units that represent the simple assertions in the sentence. Propositional Networks In the cognitive psychology literature, one sometimes finds propositions represented in a network form. Figure 5.6 illustrates the structure of a propositional network that encodes the sentence, “Lincoln, who was president of (a) (b) (c) War United States President-of Time Agent Lincoln Object Relation Lincoln Freed Object Slaves Agent Relation War Bitter Subject Relation (d) War United States President-of Time Agent Lincoln Object Relation Freed Object Slaves Agent Relation Bitter Subject Relation FIGURE 5.6 Network representations for the proposition underlying the statement: “Lincoln, who was president of the United States during a bitter war, freed the slaves.” Anderson7e_Chapter_05.qxd 8/20/09 9:44 AM Page 125 the United States during a bitter war, freed the slaves.” In this propositional network, each proposition is represented by an ellipse, which is connected by labeled arrows to its relation and arguments. The propositions, the relations, and the arguments are called the nodes of the network, and the arrows are called the links because they connect the nodes. For instance, the ellipse in Figure 5.6a represents proposition (a) from the earlier Kintsch analysis. This ellipse is connected to the relation president-of by a link labeled relation (to indicate that it is pointing to the relation node), to Lincoln by an agent link, to United States by an object link, and to war by a time link. The three network structures shown in Figures 5.6a, 5.6b, and 5.6c represent the individual propositions (a) through (c) from the earlier Kintsch analysis. Note that these three networks contain the same nodes, for example, Figures 5.6a and 5.6b both contain war. This overlap indicates that these networks are really interconnected parts of a larger network, which is illustrated in Figure 5.6d. This last network represents all the meaningful information in the original complex sentence on page 147. The spatial location of elements in a network is irrelevant to its interpretation. A network can be thought of as a tangle of marbles connected by strings. The marbles represent the nodes, the strings the links between nodes. The network represented on a 2-D page is that tangle of marbles laid out in a certain way. We try to lay out the network in a way that facilitates an understanding of it, but any layout is possible. All that matters is which elements are connected to which others, not where the components lie. A number of experiments suggest that it is helpful to think of the nodes in such networks as ideas and to think of the links between the nodes as associations between the ideas. Consider an experiment by Weisberg (1969) that used a constrained association task. In this experiment, participants studied and committed to memory such sentences as “Children who are slow eat bread that is cold.” The propositional network representation of this sentence is illustrated in Figure 5.7. After learning a sentence, participants were administered free-association tasks in which they were given a word from the sentence and asked to respond with the first word from the sentence that came to mind. Participants cued with slow almost always free-associated children and almost 126 | Representation of Knowledge Relative Subject Slow Children Bread Cold Agent Object Time Relation Past Eat Subject Relation FIGURE 5.7 A propositional network representation of the sentence: “Children who are slow eat bread that is cold.” Weisberg (1969) used such sentences in an experiment to show that the proximity of words in a propositional network has more effect on memory than their physical proximity in the sentence. Anderson7e_Chapter_05.qxd 8/20/09 9:44 AM Page 126 Propositional Representations | 127 never bread, although bread is closer to slow in the sentence than to children. However, the illustration shows that slow and children are nearer each other in the network (two links) than slow and bread (four links). Similarly, participants cued with bread almost always recalled cold rather than slow, although in the sentence bread and slow are closer than bread and cold. Again, bread and cold are closer to each other in the network (three links) than are bread and slow (five links). (A similar point was made in an experiment by R. A. Ratcliff & McKoon, 1978.) Propositional information can be represented in networks that display how concepts relate. Amodal versus Perceptual Symbol Systems The propositional representations that we have just considered are examples of what Barsalou (1999) called an amodal symbol system. By this he meant that the elements within the system are inherently nonperceptual. The original stimulus might be a picture or a sentence, but the representation is abstracted away from the verbal or visual modality. Given this abstraction, one would predict that participants in experiments would be unable to remember the exact words they heard or the exact picture they saw. As an alternative to such theories, Barsalou proposed a hypothesis called the perceptual symbol system, which suggests that all information is represented in terms that are specific to a particular perceptual modality (visual, auditory, etc.) and basically perceptual. The perceptual symbol hypothesis is an extension of Paivio’s (1971, 1986) dual-code theory that claimed that, rather than abstract propositional representations, we represent information in combined verbal and visual codes. Paivio suggested that when we hear a sentence, we develop an image of what it describes. If we later remember the visual image and not the sentence, we will remember what the sentence was about, but not its exact words. Analogously, when we see a picture, we might describe to ourselves the significant features of that picture. If we later remember our description and not the picture, we will not remember details we did not think important to describe (such as the clothes the teacher was wearing in Figure 5.3). The dual-code position does not predict that memory for the wording of a sentence is necessarily poor. The relative memory for the wording versus memory for the meaning depends on the relative attention that people give to the verbal versus the visual representation. There are a number of experiments showing that when participants pay attention to wording, they show better memory. For instance, Holmes,Waters, and Rajaram (1998), in a replication of the Bransford and Franks (1971) study that we just reviewed, asked participants to count the number of letters in the last word of each sentence. This manipulation, which increased their attention to the wording of the sentence, resulted in an increased ability to discriminate sentences they had studied from sentences Anderson7e_Chapter_05.qxd 8/20/09 9:44 AM Page 127 with similar meanings that they had not—although participants still showed considerable confusion among similar-meaning sentences. But how can an abstract concept such as honesty be represented in a purely perceptual cognitive system? One can be very creative in combining perceptual representations. Consider a pair of sentences from an old unpublished study of mine.2We had participants study one of the following two sentences: 1. The lieutenant wrote his signature on the check. 2. The lieutenant forged a signature on the check. Later, we asked them to recognize which sentence they had studied. They could make such discriminations more successfully than they could distinguish between pairs such as 1. The lieutenant enraged his superior in the barracks. 