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Journal of Experimental Psychology: General1979, Vol. 108, No. 1, 107-124

Orthographic Regularity, Positional Frequency, andVisual Processing of Letter Strings

Dominic W. Massaro, Richard L. Venezky, and Glen A. TaylorUniversity of Wisconsin—Madison

SUMMARY

Previous research has demonstrated that familiarity with the orthographic struc-ture within a letter string can facilitate the processing of the component letters.The current research was directed at discovering the psychologically relevantproperties of this structure. Two fundamental descriptions were independentlyvaried in the construction of six-letter nonword strings. A probabilistic descrip-tion based on the frequency of occurrence of letters in each position was factoriallycombined with a rule-governed description defined in terms of graphemic andphonological constraints. College sophomores and sixth-grade readers were askedto indicate whether or not a predesignaled target letter was present in these strings.For both groups of readers, orthographic regularity and summed positional fre-quency were found to have only a small facilitative effect on reaction time (RT).In contrast, RTs to say "no" increased dramatically with increases in the numberof letters in the catch string that were physically similar to the target letter. Inanother experiment, the letter string was presented for a short duration, followedimmediately by masking stimulus and then the target letter. College students indi-cated whether or not the target was present in the test string. Accuracy of per-formance was critically dependent on the orthographic regularity and summedpositional frequencies of the letters in the test string. No effect of letter similaritywas observed. The large differences that were observed between these two taskswere accounted for in terms of the stages of processing that are critical for per-formance in the tasks.

The processes by which words are recog- and Levin (1975), every major treatise onnized during reading have concerned psy- reading has discussed this issue at length,chologists and educators for almost a cen- and generally concluded, as did Huey (1908/tury. From Huey (1908/1968) to Gibson 1968), that "it is very difficult to draw final

conclusions concerning visual perception inThis research was carried out at the Wisconsin reading" (p. 102). At the turn of the Cen-

Research and Development Center for Individual- tury, work on word recognition concentratedized Schooling and was supported in part by funds on word shape and dominating lettersfrom The National Institute of Education, De- (Woodworth, 1938), using full report ofpartment of Health, Education, and Welfare. , . ., , , , _ , ,

Richard L. Venezky is now at the Department of bnefly presented words. More recently, wordEducational Foundations, University of Delaware, recognition studies have concentrated onNewark, Delaware 19711. Glen A. Taylor is now the effects of orthographic structure, usingat the Department of Psychology, Texas A & M, .. . . . . . .College Station 77843. paradigms similar to the visual recognition

Requests for reprints should be sent to Dominic paradigm initiated by Reicher (1969). InW. Massaro, Wisconsin Research and Development this taski a single letter or a letter string jsCenter for Individualized Schooling University of , , , . , . „ . .Wisconsin, 1025 West Johnson Street, Madison, presented for a short period, followed by aWisconsin 53706. visual masking stimulus. The subject is then

In the public domain

107

108 D. MASSARO, R. VENEZKY, AND G. TAYLOR

given two alternative letters and indicateswhich of the alternative letters was presentedat an indicated position in the display. Thealternatives are selected to eliminate the pos-sibility of a guessing advantage for highlyconstrained letter strings.

For example, if the subject were shownthe display WORD, the alternatives would be

D and K. In this case, know-ing that the display was a word and that WORoccurred in the first three positions wouldnot be sufficient information for the subjectto choose between D and K in the fourth posi-tion, since both alternatives spell words.Performance on this trial would be com-pared to a letter trial in which D would bepresented without context, followed by the al-ternatives D and K and a nonword trial inwhich ORWD would be presented followed bythe alternatives D and K.When Reicher initiated the paradigm, hefound a 10% advantage for words over non-words and single letters. This basic resulthas been replicated by Wheeler (1970),Johnston and McClelland (1973), andThompson and Massaro (1973). A similaradvantage has been found with pseudowords,which are spelled like English words buthave no meaning (Aderman & Smith, 1971;Baron & Thurston, 1973). This latter resultshows that orthographic structure can con-tribute to perceptual processing indepen-dently of meaning, although the latter mayalso contribute to the "word" advantage(Juola, Leavitt, & Choe, 1974; Manelis,1974).

