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3, Number 5. October J%4Rrprintcd from Journal or Vfiibal Lkahninc and Vehbal Behavior, Volume 3, Number 5. October J 961Copyright © 1964 by Academic I'rosn Inc. Ptinted in V. S. A.Copyright © 1961 by Academic I'rosn Inc.

JOURNAL OF VERBAL LEARNING AND VERBAL BEHAVIOR 3, 38S-396 (1964)

An Information-Processing Theory of Some Effects ofSimilarity, Familiarization, and Meaningfulness

in Verbal Learning 1

Herbert A. Simonand

Edward A. Feigenbaum

Carnegie Institute of Technology, Pittsburgh, PennsylvaniaUniversity of California, Berkeley, California

Among the most commonly used paradigmsin the study of verbal learning is the learningof nonsense syllables by the paired-associateor serial anticipation methods. The variablesthat have been shown to have importanteffects on the rate of learning include thelevels of familiarity and meaningfulness ofthe syllables, the amount of similarity amongthem, and the rate of presentation. In addi-tion, in the learning of lists, there are well-known serial position effects.

In previous papers (Feigenbaum, 1959,1961; Feigenbaum and Simon, 1962b), atheory has been set forth that undertakes toexplain the performance and learning proc-esses underlying the behaviour of 5s in verballearning experiments. The theory, in its origi-nal version, makes correct quantitative pre-dictions of the shape of the serial positioncurve (Feigenbaum and Simon, 1962a) andthe effect of rate of presentation on learning(Feigenbaum, 1959; Feigenbaum and Simon,1962a) as well as predictions of certain quali-tative phenomena (for example, oscillation)(Feigenbaum and Simon, 1961).

In this paper, a simplified and improvedversion of the theory is reported that retainsthese properties of the earlier theory whileproviding correct quantitative predictions of

1 This research has been supported by the CarnegieCorporation of New York and The RAND Cor-poration (Computer Science Department).

the effects of the other important variables:familiarization, meaningfulness, and similar-ity. The tests of the theory discussed hereare based on comparisons of the performanceof human ss, as reported in published experi-ments on paired-associate learning (Bruce,1933; Chenzoff, 1962; Underwood, 1953;Underwood and Schulz, 1960), with the per-formance predicted by the theory in thesame experimental situations with the same,or equivalent, stimulus material.

The theory to be described is a theory ofthe information processes underlying verballearning. The precise statement of such atheory is most readily made in the informa-tion-processing language of a digital computer,i.e., the language of computer programs.

The formal and rigorous statement of thetheory is a program called the ElementaryPerceiver and Memorizer (third version), orEPAM-111. This program is a closed modeland is used as an "artificial subject" in stan-dard verbal learning experiments (the latterbeing also simulated within the computer bymeans of an Experimenter program). Im-bedded in the theory are hypotheses about theseveral kinds of processes that are involvedin the performance of verbal learning tasks.These hypotheses take the form of subroutinesthat are component parts of the total pro-gram. Thus, there are performance subrou-tines which allow the program to produce

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responses that have previously been associ-ated with stimuli, subroutines for learning todiscriminate among different stimuli, and sub-routines for acquiring familiarity with stimuli.Top-level executive routines, which organizethese subroutines into a program, representhypotheses about the S's understanding ofthe experimental instructions and the learningstrategy he employs. The computer simulationof verbal learning behavior using the EPAM-III theory is, in essence, generation (by thecomputer) of the remote consequences of theinformation-processing hypotheses of thetheory in particular experimental situations.

In the first part of the paper a brief de-scription of EPAM-111 is provided. Sinceother descriptions of the program are avail-able in the literature (Feigenbaum, 1959,1961; Feigenbaum and Simon, 1962b), onlyso much of the detail will be presented as isessential to an understanding of the experi-ments and the interpretation of their results.In the second part of the paper, the resultswill be reported of comparisons of the behav-ior of EPAM-111 with the behavior of human5s in paired-associate learning where similar-ity is the independent variable. In the thirdpart of the paper, the results will be reportedof comparisons in which familiarization andmeaningfulness are the independent variables.

A Brief Description of EPAM-111The computer language in which EPAM-111

is written is known as IPL-V (Newell, Tonge,Feigenbaum, Green, and Mealy, 1964). Acompanion program simulates an experimentalsetting, more specifically, a memory drumcapable of exposing stimulus materials toEPAM, in either the serial or paired-associateparadigm. The simulated drum-rotation ratecan be altered as desired, as can the stimulusmaterials. An interrupt system is provided sothat the simulated experimental environmentand the simulated S can behave simulta-neously, to all effects and purposes, and caninteract, the S having access to the stimulus

material presented in the memory-drum win-dow.