2. The lieutenant infuriated a superior in the barracks. In the first pair of sentences, there is a big difference in meaning; in the second pair, little difference. However, the difference in wording between the sentences in the two pairs is equivalent. When I did the study, I thought it showed that people could remember meaning distinctions that did not have perceptual differences—the distinction between signing a signature and forging is not in what the person does but in his or her intentions and the relationship between those intentions and unseen social contracts. Barsalou (personal communication, March 12, 2003) suggested that we represent the distinction between the two sentences by reenacting the history behind each sentence. So even if the actual act of writing and forging might be the same, the history of what a person said and did in getting to that point might be different. Barsalou also considers the internal state of the individual to be relevant. Thus, part of the perceptual features involved in forging might include the sensations of tension that one has when one is in a difficult situation.3 Barsalou, Simmons, Barbey, and Wilson (2003) cited evidence that when people understand a sentence, they actually come up with a perceptual interpretation of that sentence. For instance, in one study by Stanfield and Zwaan (2001), participants read a sentence about a nail being pounded either into the wall or the floor. Then they viewed a picture of a nail oriented either horizontally or vertically and were asked to affirm whether the object in the picture was mentioned in the sentence that they just read. If they had read a sentence about a nail being pounded into the wall, they recognized a horizontally oriented nail more quickly. When they had read a sentence about a nail being pounded into the floor, they recognized a vertically oriented nail more quickly. In other words, they responded faster when the orientation implied by the sentence matched the orientation of the picture. Thus, their interpretation of the sentence seemed to 128 | Representation of Knowledge 2 It was not published because at the time (1970s) it was considered too obvious a result given studies like those described earlier in this chapter. 3 Perhaps it is obvious that I do not agree with Barsalou’s perspective. However, it is hard to imagine what he might consider disconfirming data, because his approach is so flexible. Anderson7e_Chapter_05.qxd 8/20/09 9:44 AM Page 128 contain this perceptual detail. As further evidence of the perceptual representation of meaning, Barsalou et al. cited neuroscience studies showing that concepts are represented in brain areas similar to those that process perceptions. An alternative to amodal representations of meaning is the view that meaning is represented as a combination of images in different perceptual modalities. •Embodied Cognition The perceptual symbol hypothesis of Barsalou is an instance of the growing emphasis in psychology on understanding the contribution of the environment and our bodies to shaping our cognition. As Thelen (2000) describes the viewpoint: To say that cognition is embodied means that it arises from bodily interactions with the world and is continually meshed with them. From this point of view, therefore, cognition depends on the kinds of experiences that come from having a body with particular perceptual and motor capabilities that are inseparably linked and that together form the matrix within which reasoning, memory, emotion, language and all other aspects of mental life are embedded. (p. 5) The embodied cognition perspective emphasizes the contribution of motor action and how it connects us to the environment. For instance, Glenberg (2007) argues that our understanding of language often depends on covertly acting out what the language describes. He points to an fMRI study by Hauk, Johnsrude, & Pulvermiller (2004), who recorded brain activation while people listened to verbs that involved the face, arm, or leg actions (e.g., to lick, pick, or kick). They looked for activity along the motor cortex in different regions associated with the face, arm, and leg (see Figure 1.10). Figure 5.8 shows the differential activity in these Embodied Cognition | 129 Region: Face MR signal change (arbitrary units) Arm Leg 0.02 0.04 0.06 0.08 0.1 Leg words Face words Arm words FIGURE 5.8 Brain activation in different model regions as participants listen to different types of verbs. Anderson7e_Chapter_05.qxd 8/20/09 9:44 AM Page 129 different brain regions. As participants listened to each word, there was greater activation in the part of the motor cortex that would produce that action. A theory of how meaning is represented in the human mind must explain how different perceptual and motor modalities connect with one another. For instance, part of understanding a word like kick is our ability to relate it to a picture of a person kicking a ball so that we can describe that picture. As another example, part of our understanding of someone performing an action is our ability to relate to our own motor system so that we can mimic the action. Interestingly, mirror neurons have been found in the motor cortex of monkeys, which are active when the monkeys perform an action like ripping a paper, or see the experimenter rip a paper or hear the experimenter rip the paper without seeing the action (Rizzolatti & Craighero, 2004). Although one cannot typically do single-cell recordings with humans, brain-imaging studies have found increased activity in the motor region when people observe actions, particularly with the intention to mimic the action (Iacoboni et al., 1999). Figure 5.9 illustrates two conceptions of how mappings might take place between different representations. One possibility is illustrated in the multimodal hypothesis, which holds that we have various representations tied to different perceptual and motor systems and that we have means of directly converting one representation to another. For instance, the double-headed arrow going from the visual to the motor would be a system for converting a visual representation into a motor representation and a system for converting the representations in the opposite direction. The alternative amodal hypothesis is that there is an intermediate abstract system, perhaps the propositional representation that we described earlier, and that we have systems for converting back and forth between the perceptual and motor representations and this abstract representation. So to convert a picture into an action, one first converts the visual representation into an abstract representation of its significance and then converts that representation into a motor representation. These two approaches offer alternative explanations for the research we reviewed earlier that indicated people remember the meaning of what they experience, but not 130 | Representation of Knowledge Multimodal Hypothesis Amodal Hypothesis Visual Other Verbal Motor Visual Other Verbal Motor Meaning FIGURE 5.9 Representations of two hypotheses about how information is related between different perceptual and motor modalities. The multimodal hypothesis holds that there are mechanisms for translating between each modality. The amodal hypothesis holds that each modality can be translated back and forth to a central meaning representation. Anderson7e_Chapter_05.qxd 8/20/09 9:44 AM Page 130 Conceptual Knowledge | 131 the details. The amodal hypothesis holds that this information is retained in the central meaning system. The multimodal hypothesis holds that the person has converted the information from the modality of the presentation to some other modality. The embodied cognition perspective emphasizes that meaning is represented in the perceptual and motor systems that we use