A variation on the Reicher paradigm isthe target search paradigm, in which a sub-ject attempts to determine whether or nota target letter (or letters) is present in atest string. Krueger (1970b) presented sub-jects with a target letter followed by a dis-play of 25 six-letter words or 25 six-letternonwords. The nonwords were formed byrandomly permuting the letters at each serialposition in the words. Search time was about20% faster through word than nonword dis-plays. In another experiment, Krueger(1970b) looked at relative search times forcommon and rare words and third-orderpseudowords. Subjects saw a target letter

followed by a display of two six-letter strings.Given an average RT of 1,000 msec, searchtime was 36 msec faster for common thanfor rare words while rare words were 48msec faster than the third-order pseudo-words. The pseudowords were in turn 28msec faster than the nonword strings. In afinal study (Krueger, 1970b), highly prac-ticed subjects searched for one, two, or threetarget letters in word and nonword displaysof two six-letter strings. Search time was alinear increasing function of the number oftarget letters and was faster for word thannonword displays. More important, these twoeffects were additive, indicating that theword-nonword difference was affecting over-all search time at a different processing stagethan was the number of target letters. Sinceit is commonly assumed that the number oftarget letters influences memory compari-son, the advantage of the word strings mightbe located at the recognition stage of proces-sing.

Gilford and Juola (1976) compared visualsearch to memory search using word andnonword test items. The nonwords weregenerated by rearranging (and sometimesreplacing) letters in one-syllable words togive pronounceable one-syllable anagrams.There were no significant differences be-tween the two tasks. The RTs increasedlinearly with increases in the number ofletters in the test items and were consist-ently 40 msec faster to word than nonworddisplays. Replication of Krueger's (1970b)results using both visual and memory searchparadigms, however, produces a conceptualproblem with localizing the differences be-tween words and nonwords at the recognitionstage of processing. In memory search, thetarget item was presented 2 sec after thetest item whereas the target was presented2 sec before the test item in visual search.If it is assumed that the advantage of wordsover nonwords in visual search was due toa more rapid recognition of word than non-word test items, then the target letter musthave been recognized faster with word thanwith nonword test lists in the memory searchtask. There is no justification for this in-terpretation, however, since the same single

REGULARITY, FREQUENCY, AND LETTER PROCESSING 109

target letter occurred on word and nonwordtrials and its recognition time should nothave been dependent on the nature of thepreviously presented test item.

In a second series of experiments, Krueger(1970a) had subjects search for a targetletter in a single six-letter word or nonwordstring. The target letter was presented be-fore (visual search), after (memory search),or simultaneously with the test string. Thesubjects responded about 9% faster forwords than nonwords when the target waspresented after the display and about 5%faster when the target letter was presentedbefore the display. In the simultaneous pre-sentation, the target letter was centeredimmediately above the test string or was re-peated above each letter in the test string.A 4% word advantage occurred in both vari-ations of this procedure. The only conditionthat eliminated the word advantage was onein which the letters in the string were ar-rayed vertically rather than in their standardhorizontal arrangement. Krueger's (1970a,1970b) results offer substantial support forthe utilization of orthographic structure insearching for a target letter in test strings.Krueger, Keen, and Rublevich (1974) askedadults and fourth-grade readers to search fora target letter in a list of five six-letter wordsor nonwords. The RTs were about 10%faster for the words than nonwords andabout 3% faster for (third-order) pseudo-words than nonwords. These effects did notvary with reading ability.