The Performance System. EPAM-111 incor-porates one major performance system andtwo learning processes (Feigenbaum, 1959,1961; Feigenbaum and Simon, 1962b). When

a stimulus (a symbol structure) is presented,EPAM seeks to recognize it by sorting itthrough a discrimination net. At each nodeof the net, some characteristic of the stimulusis noticed, and the branch corresponding tothat characteristic is followed to the nextnode. With each terminal node of the net isassociated an image that can be comparedwith any stimulus sorted to that node. Ifthe two are similar, in the characteristics usedfor comparisons, the stimulus has been suc-cessfully recognized. We call such a stimulusfamiliar, i.e., it has a recognizable imagein the discrimination-net memory.

An image is the internal informational rep-resentation of an external stimulus configura-tion that the learner has stored in memory.An image, thus, is comprised of the informa-tion the learner knows about, and has associ-ated with, a particular stimulus configuration.An image may be elementary or compound.A compound image has, as components, oneOr more elementary or compound imageswhich may themselves be familiar and whichmay possess their own terminal nodes in thediscrimination net. For simplicity, in thecurrent representation, letters of the Romanalphabet are treated as elementary stimuliwhose characteristics may be noticed butwhich are not decomposable into more ele-mentary familiar stimuli. On the other hand,syllables are compound stimuli, their com-ponents being, of course, letters.

A compound stimulus image, viewed fromthe bottom up, may be regarded as an asso-ciation among the component stimuli. Thus,the net may contain stimulus images thatrepresent pairs of syllables, these compoundimages having as components other compoundimages, the individual syllables.

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In performing the paired-associate task,the program uses the stimulus, present in thewindow of the memory drum, to construct acompound symbol representing the pair com-prised of the stimulus and its associated re-sponses. We may designate this compoundsymbol by S , for the second responsemember is not then visible in the drum win-dow. The compound symbol, S- , is■, IS

sorted through the net, and the image asso-ciated with the terminal is retrieved. We willdesignate this image by S'-R', for if theprevious learning has been successful, it willbe comprised of two components: an imageof the stimulus syllable and an image of theassociated response syllable. The responseimage, R', which has just been retrieved asthe second component of the compound image,S'-R', identifies a net node where an image, sayR", is stored. R" will have as its componentssymbols designating the constituent lettersof. the syllable, say X", V", and Z". Each06 these, in turn, identifies a terminal node.Associated; with the terminal for a letter ismo* only an image of the usual kind (anafferent image), but also the informationrequired to produce the letter in question, i.e.,to print it. This information, which we maycall the efferent image, is used to producethe response. Thus, the final step in thesequence is for the program to respond, say,XYZ.

It is a fundamental characteristic of thisprogram that elementary symbols and com-pound symbols of all levels are stored in thediiseriniination net in exactly the same way.Thus, a syllable is simply a list of letters,aaad an S-R is simply a list of syllables. Asingle interpretive process suffices to sort aletter, a syllable, an S-R pair, or any othersymbol, elementary or compound. Moreover,the symbols discriminated by the net arenot restricted to any specific sensory oreffector mode. All modes can be accommo-dated by a single net and a single interpretiveprocess. Afferent symbols belonging to differ-

ent sensory modes will possess different attri-butes: phonemes will have attributes like"voicing," "tongue position," andi so; on.;printed letters will have attributes like "pos-session of closed loop," "possession of diag-onal line," and so on. Because they possessentirely different attributes, they will besorted to different parts of, the net. Finally,symbols may be of; mixedi mode. In,ai symbol,S-R, for example, S may be in. the visual:mode, R in the orah

The Learning System. EPAM-lI'I uses jjusttwo learning processes, one to, construct and;elaborate images at terminal! nodes of thenet {image building),- and) other to. elaboratethe net by adding new. branches {discrimina-tion learning). The first learning process alsoserves to guide the second.

When a stimulus, S, is in view and is sortedto a terminali, the stimulus can be comparedwith the image, S', stored at the terminal.If there is no image at the terminal, theimage-building process copies a part of S andstores the copy, S', as the initial image atthe terminal. If there is already an image, S',at the terminal, one or more differencesbetween S and S' are detected, and S' iscorrected or augmented to agree more closelywith S.

When a positive difference (not a merelack of detail ) is detected between a stimulus,S, and its image, S', the discrimination learn-ing process can use this difference to constructa new test that will discriminate between Sand S. The terminal node with which S'was associated is then changed to a branchnode, the test associated with the node S', isassociated with a new terminal on one ofthe branches, and a new image of S is asso-ciated with a new terminal on another branch.Thus, the discrimination-learning process addsa new pair of branches to the discrimina-

2 We use the term "image building" rather thanthe less clumsy and more descriptive term "famil-iarization" to resolve a dilemma of nomenclature thatwill become obvious in a later section.

