Reorganization in semantic memory: An interpretation of the facilitation effect

JOURNAL OF VERBAL LEARNING AND VERBAL BEHAVIOR 16, 261-275

(1977)

Reorganization in Semantic Memory: An Interpretation of the Facilitation Effect ROSEMARIE H O P F - W E I C H E L

University of California, Los Angeles Repetitions of stimuli produce reductions in the time required to make semanticjudgments. In semantic network models, these reductions are attributed to residual activation of the memory traces. An alternate model is proposed in which information processing is accompanied by dynamic processes, including the reorganization of items into active patterns and their subsequent displacement. Category names and instances were shown to 69 subjects in a verification task with priming. The reaction times were measured for first presentations and for repetitions (mean lag = 10 trials), using high-, medium-, and low-dominant instances. Reaction times decreased with each successive repetition presented without intervening same-categorypositiveitems, but longer latencies wererestored when same-categoryitems intervened between repetitions. The results could not be attributed to activation, but matched predictions from the reorganization model. The dynamic processes provide a link between learning models and models of memory organization. A great deal of empirical information is available about the representation of categories and instances in memory, but a satisfactory theory will only become available when studies of semantic organization become integrated with theories of learning. Although some advances towards a comprehensive theory of cognition are being made (e.g., Hunt, Note 1), most empirical investigations dealing with memory emphasize either its organization or its storage and retrieval properties. There is little integration between the two. One reason for this is that organizational models generally describe a static state of long-term memory, while learning is dynamic; yet learning is a continuous process which must necessarily alter

long-term memoryt. I is therefore important to integrate the dynamic processes underlying learning with the static representations of semantic memory. One such possibility is described here. The data used to construct and validate organizational models come from the results of production tasks (e.g., Battig & Montague, 1969) in which subjects list instances in response to category names, and of verification tasks (e.g., Collins & Quillian, 1969) in which the truth of a statement involving two or more words is judged. These data show that the relationship between categories and instances can be characterized by various attributes, such as the size of the category membership. One important attribute is dominance, a term which refers to the proportion of subjects listing an This paper is based on a doctoral dissertation subinstance in response to a category name. In mitted to the University of California, Los Angeles. I wish to thank Thomas D. Wickens, my committee the Battig and Montague (1969) norms for chairman, for his continued assistance, advice, and example, 100 ~ of the subjects listed the highcritical evaluations of this research. I am also grateful dominant instance "chair", in response to to Francois Christen and to Bernard Baars for their "furniture", but only 2 ~ listed the low-domicomments on the manuscript, and to Keiko Shimanant instance "hassock". Dominance is a mura Kramer for technical assistance. Send reprint requests to the author, Pereeptronics, Inc., 6271 good predictor of the reaction time (RT) in a verification task in which subjects judge Variel, Woodland Hills, CA 91367. Copyright © 1977 by Academic Press, Inc. 261 ISSN 0022-5371 All rights of reproduction in any form reserved. Printed in Great Britain

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ROSEMARIE HOPF-WEICHEL

whether a noun is, or is not, a member of a given category (e.g., Freedman & Loftus, 1971; Loftus, 1973b; Millward, Rice, & Corbett, 1975; Smith, Shoben, & Rips, 1974; Wilkins, 1971). In general, RTs are faster for high- than for low-dominant instances. Dominance, therefore, is frequently used as a valid indicator of the relationship, or the semantic distance, between category name and instance, strengthening the notion of a static organization. In some cases, however, the manipulation of task variables has been shown to influence RT. For instance, Smith (1967), and Smith, Chase, and Smith (1973) obtained a reduction in RT on repetitions, compared to initial presentations, of the stimulus material. Collins and Quillian (1970) also obtained a reduction when they presented two consecutive sentences which, in their hierarchical model, required the same inferential step for verification. Reductions in RT, or facilitation effects, have also been reported by Meyer and Schvaneveldt (1971, 1976) and by Loftus (1973a); these were interpreted in terms of temporary activation of memory locations which facilitate the retrieval of information. A more formal account is given by Collins and Loftus (1975) in their spreading-activation model, in which long-term memory is represented by a connected graph. When a node (representing a concept) of this graph is accessed, activation spreads from the accessed node to neighboring nodes, facilitating retrieval of related information. When a second node is accessed, activation on the first node decreases gradually as a function of time and/or intervening trials. As long as activation remains in the system, retrievalis facilitated, so that when a statement is repeated, a reduction in RT will be observed. Repetition effects, however, have not been fully explored in current models, and from available data, it is not clear whether activation alone could account for them. One important prediction concerns the relationship between the number of trials separating first presentation from repetition of an

item--the lag of the item--and the reduction in RT on the repetition. If spreading-activation accounts for the reduction, the amount of facilitation is expected to decrease with increasing lag. This prediction was upheld in a study using a production task and lags of 0, 1, and 2 trials (Loftus, 1973a); almost no facilitation remained at Lag 2. This was not t,he case in an initial study (Hopf-Weichel, 1976) using a verification task and lags of 0, 2, and 10 trials. In this study, the reductions in RT were significant on all repetitions of positive instances and remained stable between Lags 2 and 10. However, while some reductions were obtained on repetitions of negative instances at Lag 0, this facilitation dissipated rapidly so that by Lag 10, the difference between initial verification and repetition was not significant. It seems that positive verifications involve processes which are not present in verifying negative instances and which may not be solely attributable to spreading-activation. Instead of altering existing organizational models to account for these data, a different approach is used, one which focuses mainly on the processes underlying verification, rather than on the actual organization ofsemanticinformation. These processes may be modeled more naturally in a context of dynamic reorganization. The hypothesis examined here, is that any processing of information is accompanied by dynamic, structural changes which may reorganize the existing relationships among items in memory. Reorganization is proposed as an alternate interpretation for the facilitation effect, but the reorganization model does not contradict the assumptions underlying network representations (e.g., Collins & Loftus, 1975; Collins & Quillian, 1969; Smith et al., 1974); rather, it is proposed as a complement to these models, which describe the stable, longterm state of memory and which represent the expected state of the processes described by the reorganization model. These two views are compatible, because the reorganization model describes processes which precede the states

