1986 Metcalfe & Fisher 1986 the Relation Between Recognition Memory and Classification Learning

7/30/2019 1986 Metcalfe & Fisher 1986 the Relation Between Recognition Memory and Classification Learning

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Memory &Cognttton1986, 14 (2), 164-173The relation between recognition memoryand classification learning

JANET METCALFEUniversity of British Columbia, Vancouver, British Columbia, Canada

an dRONALD P. FISHERFlorida International Umversity, Miami, Florida

Two experiments investigated the relation between recognition memory and classification learn-ing. The subjects were instructed that they would see a series of random-dot patterns and laterwould be asked to classify or to recognize the patterns. Following study, the subjects performeda classification task, a recognition-memory task, or both. It was found that classification-learninginstructions were superior to recognition-memory instructions for the classification task, but thatthere was little or no effect of instructions on the recognition task. When subjects performed bothrecognition and classification tasks, there was no relation between saying "old" to a probe andcorrectly classifying it, except with old exemplars, and then only when the initial instructionshad been to expect a recognition-memory test. Overall, the data show that classification and recog-nition can be experimentally separated. In addition, classification is often statistically indepen-dent of recognizing that items are old. These observed relations provide some constraints for thefurther development of models of classification learning and recognition memory.

A num ber of theorists have proposed that people inayclassify or categorize by retrieving from m emory partic-ular events that they have p reviously experienced (e.g.,Brooks, 1978; M edin& Schaffer, 1978). Reber and Al-len (1978) illustrated this exemplar view of classificationas follows:A novel four-legged beast xs perceived as a dog not be-cause it fits w~th the viewers abstract feature system fordog but rather because it reminds h~m of some specific crit-ter he has met before w hich was identif ied as a dog. (p 192)

Hintzman and Ludlum (1980) showed how a m odel thatstores only presented instances or exemplars can accountfor the differential forgetting of new p rototypical and oldexem plar test items, as is found in the em pirical data(Hom a, Cross, Cornell , Goldman, & S chwartz, 1973;Posner & Keele, 1970; Strange, Keeney, Kessel, &Jenkins, 1970). This finding had previously been thoughtto provide evidence for the special status of concepts thatwere distinct from the presented exemplars. Hintzman(1983) further elaborated this exemplar model to accountfor a wide range of memory and classification data. JacobyThis research was funded by Natural Sciences and EngineeringResearch Councd of Canada Grant No. A0505 to J. Metcalfe and byUCLA Academic Senate Research Grant No. SF27 to R. P F~sher andRobert A. Bjork. W e thank Ehzabeth Bjork, Robert Bjork, Eric E~ch,Alice Healy, Daniel Kahneman, Michael Masson, Douglas Med~n,Thomas Wickens, and two anonymous reviewers for their commentsand criticisms.Please send repnnt requests to J M etcalfe, Department of Psychol-ogy, Indiana University, Bloomington, IN 4740 5

and Brooks (1984) also argued that specific instances mayaccount for much of the classification-learning data, andKahnem an and Miller (in press) noted that normative judg-me nts may be attributable to retrieval of specific episodesor instances. The exemplar-based idea of classificationlearning stimulates the question addressed in the presentarticle, of the relation between classification and recog-nition memory for specific instances.Models that propose that classification is based onmemory for specific instances suggest that there shouldbe a relation between recognition of i tems as old and clas-sification of the items, since both judgments presumablyuse the same information. Note that the predictions arenot in terms of po sitive, negative, or zero co rrelations be-tween recognition and classification accuracy. Rather,subjects should be better at classifying items they thinkare old than those they think are new, because on aver-age the "believe old" items are more likely to be in orlike items in memory than the "believe new" items. Smithand Medin (1981, p. 164) summarized the workings ofexemplar models of classification as follows: "typical ex-emp lars dominate concept representations, and a test i temis categorized as a mem ber of a target concept if and onlyif the former retrieves a criterial number of the lattersexemplars." They are explicit about the relation that ispredicted between classification and recognition of i temsas old:

If a new pattern B3 [which is similar to old item B1] ispresented, i t should be correctly classif ied because i t w ouldmost likely access the representation associated with B1.

Copyright 1986 Psychonomlc Society, Inc. 164


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CLASSIFICATION AND RECOGNITION 165Note further that on a new-old recognition test B3 w ouldvery likely be recognized falsely as old for the same rea-son. (p. 245, our emphasis)