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tion net and attaches initial images to thebranches.

Note that a stimulus, S, can be sorted to

a terminal, T, only if S satisfies all the tests

that point to the branches leading to T. Butthe image, S', stored at T must also satisfy

these tests. Hence, there can be a positivedifference between S' and S only if S' containsmore information than is necessary to sort

S to T. For instance, let S be the syllableKAW, and suppose that all the tests leadingto the terminal T happen to be tests on thefirst letter,K. Then the image, S', stored at Tmust have X as its first symbol, but may differfrom KAW in other characteristics. It mightbe, for example, the incomplete syllable K-B.The discrimination-learning process coulddetect the difference between the W and theB in the final letters of the respective sylla-bles, construct a test for this difference, andappend the test to a new net node. Theredundancy of information in the image, inthis case the letter B, permits the furtherelaboration of the net.

Thus, learning in EPAM -111 involves cyclesof the two learning processes. Through imagebuilding, the stimulus image is elaborateduntil it contains more information than theminimum required to sort to its terminal.Through discrimination, this information isused to distinguish between new stimuli andthe stimulus that generated this terminal andgrew its image. On the basis of such distinc-tions, the net is elaborated. The interaction ofthese two processes is fundamental to thewhole working of EPAM. It is not easy toconjure up alternative schemes that will per-mit learning to proceed when the members ofa pair of stimuli to be discriminated are not

present simultaneously.The stimuli that EPAM-111 can make fa-

miliar and learn to discriminate are symbolsof any kind, elementary or compound. Thus,the letters of the alphabet can be first made

familiar, and the net elaborated to discrimi-nate among them. Then EPAM can make

familiar and learn to discriminate among syl-lables, using the now-familiar letters as uni-tary building blocks. But now, paired-asso-ciate-learning can take place without theintroduction of any additional mechanisms.Instead of postulating a new associationalprocess, we suppose that an S-R pair is asso-ciated simply by making familiar and learningto discriminate the compound object SR.

The entire EPAM-111 paired-associatelearning scheme is completed by an executiveroutine that determines under what circum-stances the several image-building and dis-crimination-learning processes will 'be acti-vated. The executive routine makes use of akind of knowledge of results. When the simu-lated S detects that he has made an incorrectresponse to a stimulus syllable, he engagesin a rudimentary diagnostic activity: dis-tinguishing between no response and a wrongresponse, and determining to what extent theresponse syllable, the stimulus syllable, andthe S-R pair are familiar. Depending on theoutcome of the diagnosis, various image-building and discrimination-learning processesare initiated.

There are many details of the EPAM-111program we have not described here, but thisgeneral sketch will give us a sufficient basisfor discussing the behavior of the program instandard paired-associate learning situations.

Effects of Intralist and InterlistSimilarity

The adequacy of EPAM-111 as a theory ofhuman rote verbal learning has been testedinitially by replicating, with the program,experiments of Underwood (1953) on intra-list similarity; of Bruce (1933) on interlistsimilarity; and a number of authors (Under-wood and Schulz, 1960) on stimulus andresponse familiarization and meaningfulness.In this section the experiments employingsimilarity as the independent variable willbe discussed; in the next section the experi-

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Table 1Comparison of EPAM with Underwood's (1953) Data on Intralist Similarity

(Relative Number of Trials to Criterion, LL = 100)

Condition of stimulus and response similarity

Underwood ( 1953) studied paired-associatelearning of nonsense syllables under variousconditions of intralist similarity of stimulussyllables and response syllables. If we useL. M, and H to designate low, medium, andhigh intralist similarity, respectively; and let,e.g., L-M stand for "low intralist similarityof stimuli, medium intralist similarity of re-sponses," then Underwood's five experimentalconditions are L-L, M-L, H-L, L-M, and L-H.Underwood also studied three different con-ditions of distribution of practice, but sincehe found no significant differences in his data,we shall not consider this variable further.

In summary, Underwood found (a) thatintralist similarity of responses had virtuallyno effect on ease or difficulty of learning;8(b) that trials required for learning increasedwith degree of intralist similarity of stimuli,the difference being about 30% between theLL and HL conditions. The first row inTable 1 summarizes Underwood's findingsaveraged over the three conditions of distri-

a In the Underwood experiment the effect of re-sponse similarity is inconsequential. In general, theevidence on the impeding or facilitating effects ofresponse similarity is mixed. What docs stand out,however, is this: The effects of response similarity,if any, are quantitatively small and insignificantwhen compared with the large effects of stimulussimilarity.

