Structural Variables That Determine the Speed of Retrieving Words from Long-term Memory ELIZABETH F. LOFTUS

Transcription

1 JOURNAL OF VERBAL LEARNING AND VERBAL BEHAVIOR 11, (1942) Structural Variables That Determine the Speed of Retrieving Words from Long-term Memory ELIZABETH F. LOFTUS New School for Social Research AND PATRICK SUPPES Stanford University The experimental study reported was designed to permit the Identification of structural features of stimuli and response words that are predictive of response latencies. The stimulus items were category names followed by an initial letter of the name of a category member, for example, fruit-p. The task for young adult subjects was to respond with the name of an appropriate member of the category, for example, plum in the case of fruit-p. Ten structural varlables were used to form a multiple linear regression model for predicting mean response latencies to stimulus items. An R2 of.61 was obtained. In accounting for this percentage of the variance the three most important structural variables were a measure of dominance in the category, frequency of the stimulus category in children s vocabulary, and frequency of the most likely response in children s vocabulary. Psychologists, using a wide range of materials, techniques, and memory tasks, have recently begun to study the retrieval of information from human memory. Subjects have been required to identify common English words that belong or do not belong to wellknown verbal categories (Landauer & Freedman, 1968), to ascertain the truth of a sentence such as A collie is a dog, or All collies are dogs (Collins & Quillian, 1969; Meyer, 1970), to judge whether two words have the same meaning (Schaeffer & Wallace, 1970), and to produce a word that satisfies various restrictions, such as a word that names a member of the categoryfruits or a word that names Thls research has been supported in part by Public Health Service Grant R03MH20280 to the New School for Social Research and in part by the National Science Foundation under Grant NSFGJ-443X to Stanford University. We acknowledge our indebtedness to John B. Carroll for suggesting that we consider some measure of the frequency of words in children s speech. We also want to acknowledge the assistance of Ronald Scheff and Hermine Warren in running the experiment and analyzing the data by Academic Press, Inc. Alt rlghts of reproduction m any form reserved. a member of the category animals and also begins with the letter Z (Loftus, Freedman, & Loftus, 1970; Freedman & Loftus, 1971). Some of these tasks require subjects to identify whether or not a given instance is a member of a particular class or category, while other tasks require the subjects to produce a word. Clearly, both procedures involve the retrieval of information from the memory store. In both procedures, the dependent variable is usually the latency with which the subject responds rather than the probability of a correct response. The reasons are obvious; first, the information being retrieved is information that the subjects have learned some time ago and know well; they make few errors. In contrast, every act of retrieval takes time, and response latency is an available, easily measured variable. In both the identification and production procedures, we find that some items are responded to quickly while others require a considerably longer response time. For example, subjects take less than 1.50 seconds to name a color that begins with the 770

2 77 1 STRUCTURAL VARIABLES OF LONG-TERM MEMORY letter W, but they take over twice as long to name an artist whose name begins with the letter D (Freedman & Loftus, 1971). Although several theoretical formulations have been offered, and many facts have been discovered, there is as yet little analysis of why one kind of information is retrieved more easily or quickly than another. The present study is an attempt to find out why. It is an attempt to understand what variables cause differential difficulty of retrieval. We especially wanted to examine the relative influence of structural factors in the stimulus items used to elicit a response from memory. The term structural indicates that the focus of attention is on the variables that characterize the specific stimulus items, for example, frequency in English. The particular task we have used is the one used by Freedman and Loftus (1971). Subjects were presented with a noun category plus a restricting letter and were required to produce an instance of the category which began with the given letter. For example, subjects who were presented with the pair fruit-p might say peach,pear, orplum, among other possibilities. A correct response would be any word begin- ning with P that names a kind of fruit. Responses such as apple or pony would be incorrect. THE THEORY For the category-letter items analyzed in this paper, the main task was to identify the factors that contributed to the difficulty of finding an appropriate response. Exactly how each factor name. The production of the response peach or pear to the stimulusfruit-p clearly depends is defined is a matter that we discuss below. We attach weights to the various factors and then use estimates of the weights to predict the relative difficulty of the individual items. We assume that the range of latencies observed in our group of subjects will show systematic variation in a way that clearly reflects a measure of item difficulty. To formulate linear structural models from which parametric predictions of relative diffi- culty can be made we need some notation. Let the jth factor of stimulus i in the set of items be denoted by Xij. The statistical parameters estimated from the data are the weights attached to the factors. We denote the weight assigned to thejth factor by aj. We emphasize that the factors identified and used in the model presented in this paper are always objective factors independent of response data. Put another way, the values of the structural variables do not depend on the experimental response data, but are defined objectively independent of the response data. Consider the analysis of the success-latency data. For a given stimulus i, let l, be the observed mean latency of a correct response for a group of subjects. The main task of the model is to predict the observed mean latency Z,. The natural linear regression model in terms of the factors Xij and the weights aj is li = 1 a, Xi, J -I- ao. There are various ways of evaluating the overall fit of the latency predictions. A rough indication of the goodness of fit of the regression model is reflected by the multiple correlation coefficient R and its square (R2), which is an estimate of the amount of variance accounted for by the regression model. The rest of this section is devoted to discussion of how each structural variable used in the regression analysis is defined. The first variables we consider involve some measure of the frequency of the category upon the recognition of fruit when it is presented. The relationship between wordfrequency and recognition has received considerable attention, with the typical finding being that words more frequently used in the language are recognized earlier or are named faster (Solomon k Howes, 1951). It seems likely that if a high-frequency word is recog- nized faster, then a high-frequency category name should be responded to more quickly.