Thus, the m ore likely an item is to be recognized as old,the better it shou ld be classified.Despite the pleasing parsimony of single-system exem-plar models, other research provides evidence that twomem ory systems with different characteristics may e x-ist. These two systems have been called episodic andsemantic memory (Tulving, 1972, 1983), memory withand without awareness (Eich, 1984; Jacoby & W ither-spoon, 1982), locale and taxon (Jacobs & Nadel, 1985;OKeefe & Nadel, 1978), and explicit and implicitmem ory (Graf& Schacter, 1985a, 1985b; Schacter, 1985;Schacter & Graf, 1985). The explicit-locale-episodic sys-tem is usually characterized as requiring that the individualbe aware that he or she is remembering a particular event.It is also thought to be high ly context sensitive (OKeefe& Nadel, 1978) and interference prone (Sch acter, 1985).Standard memory tasks suc h as free recall, cued recall,and recognition are usually thought to be explicit-locale-episodic tests (although the implicit system m ay also beused under certain circumstances; see M andler, 1980).The im plicit-taxon-semantic system does not require con-scious aw areness that an event occurred. Percep tualfluency, rather than awareness, seems to be o ne hallmarkof this system. It may be c haracterized by unidirectionalstimulus-response associations (Jacobs & Nadel, 1985;OKeefe & Nadel, 1978). Tasks such as fragment com-pletion, priming, w ord identification, anagram solving,and lexical decision are considered to be imp licit-memorytasks. Both systems are vulnerable to new information,although perhaps differentially vulnerable. Several linesof evidence suggest that the two types o f mem ory areseparable, are partially independent, and handle informa-tion in different ways. According to Schacter and Graf(1985), amnesics are often impaired on explicit-memorytasks, such as recall and recog nition, and yet spared onfragment completion, priming, and other implicit tasks(Cohen & Sq uire, 1980; Graf, Squire, & M andler, 1984;Kinsbourne & W ood, 1982; Milner, Corkin, & Teub er,1968; Moscovitch, 1982; Wood, Ebert, & Kinsbourne,1982). Norm als also have shown a pattern of results in-dicating that performance on tasks usually ascribed to onesystem (e.g., fragment comp letion task) may be indepen-dent of performance on tasks usually ascribed to the othersystem (e.g. , recognition mem ory task; see Tulving,Schacter, & Stark, 1982). Fisher and Chandler (1985)show ed that training on an episodic task transfers to anepisodic but not to a semantic task, whereas training ona semantic task transfers to a semantic but not to an epi-sodic task.It is plausible to speculate that the implicit-taxon-semantic system is m ainly responsible for categorization.Distributed perceptron mo dels and unidirectional stimulus-response matrix models, such as those o f McClelland andRum elhart (1985) and Knapp and Anderson (1984),

respectively, can do a good job of handling the data fromclassification-learning exp eriments. Th e M cClelland andRum elhart (1985) model can also handle priming effects,and both m odels are capable of redintegration, so theyhave the potential to do other implicit-mem ory tasks, suchas fragment completion. These m odels seem ideally suitedto handling much of the data that have been attributed tothe taxon or implicit-memory system. (The models havenot been applied to episodic-memory data, so the limitof application is not known.) On the o ther hand, episodic-distributed models, such as CHAR M (Eich, 1982), whichis highly context sensitive, can account for m uch data fromsuch tasks as free recall (Metcalfe & Mu rdock, 1981),cued recall, recognition (Eich, 198 2, 1985), and serialrecall (Murdock, 1983). Interestingly, the CHA RM modelrequires a semantic-memory pattern-recognition systemin addition to the episodic system . Interesting, also, is thefact that althoug h the episodic CHAR M m odel can dosome categorization, it does not m irror in detail the resultsof experiments done on prototype form ation (see Eich,1982). It may be that this failure of the episodic m odelis informative. Perhaps classification is mainly done inthe taxon or sem antic system. OKeefe and Nadel (1978),p. 100) argued that concepts and categories arerepresented in the taxon system. Children seem to be ableto form concepts before the hippocampus--the anatomi-cal structure necessary for explicit-episodic memories--is fully functioning. Jacobs and Nadel (1985, Footnote 3)also noted "the central role of all taxon systems in proto-type or concept formation." The idea that there m ay betwo memory systems--one implicit and mainly respon-sible for concept formation, and one explicit and mainlyresponsible for recognition memory--provides a contrapo-sition to the exemplar-only view of classification learn-ing. The relation between recognition memory and clas-sification learning is of considerable interest from thedual-memory perspective. This view suggests that thesetwo tasks m ight be dissociable.Reber and Allen (1978) conducted a study on the learn-ing of synthetic gramm ars in which they asked subjectsto introspect about information and decision processes thatwere relevant to how they were able to tell whe ther a par-ticular probe letter string w as or w as not gramm atical.Of spec ial interest in the present context are those in-trospections indicating that subjects classified letter stringsas grammatical because they judged the strings to be old(i.e., because they recognized the string, or thought theydid). Reber and Allen noted that these kinds of responsedeserve special treatment, since they represent the primaryevidence for the idea that classification is done on the ba-sis of specific instances. They expected that if subjectsclassified primarily by referencing back to the stimuli fromthe learning session, then they w ould find a large num -ber of items on w hich the subjec ts justified their classifi-cation responses by citing an item as being an "o ld" itemor one that "reminds me of .... " In contrast to expecta-tions, Reber and Allen found that these kinds of introspec-tion were infrequent. In addition, subjects w ere often