The syllables employed in the EPAM simu-lation were the same as those used by Under-wood. 4 Row 2 in Table 1 summarizes the datafrom the EPAM tests. Response similarityfacilitated learning very slightly, while stim-ulus similarity impeded learning by as muchas 40%. Since relative learning times arereported in both cases, there is one freeparameter available for matching the twoseries. (In the normal course of events, thecompound images, S'-R', are discriminatedfrom each other on the basis of stimulus in-formation, not response information. Highintralist stimulus similarity makes difficultiesfor EPAM in discriminating and retrievingthese images, and hence impedes learning.Response similarity, of course, has no sucheffect.)

The qualitativefit of the EPAM predictionsto the Underwood data is better than thequantitative fit, although, considering the (apriori) plausible range of impact of the sim-ilarity variable on difficulty of learning, eventhe quantitative fit is not bad. Nevertheless,we sought a much bettter quantitative pre-diction. This search led us into the followingconsiderations. The prediction that is seri-

4 We are indebted to Professor Underwood formaking these sets of syllables available to us.

Data L-L L-M M-L L-H H-LUnderwood 100 96 109 104 131EPAM-111

("visual only")EPAM-111

100 88 141 91 146

("aural only") 100 100 100 100 114EPAM-111

("visual" and "aural" mixed, 1:1) 100 94 121 96 130EPAM-111

("visual" and "aural" mixed, 1:2) 100 96 114 97 125

tents on familiarization and meaningfulness bution of practice. The numbers are relativeill be considered. numbers of trials to criterion, with the numberUnderwood ( 1953) studied paired-associate for the LL condition taken as 100.

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ously out of line in Table 1 is the predictionfor the M-L condition. The more carefully onescrutinizes the Underwood experiment andthe Underwood materials, the more puzzlingthe experimental results become. Why doSs, as the results indicate, respond in theM-L condition so similarly to the way theyrespond in the L-L condition, while their re-sponses in the H-L condition are so differentfrom responses in either the M-L or L-L con-ditions? The answer is not to be found inthe Underwood materials. We have analyzedthe Underwood definition of "medium sim-ilarity" in terms of the information neces-sary to discriminate the items on a list of agiven length (in EPAM-like fashion) andhave found that Underwood's definition isquite careful and correct. By his definition,one should expect "medium similarity" liststo be midway in effect between his "low sim-ilarity" and his "high similarity" lists.

The answer, we believe, lies in the recoding,or "chunking," behavior of ss, which wouldmake the "medium similarity" stimulus listformally identical with the "low similarity"stimulus list under Underwood's definition.Suppose that many 5s were pronouncing theUnderwood CVC's, i.e., recoding the itemsinto the aural mode, instead of dealing withthem directly in the visual-literal (presenta-tion) mode. The recoded ("aural") syllableswill be "chunked" into two parts: a con-sonant-vowel pair, and a consonant. In otherwords, the visual-literal stimulus objects ofthree parts (CVC) quite naturally recode in-to "aural" stimulus objects of two parts (CCor CC '). In Underwood's "medium sim-ilarity" lists, none of C chunks are duplicated,nor are any of the C ' chunks. The recoding,therefore, has transformed the "mediumsimilarity" list into a "low similarity" list,by Underwood's definition.

To test this hypothesis for sufficiency fromthe point of view of the theory, we reran theEPAM (simulated) experiments using "aural"recodings of the original syllables. The mod-

ified predictions are given in Row 3 of Table1. As the analysis above indicates, the M-L

condition is now no different from the L-Lcondition, but the prediction of difficulty forthe H-L condition is too low. Assuming thatsome 5s are processing in the visual-literalmode and some in the "aural" mode, we havecomputed the average (1:1) of the two setsof EPAM predictions. This is given in Row4 of Table 1. If we weight the average 2:1 infavor of Ss doing "aural" recoding, the re-sult is as given in Row 5 of Table 1. Eachof these averaging procedures gives a pre-diction which is much better than that for theUnderwood lists non-recoded.

It is clear that we still have much to learnabout this low-vs.-medium similiarity prob-lem. In this regard, we are currently attempt-ing a direct experimental test of the "aural"recoding hypothesis.

Bruce's 5s (1933) learned two successivelists of paired-associate nonsense syllables.On the second list, response syllables, orstimulus syllables, or neither, could be thesame as the corresponding syllables on thefirst list. Thus, using current designations,Bruce's three conditions were (A-B,C-D),(A-B,A-C), and (A-B,C-B), respectively. Insummary, he found that learning of the secondlist was somewhat easier than learning of thefirst when all syllables were different (A-B,C-D), much easier when the response syl-lables were the same (A-B, C-B), and alittle harder when the stimulus syllables werethe same (A-B, A-C) (see Table 2). Therelative difficulties are compared using theA-B,C-D group as the norm.