James and Smith (1970) and Gibson,Tenney, Barron, and Zaslow (1972) failedto find faster search times through pro-nounceable pseudowords than nonwords, butKrueger (1975) argued that these null ef-fects may be due to the utilization of a lesssensitive between-subjects design. Gibsonet al. (1972) pointed out, however, thatsince a single target letter was used, theirsubjects did not have to recognize each ofthe test letters but could make decisions onthe basis of letter features alone. To testthese two explanations, Krueger and Weiss(1976) used a within-subject design andheld the target letter constant across trialsin a target search task with lists of 30 six-

letter items. Subjects searched through wordlists about 4% faster than through nonwordlists. This significant result supports the com-monly accepted idea that comparisons be-tween subjects are insensitive (and inappro-priate) in visual information-processingtasks.

Mason (1975) evaluated the contributionof single-letter positional frequency in aletter search task. Good and poor sixth-grade readers indicated whether or not asix-letter string contained a predeterminedtarget letter. Mason (Experiment 2) foundthat good readers were faster (on both yesand no trials) for strings with high than withlow positional frequencies. Poor readersshowed no difference. The results supportthe idea that the time to process the lettersin a string is influenced by the likelihood ofletters occurring in their more commonspatial positions.

In each of the experiments just reviewed,orthographic structure was assumed to facili-tate letter search: yet the definitions of thisstructure were widely disparate, varyingfrom single-letter positional frequency (Ma-son, 1975) to third-order approximations toEnglish (Krueger et al., 1974) to pronounce-ability (Gibson et al., 1972). The presentstudy addresses the issue of defining ortho-graphic structure. In doing so, the degree towhich the target search task was sensitiveto the orthographic structure of letter stringsalso becomes a relevant question.

A Model for Visual Processing

The conceptual framework for the pres-ent research derives from an information-processing stage model (Massaro, 1975a,1975c; Massaro & Klitzke, 1977). Althoughthis model has received considerable supportfrom a wide variety of information-proces-sing tasks, our purpose in presenting themodel here is not to justify it over othermodels, but to provide a precise frameworkfor both the theoretical hypotheses and theexperimental tests of the current research.In the current model, a segment of text isregistered as a light pattern on the retina,which stimulates the visual receptors. The


MEANING

Figure 1. Flow diagram of the processing of printed text.

first process, called feature detection, detectsbasic letter features, which are transmittedto preperceptual visual storage (see Figure1). The feature detection process takes time,and it is assumed that different features re-quire different times for detection. For ex-ample, the general circular shape of theletters c and o may be detected before in-formation about about whether or not a gapis present. As the visual features enter pre-perceptual visual storage, the primary recog-nition process attempts to synthesize theseisolated features into a sequence of letters andspaces in synthesized visual memory. To dothis, the primary recognition process can uti-lize information held in long-term memory.For the accomplished reader, this includes alist of features for each letter of the alphabetalong with information about the ortho-graphic structure of the language. The pri-mary recognition process utilizes both visualfeatures and information about orthographicstructure of the language in its synthesis ofthe letter string.

The primary recognition process operateson a number of letters in parallel. The visualfeatures that are read out at each spatial lo-cation define a set of possible letters for thatposition. The recognition process choosesfrom this candidate set the letter alternativethat has the best correspondence in terms ofvisual features. However, the selection of a"best" correspondence can be facilitated byknowledge of orthographic structure. If, forexample, th—in has been resolved in a letterstring and the features available for the thirdletter match either c or e, the reader might

accept e without waiting for further visualinformation since them is irregular whilethem is not. The primary recognition processtherefore attempts to utilize both the visualinformation in preperceptual storage andknowledge about the structure of legal letterstrings.

The primary recognition process transmitsa sequence of recognized letters to synthe-sized visual memory. The secondary recog-nition process transforms this synthesizedvisual percept into a meaningful form ingenerated abstract memory. The secondaryrecognition process makes this transforma-tion by finding the best match between theletter string and a word in the long-termlexicon. Generated abstract memory corre-sponds to the short-term or working memoryof most information-processing models. Inour model, this memory is common to bothspeech perception and reading. Recedingand rehearsal processes build and maintainsemantic and syntactic structures at the levelof generated abstract memory. The readeris referred to Massaro (1975b) and Venezkyand Massaro (in press) for a more completedescription of the model.