to interact with the world. •Conceptual Knowledge Consider the picture in Figure 5.3a.When we look at this picture, we do not see it as just a collection of specific objects. Rather, we see it as a picture of a teacher instructing a student on geography. That is, we see the word in terms of categories like teacher, student, instruction, and geography. As we saw, people tend to remember this categorical information and not the specific details. For instance, the participants in the Mandler and Ritchey (1977) experiment forgot what the teacher wore but remembered the subject she taught. You cannot help but experience the world in terms of the categories you know. For instance, if you were licked by a four-legged furry object that weighed about 50 pounds and had a wagging tail, you would perceive yourself as being licked by a dog.What does your cognitive system gain by categorizing the object as a dog? Basically, it gains the ability to predict. Thus, you can have expectations about what sounds this creature might make and what would happen if you threw a ball (the dog might chase it and stop licking you). Because of this ability to predict, categories give us great economy in representation and communication. For instance, if you tell someone, “I was licked by a dog,” your listener can predict the number of legs on the creature, its approximate size, and so on. The effects of such categorical perceptions are not always positive—for instance, they can lead to stereotyping. In one study, Dunning and Sherman (1997) had participants study sentences like Elizabeth was not very surprised upon receiving her math SAT score. or Bob was not very surprised upon receiving his math SAT score. Participants who had heard the first sentences were more likely to falsely believe they had heard “Elizabeth was not very surprised upon receiving her low math SAT score,” whereas if they had heard the second sentence, they were more likely to believe they had heard “Bob was not very surprised upon receiving his high math SAT score.” Categorizing Elizabeth as a woman, the participants brought the stereotype of women as poor at math to their interpretation of the first sentence. Categorizing Bob as male, they brought the opposite stereotype to their interpretation of the second sentence. This was even true among participants (both male and female) who were rated as not being sexist in their attitudes. They could not help but be influenced by their implicit stereotypes. Anderson7e_Chapter_05.qxd 8/20/09 9:44 AM Page 131 Research on categorization has focused both on how we form these categories in the first place and on how we use them to interpret experiences. It has also been concerned with notations for representing this categorical knowledge. In this section, we will consider a number of proposed notations for representing conceptual knowledge. We will start by describing two early theories. One concerns semantic networks, which are similar to the propositional networks we just considered. The other is about what are called schemas. Both theories have been closely related to certain empirical phenomena that seem central to conceptual structure. The categorical organization of our knowledge strongly influences the way we encode and remember our experiences. Semantic Networks Network representations have been used to encode conceptual knowledge as well as propositional knowledge. Quillian (1966) proposed that people store information about various categories—such as canaries, robins, fish, and so on—in a network structure like that shown in Figure 5.10. In this illustration, we represent a hierarchy of categorical facts, such as that a canary is a bird and a bird is an animal, by linking nodes for the two categories with isa links. Properties that are true of the categories are associated with them. Properties that are true of higher-level categories are also true of lower level categories. Thus, because animals breathe, it follows that birds and canaries breathe. Figure 5.10 can also 132 | Representation of Knowledge Level 1 Level 2 Level 3 Canary Can sing Is yellow Bird Has wings Can fly Has feathers Animal Has skin Can move around Eats Breathes Ostrich Has long thin legs Is tall Can’t fly Shark Can bite Is dangerous Fish Has fins Can swim Has gills Salmon Is pink Is edible Swims upstream to lay eggs FIGURE 5.10 A hypothetical memory structure for a three-level hierarchy using the example canary. Quillian (1966) proposed that people store information about various categories in a network structure. This illustration represents a hierarchy of categorical facts, such as that a canary is a bird and a bird is an animal. Properties that are true of each category are associated with that category. Properties that are true of higher level categories are also true of lower level categories. (After Collins & Quillian, 1969. Adapted by permission of the publisher. © 1969 by Academic Press.) Anderson7e_Chapter_05.qxd 8/20/09 9:44 AM Page 132 Conceptual Knowledge | 133 represent information about exceptions. For instance, even though most birds fly, the illustration does represent that ostriches cannot fly. Collins and Quillian (1969) did an experiment to test the psychological reality of such networks by having participants judge the truth of assertions about concepts, such as 1. Canaries can sing. 2. Canaries have feathers. 3. Canaries have skin. Participants were shown these along with false assertions such as “Apples have feathers.” They were asked to indicate whether a statement was true or false by pressing one of two buttons. The time from presentation of the statement to the button press was measured. Consider how participants would answer such questions if Figure 5.10 represented their knowledge of such categories. The information to confirm sentence 1 is directly stored with canary. The information for sentence 2, however, is not directly stored at the canary node. Instead, the have feathers property is stored with bird, and sentence 2 can be inferred from the directly stored facts that A canary is a bird and Birds have feathers. Again, sentence 3 is not directly stored with canary; rather, the has skin predicate is stored with animal. Thus, sentence 3 can be inferred from the facts that a canary is a bird and a bird is an animal and animals have skin. So, all the information required to verify sentence 1 is stored with canary; for sentence 2, participants would need to traverse one link, from canary to bird, to retrieve the requisite information; for sentence 3, they would have to traverse two links, from canary to animal. If our categorical knowledge were structured like Figure 5.10, we would expect sentence 1 to be verified more quickly than sentence 2, which would be verified more quickly than sentence 3. This is just what Collins and Quillian found. Participants required 1310 ms to judge statements like sentence 1, 1380 ms to judge statements like sentence 2, and 1470 ms to judge statements like sentence 3. Subsequent research on the retrieval of information from memory has somewhat complicated the conclusions drawn from the initial Collins and Quillian experiment. How often facts are experienced has been observed to have strong effects on retrieval time (e.g., C. Conrad, 1972). Some facts, such as Apples are eaten—for which the predicate could be stored with an intermediate concept such as food, but that are experienced quite often—are verified as fast as or faster than facts such as Apples have dark seeds, which must be stored more directly with the apple concept. It seems that if a fact about a concept is encountered frequently, it will be stored with that concept, even if it could also be inferred from a more general concept. The following statements about the organization of facts in semantic memory and their retrieval times seem to be valid conclusions from the research: 1. If a fact about a concept is encountered frequently, it will be stored with that concept even if it could be inferred from a higher order concept. Anderson7e_Chapter_05.qxd 8/20/09 9:44 AM Page 133 2. The more frequently a fact about a concept is encountered, the more strongly that fact will be associated with the concept. The more strongly facts are associated with concepts, the more rapidly they are verified. 