Nonsense-syllable lists of low Glaze valueand low intralist similarity were used whenthe experiment was replicated with EPAM.The normalized results are shown in thesecond line of Table 2. The effects in thesimulated experiment were qualitatively thesame as in the actual data. If we comparethe conditions A-B,A-C and A-B,C-B withA-B,C-D, we find that identity of stimulus

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Table 2Comparison of EPAM with Bruck's Data on Interlist Similarity

(Relative Number oe Trials to Criterion)

Condition of stimulus and response similarity

A-B, A-C A-B, C-B A-B, C-D75 100

syllables impeded learning less, and identityof response syllables facilitated learning tothe same degree in the simulation as for thehuman ss. The ratio of difficulty for theA-B,A-C compared with the A-B,C-B condi-tions, where total number of different syllablesdiscriminated and learned was the same, was1.73 for the human 5s and 1.49 for EPAM.

From our analysis of the data of the Un-derwood and Bruce experiments, we concludethat EPAM provides satisfactory explana-tions for the main observed effects of intralistand interlist stimulus and response similarityupon the learning of paired-associate non-sense syllables. The effects predicted byEPAM-111 are in the right direction and areof the right order of magnitude, althoughthere is room for improvement in the quanti-ative agreement.

Familiarity and Meaningfulness

Among the other independent variablesthat have been shown to have major sig-nificance for the ease or difficulty of learn-ing nonsense syllables are familiarity andmeaningfulness. A thorough discussion of thedefinition of these two variables can be foundin Underwood and Schulz (1960).

The degree of familiarity of a syllable is usuallynot measured directly; instead, it is measured bythe amount of Familiarisation training to which 5has been exposed with that syllable. In the follow-ing discussion, they are not synonymous. "Familiari-zation" will be used when reference is made tospecific experimental conditions and operations."Familiarity," on the other hand, will refer to acondition internal to an 5: the state of informationabout a syllable in the memory of an 5 who hasgone through some kind of familiarization training.Thus, familiarity is an intervening variable not

directly observable. The use of an intervening vari-able hardly needs to be defended, since it is the rulerather than the exception in theory-building in thenatural sciences as well as the behavioral sciences.

Familiarization training is accomplished by caus-ing 5 to attend to the syllable in question in thecontext of some task other than the paired-associatelearning task to be given him subsequently. It shouldbe noted that there is no way of discovering, withthis definition, how familiar a syllabic may be foran 5 due to his experience prior to coming into thelaboratory. Although the syllables are not meaning-ful words, the consonant-vowel combinations con-tained in them occur with varying frequency inEnglish. The mcaningfulness of a syllable, on theother hand, is generally determined by measuring thenumber of associations that Ss make to it in aspecified period of time. Nonsense syllables for learn-ing experiments are generally selected from availablelists that have been graded in this way for mean-ingfulncss.

Since high familiarity and high meaningfulnessboth facilitate nonsense-syllable learning, there hasbeen much speculation that the two phenomenamight be the "same thing." This, in fact, is thecentral hypothesis examined in the Underwood andSchulz monograph. In one sense, meaningfulness andfamiliarity are demonstrably not the same, for asubstantial amount of familiarization training canbe given with low-meaningful syllables without sig-nificantly increasing their meaningfulness. However,Underwood and Schulz (1960) adduce a large bodyof evidence to show that there is a strong relationrunning the other way i.e., that meaningfulness ofwords is correlated with their frequency of occur-rence in English, and that ease of learning nonsensesyllables is correlated with the frequency, in English,of the letters that compose them (for syllables oflow pronunciability), or with their' pronunciability.

The data are of course greatly complicated by thefact that 5s may handle the material in either thevisual or the aural mode, and that most 5s probablyencode into the latter, at least part of the time.Hence, for relatively easily pronounceable syllables,frequency of phoneme pairs in the aural encoding isa more relevant measure of frequency than frequency

A-B,C-D condition= 100 130 75 100EPAM-111 A-B, C-D condition = 100 112 75 10Q

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of the printed bigrams or trigrams. Thus, the find-ing by Underwood and Schulz that pronunciabilityis a better predictor than trigram frequency of easeof learning does not damage the hypothesis thatfamiliarity of the component units is the criticalvariable, and that familiarity, in turn, is a functionof previous exposure.

We conclude that high meaningfulness implies highfamiliarity, although not the converse. If this is so,then the correlation of meaningfulness with ease oflearning may be spurious. Familiarity may be thevariable that determines ease of learning, and mean-ingful syllables may be easy to learn only becausethey are highly familiar.