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16 6 METCALFE AND FISHERwrong ab out whether an i tem w as , in fact , o ld . How ever ,w hen they d id c las s ify an i t em as gram m a t i ca l becausei t rem inded them o f an o ld ex em plar , they w ere usua l lyr ight in the gram ma tica l ity dec is ion . To our kn owledge ,these introspect ive reports comprise the only empir ica lev idence that has d irect bear ing on the re lat ion betw eensay ing " o ld" to an i t em in recogni t ion m em ory and cor -r e c t ly c l a s s if y in g t h a t i te m . T h e p r i m a r y g o a l o f t h epresent s tudy wa s to further invest igate the re lat ion be-tw een recogn i t ion m em ory and c las s i f ica t ion , in an ob-j ec t ive form .The other aspect of the re lat ion between c lass if icationand recog nit ion that is of interest is the goodn ess of per-formance o n the recogni t ion and c lass if ication tasks as afunct ion of the instruct ions given to sub jects . A lthoug hmod els of c lass if icat ion learning m ake no predict ions onthis issue, the results could be of interest from a practicalor pedagogica l s tandpoint . Accurate recogni t ion may beinversely related to good c lass i f icat ion perform ance, ori t might be the case that the com patibi l ity between instruc-t ions and test (Bransford, Franks, Morris , & Stein, 1979)i s a n imp o r t a n t f a c to r in p e r f o r m a n c e . L u r ia s ( 196 8 )mnemonist , while exhibit ing spectacular recognit ion abil-i t ies , was almost total ly unable to conceptualize and clas-s i fy . R e b e r a n d Al le n ( 1978 ) f o u n d t h a t u n d e r p a ir e d -associate learning instructions, subjects were better ableto remem ber str ings of letters than un der incidental learn-ing instructions, but they were less able to determine thegram m at i ca l ity o f the s t r ings . M ed in and S m i th (1 9 81 )found that subjects were be st at classi fying when they weres imply told to expect a c lass i f icat ion task; perform ancedeteriorated w ith more expl ic i t instruct ions , such as tomem orize a rule plus exceptions or to try to form a gen eralim press ion an d use th i s im p ress ion to c las s ify . R eber(1976) a l so found that instruct ions to try to f ind an d usea rule hurt c lassif ication performance. H owever, s ince thisstrategy also impa ired recognition performance, the searchfor an obscure ru l e m ay have been s im ply a d i s t rac tingactivity. Although m any different strategies have been in-vest igated, there are no previous studies in which sub-jects were told to expect a recognit ion test or a c lassif ica-t ion test and then w ere given e i ther a c lass i f icat ion testor a recogn it ion test or both. Suc h a study is of interestbecause i t should tap the usua l encoding that i s done forone task or the other . In the exper iments that fo l low, wem a n ip u la t e th e e n c o d in g b y m e a n s o f o u r in s tr u c t io n sabou t the task that w i l l be g iven a t t im e o f t e s t .

E X P E R IM E N T 1M e t h o dM a t e r ia l s . T h e m a t e r i a l s w e r e 3 6 r a n d o m - d o t p a t t er n s , e a c h o fw h i c h c o n s i s t e A o f s i x b l a c k d o t s ( . 5 cm i n d i a m e t e r ) r a n d o m l yp l a c e d o n a n 8 . 5 x 1 1 i n . w h i t e b a c k g r o u n d . T h e r e w e r e 1 O p a t -t ern s i n ea ch o f th ree ca teg o r i e s , a rb i tra r i l y la b e l ed A , B , a n d C .E a ch ca teg o ry co n ta i n ed o n e p a t t ern th a t s erv ed a s th e p ro to ty p e ,e ight patterns that were sm al l d i s tort ions o f the proto type , and on ep a t tern th a t w a s a l a rg e d i s to r ti o n o f th e p ro to ty p e . T h e s m a l l d i s-

tOrtlons were constructed by moving each of the six dots in the pro-totype .5 in. in one of eight com pass-point direct ions N, NE, E,SE, S, SW , W , NW. Each dot w as moved in a different direct ion.The large distortions were co nstructed in a similar fashion, but bymoving each of the six dots [ in. m one of the eight directions.Procedure and des ign . Subjects heard one of two sets of instruc-tions before studying a list of random-dot patterns. They w ere toldto study the patterns in anticipation of either a recog nition test ora classification test. The instructions were read verbatim from oneof the followmg texts