3 LOFTUS AND SUPPES 772 The first three variables are measures of the frequency in English (from KuCera & Francis, 1967) of the category name. The first variable is the exact frequency of the category name. In the above example, it is the exact frequency of the word fruit. The second variable combines the frequency of the singular and plural forms of the category name; the frequency of fruit and fruits is combined. Both variables 1 and 2 were expected to vary inversely with reaction time; in other words, we expected that the higher the frequency of the category name, the shorter the reaction time. The third variable is simply the reciprocal of variable 2, the total category frequency. The reason for including this variable is that the total category frequency ranged from a value of 2 for the word seasoning(s) to a value of 1413 for state(s). A regression equation often has difficulty fitting such a large range of values. The reciprocal of total frequency, on the other hand, ranges from O to 1. This variable was expected to vary directly with reaction time. The second set of variables involves some measure of the frequency in English of the possible correct responses. Specifically, we were interested in the effect on reaction time of the frequency in English of the one correct response that had the highest such frequency. These variables were included in the analysis because previous research (Loftus, Freedman, k Loftus, 1970; Freedman & Loftus, 1971) demonstrated that items that have higher frequency responses tend to have faster reaction times. The findings from these and other studies are further demonstrations of the frequency-reaction time relationship found by Marbe quite long ago (Thumb & Marbe, 1901 ; cited in Woodworth & Schlosberg, 1954). Variable 4 is the exact frequency (from KuCera & Francis, 1967) of the correct response that had the highest frequency. For example, the most frequent correct response to the stimulus dwelling-h is the word house, which occurs 591 times in the 1,014,232 words sampled by KuCera and Francis (1967). Thus, the exact response frequency variable would have avalue of 591 for the stimulus dwelling-h. It is important to note that the value of the exact response frequency variable depends not on the frequency of the response most frequently emitted by subjects in the experiment, but rather on the highest KuCera- Francis frequency member of the category. In other words, the value of the exact response frequency variable for any particular stimulus is independent of any data collected from sub- jects in the experiment. The total response frequency variable, variable 5, includes the frequency of house (591) and the frequency of houses (83), resulting in a total value of 674. Both variables 4 and 5 were expected to vary inversely with response latency; in other words, we expected that the higher the frequency of the most frequent response, the shorter the reaction time. Variable 6 is simply the reciprocal of the total response frequency, and the reasons for using the reciprocal are the same as those already stated for variable 3. The seventh variable is category length, or the number of letters in the category name. Cattell (1886; cited in Woodworth & Schlosberg, 1954) was one of the earliest psychologists to note and measure a difference in reading time between short words and long words. The average reading times for two trained subjects were 388 msec for short words and 431 msec for long words. It seems likely that if a short word is read faster, then a short category name should be responded to more quickly, all other things being equal. The eighth variable is the dominance variable, whichisdefined as the likelihood that a particular response will be given when subjects are asked to name words that fit a particular category. That is, rather than the frequency in the English language in general, it is the frequency with which a word is given as an exemplar of a category. Information on dominance was obtained from Battig and Montague (1 969). The dominance variable for each item is assigned a value that represents the highest rank position in the category of an