3. Inferring facts that are not directly stored with a concept takes a relatively long time. Thus, both the strength of the connections between facts and concepts (determined by frequency of experience) and the distance between them in the semantic network have effects on retrieval time. When a property is not stored directly with a concept, people can retrieve it from a higher order concept. Schemas Consider our knowledge of what a house is like. We know many things about houses, such as • Houses are a type of building. • Houses have rooms. • Houses can be built of wood, brick, or stone. • Houses serve as human dwellings. • Houses tend to have rectilinear and triangular shapes. • Houses are usually larger than 100 square feet and smaller than 10,000 square feet. The importance of a category is that it stores predictable information about specific instances of that category. So when someone mentions a house, for example, we have a rough idea of the size of the object being referred to. Semantic networks, which just store properties with concepts, cannot capture the nature of our general knowledge about a house, such as its typical size or shape. Researchers in cognitive science (e.g., Rumelhart & Ortony, 1976) proposed a particular way of representing such knowledge that seemed more useful than the semantic network representation. Their representational structure is called a schema. The concept of a schema was first articulated in AI and computer science. Readers who have experience with modern programming languages should recognize its similarity to various types of data structures. The question for the psychologist is what aspects of the schema notion are appropriate for understanding how people reason about concepts. I will describe some of the properties associated with schemas and then discuss the psychological research bearing on these properties. Schemas represent categorical knowledge according to a slot structure, in which slots specify values of various attributes that members of a category possess. So we have the following partial schema representation of a house: House • Isa: building • Parts: rooms 134 | Representation of Knowledge Anderson7e_Chapter_05.qxd 8/20/09 9:44 AM Page 134 Conceptual Knowledge | 135 • Materials: wood, brick, stone • Function: human dwelling • Shape: rectilinear, triangular • Size: 100–10,000 square feet In this representation, such terms as materials and shape are the attributes or slots, and such terms as wood, brick, and rectilinear are the values. Each pair of a slot and a value specifies a typical feature. The fact that houses are usually built of materials such as wood and brick does not exclude such possibilities as cardboard. Thus, the values listed above are called default values. For instance, the fact that we represent that birds can fly as part of our schema for birds does not prevent us from seeing ostriches as birds.We simply overwrite this default value in our representation of an ostrich. A special slot in each schema is its isa slot, which is like the isa link in a semantic network and points to the superset. Basically, unless contradicted, a concept inherits the features of its superset. Thus, with the schema for building, the superset of house, we would store such features as that it has a roof and walls and that it is found on the ground. This information is not represented in the schema for house because it can be inferred from building. As illustrated in Figure 5.10, these isa links can create a structure called a generalization hierarchy. Schemas have another type of structure, called a part hierarchy. Parts of houses, such as walls and rooms, have their own schema definitions. Stored with schemas for walls and rooms would be the information that they have windows and ceilings as parts. Thus, using the part hierarchy, we would be able to infer that houses have windows and ceilings. Schemas are abstractions from specific instances that can be used to make inferences about instances of the concepts they represent. If we know something is a house, we can use the schema to infer that it is probably made of wood or brick and that it has walls, windows, and ceilings. The inferential processes for schemas must also be able to deal with exceptions: We can still understand what a house without a roof is. Finally, it is necessary to understand the constraints between the slots of a schema. If we hear of a house that is underground, for example, we can infer that it will not have windows. Schemas represent concepts in terms of supersets, parts, and other attributevalue pairs. Psychological Reality of Schemas One property of schemas is that they have default values for certain slots or attributes. This property provides schemas with a useful inferential mechanism. If you recognize an object as being a member of a certain category, you can infer—unless explicitly contradicted—that it has the default values associated with that concept’s schema. Brewer and Treyens (1981) provided an interesting Anderson7e_Chapter_05.qxd 8/20/09 9:44 AM Page 135 demonstration of the effects of schemas on memory inferences. Thirty participants were brought individually to the room shown in Figure 5.11. Each was told that this room was the office of the experimenter and was asked to wait there while the experimenter went to the laboratory to see whether the previous participant had finished. After 35 s, the experimenter returned and took the waiting participant to a nearby seminar room. Here, the participant was asked to write down everything he or she could remember about the experimental room. What would you be able to recall? Brewer and Treyens predicted that their participants’ recall would be strongly influenced by their schema of what an office contains. Participants would recall very well items that are part of that schema; they would recall much less well office items that are not part of the schema; and they would falsely recall items that are part of the schema but not in this office. Brewer and Treyens found just this pattern of results. For instance, 29 of the 30 participants recalled that the office had a chair, a desk, and walls. Only 8 participants, however, recalled that it had a bulletin board or a skull. On the other hand, 9 participants recalled that it had books, which it did not. Thus, we see that a person’s memory for the properties of a location is strongly influenced by that person’s default assumptions about what is typically found in the location. A schema is a way of encoding those default assumptions. People will infer that an object has the default values for its category, unless they explicitly notice otherwise. Degree of Category Membership One of the important features of schemas is that they allow variation in the objects that might fit a particular schema. There are constraints on what typically occupies the various slots of a schema, but few absolute prohibitions. Thus, if schemas encode our knowledge about various object categories, we ought to see a shading from less typical to more typical members of the category as the features of the members better satisfy the schema constraints. There is now considerable evidence that natural categories such as birds have the kind of structure that would be expected of a schema. Rosch did early research documenting such variations in category membership. In one experiment (Rosch, 1973), she instructed participants to rate the typicality of various members of a category on a 1 to 7 scale, where 1 meant very typical and 7 meant very atypical. Participants consistently rated some members as more typical than others. In the bird category, robin got an average rating of 1.1, and chicken a rating of 3.8. In reference to sports, football was thought to be very typical (1.2), whereas weight lifting was not (4.7). Murder was rated a very typical crime (1.0), whereas vagrancy was not (5.3). Carrot was a very typical vegetable (1.1); parsley was not (3.8). 