(A ) Recognition m structton~ In this experxment, I am go ing toshow you som e pictures that 3ou may think of as constellations ofstars If you wish. I will sho~ you three different lists of 6 patternseach for a total of 18 p atterns The first 6 patterns will be calledList A patterns, the second List B. and the third List C [F or halfof the subjects, the order was C, B. A ] Your task is to try to remem-ber each of the 18 patterns so that you can recognize them laterI am later going to present some of these same 18 patterns mixedin with other very similar patterns that you have not seen, and Iwill ask you to say w hether a given pattern is old or new(B ) Classification mstructlons. In this experiment, I am going toshow you some pictures that you m ay think of as constellations ofstars ~f you wish. I will show you three different lists of 6 patterns

each for a total of 18 p atterns. The first 6 patterns will be calledList A patterns, the second List B, and the third List C Y our taskis to study the patterns so that later you can correctly classify themas A, B, or C. I am later going to present you w ith some of thepatterns mixed in with some new patterns that you have not seen,and I will ask you to sort all of the patterns into Type A, B, or CThe stimu li were arranged m three six-page bo oklets (lists), witheach booklet containing s ix exem plars f rom one category. The s ixexemplars were chosen randomly from among the eight small ths-tortions of each prototype. Subjects examined each pattern for10 sec, and then on the experimenters signal ("turn") turned tothe next page of the boo klet to examine the next pattern. After thesubjects examined the last page of each booklet, there was a briefpause during which t ime the experimenter reminded the subjectswhich list they had just finished and which w as to be examined nexthnmediately after the learning phase, the subjects performed either

a recognition test, a classification test. or both. The same sequ enceof 24 randomly ordere d test patterns was used in both the recogni-tion and the classification tests The test bo oklets included 8 pat-terns from each category , the prototype, four small distortions thathad been presented during acquisition (old exemplars), two smalldistortions that had not been presented before (new exemplars), andthe large distortion.For the recognition test, subjects judged whether each test pat-tern had been seen before (old) or not (new). The subjects gavea 6-point confidence judgm ent (1 = guess , 6 = posi t ive) after eachdecision. For the classification test, subjects judged whether eachof the test patterns was an A, B , or C pattern, and gave a 6-pointconfidence judgment. Testing w as subject-paced, and the subjectswere asked to respond to every p robe, even if unsure.S u b j ec t s . T h e subjects were 211 UCLA undergraduates who par-ticipated to fulfill a course reqmrement. The subjects were run insmall groups of 5 to 16. One hu ndred seven subjects received ther e c ogn i t i on i n s t r u c t i on s . Of these, 51 performed the r e c o g n i t i o ntest and 56 w ere given the classification test, as thear first, or only,test. Fifty-one of the subjects who were given the recognition in-structions performed b oth the recognition and classification tests,27 in the o rd er reco g n i t io n followed by classification, and 24 inthe reverse order. One h undred four subjects received the classifi-cation instructions. Of these, 50 performed the reco gnition test and54 w ere given the classification test as their first, or only, test. Forty-eight subjects who were given the classification instructions per-formed bo th tests, 22 in the order recognition followed b y classifi-cation, and 26 in the reverse order.


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CLASSIFICATION AND RECOGNITION 167R esul t s and D i scuss ionA criterion ofp < .05 w as chosen for considering aneffect to be significant. If an effect is mentioned, but didnot reach the .05 level, the p value of the trend is specifi-cally stated.Recognition tt~t. In this section, we examine the resultsof only the first test. The recognition data were analyzedin terms of the proportion of items that subjects thoughtwere o ld. These data are presented in Figure 1, top p anel.As the figure sh ows , the probability of calling an itemold did not depend upon instructions [F(1,99) = 1.52,M S e = .06]. There w as no interaction between instruc-t ions and the type o f probe [F (3,297) = . 38, M S e = . 0 4 1 .How ever, there w as considerable discrimination amongthe different probe types [F(3,297) = 181.49, M S e =.04]. Subjects in both instructional conditions thought thatthe nonpresented prototypes were old most of the time,followed by the old exem plars, the new exemplars, andfinally by the large distortions, which were rarely calledold. All comparisons among probe types w ere significantby Duncans multiple-range test. Subjects discriminatedamong the probes, and thus, presumably w ere makingcontact with stored mem orial intbrmation. However, therewas no indication that contact with the m emorial infor-mation was affected by the instructions.Four oth er analyses were conducted on the recognitiondata. First, the data were analyzed in terms of prop ortioncorrect, collapsed over List A, B, or C. Again, there wasno effect of instructions on correct recognition [F(1,99)= 1.09, M S e = .05]. Th ere was no interaction betweeninstructions and the type of pro be [F(3,297) = .53, M S e= .04]. H owe ver, there was a significant effect of typeof probe [F(3,297) = 152.55, M S e = .04]. The probabil-ity of correct recognition was best for the large distortionsand the old exemplars. The new exem plars were about atchance, and the new prototypes tended to be wronglyrecognized as being old. Bearing in mind that subjects wereonly correct in calling the old exem plars old, the meanvalues for this effect are redundant with those given inFigure 1.Second, the data were collapsed over probe type andanalyzed by lists, to see if input order was im portant.There w as no m ain effect of instructions, of list (ABCvs. CBA), or of order (first, second, or third listpresented) (Fs < 1). None of the interactions was sig-nificant except that between list and order [F(2,192) =12.18, M S e = 1.19]. This interaction can be interpretedas indicating that List A was easier than List C. This anal-ysis was fairly uninteresting since there was no hint ofan interaction with instructions. The ov erall mean propor-tion correct was .57 for prototype instructions and .58for recog nition instructions.Third, the data, collapsed over probe type, w ere ana-lyzed on the basis of the num ber of items that were calledold. Again, there w ere no interactions with instructions.There w as an effect of order, showing that items in thefirst list were more likely to be called old than the thirdthan the second [F(2,192) = 47.04, M S e = 2.10]. Therewas also an interaction between list and order that can