The idea that familiarity is the criticalvariable in learningrests on the idea, certainlynot original with EPAM model, that thereare two stages in paired-associate learning:(1) integration of responses, and (2) as-sociation of responses with stimuli. Under-wood and Schulz (1960) have used this idea,and it plays an important role in their analy-sis. It also plays an important role in thestructure of EPAM (Feigenbaum, 1959,1960). From our earlier description it canbe seen that these two stages of learning arealso present in EPAM-111, but that bothstages make use of the same pair of learningprocesses: image building and discriminationlearning.

If response intgration is the mechanismaccounting for the relation between meaning-fulness and familiarity, on the one hand, andease of learning, on the other, then thereshould be a point of saturation beyond whichadditional familiarization will not furtherfacilitate learning, i.e., the point at whichthe syllables are so familiar that they arecompletely integrated. In the EPAM-111mechanism this would be the point where thesyllable images were complete and where thetests in the net were fully adequate to dis-criminate among them.

There is strong empirical support for thehypothesis of saturation. At the high end ofthe meaningfulness scale, further increases inmeaningfulness of syllables have relativelylittle effect on ease of learning, but the effects

are large over the lower range of the scale. Infact, and this is the most striking evidencerelevant to the issue, the experiments onmeaningfulness in the literature reveal a re-markably consistent upper bound on the effectof that variable. Underwood and Schulz(1960) survey a large number of the experi-ments reported in the literature, of bothpaired-associate and serial learning of CVCsyllables, and find rather consistently that theratio of trials to criterion for the least andmost meaningful conditions, respectively, liesin the neighborhood of 2.5. That is to say,syllables of very low meaningfulness takeabout two and one-half times as long to learnas syllables of very high meaningfulness (andabout two and one-half times as many errorsare made during learning).

Before the significance of this 2.5/1 ratio is con-sidered further, it is necessary to discuss one dif-ficulty with the hypothesis that familiarization andmeaningfulness (via familiarity) facilitate learningprimarily by virtue of responses being integratedprior to the associattonal trials. The effects reportedin the literature with meaningfulness as the independ-ent variable are generally much larger than theeffects reported for familiarization. No ' one hasproduced anything like a 2.5/1 gain in learning speedby familiarization training.

There is now some evidence, primarily in a doc-toral dissertation by Chenzoff (1962), that the mainreason for this discrepancy is that the familiarizationtraining in experiments has been too weak, hasstopped too soon. It appears that no one has carriedout familiarization training with his 5s to the pointwhere the syllable integration achieved is compar-able to the integration of syllables of high meaning-fulness.

Chcnzoff's experiment can be summed up as fol-lows. First, in his experiment he manipulated bothmeaningfulness and familiarization of both stimuliand responses. Thus, he had 16 conditions: all pos-sible combinations of H-H, L-H, H-L, and L-Lfor stimulus and response meaningfulnesss with F-F,U-F, F-U, and U-U for familiarity. Second, he em-

5 The two levels of the meaningfulness variablewere constructed as follows (using CVC's):

H: 53-100 Glaze, 85-100 Krueger, 67-97 Archer,2.89-3.66 Noble <m'), 3.08-3.87 Noble (a').

L: 0-53 Glaze, 39-72 Krueger, 9-48 Archer,0.92-1.83 Noble (m'), 1.38-1.94 Noble (a').

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" Reciprocal of number of correct responses ; H-H or F-F = 1.0.6 Relative number of errors to criterion; H-H or F-F = 1.0.

ployed a more thorough familiarization training tech-nique for the F condition than had any previous in-vestigator. The syllables were presented one at atime to 5 at about a 2.5-sec rate. The 5 was requiredto pronounce each syllable. After five trials, 5 wasasked to recall the syllables in any order. If an in-correct syllable was given, S was told that it wasnot a member of the list. If, within 30 sec, 5 couldnot perform completely correctly, five more familiari-zation trials were administered. This continued untilS learned the list. The range of number of trials forthe various 5s was 10-30; the median and modewere IS trials.

With this training, the effects, of familiarizationwere qualitatively similar to, and more than halfas large in magnitude as, the effects of meaning-fulness. Specifically:

(1) For the H-H (high meaningfulness) condi-tions, amount of familiarization of stimuli, responses,or both had no effect on case of learning; thesaturation was complete [Table 3, Column (1)].

(2) For the L-L (low meaningfulness) conditions,unfamiliarized syllables (U-U) took 1.8 times aslong to learn« as familiarized syllables (F-F). Re-sponse familiarization (U-F) had a greater effectthan stimulus familiarization (F-U) ; the ratios were1.2 and 1.6, respectively [Table 3, Column (3)].