136 | Representation of Knowledge FIGURE 5.11 The “office room” used in the experiment of Brewer and Treyens to demonstrate the effects of schemas on memory inferences. As they predicted, their participants’ recall was strongly influenced by their schema of what an office contains. (From Brewer & Treyens, 1981. Reprinted by permission of the publisher. © 1981 by Cognitive Psychology.) Anderson7e_Chapter_05.qxd 8/20/09 9:44 AM Page 136 Conceptual Knowledge | 137 Rosch (1975) also asked participants to identify the category of pictured objects. People are faster to judge a picture as an instance of a category when it presents a typical member of the category. For instance, apples are seen as fruits more rapidly than are watermelons, and robins are seen as birds more rapidly than are chickens. Thus, typical members of a category appear to have an advantage in perceptual recognition as well. Rosch (1977) demonstrated another way in which some members of a category are more typical. She had participants compose sentences for category names. For bird, participants generated sentences such as I heard a bird twittering outside my window. Three birds sat on the branch of a tree. A bird flew down and began eating. Rosch replaced the category name in these sentences with a typical member (robin), a less typical member (eagle), or a peripheral member (chicken) and asked participants to rate the sensibleness of the resulting sentences. Sentences involving typical members got high ratings, sentences with less typical members got lower ratings, and sentences with peripheral members got the lowest ratings. So the evidence shows that when people think of a category member, they generally think of typical instances of that category. Failing to have a default or typical value does not disqualify an object from being a member of the category, however. People should have great difficulty and should be inconsistent in judging whether items at the periphery of a category are actually members of that category. McCloskey and Glucksberg (1978) looked at people’s judgments about what were or were not members of various categories. They found that although participants did agree on some items, they disagreed on many. For instance, whereas all 30 participants agreed that cancer was a disease and happiness was not, 16 thought stroke was a disease and 14 did not. Again, all 30 participants agreed that apple was a fruit and chicken was not, but 16 thought pumpkin was a fruit and 14 disagreed. Once again, all participants agreed that a fly was an insect and a dog was not, but 13 participants thought a leech was and 17 disagreed. Thus, it appears that people do not always agree among themselves.McCloskey and Glucksberg tested the same participants a month later and found that many had changed their minds about the disputed items. For instance, 11 out of 30 reversed themselves on stroke, 8 reversed themselves on pumpkin, and 3 reversed themselves on leech. Thus, disagreement about category boundaries does not occur just among participants—people are very uncertain within themselves exactly where the boundaries of a category should be drawn. Figure 5.12 illustrates a set of materials used by Labov (1973). He was interested in studying which items participants would call cups and which they would not.Which do you consider to be cups 17 19 16 18 12 15 11 14 10 13 2 3 4 FIGURE 5.12 The various cuplike objects used in Labov’s experiment that studied the boundaries of the cup category. (After Labov, 1973, in Bailey & Shuy, 1973. Adapted by permission of the author. © 1973 by Georgetown University Press.) Anderson7e_Chapter_05.qxd 8/20/09 9:44 AM Page 137 and which do you consider bowls? The interesting point is that these concepts do not appear to have clear-cut boundaries. In one experiment, Labov used the series of items 1 through 4 shown in Figure 5.12 and a fifth item, not shown. These items reflect an increasing ratio of width of the cup to depth. For the first item, that ratio is 1, whereas for item 4 it is 1.9. The ratio for the item not shown was 2.5. Figure 5.13 shows the percentage of participants who called each of the five objects a cup and the percentage who called it a bowl. The solid lines indicate the classifications when participants were simply presented with pictures of the objects (the neutral context). As can be seen, the percentages of cup responses gradually decreased with increasing width, but there is no clear-cut point where participants stopped using cup. At the extreme 2.5-width ratio, about 25% percent of the participants still gave the cup response, whereas another 25% gave bowl. (The remaining 50% gave other responses.) The dashed lines reflect classifications when participants were asked to imagine the object filled with mashed potatoes and placed on a table. In this context, fewer cup responses and more bowl responses were given, but the data show the same gradual shift from cup to bowl. Thus, it appears that people’s classification behavior varies continuously not only with the properties of an object but also with the context in which the object is imagined or presented. These influences of perceptual features and context on categorization judgments are very much like the similar influences of these features on perceptual pattern recognition (see Chapter 2). Different instances are judged to be members of a category to different degrees, with the more typical members of a category having an advantage in processing. Event Concepts It is not only objects that have a conceptual structure.We also have concepts of various kinds of events, such as going to a movie. Schemas have been proposed as ways of representing such categories. We can encode our knowledge about stereotypic events according to their parts—for instance, going to a movie involves going to the theater, buying the ticket, buying refreshments, seeing the movie, and returning from the theater. Schank and Abelson (1977) proposed versions of event schemas that they called scripts. They pointed out that many circumstances involve stereotypic sequences of actions. For instance, Table 5.1 shows their hunch as to what the stereotypic aspects of dining at a restaurant might be and represents the components of a script for such an occasion. Bower, Black, and Turner (1979) reported a series of experiments in which the psychological reality of the script notion was tested. They asked participants to name what they considered the 20 most important events in an episode, 138 | Representation of Knowledge 1.0 1.2 00 25 1.5 1.9 2.5 50 75 100 Cup Bowl Bowl Cup Neutral context Food context Relative width of cup Response (%) FIGURE 5.13 Results from Labov’s experiment demonstrating that the cup category does not appear to have clear-cut boundaries. The percentage of participants who used the term cup versus