REORGANIZATIONIN MEMORY

represented by network models. Nevertheless, there are some conceptual differences between the two. In the dynamic model, when a stimulus is received, it activates a pattern containing information which makes the stimulus meaningful. Instead of conceptualizing an informational event as a node in a network or a trace in memory, it is the pattern itself which represents the information, a notion borrowed from John (1967). Patterns must be active before the information they represent can be processed, but all active items are equally retrievable. More than one pattern can be active at the same time. If concurrently active patterns contain related information, they may merge into a single pattern and reorganization occurs; if the patterns are made up of unrelated elements, they will remain independent. The independent variable used to test reorganization will be repetitions of verifications involving category membership, and the dependent variable will be the RT on these verifications. The specific hypothesis to be examined is that when an item is verified as a member of a category, the verification process itself has the potential for reorganizing the existing relationship between the item and the category name, so that items which are identified as belonging together become more closely organized. The category name will be primed for 1 second before the to-be-verified noun is presented, and the response latency, timed from the presentation of the to-beverified noun, measured. The nouns will be either members of the category (positive instances) or unrelated to it (negative instances). A QUANTITATIVEMODEL FOR REORGANIZATION

When a category name is presented, information is generated which defines the category. This typically includes some of its best representatives (Rosch, 1973), or high-dominant instances, which are primed along with the category to form an active set. Thus, if the to-

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be-verified instance is already active when presented, the verification time will be very rapid, while if the instance is not already active, its meaning must be generated before it can be judged against the category name. This takes extra time and the RT will be slower. If the instance is a member of the category, the two patterns representing category name and instance, will have common elements and they will tend to become reorganized into a single pattern. ~Ihis is the pattern which will become activated the next time the category name is presented. If the to-be-verified instance is the one that was reorganized earlier, the RT will now be rapid. When a negative instance is presented, its representative pattern will not be compatible with that of the category name, its verification time will be slow, and no reorganization will be possible. These processes will now be restated as a more formal model.

Assumptions So as to simplify this discussion, the following notation will be adopted: The category name will be Ci and the general term for an instance will be Ij, which might be either a positive or a negative member of C~. Three dominance levels will be used, high, medium, and low, and positive instances will be identified in terms of this classification: HDI, MDI, or LDI. The sets of items in the active state, SA, are distinguished from those in the inactive state, Sx. The processes hypothesized to underly verification of an instance are shown in Figure 1. These may be divided into three stages. Stage 1 includes all processes preceding the actual verification, Stage 2 those underlying the response time, and Stage 3 includes the processes which may take place following the overt response and which are responsible for altering the existing organization. Stage 1.1. Any item is either in SA or in Sx. This is a simplifying assumption of the present model. A more general version would include the notion that items are differentially accessible from Sx, and would require a continuum

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ROSEMARIEHOPF-WEICHEL STAGE1

PRESENTATION OF CATEGORY NAME

STAGE2

STAGE3

PRESENTATION OF NOUN

OUTCOMES IN S L

READ CATEGORY Ci

i

Si FOR Ci

@

---~,l~Es~o~:Y eES F---->II± " ONC"ANI~ED

]OgNE;ATE I

,

c~ FOR~j I

, ~ l p

I

P0N

FROMSi CNOT~j>

I ADD Ij TO Si ~

I~ESPON~+"YES"' 5~I .... ~ ~ ,~-~I~

.EMOVEITEM

lI~- ~

UNCHANOED I

[ S- UNCHANGED

L

0NCHANOED[

FIG. 1. Processes underlying verification of category membership in the priming paradigm. Stage 1 comprises the presentation of the category name, Stage 2 the presentation of a positive or a negative instance, and Stage 3 represents the reorganization of memory following the response. of states, each representing different degrees of activity. 2. The presentation of a category C~ activates a pattern Si. Si represents the meaning of the category name, and includes some of its representative instances. In general, these will be the best representatives of C~, namely HDIs, but some M D I s may also be generated at this time. The probability that any H D I is entered into Si will be all, and that any M D I is entered into S~ will be aM, where a n > aM, though both may be related to task variables, such as the 1-second priming interval. The initial probability that an L D I is activated on the first presentation of C~ is defined to be zero. 3. The size of Si is not fixed, but it is limited by the amount of time available before Ij is presented, and by the number of instances which best represent the category. It is only

necessary to generate enough instances to make the category name meaningful (so that Si is independent of category size), and since this is less than the number of instances that can be produced, a will be smaller than the dominance of Ij, although a and Is-dominance are related. Stage 2. 4. When Ij is presented, the subject identifies its membership with respect to Ci. I f I s is one of the items in Si, simple matching is required, resulting in a rapid positive response. This response takes time z and is the lower limit of RT. 5. I f n o match is found, the subject generates one or more category names for Is, shown in Figure 1 as C'j. These names are then compared to Ci. I f a match is found, a positive response is made, otherwise a negative response. The time to generate C'j and to compare it to Cl is assumed to be v, so that the total