be interpreted as indicating that List C items (whether firstor last) were mo re likely to be called old than A itemsthan B items [F(2,192) = 19.09, M S e 2.10]. Since thesematerials effects did not interact with instructions, theywill not be discussed further.Fou rth, the overall mean confidence ratings, collapsedover probe type and order, were compared with those rat-ings on just the correct items. There was no effect of in-structions, and no interactions with instructions. The onlysignificant effect revealed by the analysis showed that peo-ple were slightly more confident on correct responses(3.92) than they w ere overall (3.83) [F(1,96) = 14.99,M S e = .045]. The lack of an effect of instructions on con-fidence ratings (as shown abov e with propo rtion correct)suggests that this lack o f an effect for instructions in recog-nition is not due just to an insensitive measure of propor-tion correct.

Classification test. The bottom panel of Figure 1presents the unconditional classification data. As can beseen, the group given classification instructions classifiedbetter than did the group given recognition instructionsIF(I,108) = 13.85, M S e = .12]. This effect is in con-trast to the failure to find a difference with instructionsin the recognition-memory task. There w as an effect ofprobe type [F(2,324) = 34.17, M S e = .05]. Goodnessof classification decreased for prototypes, old exemplars,new exemplars, and large distortions. There w as also asignificant interaction between instructions and the typeof probe [F(3,324) = 4.04, M S e = .05]. Th is interac-tion is attributable to the large distortions, which wereclassified about equally poorly in both instructional con-ditions. Duncans m ultiple-range tests showed that therewere significant differences betw een instructions for eachof the prototype, old exemp lar, and new exemp lar probes.The differences among these pro bes were not significantunder classification instructions, although all three gavesignificantly better results than did the large distortionprobe. U nder recognition instructions, there w as a sig-nificant difference between the prototype and new exem-plar probes, but none of the other comp arisons amongprototype, old exemplars, and new exem plars were sig-nificant. All three of these probes p roduced significantlybetter results than did the large distortion probes. Therewas no significant difference between the large distortionprobes depending upo n instructions. The superiority ofthe classification instructions is thus show n with all prob esexcept with the large distortion probe. W e did not takesimilarity judgments on the materials. However, the factthat under both instructional conditions classification per-formance was well above chance, even on nonpresentedprobes, indicates that the within-category similarity wasconsiderably higher than the between-category similarity.These findings suggest that contact with episodicmemory, as evidenced by the recognition scores, was thesame in the two instructional conditions. There was neithera main effect of instructions, nor an interaction betwe eninstructions and probe type, w hen the dependent measurewas the prob ability of calling an item old. Althoug h theinstructional manipulation did not affect contact with epi-


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168 METCALFE AND FISHER

RECOGNITION TESTI RECOGNITION INSTRUCTIONSCLASSIF)CAT l ON INS3"RUC3" |ON S

CLASSIFICATION TEST

OLD NEW LARGEPROTOTYPE EXEMPLAR EXEMPLAR DISTORTION

PROBE TYP EFi g u re 1 . E x p er i m en t 1 , r eco g n i t i o n a n d c l a s s i f ica t i o n . T h e to p p a n e l s h o w s th e p ro b a b i l i ty th a t n ewp r o t o t yp e s , o ld e x e m p l a r s , n e w e x e m p l a r s , a n d n e w d i s t o r ti o n s w e r e c a l l e d o l d i n r e c o g n i t io n . The bottompanel sh ow s t h e p r ob a b i l i ty o f c or r e c t c l as s i f ic a t i on o f t h e se probes.

sodic mem ory as show n in the recognition task, there wasa large effect in the classification task. Sim ilarly, therewas no instructions x probe type interaction in the recog-nition task, but there w as a reliable interaction in the clas-sification task. Classification learning and recog nitionmem ory thus appear to be separable experimentally.To further investigate the relation between reco gnitionmem ory and classification learning, we looked at the con-ditional probabilities of classification given recognition

as old or new, for those subjects who w here given boththe recognition and classification tests. If both recogni-tion and classification judgments are based on the sameinformation, and if the processes are related, we shouldfind an item-by-item dep endence relation between recog -nition memory and classification. Specifically, if peoplecall an item old, they should be m ore likely to classifythat item correctly than if they call an item new. This find-ing would corro borate the data on subject introspections


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CLASSIFICATION AND RECOGNITION 169found by R eber and Allen (1978). If recognition and clas-sification were handled by different memory systems, wemigh t expect little or no dependence.C ondi t iona l ana lyses . The difference between propor-tion of correct classification of those items judged to beold versus items judged to be new in recognition was cal-culated for each subject. As can be seen from Table 1,under classification-learning instructions, it did not mat-ter for classification performance w hether an item w asjudged to be o ld or new. In fact, the items judged as newwere actually slightly m ore likely to be classified correctlythan were the items judged to be o ld. With recognitioninstructions, there was a tendency for classification per-formance to depend upon recognizing an item as old. At test showed that there w as a reliable difference betweenthe two instructional conditions. There w as no dependencebetween recognition and classification with classificationinstructions, whereas dependence was found with recog-nition instructions [t(27) = 2.13].