the term bowl to describe the objects shown in Figure 5.10 are plotted as a function of the ratio of cup width to cup depth. The solid lines reflect the neutral-context condition, the dashed lines the food-context condition. (After Labov, 1973, in Bailey & Shuy, 1973. Adapted by permission of the author. © 1973 by Georgetown University Press.) Anderson7e_Chapter_05.qxd 8/20/09 9:44 AM Page 138 Conceptual Knowledge | 139 such as going to a restaurant.With 32 participants, they failed to get complete agreement on what these events were. No particular action was listed as part of the episode by all participants, although considerable consensus was reported. Table 5.2 lists the events named. The items in roman type were listed by at least 25% of the participants; the italicized items were named by at least 48%; and the boldfaced items were given by at least 73%. Using 73% as a criterion, we find that the stereotypic sequence was sit down, look at menu, order meal, eat food, pay bill, and leave. Bower et al. (1979) went on to show a number of the effects that such action scripts have on memory for stories. They had participants study stories that included some but not all of the typical events from a script. Participants were then asked to recall the stories (in one experiment) or to recognize whether TABLE 5.1 The Schema for Going to a Restaurant Scene I: Entering Customer enters restaurant Customer looks for table Customer decides where to sit Customer goes to table Customer sits down Scene 2: Ordering Customer picks up menu Customer looks at menu Customer decides on food Customer signals waitress Waitress comes to table Customer orders food Waitress goes to cook Waitress gives food order to cook Cook prepares food Scene 3: Eating Cook gives food to waitress Waitress brings food to customer Customer eats food Scene 4: Exiting Waitress writes bill Waitress goes over to customer Waitress gives bill to customer Customer gives tip to waitress Customer goes to cashier Customer gives money to cashier Customer leaves restaurant From Schank & Abelson (1977). Reprinted by permission of the publisher. © 1977 by Erlbaum. TABLE 5.2 Empirical Script Norms at Three Agreement Levels Open doora Enterb Give reservation name Wait to be seated Go to table Sit downc Order drinks Put napkins on lap Look at menu Discuss menu Order meal Talk Drink water Eat salad or soup Meal arrives Eat food Finish meal Order dessert Eat dessert Ask for bill Bill arrives Pay bill Leave tip Get coats Leave aRoman type indicates items listed by at least 25% of the participants. bItalic type indicates items listed by at least 48% of the participants. cBoldface type indicates items listed by at least 73% of the participants. After Bower, Black, & Turner (1979). Adapted by permission of the publisher. © 1979 by Cognitive Psychology. Anderson7e_Chapter_05.qxd 8/20/09 9:44 AM Page 139 various statements came from the story (in another experiment). When recalling these stories, participants tended to report statements that were parts of the script but that had not been presented as parts of the stories. Similarly, in the recognition test, participants thought they had studied script items that had not actually been in the stories. However, participants showed a greater tendency to recall actual items from the stories or to recognize actual items than to misrecognize foils that were not in the stories, despite the distortion in the direction of the general schema. In another experiment, these investigators read to participants stories composed of 12 prototypical actions in an episode; 8 of the actions occurred in their standard temporal position, but 4 were rearranged. Thus, in the restaurant story, the bill might be paid at the beginning and the menu read at the end. In recalling these stories, participants showed a strong tendency to put the events back into their normal order. In fact, about half of the statements were put back. This experiment serves as another demonstration of the powerful effect of general schemas on memory for stories. These experiments indicate that new events are encoded with respect to general schemas and that subsequent recall is influenced by the schemas. I have talked about these effects as if participants were misrecalling the stories. However, it is not clear that these results should be classified as acts of misrecall. Normally, if a certain standard event, such as paying a check, is omitted in a story, we are supposed to assume it occurred. Similarly, if the storyteller says the check was paid at the beginning of the restaurant episode, we have some reason to doubt the storyteller. Scripts or schemas exist because they encode the predominant sequence of events in a particular kind of situation. Thus, they can serve as valuable bases for predicting missing information and for correcting errors in information. Scripts are event schemas that people use to reason about prototypical events. Abstraction versus Exemplar Theories We have already described semantic networks and schemas as two ways of representing conceptual knowledge. It is fair to say that although each has merits, the field of cognitive psychology has concluded that both are inadequate. We already noted that semantic networks do not capture the graded character of categorical knowledge such that different instances are better or worse members of a category. Schemas can do this, but it has never been clear in detail how to relate them to behavior. The field is currently struggling between two alternative ways of theorizing about conceptual knowledge. One type of theory holds that we have actually abstracted general properties from the instances we have studied; the other type holds that we actually store only specific instances, with the more general inferences emerging from these instances. We will call these the abstraction theories and the exemplar theories. The debate between these two perspectives has been with us for centuries—for instance, in the debate between the British philosophers John Locke and George Berkeley. Locke claimed that he had an abstract idea of a triangle that was neither oblique or right-angled, neither equilateral, isosceles, or scalene, but all of these 140 | Representation of Knowledge Anderson7e_Chapter_05.qxd 8/20/09 9:44 AM Page 140 Conceptual Knowledge | 141 at once, while Berkeley claimed it was simply impossible for himself to have an idea of a triangle that was not the idea of some specific triangle. The schema theory we have considered is an abstraction theory, but others of this type have been more successful. One alternative assumes that people store a single prototype of what an instance of the category is like and judge specific instances in terms of their similarity to that prototype (e.g., Reed, 1972). Other models assume that participants store a representation that also encodes some idea of the allowable variation around the prototype (e.g., Hayes- Roth & Hayes-Roth, 1977; Anderson, 1991). Exemplar theories could not be more different. They hold that we store no central concept, but only specific instances. When it comes time to judge how typical a specific object is of birds in general, we compare it to specific birds and make some sort of judgment of average difference. Exemplar theories include those of Medin and Schaffer (1978) and Nosofsky (1986). Given that abstraction and exemplar theories differ so greatly in what they propose the mind does, it is surprising that they generate such similar predictions over a wide range of experiments. For instance, both types predict better processing of central members of a category. Abstraction theories predict this because central instances are more