REORGANIZATION IN MEMORY

response time for instances which are not in S~ is -c + v. 1 In summary then, only two response times are postulated, -c and z + v, no matter what processes underlie the verification of an instance which is not already in S~. These response times apply to the verifications of positive instances only; for the present, the model makes no predictions concerning negative verifications. Stage 3. The following assumptions are most divergent from those underlying network representations. 6. If two simultaneously active sets are compatible (i.e., they contain similar information, or common features), they may merge into a single set, i.e., reorganization takes place. With probability p, an identified instance, Ij, of a category C~, will become reorganized into the active set of C~. This implies that when Ci is presented next, Ij will be in Si, so that it will be verified with time z rather than z + v. Actually, this assumption is simply a version of the association principle that related items which are processed at the same time tend to be recalled together. Because instances having different dominance values may be differentially susceptible to reorganization,HDIs, MDIs, and LDIs will be assigned different reorganization parameters, Pn, PM, and PL. 7. If tWO patterns are active at the same time, but have no common elements, they remain discrete. This occurs when Ij is negative, since the category names generated for Ij are not compatible with C~.2 Although the model 1 Note that the variability in observed response times is the result of averaging over subjects who individually respond either with time r or T + v, depending on the state of Ii (individual variability, or noise, is expected of course, but not included in the mathematical representation). This is quite different from what would be expected with a network representation, where response time is a function of the aggregate of all paths connecting two concepts (Collins & Loftus, 1975). 2 This is a simplification which restricts the choice of stimuli in testing the model, namely to positive and negative instances with the least semantic confusibility between them.

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makes no predictions concerning verification times of negative instances, it does imply that the presentation of a negative instance has no effect on the contents of Si. 8. When a new item is entered into Si as a result of reorganization, an existing member of Si is displaced with probability ft. This assumption is necessary because of the limited size of Si (see Assumption 3), and it implies that if the displaced item is later presented for verification, it should show an increase rather than a reduction in RT. As for reorganization, three different displacement parameters are used, depending on the dominance index of the displaced item, fin, 6r~, and I~L. 9. At the end of a trial, the contents of Si remain stable until activated again by the presentation of Ci. The model does not specify how long any set remains active, and changes are possible as long as the relevant patterns remain active, but the model assumes that no other changes occur besides those specified. This implies that reorganization and displacement are not related to lag; they both represent structural changes which should not be affected by time or intervening trials. 10. The presentation of Ij has no effect on inactive members of the same category. This is a simplifying assumption, appropriate only if positive instances are selected which are not closely related to each other, beyond their membership in the same category. For example, the presentation of "wife" as an instance of "relative" might facilitate the future verification of "husband", or vice versa, so that such related words should be avoided in testing the model. 11. Presentations of categories other than Ci do not affect the outcome of Si. These are the main assumptions of the reorganization model. To summarize, the model includes two parameters which specify the initial probability that an instance enters the active set when the category name is presented, three reorganization parameters, three displacement parameters, and two parameters specifying response times. Although treated

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ROSEMARIE HOPF-WEICHEL

separately, reorganization and displacement are both manifestations of a mechanism which reorganizes the contents of S~.

The Mathematical Representation

hm. " H M " means that when Ci is presented, both the HDI and the MDI are in Si; " H m " means that only the HDI is in Si, while the MDI remains latent, and so on. According to Assumption 2, the initial vector for this process is:

In order to derive actual predictions, these assumptions can be expressed as a Markov process. Predictions only apply to the expected (an aM, an (1 -- aM), (1 -- ~n) aM, (1 -- an) (1 - aM)). latencies on positive verifications and will be derived for specific sequences of presentations. The transition matrix for the presentation of an The concept of sequences of positive pre- HDI is: sentations, or "within-category sequence", is HM Hm hM hm important because it is the relevant unit of 1 0 0 0 the reorganization model. It will be referred HM 0 1 0 0 to as "SQ" to distinguish it from the sequence Hm pn(1 - 5M) priSM (1 -- p~) 0 of all trials which make up a particular experi- hM 0 Pu 0 (1 - Pn) ment. SQ is simply represented by listing the hm order of presentation of the instances in a The transition probabilities are specified by category; for example, " H - W - L " means that Assumptions 6 and 8: Any HDI which is althe first positive instance presented from C~ ready active on Trial 1 remains in Si with prowas an HDI, that it was repeated and then folbability 1.0; any HDI which is not in Si has a lowed by an LDI. In order to differentiate first probability Pn of entering Si when the HDI is presentations from repetitions, primes are presented, and (1 - Pn) of not entering Sj. If used on repetitions (e.g., H' refers to the repethe HDI does become reorganized, there is a tition of H) and a double prime (e.g., H') probability 6M that it will displace the active indicates a second repetition of the same inMDI, and conversely, (1 - 5M) that the HDI stance. Dashes indicate lags during which posiwill not displace the MDI, but simply be added tive instances from unrelated categories and to Si. If the MDI is inactive, the presentation negative instances from any category, are of an HDI has no effect on its state (Assumppresented. If two different instances of the tion 10). same dominance type are used from the same When the HDI is tested, the mean latency category, they are simply identified as such: for each state is given by the vector: "M1-MI'-L-M2". The position of negatives is not given in SQ because they are not expected (~, ~, z + v, r + v) to affect the contents of Si (Assumption 7). Additional investigations will be needed to test (Assumptions 4 and 5). The expected latency on the repetition of HDI therefore is: this assumption. Predictions must be derived for each posi= z(~n aM + all(1 - aM)) tion within SQ and for all SQs used in the ÷ "c(1 - an) a~ (Pn (1 - 5~) + Pn 6M) experiment. Since each instance is assumed to + z(1 - an) (1 - aM) PH be in one of two states, SA and Sx, the total + (Z + V) ((1 -- Ula)aM (1 -- Pr~)) number of states in the transition matrix de+ (Z + V) ((1 -- ~.) (1 -- aM) (1 -- p,)) pends on the number of positive instances per -- • + v(1 - ~ . ) (1 - p n ) . category. These states are denoted by using capital letters for items in SA, and lowercase This is the time necessary to verify any inletters for items in Sx. Thus, for SQ = H - H ' - stance in S~(z), plus a proportion of the time M, there are four states: HM, Hm, hM, and necessary to verify any instance in Sx (v), this