4 773 STRUCTURAL VARIABLES OF LONG-TERM MEMORY available correct response. For example, in the one. For the stimulus insect-a, for example, Battig and Montague (1969) norms of re- variable 9 has a value of 1 and variable 10 has sponses to the category Bower, the first word that would satisfy the pairjlower-p is actually the ninth most frequent word given to the category Bower. Thus, the pair Bower-P was assigned the number 9. Chemistry is the most frequent response given to science; the pair science-c was accordingly assigned the number 1. Previous investigators have found that a value of 3. We refer to these two variables as high-frequency poolsize and total poolsize. The last two variables are unusual in that they have not been discussed in any analysis of semantic memory retrieval that we know of, except in the work of J. B. Carroll,2 who suggested the idea of these two variables to us. Almost every student of verbal learning and verbal behavior knows of the existence of higher dominance produces faster reaction Thorndike and Lorge s Word Book (1944). times (Freedman & Loftus, 1971). In a slightly Veryfew, however, have used the J count, different experiment in which subjects had to which is a Thorndike count of 120 juvenile decide whether or not word a was a member of a category, Wilkins (1971) showed that instances with higher dominance were categorized faster than instances of similar Thorndike-Lorge frequency but lower dominance. The next two variables involve some measure of response poolsize, or the number of possible correct responses. The stimuli were chosen so that the overlap of category books. The J count includes only books recommended for boys and girls in Grades 3 to 8. Presumably the high-frequency words in the J count, such as city, color, and power, are words learned relatively early in childhood. Low-frequency words in the J count, such as seasoning, profession, or fuel, are words learned later that must be added to the basic semantic structure already built. Collins and name with the letter varied from a minimum Quillian (1971) hinted at the importance of of one (animal-z, with zebra being the only correct response in most people s memory store) to very 1argeJish-S, with several names being possible responses. Using the Battig this early-late distinction, but did not suggest any systematic way to find out which words are early words and which are not. We expected that early words, because they are in and Montague (1969) norms, we counted some sense the foundation of the semantic the number of responses given to a particular category which also began with the letter restrictors used in the present study. For the stimulus insect-a, for example, we counted structure, would be retrieved more quickly than late words. Variable 1 l, then, is the frequency of the category name in the J count; variable 12 is the frequency of the correct the number of insects givenby Battig and response that has the highest frequency in the Montague s subjects that began with the letter A. One insect-a response, ant, wasgiven quite frequently, that is to say, with a total J count. We refer to these variables as the children s category frequency variable and children s response jirequency variable, refrequency of 10 subjects or more. Two spectively. insect-a responses, aphid and annelid, were In summary, the variables we investigated given by fewer than 10 subjects. These facts are : were the basis for variables 9 and 10. Variable X,, the exact category frequency variable: 9 is the number of high frequency correct the exact frequency in English of the responses (given by 10 subjects or more in the category; Battig and Montague norms), while variable 10 is the total number of different responses J. B. Carroll, Word frequency versus age of memory given which began with the appropriate letter, in predicting naming latencies, submitted for regardless of how many subjects gave each publication.

5 LOFTUS AND SUPPES 774 the total category frequency variable : the combined frequency in English of the singular and plural forms of the category name ; the reciprocal category frequency variable: the reciprocal of the total category frequency variable ; the exact response frequency variable : the exact frequency in English of the response that has the highest frequency; the total response frequency variable: the combined frequency in English of the singular and plural forms of the response that has the highest frequency; X,, the reciprocal response frequency variable: the reciprocal of the total response frequency variable ; X,, the category length variable: the number of letters in the category name; X,, the dominance variable: the highest rank position in the category of an available correct response (from Battig & Monta- gue, 1969) ; X,, the high-frequency poolsize variable: the number of available high-frequency correct responses (from Battig & Montague, 1969) ; Xlo, the total poolsize variable : the total number of available correct responses (from Battig & Montague, 1969); X,,, t he children s category frequency variable : the frequency of occurrence of the category name in the Thorndike-Lorge (1944) J count, which is a count that includes only books recommended for children ; XlZ, the children s response-frequency variable : the frequency of occurrence in the J count of the response that had the highest such frequency. Subjects METHOD The subjects were 40 students at the New School for Socla1 Research. Each subject took part in one experimental session that lasted about 30 minutes. Materials Stimuli were printed on 5 x 8-inch cards. Each stimulus consisted of a category plus a restricting letter (e.g., flower-p). Thirty single-word categories were selected so as to include a wide range of category frequencies. An attempt was made to select categories that were low in frequency (e.g., furniture), high in frequency (e.g., country), and of intermediate fre- quency. Categories that had appeared in previous studies were selected so as to provide some continuity with previous findings. The particular pairings of categories with letters were selected with several criteria in mind. The categories and letters were paired so as to provide a wide range of dominance values, ranging from high dominance (e.g., tree-o, with oak being the most frequent word given to tree) to low (e.g., vegetable- O, with onion being the 18th most frequent response given to vegetable). Similarly, pairings were chosen to include a wide range of response frequency values. The 30 pairs are shown in Table 1. Each pair was always presented with the category first, and with an interval of 1.0 second between the category and letter. Each subject received a random permutation of the 30 items. Procedure Each subject was told that we were conducting a study on how memory worked, that he would see items consisting of categories and letters, and that he was to respond with a word in the category that began with the given letter. He was given examples and told to respond as quickly as possible, but to avoid errors. The subject sat in front of a screen in which was a window covered by half-silvered glass. The index card containing the stimulus was placed in a dark enclosure behind the mirror and was presented by illuminating the enclosure. A microphone was placed m front of the subject and he responded by speaking into it. A trial consisted of the following. As a card with the item printed in large type was placed in the darkened enclosure behind the half-silvered mirror, the experimenter said, Ready, and pressed a button that illuminated the category. After a l-second interval, the letter was automatically illuminated and simultaneously an electric timer with a dc clutch was started. The subject s verbal response activated a voice key that stopped the clock and terminated the trial. A warm-up period of 25 trials preceded the experimental trials. RESULTS The first step in analysis was to the obtain mean latency for correct responses to each of the 30 stimuli. These means are given in and are ranked according to observed reaction