E X P E RI M E N T 2Experiment 2 was sim ilar to Experiment 1 in overalldesign. We w ere primarily interested in further investigat-ing the finding from Experim ent 1, that classification per-formance was better with classification instructions thanwith recognition instructions, and more importantly, thatclassification perform ance can be independent of saying"old" in a recognition-memo ry task. To obtain more dataon the dependency relation, all subjects were tested forboth reco gnition and classification. Subjects w ere testedfor recognition of a particular probe and then were testedimmediately for classification of that probe. Finally, weincreased the numb er of old and new exem plar probesto provide enough observations to calculate the conditionalprobabilities separately for both prob es.

M e t h o dM a t e r ia l s . The stimuli were similar to those used in Experi-ment 1. Three new prototypes were constructed, one for eachcategory (A, B, and C). From each prototype, we constructed 12exemplars that were sm all distort ions and 1 exemplar that was alarge distortion.P r o c e d u r e . The instructions and learning phases were identicalto those of Experiment 1. Subjects examined six small distortionsof the prototype in each category, one at a time, at a 10-sec rate.Imm ediately after the learning phase, subjects were given a 42-pagebooklet containing 14 patterns from each of the three categories:t h e p r o t o t y p e , t h e s i x s m a l l d i s t o r t io n s t h a t h a d b e e n p r e s e n t e d e a r l i e r(old exem plars) , six small distortions that had not been presented

T a b l e 1Simple Probabi l i ty o f Class i f i ca t ion and Di f ferencesi n C o n d i t i o n a l P r o b a b i l it i e s, E x p e r i m e n t 1Old Exemplars

P(Correct Classification/Said Old) P(CorrectInstmctions -P(Correct Classification/Said New) Classification)Classification - .04 .66Recognition .12 .50

earlier (new exem plars), and one large distortion. In all there w ere18 old test patterns and 24 new test patterns. The 42 patterns wereordered randomly. All sub jects made both a recognition and a clas-sification decision about each test pattern before turning to the nextpattern. Subjects were instructed to decide whether a given patternwas old or new (plus give a confidence judgment: 1 = guess, 3 =positive) and then judge whether the pattern was a Typ e A, B, orC (and give a 3-point confidence judgment).Subjects. The subjects were 100 UCLA undergraduates who par-t icipated to fulf i l l a course requirement. There w ere 51 sub jectsin the recognition-instructions group and 49 in the classification-instructions group .

ResultsRecognition test. When the data were analyzed in termsof the prob ability of calling an item old, there w as an ef-fect of instructions [F(1,98) = 4.81, M S e = .05]. Probeswere more likely to be c alled old with classification thanwith recognition instructions. However, there was notrend at all toward an interaction between instructions andtype of probe [F(3,294) = .40, M S e = .04]. A n interac-tion had been found o n the classification task in Experi-ment 1 and was also found in this experiment, as will bediscussed shortly. The main effect of instructions, then,seems to be due to a response bias during recognitionrather than to differential remembering. As had been thecase in Experiment 1, there was a main effect of type ofprobe |F(3,294) = 81.02, MS e = .04]. Duncansmultiple-range tests showed that prototypes were calledold significantly m ore than were old exe mp lars. Thedifference between old ex emplars and new exem plars wassignificant, as was the difference betw een new exemp larsand the large distortions, wh ich w ere rarely called old.The data, plotted in terms of the probability of calling thevarious probes old, are presented in the top panel ofFigure 2.

It was thoug ht that the confidence ratings might pro-vide a more sensitive measure than did the simple proba-bilities of calling items old, and might show evidence foran interaction betwe en probe type and instructional con-dition. Thus, the m ean confidence ratings for the called-old items were calculated for each subject who had at leastone "old" response for each probe type. There were 6 1such subjects. There was no effect of instructions(F < 1). There was an effect of probe typ e such that con-fidence was higher for the prototype (2.31) and old ex-emplars (2.32) than for the new ex emplars (2.12) and newdistortions (2.18) [F(3,177) = 3.77, MS e = .16].How ever, there was no interaction between probe typeand instructions (F < 1). Th us, the confidence analysissupports the idea of a simple response bias rather thanmem ory difference between instructional conditions.There w as no effect of instructions on correct recogni-tion [F(1,98) = 1.34, M S e = .04], nor w as there an in-teraction between instructions and type of probe [F (3,294)= 1.75, M S e = .04]. T here w as a significant effect oftype of probe [F(3,294) = 71.82, M S e = .04]. Theseresults were consistent with those found in Experiment 1.W hen the data were collapsed across probe type andanalyzed for correct recognition by lists, there was also