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REORGANIZATION IN MEMORY

p r o p o r t i o n being given by the p r o p o r t i o n o f • H D I s which were originally in Sx(1 - eu) and did not become reorganized (1 - PH). Similar predictions can be calculated for any particular presentation order and tested by appropriate selections of SQs.

TEST OF THE MODEL The particular SQs used in the experiment were directly derived f r o m considerations of the model's predictions and were designed so as to simplify p a r a m e t e r estimations (HopfWeichel, 1976). A verification task with repetitions and priming of the category name was used.

Method Stimuli. The stimulus material consisted of category names and instances taken f r o m the Battig and M o n t a g u e (1969) norms. There were 25 categories which were selected according to the production frequency of their m o s t frequently listed instance, with those having the highest H D I s being chosen. F o r each category, one H D I , one M D I , and two L D I s were selected. All L D I s were those with the lowest possible dominance over 2 ~ , and M D I s were taken f r o m the middle-to-high ranges of production frequencies. All instances were between three and nine letters long, and no c o m p o u n d words were used. The dominance indices for H D I s ranged f r o m 84 to

TABLE 1 CATEGORIES AND INSTANCES USED IN THE EXPERIMENT

Category name Animal Carpentry Color Elective office Flower Fruit Furniture Gem stone Relative Religious building Unit of distance Unit of time Alcoholic beverage Cloth Crime Dwelling place Earth formation Food flavoring Footgear Part of the body Reading material Sport Tree Vehicle Weapon

H instances Dog (96)" Hammer (98) Red (98)b President (98) Rose (95) Apple (97) Chair (100) Diamond ( 9 8 ) Aunt (98) Church (98) Mile (99) Second (96) Beer (87) Cotton (91) Murder (88) House (90) Mountain ( 9 1 ) Salt (93) Shoe (87) Arm (90) Book (84) Football (90) Oak (89) Car (92) Gun (89)

M instances Cow (64) Nail (56) Purple (64) Senator (61) Tulip (47) Pear (74) Bed (74) Sapphire ( 5 5 ) Cousin (77) Temple (62) Meter (71) Month (73) Gin (70) Silk (66) Robbery (43) Apartment ( 7 1 ) Valley (51) Sugar (38) Sandal (58) Foot (67) Newspaper (67) Tennis (74) Maple (71) Bus (68) Rifle (37)

Lt instances

L2 instances

Turtle (3) Lathe (5) Beige (8) Judge (6) Aster (3) Fig (4) Rocker (2) Garnet (10) Wife (8) Shrine (10) Fathom (6) Fortnight (5) Sherry (5) Canvas (3) Extortion (3) Palace (3) Crater (4) Thyme (7) Sneakers (8) Thumb (3) Brochure (3) Judo (2) Cypress (2) Jeep (6) Dagger (3)

Camel (6) Vise (3) Lavender (8) Sheriff (7) Azalea (7) Melon (2) Divan (2) Onyx (13) Grandson (2) Monastery (6) League (6) Epoch (2) Cognac (2) Taffeta (3) Perjury (4) Tepee (7) Cavern (5) Ginger (3) Clogs (2) Waist (4) Bulletin (3) Diving (3) Cedar (8) Taxi (7) Arrow (5)

a Numbers in parentheses give the dominance index of the instances. b In some cases, the proportion of "first responses" (Battig & Montague, 1969) dictated the selection of the high-dominant instances.

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ROSEMARIE HOPF-WEICHEL

100 ~, with a mean of 93 ~ ; MDI dominance ranged from 37 to 77 ~ , with a mean of 62 ~ ; and the range of LDIs was 2 to 13~, with a mean of 5 ~ . The actual categories and instances, along with their dominance indices, are shown in Table 1. The two LDIs per category were only used for SQs 22-25; in all other cases, the first LDI was used. Although this represents a pool of 100 positive instances, only 56 were used in any single experimental run. A set of 56 negative instances was selected from unused categories, and with minimal semantic similarity to the 25 category names. Their dominance indices with respect to their own category names varied over the entire production frequency range, and in all cases, they were concrete nouns with an average word-frequency (Thorndike & Lorge, 1944) which was approximately equal to that of the positive instances. SQs. The SQs selected to test the model are shown in Table 2. There are 25 SQs, representing 16 unique orders, each consisting of from two to three positive instances. In SQs TABLE 2 EXPERIMENTAL DESIGN