6 775 STRUCTURAL VARIABLES OF LONG-TERM MEMORY TABLE 1 MEAN RESPONSE LATENCY IN SECONDS FOR EACH CATEGORY-LETTER PAIR TABLE 2 ORDER OF INTRODUCTION OF THE VARIABLES IN THE REGRESSION WITH CORRESPONDING CORRELATIONS 1 Dwelling-M 2 Fruit-P 3 TOY-D 4 Tree-O 5 State-I 6 Color-W 7 Relative-U 8 Vehicle-B 9 Ship-S 10 Bird-P 11 Science-C 12 Sport-S 13 Animal-Z 14 Flower-P 15 Fish-S Furniture-D City-M Profession-T Insect-A Fuel-C Snake-C Gem-O Country-A Vegetable-O Metal-C Seasoning-G Weapon-N Disease-L Cloth-D Crime-F 1.78 Multiple 1.79 regression Variable l X, = dominance X,, = children s category frequency X,, = children s response frequency X, = reciprocal category frequency X, = category length X,, = total poolsize X, = high-frequency poolsize X, = total response frequency X, = exact response frequency O6 1 O X, = exact category frequency X, = total category frequency X, = reciprocal response frequency.778 times. The overall mean latency of correct responses for 40 subjects was 1.88 seconds. The next step was to obtain regression coefficients for each of the 12 variables described earlier. A stepwise, multiple linear regression analysis program, BMD02R, adapted for New York University s IBM 360 computer, was used to obtain regression coefficients, multiple correlation R and R. The regression equation was 1, =.004Xil-.002Xiz Xi Xi X,, +.081XiG -.051Xi X,, -.087X, Xilo -.OOlXill X7, with a multiple R of.78, a standard error of estimate of.53, and an R of.61. Table 2 presents the independent variables in order, as introduced in the stepwise regression, with corresponding multiple correlation~.~ A stepwise multiple linear regression is actually a sequence of multiple linearegression equations computed in a stepwisemanner.ateachstepone variable is added to the regression equation. The variable added is the one that makes the greatest reduction in the error sum of squares. That is to say, it is the variable that has the highest partial correlation with the dependent variable partialled on the variables already added. The order in which the variables were introduced indicates that X,, the dominance variable, is the most important of the 12 variables. The children s category frequency variable, XII, and the children s response frequency variable, XI, are introduced next, and raise the multiple correlation coefficient considerably. A rough indication of the goodness of fit of the regression line is given by the final multiple correlation coefficient, R, and its square, R, which is an estimate of the amount of variance accounted for by the regression model, which in this case is 61 percent. Figure 1 shows the predicted and observed reaction times for each of the 30 items. The latencies are plotted as a function of the rank of observed latency. Consequently, the curve of the observed latencies is monotonically decreasing and smoother than the predicted curve. An inspection of the two curves shows a reasonable fit for the regression model, but the model does not fit the very difficult items well. A more detailed look at the correspondence between the observed and expected latencies shows that a few items were extremely discrepant. These items include metal-c, cloth-d, disease-l, and toy-d; the first three were among the most difficult, while the fourth was among the easiest. The large deviations