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17 0 METCALFE AND F ISHER

RECOGNITION TEST

RECOGNITION INSTRUCTIONSCLASSIFICATION INSTRUCTIONS

CLASSIFICATION TEST

OLD N EW LARGEPROTOTYPE EX EMPLAR EXEMPLAR DISTOR T ION

PROBE TYPEF i g u r e 2. Experiment 2, r e c o g n i t io n a n d c l a s s if i c a ti o n . T h e top panel shows the probabi l i ty that newprototypes, old exemplars, new exemplars , and new di s tort ions were ca l l ed o ld in recogni t ion. The bottomp a n e l s h o w s th e p ro b a b i l ity o f co rrec t c l a s s i f ica t i o n o f th es e probes.

no effect of instructions. The means were .56 and .55 forrecognition and classification instructions, respectively.There we re no significant interactions with instructions.The o nly significant effect that this analysis revealed w asa list order interaction, which indicated that recogni-tion was best on L ist C, then List A, and finally List B.W hen the data, collapsed over probe type, were analyzedfor the numb er of items called old, there was an effectof instructions such that more items (7.78 or 5 6%) w ere

called old with classification instructions than with recog-nition instructions (7.05 or 50%). This is the sameresponse bias reported above. There were no significantinteractions with instructions. There was an effect of ordersuch that items from List B w ere most likely to be calledold.The analysis of confidence ratings on correct respond-ing collapsed over probe type show ed no effects at all ex-cept one of list order. Subjects given presentation order


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CLASSIFICATION AND RECOGNITION 1 7 1ABC had higher confidence (2.22) than those given theCBA order (2.06) [F(1,95) = 6.00, M S e = .02]. W e haveno explanation for this effect.Overall, the recognition results showed that sub jects dis-criminated among probe types in both instructional con-ditions and tended to call more items old, regardless ofthe correctness of this judgment, when given classifica-tion as oppos ed to recognition instructions. This latter ef-fect seem s to be a response bias, since there w as no in-teraction betw een instructions and probe type (as wasfound in the classification results described below) on anydependent measure.C l a s s i f ic a t i o n t e s t . The classification results arepresented in the bottom panel of Figure 2. A s had beenthe case in Experiment 1, there w as a significant maineffect of instructions, such that performance was betterwith classification instructions than with recognition in-structions [F(1,98) = 14.54, M S e = .07]. There was amain effect of probe type [F (3,294) = 10.39, M S e = .05] .The order of goodness of classification was p rototypes> old exem plars > new exem plars > large distortions.Duncans tests showed that prototypes were significantlybetter classified than new exemplars, and that prototypes,old exemplars, and new exemplars were all better classi-fied than large distortions. There w as also an interactionbetween the type of probe and instructions [F(3,294) =4.11, MSe = .05]. A s had been the case in Experiment 1,the interaction w as attributable m ainly to the large dis-tortions, which w ere not differentially classified depend-ing upon instructions. Duncans tests showed that the p ro-totypes, old exemplars, and new exemplars were all betterclassified in the classification- than in the recog nition-instructions condition. In addition, the post hoc testsshowed that the differences among probes given abovefor the main effect apply only to the classification-instructions condition. There w ere no significant differ-ences among probe types with recognition instructions.It may be noted, however, that subjects were well abovethe guessing p robability of .33, even under recognitioninstructions, and even for the large distortions probe swh ich were classified the worst [t(50) = 2.07].Conditional analysis. Table 2 show s the (simple) prob-abilities of co rrectly classifying an item and the differ-ence in classification probability given that an item wasrecognized as old as compared to new. Separate differ-ence scores w ere calculated for the old and the new ex-emplars for each subject, and an ANOV A w as conductedon these difference scores. There w as neither a main ef-fect of instructions, nor a main effect of type of prob e(Fs < 1). How ever, the interaction between instructionsand probe type was significant [F(1,98) = 4.06, M S e = 06]. A D uncans multiple-range test showed that this in-teraction was attributable to the fact that there was a largedifference with the old exem plar probes wh en the instruc-tions had been to expect recognition. In this treatmentcomb ination, subjects were m ore likely to correctly clas-sify an item that they h ad recognized as old than one theythought was new. This dependence between recognition

T a b l e 2Simple Probabi l i t i es o f Class i f i ca t ion and Di f ferencesi n C o n d i t io n a l P ro b a b i l it i e s , E x p er i men t 2P(Correct Classification/Said Old) P(CorrectInstructions -P(Correct Classlfication/Sa~d New ) Classification)

Old ExemplarsClassification .04 .623Recognition 15 .476New ExemplarsClassification .09 .556Recognition .05 .435

and classification was not found in the othe r three treat-ment combinations, nor were the difference scores in theother three conditions reliably different from one another.Only the old exemplar probes in the recognition-instructions condition produced difference scores thatwe re statistically greater than zero (Bruning & Kintz,1968).These results replicate those found in Experiment 1 inwhich only the o ld exemplar probes were examined. De-pendence between recognition and classification wasfound, in both experiments, only when subjects expecteda recognition test a n d when the actual old items w ere givenas test stimuli. Otherwise, recognition and classificationwe re statistically independent.