(3) When familiarization training was provided,the effects of meaningfulness upon ease of learningwere reduced by about two thirds. In the F-F con-ditions, the L-L pairs took only 1.2 times as longto learn as the H-H pairs, the L-H and H-L pairsfalling between the two extremes. Saturation was not

fi Because of the form in which Chenzoff presentedhis data, the actual measure of speed of learningused here is the reciprocal of the number of correctresponses between particular (fixed) trial boundaries,relative to the (H-H, F-F) condition taken as anorm of 1.0 (see Table 3).

quite complete but was clearly visible [Table 3,Column (2)].

(4) In the absence of familiarization training, theusual large effects of meaningfulness were visible.In the U-U conditions, the L-L pairs took 2.2 timesas long to learn as the H-H pairs [Table 3, Column(4)].

Thus, except for the quantitative deficiencyin the effect of familiarization, Chenzoffshows meaningfulness and familiarity to beequivalent. But they are not additive becauseof the saturation effect.

Further and very strong evidence .for thesyllable-integration hypothesis is obtainablefrom the predictions of EPAM-111. By pre-senting syllables with appropriate instruc-tions, EPAM can attain familiarity withstimulus syllables, response syllables, or both.Amount of familiarity can be manipulated byvarying the number of familiarization trials.In particular, familiarity can be carried tosaturation—to the point where complete syl-lable images are stored in the discriminationnet. The maximum effects predicted byEPAM-111 for familiarization are of the samemagnitude as the maximum effects of mean-ingfulness observed in the empirical studies.Table 3 shows, for the four conditions, andtaking the L-L conditions as the norm, therelative rates of learning as predicted byEPAM-111 [Column (s)], and as reportedby Chenzoff 's 5s [Column (4)]. Except forthe rather high value for the H-L conditionfor Chenzoff's ss, which is in disagreement

Table 3Effects of Familiarization and Meaningfulness

Chenzoff 's (J 962) data" EPAM-lII*

Meaningfulness(or familiarity)

(1)High

meaning-fulness

(2)High

familiar-ization

(3)Low

meaning-fulness

(4)No

familiar-ization

(5)No previous

familiar-ization

(6)Previousfamiliar-ization

H-H or F-F 1.0 1.0 1.0 1.0 1.0 1.0

L-H or U-F 10 1.1 1.2 1.2 1.3 1.0H-L or F-U 1.0 1.2 1.6 1.2 1.8 1.5

1.2 1.8 2.2 2.5 1.7L-L or U-U 1.0

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with the other experiments in the literatureon this point, the quantitative agreement withthe EPAM-111 predictions is remarkablyclose. In particular, EPAM predicts the 2.5maximum ratio that has been so often ob-served. Since syllable integration is the mech-anism that EPAM employs to achieve thisresult, this implication of the theory providessupport for the hypothesis that syllable in-tegration is the mediating mechanism inproducing the effect of meaningfulness (andfamiliarization) upon ease of learning.

Degree of Familiarization

"If the present interpretation of the mech-anism of familiarity is correct, then the ef-ifects of a given amount of familiarizationdraining will depend, in a sensitive way, uponihow familiar the syllables were at the begin-ning of training. There is no way of knowingithis exactly, although it is reasonable to as-sume that nonsense syllables of low associa-tion value .are close to the zero level of(familiarity. .(See, however, the findings ofiUnderwood and Schulz on differential pro-j&unciability of such syllables.)

To examine the effects of varying amounts.of familiarization training upon the ease or.difficulty of paired-associate learning, EPAMwas tested with various combinations of zeroito jive trials of stimulus- and response-sylla-ible familiarization. The results are shown inTable 4 in terms of number of errors tocriterion.

Under the conditions employed in these ex-jperiments, the maximum possible effects offamiliarization were obtained with a combina-tion of three trials of response familiarizationand one trial of stimulus familiarization; ad-ditional familiarization did not facilitatelearning. The asymptote, 21 errors, for thisamount of familiarization was not attainablewith any amount of response familiarizationin the absence of stimulus familiarization, orwith any amount of stimulus familiarizationwithout response familiarization. The asymp-

Table 4Effects of Stimulus and Response-Familiarization

(Number.- of .Errors to Criterion inPaired-Associate ;Learning)

Responsefamiliar-ization

Stimulus familiarization(trials)

(trials) 3210

383852 440323235481242424392

3 2127 2121

totes in the latter two cases were 27 errors and38 errors, respectively, and were reached withthree trials and two trials, respectively, offamiliarization.

The detail of Table 4 shows some exceed-ingly complicated relations. For example, Msyllables have received no prior familiariza-tion, one trial of stimulus familiarizationreduces errors more than one trial of responsefamiliarization (reductions of eight and 'fourerrors, respectively) from 52 in the no-^famJl-iarization case. On the other hand, for syl-lables that had already received one trial ofstimulus and response familiarization, an ad-ditional trial of stimulus familiarization re-duced errors only by three, while an additionaltrial of response familiarization reduced errorsby 11, from a level of 35. Other similar re-sults may be read from Table 4. Many o|the numerous small anomalies in the litera-ture on familiarization training may be at-ributable to the lack of control over theamount of prior familiarity that 5s had withthe syllables used in the experiments.