Defining Orthographic Structure

Proposals for describing orthographicstructure can be classed as either rulegoverned or frequency governed. The fre-quency-governed approaches include orderedapproximations to English (Miller, Bruner,& Postman, 1954), as well as bigram counts(Herrmann & McLaughlin, 1973) and single


letter positional frequencies (Mason, 1975).These descriptions are usually based oncounts of letters or letter sequences thatoccur in words in running text, althoughword types might also be used (Solso &King, 1976).

Rule-governed approaches, in contrast,attempt to define orthographic structure interms of the more general linguistic patternsof English spelling. This results in rules orcriteria that are based upon both phono-logical and graphemic constraints, such asthe nonoccurrence of initial consonant clus-ters composed of a voiced consonant followedby a voiceless one, or the use of ck ratherthan c or k in word final position after achecked vowel spelling. Although no com-prehensive set of rules has yet been pub-lished, a sketch of the major orthographicconstraints is given in Venezky and Massaro(in press). Rule-governed approaches haveso far been based on word types rather thanword tokens and have yet to incorporatefrequency measures, although these are notexcluded by definition.

The most important difference betweenthe two approaches is that rule-governedapproaches can, at least in theory, generatesubstrings that do not occur in Englishwords and can eliminate some that do. Forexample, if gh were accepted as a regularspelling for /g/ in word initial position,then the initial spellings, ghl and ghr, mightbe generated as regular clusters, even thoughneither occurs initially in real English words.(Note, however, the parallel with phr, phi,rhl, chr, and thr, all of which do occur inEnglish.)

Although some frequency approaches(e.g., position-sensitive fourth-order approx-imations) will yield strings similar to thoseproduced by a rule-governed approach, mostfrequency measures will not. Single-letterpositional frequencies, for example, havebeen offered as descriptions of orthographicstructure; yet empirical tests done so farhave generally confounded a rule-governeddescription with a single-letter positionalfrequency description (Mason, 1975). Thepositional frequency of a given letter in aletter string is measured by the frequency

with which the letter occurs in the samespatial position in words of the same length.The positional frequency of a letter stringis the sum of the positional frequencies ofeach of the individual letters in the string.Positional frequencies can be computed fromthe tables of Mayzner and Tresselt (1965),which give the total number of occurrencesof each letter at each spatial position in asample of 20,000 words of running text.

Summed positional frequency and rule-governed regularity can be varied indepen-dently, however. Consider, for example,some of the permutations of the letters thatmake up the word prince, which has asummed positional frequency of 1,008 ac-cording to the Mayzner and Tresselt tables.The strings picner and encrip are both ortho-graphically regular by a rule-governed de-scription, yet their summed positional fre-quencies are 1,579 and 426, respectively.Similarly, rcenip and cpnier are ortho-graphically irregular, but have summedpositional frequency counts of 504 and 1,582,respectively. It is not possible to determinefrom Mason's (1975) results alone whetherdifferences in positional frequency are for-tuitous effects of orthographic regularity asdefined by phonological and graphemic rules,or if both were responsible for her results.

To evaluate the degree to which summedpositional frequency and orthographic reg-ularity are psychologically relevant measuresof orthographic structure, we selected letterstrings that were high or low in summedpositional frequency, and then chose bothregular and irregular strings from both ofthese categories according to an extensionof the rules proposed by Venezky andMassaro (in press). (These rules are ex-plained in Appendix A.) If summed posi-tional frequency is a psychologically relevantdescription of orthographic structure, itshould predict performance regardless ofregularity. Likewise, if a rule-governed de-scription is psychologically relevant it shouldpredict performance independently of posi-tional frequency. In the present experiments,we independently varied these two descrip-tions of orthographic structure to provide a


direct assessment of the contribution of eachto letter processing.