similar to the abstract representation of the concept. Exemplar theories predict this because central instances will be more similar, on average, to other instances of a category. There appear to be subtle differences between the predictions of the two types of theories, however. Exemplar theories predict that people should be influenced by studying specific instances similar to a test instance and that such influences should go beyond any effect of some representation of the central tendency. Thus, although we may think that dogs in general bark, we may have experienced a peculiar-looking dog that did not, and we would then tend to expect that another similar-looking dog would also not bark. Such effects of specific instances can be found in some experiments (e.g., Medin & Schaffer, 1978; Nosofsky, 1991). On the other hand, some research has shown that people will infer tendencies that are not in the specific instances (Elio & Anderson, 1981). For example, if one has encountered many dogs that chase balls and many dogs that bark at the postman, one might consider a dog that both chases balls and barks at the postman to be particularly typical. However, we may never have observed any specific dog both chasing balls and barking at the postman. Much of the past research on categorization has been an effort to determine whether abstraction theories or exemplar theories are correct. The recent trend, however, has been a recognition that people may sometimes use abstractions and other times use instances to represent categories (Anderson & Betz, 2001; Ashby, Alfonso-Reese, Turken, & Waldron, 1998; Erickson & Kruschke, 1998; Gobet, Richman, Staszewski, & Simon, 1997; Palmeri & Johansen, 1999; Smith & Minda, 1998). Perhaps the clearest evidence for this expanded view comes from neuroimaging studies showing that different participants use different brain regions to categorize instances. For example, Smith, Patalano, and Jonides (1998) had participants learn to classify a set of 10 animals like those shown in Figure 5.14. One group was encouraged to use rules such as “An animal is from Venus if at least three of the following are true: antennae ears, curly tail, hoofed Anderson7e_Chapter_05.qxd 8/20/09 9:44 AM Page 141 feet, beak, and long neck. Otherwise it is from Saturn.” Participants in a second group were encouraged simply to memorize the categories for the 10 animals. Smith et al. found very different patterns of brain activation as participants classified the stimuli. Regions in the prefrontal cortex tended to be activated in the abstract-rule participants, whereas regions in the occipital visual areas and the cerebellum were activated in the participants who memorized instances. Smith and Grossman (in press) review evidence that this second exemplar system also involves brain regions supporting memory such as the hippocampus (see Figure 1.7). There may be multiple different ways of representing concepts as abstractions. Although the Smith et al. study identified an abstract system that involves explicit reasoning by means of rules, there is also evidence for abstract systems that involve unconscious pattern recognition—for instance, our ability to distinguish dogs from cats, without being able to articulate any of the features that separate the two species. Ashby and Maddox (2005) argue that this system depends on the basal ganglia (see Figure 1.8). As they review, damage to the basal ganglia (as happens with Parkinson’s and Huntington’s disease) results in deficits in learning such categories. The basal ganglia region has been found to be activated in a number of studies of implicit category learning. Categories can be represented either by abstracting their central tendencies or by storing many specific instances of categories. Natural Categories in the Brain The studies we have been discussing look at the learning of new laboratorydefined categories. There has always been some suspicion about how similar such laboratory-defined categories are to the kinds of natural categories that we have acquired through experience, such as birds or chairs. Laboratory categories display the same sort of fuzzy boundaries that natural categories do and share a number of other attributes. However, natural categories arise over a much longer time period than a typical laboratory task. Over their long learning history, people come to develop biases about such natural categories 142 | Representation of Knowledge FIGURE 5.14 Examples of the drawings of artificial animals used in the PET studies of Smith, Palatino, and Jonides showing that people sometimes use rule-based abstractions and sometimes use memory-based instances to represent categories. (After Smith, Palatino, & Jonides, 1998. Adapted by permission of the publisher. © 1998 by Cognition.) Anderson7e_Chapter_05.qxd 8/20/09 9:44 AM Page 142 Conceptual Knowledge | 143 as living things and artifacts.Much of the research documenting these biases has been done with primary-school children who are still learning their categories but have advanced beyond younger children. For instance, if primary-school children are told that a human has a spleen, they will conclude that dogs have spleens too (Carey, 1985). Similarly, if they are told that a red apple has pectin inside, they will assume that green apples also have pectin (Gelman, 1988). Apparently, children assume that if something is a part of a member of a biological category, it is an inherent part of all members of the category. On the other hand, if told that an artifact such as a cup is made of ceramic, they do not believe that all cups are made of ceramic. The pattern is just the opposite with respect to actions. For instance, if told that a cup is used for “imbibing” (a term they do not know), they believe that all cups are used for imbibing. In contrast, if told that they can “repast” with a particular red apple, they do not necessarily believe that they can repast with a green apple. Thus, artifacts seem distinguished by the fact that there are actions appropriate to the whole category of artifacts. In summary, children come to believe that all things in a biological category have the same parts (like pectin in apples) but all things in an artifact category have the same function (like imbibing for cups). Cognitive neuroscience data suggest that there are different representations of biological and artifact categories in the brain. Much of this evidence comes from patients with semantic dementia, who suffer deficits in their categorical knowledge because of brain damage. Patients with damage to different regions show different deficits. Patients who have damage to the temporal lobes suffer deficits in their knowledge about biological categories such as animals, fruits, and vegetables (Warrington & Shallice, 1984; Saffran & Schwartz, 1994). These patients are unable to recognize such objects as ducks, and when one was asked what a duck is, the patient was only able to say “an animal.”However, knowledge about artifacts such as tools and furniture is relatively unaffected in these patients. On the other hand, patients with frontoparietal lesions are impaired in their processing of artifacts but unaffected in their processing of biological categories. Table 5.3 compares example descriptions of biological categories and artifact categories by two patients with temporal lobe damage. These types of patients are more common than patients with deficits in their knowledge of artifacts. It has been suggested (e.g.,Warrington & Shallice, 1984; Farah & McClelland, 1991) that these dissociations occur because biological categories are more associated with perceptual categories such as shape, whereas artifacts