Presentation order SQ

1

2

1&2 3&4 5&6 7&8 9&10 11 &12 13 14 15 16 17 18 19 20 21 22-25

H H M M L L H M L H M L H M L L~

H' H' M' M' L' L' H' M' L' H' M' L' H' M' L' LI'

Position number 3 4 5 6 M L H L H M H" M" L" H" M" L"

M' L' H' L' H' M' M H H

L M L H M H M' H' H'

L' M' L' H' M' H'

7 H" H" M" M" L" L"

H" M" L" L2

L2'

LI"

1-12, the three instance types (shown as H, M, and L) each appear in all possible permuta-" tions for Positions 1, 3, and 5, and are repeated in Positions 2, 4, and 6; there is a second repetition of the initial item in Position 7. In these SQs, each repetition serves as a test of the reorganization parameter p, while trials with an HDI or MDI in Positions 3 to 7 provide data for the displacement parameters, 6n and 6MThe second repetition in Position 7 is a particularly good test of~, because it can be compared directly with data from the third presentations in SQs 13-25. In SQs 13-18, the instance in first position is presented three times without intervening event from the same category, and provides a baseline for comparison with data on Position 7 in SQs 1-12. In order to estimate practice effects, the same order of presentation is used in SQs 13-15 and 16-18, but SQs 13-15 appear early in the experiment and SQs 16-18 late. SQs 19-21 are included because the total number of trials intervening between first and third presentations is much less in SQs 13-18 than in SQs 1-12. If the displacement effect is to be evaluated, then the number of trials between first and third presentations should be comparable. Consequently, the mean number of trials spanned in SQs 19-21 and 1-12 are equated. Since the initial probability that an LDI is entered into S~ is zero (Assumption 2), 6L can only be estimated on trials in which an LDI is repeated following an interruption from other same-category items (Position 7 of SQs 9-12). SQs 22-25 are included to provide additional data for estimating 6L. Stimulus sequence. The first consideration in making up the stimulus sequence was that of lag size. There were two types of lag; one for repetitions of the same instance, and the other for repetitions of the same category with different positive instances. The lag of primary interest is the lag for repetitions of the same instance. Since pilot data (Hopf-Weichel, 1976) indicated that there was no difference in the amount of reduction obtained on repetitions between Lags 2 and 10, the lags for the present experiment were randomized to vary

REORGANIZATION IN MEMORY

between 5 and 15 trials. The lower limit of 5 trials was selected to avoid any possible need to interpret the results in terms of short-term memory effects. The second type of lag specified the interval between two presentations of different positive instances from the same category (for example, the lag between Positions 2 and 3 in SQs 1-12). These were distributed as follows: 50 ~ were between 21 and 30 trials long, 30 ~ between 11 and 20 trials, and 20 ~ between 1 and 10 trials. There were no Lag 0 trials, either for category or instance repetitions. The entire sequence had 274 trials, with equal numbers of positives and negatives. The trial for the first positive event of each SQ was assigned randomly, with the rest of the positions being determined by the lag specifications for instance and category repetitions. There were two exceptions: The instances of SQs 16-18 were presented only during the last 100 trials, and those of SQs 22-25 were distributed evenly over the entire stimulus sequence. The 56 negative instances were randomly assigned to the empty trials, except that no negative event occurred before the three presentations of the first positive item in SQs 1315 and 19-21, and no negative item was assigned to SQs 16-18. All other SQs had between one and three negatives, each negative being repeated once or twice in approximately the same proportion as the positives. Repetition lags were randomized within the available trials. The runs of positive and negative events were checked so that their overall characteristics would match those of a random binary sequence. Two sequences were constructed which had different lag intervals for category and instance repetitions. Half the subjects were assigned to Sequence 1 and half to Sequence 2. The actual categories and instances shown to the subjects were individually randomized preceding each experimental run. There was random assignment of (a) the categories to SQ, (b) SQ to trial number for the following sets of SQ: 1-12, 13-15, 16-18, 19-21, 22-25, and (c) the

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negative instances. This randomization procedure means that any category could be assigned to any of the SQs, and that any of the SQs within each set could occur on the preassigned trials, thereby insuring against spurious stimulus and SQ selection effects. There were also 20 practice trials consisting of categories and instances not used in the actual run. This same set of items was again used for the first 10 trials of the experimental sequence and served as additional practice. Data from these trials were not recorded. Procedure. The subjects were 69 male and female undergraduates, fulfilling a requirement for a course in introductory psychology at UCLA. The subjects were randomly assigned to the two trial sequences and to two response modes. As the subjects entered the laboratory, they were shown to a dimly lit cubicle and seated in front of a table. On the table there was a cathode-ray display console for the stimuli and a typewriter-type response console, two buttons of which (Z a n d / ) were used for the responses; half the subjects pushed the right-hand button for a "yes" answer, and half the subjects pushed the lefthand button. Subjects were told to make themselves comfortable and were given instructions as to the purpose and the exact procedure of the experiment. They were told that the sequence would contain repetitions, but not the reason for the repetitions. The importance of speed and accuracy was stressed a number of times. A trial consisted of the presentation of a category name and a noun. The noun appeared 1 second after the category name. Both words remained in view until the subject responded; the response was followed by feedback. After 1 second, a new category name was displayed, and so on, for 274 trials. The presentation of the input sequence and the recording of latencies and errors were controlled by a Hewlett-Packard 2116-B computer. The experimenter remained in the cubicle with the subject for the 20 practice trials, to ensure that the instructions had been properly