GENERAL DISCUSSIONThe results of the two experiments presented here showthat classification learning and recognition mem ory areexperimentally separable and, under some conditions, arealso statistically independent. The reason for claiming ex-perimental separability is that an instructional manipula-

tion improved classification performance without alter-ing recognition performance (Experiment 1) or with verylittle effect on recognition (Experiment 2). The statisti-cal independence in some treatment combinations doesnot appear to be artifactual because we also found depen-dence in one condition. Furthermo re, the pattern of in-dependence between recog nition of items as old and clas-sification under classification-learning instructions andsome dependence under recognition-memo ry instructions,with the old exemplar probes, replicated over the two ex-periments. The dependence relation that is expected bya simple interpretation of exem plar-only m odels exists,but it is not a universal phenom enon. In particular, wewere able to find dependence only when subjects encodedin a way that presumably would give priority to thespecific exem plars--in preparation for a reco gnition test.W hen subjects encoded in order to be able to classify later,recognition and classification performance were in-dependent.These experiments do not sho w an inverse relation be-tween classification learning and recognition mem ory.People w ho w ere told that they wo uld get a recognitiontest were not better on recognition, as would be predictedby a com patibility hypothesis. Classification instructions,


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1 7 2 M E T C A L F E A N D F IS H E Rhowever , d id improve performance on the c lass i f icat iontask. Our resul ts are cons is tent wi th those found by Re-ber and A l l en ( 1 9 7 8) , inso far as the i r ep i sod ic - m em oryinstructions for paired-associate recal l and ours for recog-nit ion mem ory both impaired c lass i f icat ion performance .In addi t ion , the one treatment com binat ion in our exper i -m en ts in w h ich w e found a re la t ion be tw een " sa id o ld"and co rrec t c las s if i ca t ion correspo nds to the ep i sod ic -m e m o r y - i n s tr u c t io n s c o n d i t io n o f R e b e r a n d A l l en ss tudy, in which a depen dency re lat ion was m ost l ike ly toO C C U r .The fact that a different pattern of dependencies , as wellas a d i f ference in s imple probab i l it ies , occurred depen d-i ng upon i ns t ruc t i ons s ugg es t s that t he i ns t ruc t i ona lmanipulation may have resulted in different strategies. Itseems u nlikely to us that the strategy used under classif i-cation instructions was one of trying to abstract rules. BothM edin and Sm iths (1981) and Rebers (1976) experimentsindicated that c lassif ication perform ance is w orse, not bet-ter , under rule-based s trateg ies than under recog ni t ion-m e m o r y - b a s e d s tr a t e g ie s . T h e r e s u l ts s e e m m o s tinterpretable by the conjecture that there may be tw o sys-tems that are differentially used if one is expecting a recog-nit ion as com pared to a c lass i f icat ion test. We hav e twosorts of ev idence for a d is t inct ion . Firs t , the instruct ionsproduced different effects on the recognition and classif i-cat ion task s . S econd , c las s i fi ca t ion w as independ ent o fsaying "old" in recogni t ion in a l l treatment com binat ionsexcept the on e that ga ve expl ic i t pr ior i ty to spec i f ic ex-emplars . W e acknow ledge that the idea that two types ofinformat ion deve lop ( e .g . , exemplar and prototype infor-ma t ion, Posner & Keele , 1970; a l though other poss ib i l i-t ies exis t , Jacoby, 1984; Medin , Al ton, & M urphy, 1984;and see Sm ith & M edin , 1981, for a rev iew) might poten-t ia l ly h a n d l e t h e r e s u l t s p r e s e n t e d i n t h i s a r t i c l e . I t i s e v e nconceivable that some version of an exemplar-only modelmight produce these resul ts . The measures taken to in-duce an ex em plar - on ly m ode l to y i e ld the pat t ern foundin these data could generate interesting new predictions.The dissociat ion between recognit ion and class i f icat ionfound here , how ever , seems to us to be most com pat ib lewit h r e c e n t r e s e a r c h p o in t in g t o a d i f fe r e n c e b e t we e nsemant ic and ep isodic mem ory (Tulv ing , 1983) , betweenlocale and taxon systems (Jacobs & Nadel , 1985; OKeefe& N adel , 1978) , or between expl ic i t and impl ic it mem ory(Graf& Schacter , 1985a, 1985b; Schacter , 1985; Schac-ter & G raf , 1985).

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(Manuscript received June 19, 1985;revision accepted for publication November 11, 19 85.)

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1986 Metcalfe & Fisher 1986 the Relation Between Recognition Memory and Classification Learning