are more associated with the actions that we perform with them. Farah and McClelland offer a computer simulation model of this dissociation that learns associations among words, pictures, visual semantic features, and functional semantic features. By selectively damaging the visual features in their computer simulation, they were able to produce a deficit in knowledge of living things; and by selectively damaging the functional features, they were able to produce a deficit in knowledge of artifacts. Thus, loss of categorical information in such patients seems related to loss of the feature information that defines these categories. Anderson7e_Chapter_05.qxd 8/20/09 9:44 AM Page 143 Recent brain-imaging data also seem consistent with this conclusion (see A. Martin, 2001, for review). In particular, it has been shown that when people process pictures of artifacts or words denoting artifacts, the same regions of the brain that have been shown to produce category-specific deficits when damaged tend to be activated. Processing of both animals and tools activates regions of the temporal cortex, but the tool regions tend to be located above (superior to) the animal regions. There is also activation of occipital regions (visual cortex) when processing animals. In general, the evidence seems to point to a greater visual involvement in the representation of animals and a greater motor involvement in the representation of artifacts. There is some debate in the literature over whether the real distinction is between natural categories and artifacts or between visual-based and motor-based categories (Caramazza, 2000). There are differences in the way people think about biological categories and artifact categories and differences in the brain regions that support these two types of categories. •Conclusions We remember only a tiny fraction of what we experience. Estimates of the storage capacity (e.g., Treves & Rolls, 1994; Moll & Miikkulainen, 1997) of the brain differ substantially, but they are all many orders of magnitude less than 144 | Representation of Knowledge TABLE 5.3 Performance of Two Patients with Impaired Knowledge of Living Things on Definitions Task Patient Living Things Artifacts 1 Parrot: Don’t know Tent: Temporary outhouse, living home Daffodil: Plant Briefcase: Small case used by students Snail: An insect animal to carry papers Eel: Not well Compass: Tool for telling direction you Ostrich: Unusual are going Torch: Handheld light Dustbin: Bin for putting rubbish in 2 Duck: An animal Wheelbarrow: Object used by people Wasp: Bird that flies to take material about Crocus: Rubbish material Towel: Material used to dry people Holly: What you drink Pram: Used to carry people, with wheels Spider: A person looking and a thing to sit on for things, he was a spider Submarine: Ship that goes underneath for his nation or country the sea After Farah & McClelland (1991). Adapted by permission of the publisher. © 1991 by Journal of Experimental Psychology: General. Anderson7e_Chapter_05.qxd 8/20/09 9:44 AM Page 144 Key Terms | 145 1. Jill Price, a person with superior autobiographical memory described at the beginning of the chapter, can remember what happened on almost any day of her life (see her interview with Diane Sawyers: http:// abcnews.go.com/Health/story?id=4813052&page=1). For instance, if you ask her, she can tell you the date of the last show of any former TV series she watched. On the other hand, she reported great difficulty in remembering the dates in history class.Why do you think this is? 2. Take some sentences at random from this book and try to develop propositional representations for them. 3. Barsalou (2008) claims little empirical evidence has been accumulated to support amodal symbol systems. What research reviewed in this chapter might be considered evidence for amodal symbol systems? 4. Consider the debate between amodal theories and multimodal theories and the debate between exemplar and abstraction theories. In what ways are these debates similar and in what ways are they different? Questions for Thought Key Terms abstraction theory amodal hypothesis amodal symbol system arguments default values dual-code theory embodied cognition exemplar theory isa link link mirror neuron mnemonic technique multimodal hypothesis node perceptual symbol system proposition propositional network propositional representation relations schema script slot what would be required to store a faithful video recording of our whole life. This chapter has reviewed the studies of what we retain and what we forget— for instance, what subject was being taught, but not what the teacher was wearing (Figure 5.3), or that we were in an office, but not what was in the office (Figure 5.11). The chapter also reviewed three perspectives on the basis for this selective memory. 1. The multimodal hypothesis (Figure 5.9a) that we select aspects of our experience to remember and often convert from one medium to another. For instance, we may describe a room (visual) as an “office” (verbal). This hypothesis holds that we maintain the perceptual-motor aspects of our experience but only the significant aspects. 2. The amodal hypothesis (Figure 5.9b) that we convert our experience into some abstract representation that just encodes what is important. For instance, the chapter discussed how propositional networks (e.g., Figure 5.7) captured the connections among the concepts in our understanding of a sentence. 3. The schema hypothesis that we remember our experiences in terms of the categories that they seem to exemplify. These categories can either be formed from a bundle of specific experiences or an abstraction like a category. These hypotheses need not be mutually exclusive, and cognitive scientists are actively engaged in trying to understand how to coordinate these explanations. Anderson7e_Chapter_05.qxd 8/20/09 9:44 AM Page 145 146 6 Human Memory: Encoding and Storage Past chapters have discussed how we perceive and encode what is in our present. Now we turn to discussing memory, which is the means by which we can perceive our past. People who lose the ability to create new memories become effectively blind to their past. I would recommend the movie Memento as providing a striking characterization of what it would like to have no memory. The protagonist of the film, Leonard, has anterograde amnesia, a condition that prevents him from forming new memories. He can remember his past up to the point of a terrible crime that left him with amnesia, and he can keep track of what is in the immediate present, but as soon as his attention is drawn to something else, he forgets what has just happened. So, for instance, he is constantly meeting people he has met before, who have often manipulated him, but he does not remember them, nor can he protect himself from being manipulated further. Although Leonard incorrectly labels his condition as having no short-term memory, this movie is an accurate portrayal of anterograde amnesia—the inability to form new long-term memories. It focuses on the amazing ways Leonard tries to connect the past with the immediate present. This chapter and the next can be thought of as being about what worked and did not work for Leonard. This chapter will answer the following questions: • How do we maintain a short-term or working memory of what just happened? This is what still worked for Leonard. • How does the information we are currently maintaining in working memory prime knowledge in our long-term memory? • How do we create permanent memories of our experiences? This is what did not work any more for Leonard. • What factors influence our success in creating new memories? Anderson7e_Chapter_