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ROSEMARIEHOPF-WEICHEL

understood. Very few subjects had any questions at the end of practice which served primarily to familiarize the subjects with the display and response consoles. Any questions were answered by paraphrasing the instructions. After practice, the experimenter left the cubicle, reemphasizing the need to work rapidly, as long as no errors were made. The entire experiment lasted approximately 30 minutes. Results and Discussion There were 3.85 ~ errors on positive, and 2.79 ~ on negative trials. Of the positive errors, 7 7 ~ (2.96 ~ ) occurred on first presentations, 1 6 ~ (.62~) on second, and 7 ~ (.27~) on third presentations. Of the first presentation errors, 69 ~ (2.04 ~ ) were errors in verifying LDIs, and 31 ~ (.92~) in verifying HDIs or MDIs. Of the negative errors, 5 7 ~ (1.59~) were on first presentations, with 3 2 ~ (.89 ~ ) and 11 ~ (.31 ~ ) on second and third presentations, respectively. All results discussed are based on the mean latencies of 69 subjects. Latencies on errors were not counted. Performance on negative events will not be discussed, since neither their positions, nor their repetition lags were controlled. Obviously, the assumption ( # 7) that negative events have no effect on Si will have to be tested in the future.

The data were used to obtain the best estimates for the 10 parameters of the quantitative model. These estimates are the result of a numerical minimization (Wickens, Note 2) of the squared deviations between data and model. The first estimates obtained showed that the values of the three reorganization parameters were very close to each other: Pn = .665, PM = .645, PL .660. These parameters were, therefore, combined into a single reorganization parameter, p, and new estimates obtained for an 8-parameter model. Both sets of estimates are shown in Table 3. The results shown in Figures 2 and 3 include the predictions from the 8-parameter model. Overall, the model fit the data remarkably well. Figures 2a to 2c compare the data to the model on each position of SQs 1-12. The amount of reduction obtained on repetitions of the same verification is evident when RTs at Positions 1, 3, and 5 (initial presentations) are compared with RTs at Positions 2, 4, and 6 (repetitions). There is a reduction at all positions, with the greatest difference on LDIs. It is unlikely that a facilitation hypothesis could account for these differences, considering that the repetitions occurred after a mean lag of 10 trials. However, a more direct comparison of facilitation and reorganization is possible from the data shown in Figure 3. Figure 3 shows the performance on three =

TABLE 3 LEAST SQUARES PARAMETERESTIMATES

Parameters

10-parameter model

8-parameter model

• v ~a core Pn PM PL 6a

479 msec 386 msec .55 .38 .67 .65 .66 .11

480 msec 384 msec .56 .37

OM

.15

t~L

.40

.11 .16 .40

Squared deviation = 43306

43397

Response times Initial probabilities Reorganization Displacement

~ .66

REORGANIZATION

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900

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0

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.

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FIG. 2. Response latencies (solid lines) and predictions from the model (dotted lines) on SQs 1-12. Panel (a): SQs 1-4, panel (b): SQs 5-8, panel (c): SQs 9-12.

presentations of the same instance in different SQs. The basic case is represented by SQs 1315 in which the three presentations occur without interruptions by other instances from the same category, and with lags varying between 5 and 15 trials. The same is true for SQs 16-18, except that the latter occur late in the stimulus sequence and serve as a basis for evaluating practice effects. In SQs 1-12, the three presentations of Ij are interrupted by other positive instances from the same category, where--according to the model--each interruption re-

IN MEMORY

271

presents an opportunity for Ij to be displaced from Si. Since there are more trials intervening between Presentations 2 and 3 in SQs 1-12 than 13-18, an appropriate comparison is possible with SQs 19-21, whose lags match those of SQs 1-12. It is clear from Figure 3 that the interruptions depress performance on Presentation 3 of SQs 1-12 but not of 19-21 ; therefore, it is not the number of trials, but the type of trial that affects performance. In other words, the hypothesis that Ij may be displaced by intervening relevant (same-category) items, but that intervening irrelevant items have no effect, is supported by the present results. This result is important because it provides a parameter-free test of the assumptions underlying the reorganization model and evidence against a spreading-activation explanation of the observed reductions. If spreadingactivation were the only process underlying the repetition effects, the presentation of intervening relevant items should facilitate--or at least not interfere with--verification of the third presentation in SQs 1-12, since presentation of the intervening items would maintain the activation on the relevant nodes. At the same time, RT on the third presentation item in SQs 19-21 should be slower, since activation would dissipate during the intervening trials. At the very least, under the spreadingactivation assumption, one would expect the differences in lags between Presentations 2 and 3 to differentiate between performance on SQs 13-18 (mean lag of 10 trials) and SQs 1921 (mean lag of 72 trials). This was clearly not the case. Performance on SQs 1-12 was different than on SQs 13-21, but performance on 13-18 was the same as on 19-21, indicating that lag has no effect on RT. This was confirmed by a 3-factor within-subjects analysis of variance, performed on data from the three "same-item" presentations of SQs 1-21. The three factors of dominance, SQ, and repetition were all significant (dominance: F(2, 136)= 65.3, p < .01, M S = 22,856; SQ: F(3, 204)= 23.7, p < .01, M S = 10,273; repetition: F(2, 136) = 161.9, p < .01, M S = 110,135). There