7 LOFTUS AND SUPPES I 5 IO ITEMS FIG. 1. Latency ranked according to observed difficulty. between the observed and predicted results for certain items, such as the four just mentioned, emphasize the need for a more elaborate theory. Most of the predictions can be made by a smaller number of variables, and the inclusion of additional variables adds little. In the present case, most of the variance can be accounted for by variables X,, X,,, XI,, X,, X,, Xlo, X,, and X,. Variables X,, X,, X,, and X, are obviously redundant. If we reduce the number of variables in the regression equation to include only these eight, the reduction in multiple R and R2 is slight. The regression equation becomes L, = 1.169XZ3 +.OOlXiS -.O75Xi X, Xz Xz10 -.OOlXill -.OO1XZ with a multiple R of.76, a standard error of estimate of.50, and R2 of.57. DISCUSSION The regression results we have reported probably go about as far as can be gone by this approach in accounting for differences in mean response latencies to the kind of stimuli we reported. Certainly we have not been totally successful, but we do wish to emphasize the relative power of regression methods

8 777 STRUCTURAL VARIABLES OF LONG-TERM MEMORY as opposed to simple tests of significance. Using an P test or a t test, a particular structural variable may be shown to be significantly related to the dependent variable of response latency, and yet account for only a small part of its variance. We believe that the regression methods we have applied can be used more extensively than they have to explore in detail the features of stimuli or responses that seem to make a difference in ease of information retrieval from memory. On the other hand, we are under no illusion that the kind of structural variables we have studied in this paper provides a direct approach to characterizing the mechanisms of memory storage. Their significance as predictors does seem to show that no overly simple process model organized on the principles familiar from the current theory of automata will be adequate. If, for example, other studies continue to show the importance of the children's category and response variables, which exhibit the fundamental nature of early experiences, then an adequate process model will need to take into account long-term developmental trends that do not seem easy to characterize explicitly. Without being in any sense definitive, the results we have reported naturally suggest some theoretical speculations about the organization of long-term memory. A simple model would postulate a sequence of files with access time an increasing function of position in the sequence. Category names are stored in these files, and within each file the names of kinds of objects that fall under the category together with associated information. Within each file the organization is hierarchical, and position in the hierarchy is determined to first approximation by the variables we have considered. Detailed development and test of such a model would seem to require refined analysis of data from individual subjects rather than analysis of data averaged across subjects, because file position of category names and hierarchical position of possible responses surely vary according to individual usage and experience from person to person. We plan to turn to such individual analyses in future work. REFERENCES BATTIG, W. F., & MONTAGUE, W. E. Category norms for verbal items in 56 categories :A replication and extension of the Connecticut category norms. Journal of Experimental Psychology, 1969, 80, 3. COLLINS, A. M., & QUILLIAN, M. R. Retrieval timefrom semantic memory. Journal of Verbal Learning and Verbal Behavior, 1969,8, COLLINS, A. M., & QUILLIAN, M. R. Categories and subcategories in semantic memory. Paper presented at the Psychonomic Society convention in St. Louis, Mo., FREEDMAN, J. L., & LOFTUS, E. F. The retrieval of words from long-term memory. Journal of Verbal Learning and Verbal Behavior, 1971, 10, KUCERA, H., & FRANCIS, W. N. Computational analysis of present-day Amerzcan English. Providence: Brown university Press, LANDAUER, T. K., & FREEDMAN, J. L. Information retrieval from long-term memory: Category size and recognition time. Journal of Verbal Learning and Verbal Behavior, 1968,7, LOFTUS, E. F., FREEDMAN, J. L., & LOFTUS, G. R. Retrieval of words from subordinate and superordinate categories ín semantic hierarchies. Psychonomic Science, 1970,21, MEYER, D. E. On the representation and retrieval of stored semantic Information. Cognitme Psychology, 1970,21, SCHAEFFER, B., & WALLACE, R. The comparison of ' word meanings. Journal of Experimental Psychol- ogy, 1970,86, SOLOMON, R. L., & HOWES, D. H. Word-frequency, personal values, and visual duration thresholds. Psychological Review, 1951,58, THORNDIKE, E. L., & LORGE, I. The teacher's word book of 30,000 words. New York: Columbia University Press, WILKINS, A. J. Categorizatlon time and category size. Journal of Verbal Learning and Verbal Behavior, 1971, 10, WOODWORTH, R. S., & SCHLOSBERG, N. Experimental psychology. New York: Holt, Rmehart, and Winston, (Received April 18, 1972)