In Table 3, Column (6), we show thepredicted effect, estimated from the EPAMdata of Table 4, of familiarization trainingwith syllables that were already somewhatfamiliar before the experiment began (i.e.,that had previously received one simulatedtrial each of stimulus and response familiari-zation). Under the F-F condition, we wouldhave 21 errors to criterion; under the U-Fcondition (one stimulus familiarization trial,

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three response trials), 21; under the F-Ucondition (three and one stimulus and re-sponse familiarization trials, respectively),32; and under the U-U condition (one S andone R familiarization trial), 35. The result-ing indexes of relative difficulty for the fourconditions are 1.0, 1.0, 1.5, and 1.7, re-spectively, as shown in Column (6). Thesemay be compared with the values 1.0, 1.2,1.6, 1.8, for the actual data in Column (B).In other words, the fact that the effects shownin Column (3), and even in Column (4), aresomewhat smaller than the predictions inColumn (5) may be due simply to the factthat the syllables were already slightly famil-iar to the 5s at the beginning of the experi-ment.

ConclusionIn this paper we have compared the predic-

tions of EPAM-111, a theory of human verballearning, with data from the experiments ofBruce, Chenzoff, Underwood, and others, onthe effects of intralist and interlist similarity,of familiarization, and of meaningfulness up-on difficulty of learning. We find that there isgood quantitative, as well as qualitative,agreement between the published data andthe predictions of the theory. Finally, wehave used our findings to discuss the relationbetween familiarity and meaningfulness, andhave shown that most of the known facts canbe explained by supposing that a symbolicstructure necessarily becomes familiar in theprocess of becoming meaningful, but that theconverse is not necessarily the case.

Summary

Results obtained by simulating variousverbal learning experiments with the Ele-mentary Perceiving and Memorizing Program(EPAM), an information-processing theoryof verbal learning, are presented and dis-cussed. Predictions were generated for ex-periments that manipulated intralist simi-larity (Underwood, 1953); interlist simi-

larity (Bruce, 1933); and familiarity andmeaningfulness. The stimulus materials werenonsense syllables learned as paired-asso-ciates.

A description of the EPAM-111 model isgiven.

The predictions made by the model aregenerally in good agreement with the experi-mental data. It is shown that the quantitativefit to the Underwood data can be improvedconsiderably by hypothesizing a process of"aural recoding."

The fit of the EPAM predictions to dataof Chenzoff (1962) lends support to thehypothesis that the mechanism by means ofof which a high degree of meaningfulness ofitems facilitates learning is the high famil-iarity of these items.

The effects of varying degrees of stimulusand response familiarization on ease of learn-ing were studied, and are shown to be sur-prisingly complex.

References

Bruce, R. W. Conditions of transfer of training../. exp. Psychol., 1933, 16, 343-361.

Chenzoff, A.P. The interaction of meaningfulnesswith S and R familiarization in paired-associ-ate learning. Unpublished doctoral dissertation,Carnegie Institute of Technology, 1962.

Feigenbaum, E. A. An information processing theoryof verbal learning. Report P-1817. Santa Monica,Calif.: The RAND Corporation, 1959.

Feigenbaum, E. A. The simulation of verbal learn-ing behavior. Proceedings of the. 1961 WesternJoint Computer Conference, 1961, 19, 121-129.

Feigenbaum, E. A., and Simon, H. A. Forgetting inan association memory. Reprints of the 1961National Conference of the Association for Com-puting Machinery, 1961, 16, 2C2-2C5.

Feigenbaum, E. A., and Simon, H. A. A theory ofthe serial position effect. Brit. J. Psychol., 1962,53, 307-320. (a).

Feigenbaum, E. A., and Simon, H. A. Generalizationof an elementary perceiving and memorizingmachine. Information processing 1962, Pro-ceedings of IFIP Congress 62. Amsterdam:North-Holland Publishing Co., 1962, 401-406. (b)

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Newell, A., Tonge, F. M., Feigenbaum, E. A.,Green, B. F., Jr., and Mealy, G. H. Informa-tion processing language V manual. (2nd cd.)Englewood Cliffs N.J.: Prentice-Hall, 1964.

Underwood, B. J. Studies of distributed practice:VIII. Learning and retention of paired nonsense

syllables as a function of intralist similarity./. exp. Psychol., 1953, 45, 133-142.

Underwood, B. J., and Schulz, R. W. Meaningfulness and verbal learning. Philadelphia: Lippin-cott, 1960.

(Received: November 18, 1963)