272

ROSEMARIE

H IGH - D O M I N A N T

HOPF-WEICHEL

MEDIUM - D O M I N A N T

LOW 7 DOMINANT

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DATA

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PRESENTATIONS

FIG. 3. Response tatencies (solid lines) on three presentations of the same instance for each instance type (high-, medium-, or low-dominant) in SQs 1-12, 13-15, 16-18, and 19-21. The predictions from the model (dotted lines) are the same for all first and second presentations; on third presentations, the predictions are the same for SQs 13-21. were also two significant interactions, dominance by repetition, F(4, 272) = 16.6, p < .01, M S = 5,406; and SQ by repetition, F(6, 408) = 3.3, p < .01, M S = 1,068. The significant SQ by repetition interaction was expected, if there was a difference in third-presentation performance between SQs 1-12 and 13-21, but it was also expected not to be significant on a post-hoc test comparing only performance on SQs 13-21. In an analysis of variance without SQs 1-12, the three main effects of dominance, SQ, and repetition remained significant (dominance: F(2, 136) = 33.5,p < .01, M S = 24,575; SQ: F(2, 136) = 8.3, p < .01, M S = 8 , 1 3 8 ; repetition: F(2, 136)=128.5, p < .01, M S = 169,954), as did the dominance by repetition interaction, F(4, 272)= 9.4, p < .01, M S = 14,405, but the SQ by repetition interaction was not significant, F(4, 272) = .8, p > .05, M S = 315, thereby favoring reorganization over facilitation. The significance of SQ is attributable to the practice effects which can be seen in Figure 3. Responses on SQs 16-18 are somewhat faster than on the other SQs for all three instance types; but this is manifested only as a general lowering of RT and has no influence on the

process of reorganization, as evidenced by the absence of an SQ by repetition interaction. Such effects might be represented in the model by allowing v to vary with practice. The significant dominance by repetition interaction indicates that there is a greater amount of reduction on LDIs than on MDIs, and on MDIs than on HDIs; this is attributable to differences in the initial difficulty in verifying these item types, a result which conforms with the established finding that dominance predicts RT in this task. But the amount of observed reduction is not related to the probability of reorganization which is invariant over item types (p = .66). This is interesting, especially when contrasted with the estimates obtained for the displacement parameters. As seen in Table 3, the probability of displacement is inversely related to instance dominance and is in all cases smaller than p. Although reorganization and displacement both induce changes in the contents of Si, instance dominance influences only the displacement process, and not that of reorganization. This suggests that the familiarity of an item (as indicated by its dominance) is irrelevant in determining how easily an item is en-

REORGANIZATION IN MEMORY

tered into the active state (or stored), but that less familiar items are more easily displaced than familiar items. The response time parameters, -c and v are also shown in Table 3. The time to verify an instance from Si (-c)is 480 milliseconds and the best estimate of y, the additional time necessary to verify an instance from Sx, is 384 milliseconds. There is some independent evidence that ~ represents a minimum time for verification of instances with priming. Using a similar task (subjects had to judge whether two instances belonged to the same category), Rosch (1975) gave two subjects extensive practice (21 runs over a 2-week period) on a restricted set of categories and instances; at the end, the mean response time of the two subjects was slightly below 500 milliseconds, and there was no difference in RT for HDIs and LDIs. The hypothesis that the initial probability, e, is related to dominance and that ei~ > eU, is supported: eH= .56 and eM = .37. The mean dominance of HDIs and MDIs was 93 % and 62 %, respectively, and the ratio of a to dominance is the same for the two item types: 56/93 ~ 37/62 ~ .60. While interesting, this result should be replicated before further conclusions can be drawn.

273

tation of negative items has no effect on the contents of Si; although this still needs to be tested formally, a minimum condition of the experimental design was to select negative instances which were semantically as distinct as possible from the positives. Assumption 11 states that the processing of information within each category proceeds independently from any other category. In order to minimize sequence effects (a different category was presented approximately every 3 seconds), the category names themselves were selected so as to minimize semantic confusion (for example, the category "animal" was used, but not "bird" or "insect"). Had greater semantic similarity been present in both these cases, it might easily have decreased the model's predictability. But it is precisely because semantic confusion was avoided, and because categories can be represented as well-defined, semantically distinct structures, that the processes of reorganization and displacement could be empirically isolated. These characteristics, as well as the nature of the stimuli--involving simple but welllearned items--must be kept in mind when trying to generalize the reorganization model beyond the present results. For example, Hayes-Roth and Hayes-Roth (1975) in an analogous task, but using artificial knowledge GENERAL CONCLUSIONS structures, also reported reductions in RT on The results of the present experiment agree repetitions, but the probability of reorganizaclosely with the reorganization model's pre- tion (or in their model, the formation of new dictions. The model represents the processes links) following just one verification, was much which accompany verification of category lower than the .66 reported here. In the Hayesmembership; these include the reorganization Roth and Hayes-Roth study, the underlying and displacement of items into and from active structure was new to the subjects and reorganpatterns composed of all items necessary to ization occurred slowly, while the structures representing categorical relationships have make the input meaningful. Much work remains to be done of course, been established for a long time, their contents particularly with respect to the processes un- are very well known, and reorganization was derlying verification of negative items, but rapid. It may be that factors which determine some general comments are possible. In inter- the familiarity of the structure itself (in preting the results, it is first necessary to con- contrast to the item's familiarity within the sider two of the model's assumptions which, structure), influence the probability of reto some extent, dictated the selection of organization, and if so, this is an important stimuli. Assumption 7 states that the presen- consideration for future research.

274

ROSEMARIEHOPF-VCEICHEL

It is possible to interpret the present results within a framework of a network representation. However, existing network models would need to be modified to incorporate the reorganization findings, since these models are basically static. Even though the concept of activation introduces a dynamic aspect into organizational theories, it does so only with respect to the retrieval process; the relationships of words to each other are not expected to be changed when the structure is accessed. While it is relatively simple to include processes which allow for additions to existing structures (e.g., Anderson & Bower, 1973; N o r m a n & Rumelhart, 1975), there is a possibility that learning produces changes in the structures themselves, such that the relative importance of items and their connections becomes shifted. Hayes-Roth and Hayes-Roth (1975) dealt with this problem by postulating the formation of new links. In the present study, the processes of reorganization and displacement were emphasized and structural characteristics minimized. However, the reorganization model is consistent with assumptions underlying network representations, as shown by the values obtained on the displacement parameters: Since the probability of displacement is inversely related to dominance, LDIs are more likely to be displaced from Si than H D I s ; therefore, whenever Si is activated, it is more likely to contain H D I s than LDIs, a prediction consistent with normative data. The values of the initial probability, an and ~M, being proportional to dominance, similarly reflect normative characteristics. Static representations are useful because they represent the expected value of the processes modeled here, but if a connection is to be made between learning models and structural representations of memory, dynamic processes such as reorganization must be taken into account. REFERENCES ANDERSON,J. R., & BOWER,G. H. Human associative memory. New York: Winston, 1973.

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 Monograph,

1969, 80 (3, Pt. 2). COLLINS,A. M., & Lorros, E. F. A spreading-activation theory of semantic processing. Psychological Review, 1975, 82, 407-428. COLLINS,A. M., & Q~LLIAN, M. R. Retrieval time from semantic memory. Journalof VerbalLearning and Verbal Behavior, 1969, 8, 240-247. COLLINS,A. M., & QUILLIAN,M. R. [Facilitating retrieval from semantic memory: The effect of repeating part of an inference.] In A. F. Sanders (Ed.), Attention and performance 11I. Amsterdam: North-Holland, 1970. (Reprinted from Acta Psychologica, 1970, 33.) FREEDMAN,J. L., & LOFTUS,E. F. Retrieval of words from long-term memory. Journal of Verbal Learning and Verbal Behavior, 1971, 10, 107-I 15. HAYES-RoTI-I, B., & HAYES-ROTH,F. Plasticity in memorial networks. Journal of Verbal Learning and VerbalBehavior, 1975, 14, 506--522. HOvr-WEICrIEL,R. Semantic reorganization in memory (Doctoral dissertation, University of California, Los Angeles, 1976). Dissertation Abstracts International, 1976, 37(4). (University Microfilms No. 76-22, 196.) JOHN, E. R. Mechanisms of memory. New York: Academic Press, 1967. LOFTOS, E. F. Activation of semantic memory. American Journal of Psychology, 1973, 86, 331337. (a) LOFTUS, E. F. Category dominance, instance dominance, and categorization time. Journal of ExperimentalPsychology, 1973, 97, 70-74. (b) MEYER,D. E., & SCHVANEVELDT,R. W. Facilitation in recognizing pairs of words: Evidence of a dependence between retrieval operations. Journal of ExperimentalPsychology, 1971, 90, 227-234. MEYER, D. E., & SCHVANEVELDT,R. W. Meaning, memory structure, and mental processes. Science, 1976, 192, 27-33. MILLWAV,D, R. B., RrCE, G., & CORBETT,A. Category production measures and verification times. In A. Kennedy & A. Wilkes (Eds.), Studies in long-term memory. London: Wiley, 1975. NORMAN,D. A., & RtrUELHART,D. E. Explorations in cognition. San Francisco: Freeman, 1975. RoscH, E. On the internal structure of perceptual and semantic categories. In T. E. Moore (Ed.), Cognitive development and the acquisition of language.

New York: Academic Press, 1973. RoscH, E. Cognitive representations of semantic categories. Journal of Experimental Psychology: General, 1975, 104, 192-233.

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REORGANIZATIONIN MEMORY SMITH,E. E. Effects of familiarity on stimulus recognition and categorization. Journal of Experimental Psychology, 1967, 74, 324-332. SMITH,E. E., CHASE,W., & SMITH,P. Stimulus and response repetition effects in retrieval from shortterm memory: Trace decay and memory search. Journal ofExperimentalPsychology, 1973, 98, 413422. SMITH,E. E., SHOBEN,E. J., & RIPS,L. J. Structure and process in semantic memory: A featural model for semantic decisions. Psychological Review, 1974, 81, 214-241. THORNDIKE,E. L., & LORGE,I. The teacher'swordbook of 30,000 words. New York: Teacher's College, Columbia University, 1944.

WILKINS,A. J. Conjoint frequency, category size, and categorization time. Journalof VerbalLearningand Verbal Behavior, 1971, 10, 382-385. REFERENCENOTES 1. HUNT,E. B. Imageful thought. Paper presented at a conference on Semantic Factors in Cognition. University of California, Santa Barbara, May 26, 1976. 2. WICKENS, W. D. Parameter estimation in Markov chain learning models. Unpublished master's thesis, Brown University, 1967. (Received September 8, 1976)