Target Search and Orthographic Structure

A target search task was selected in viewof the substantial evidence that orthographicstructure facilitates performance in this task.According to the model utilized here, theinfluence of orthographic structure on per-formance in the target search task shoulddepend on whether the subject is capable ofresponding at the level of letter features (i.e.,at the feature detection stage) or wholeletters (i.e., after the primary recognitionstage). If primary recognition must be com-pleted before a decision can be made, wepredict a positive effect of orthographicstructure in the task; no influence of ortho-graphic structure is predicted if a decisioncan be initiated at the level of feature detec-tion. At first glance, we might expect thatprimary recognition must be completedbefore a decision can be made in the searchtask. There is some evidence, however, thatreaders do not have to recognize all of theletters in the test letter string to perform thetarget search task (Estes, 1975; Massaro &Klitzke, 1977). Consider the case in whichthe subject is looking for the target letter bin the letter strings cose, peom, deom, anddelm, respectively. In all cases the answeris no, but the subject may be able to decideon this answer faster with some of thestrings than with others. We assume thatrecognition of the string is a temporallyextended process and that some letter fea-tures are resolved before others. There issome evidence that overall letter shape canbe resolved and made available to laterstages of processing before the letter is com-pletely recognized (Bouma, 1971; Massaro& Schmuller, 1975). In a well-known experi-ment (Neisser, 1964), subjects searchedfaster for the uppercase letter Z in a list ofcurved letters (0, D, U) than in a list oflinear letters (T, V, M). This result indi-cates that complete letter recognition is notnecessary in the target search task. Apply-ing this analysis to the example strings, asubject might be able to respond "no" to the

first two example strings very quickly sincenone of the letters has the same overallascender shape as the target letter. In con-trast, the third and fourth strings willrequire additional processing time. Otherfeatures of the ascending letters of thesestrings must be resolved to distinguish theseletters from the target letter. Accordingly,we might expect that the time to say "no"will be longest for the string with two ascen-ders, intermediate for the string with oneascender, and shortest for the strings withno ascenders. If subjects are utilizing thisprocessing strategy, then we would notexpect a large effect of orthographic struc-ture of the letter string, but rather a signifi-cant influence of the number of letters similarto the target letter.

It should be pointed out that for thismodel the number of letters in the teststring of the same overall shape as the targetshould not be important on target trials. Onthese trials, the.target letter must be com-pletely resolved before a yes response can bemade. Therefore, the critical variable ontarget trials is how quickly the target isrecognized rather than the number of lettersin the test string that have the same overallshape as the target.

To review, the two central issues ad-dressed by the current research involve anevaluation of the psychological reality of twodescriptions of orthographic structure andthe degree to which orthographic structureinfluences performance in the target searchtask. To evaluate these hypotheses at twolevels of reading experience, both adult read-ers and sixth graders were tested.

Experiment 1

Method

Subjects. Eleven adult subjects who respondedto an advertisement posted in the university com-munity participated 1 hr a day for 2 consecutivedays. Each subject was paid $4 at the end of theexperiment.

Stimulus lists. The stimulus lists were gen-erated from the most frequent 150 six-letter Englishwords in Kucera and Francis (1967). A computerprogram generated all 720 permutations of the sixletters in each word, and computed the summedpositional frequency for each permutation from


Mayzner and Tresselt's (1965) counts for theabsolute frequency of occurrence of single lettersat each position in six-letter words. The programlisted the 30 permutations with the highest summedpositional frequencies and the 30 permutations withthe lowest summed positional frequencies. Fromthese lists one orthographically regular and oneorthographically irregular item were chosen fromthe items with the highest summed positional fre-quency, using the criteria given in Appendix A. Asimilar pair of items was chosen from the itemsthat ranked lowest in summed positional frequency.Because of the difficulty of finding orthographicallyregular items in the low positional frequency group,all of the original ISO words could not be used.The four different types of items could be gen-erated from 80 of the words, giving a total of 320stimulus items in the experiment. Table Bl ofAppendix B lists the words, the 4 permutationsthat were used in the experiment, and the summedpositional frequency of each permutation.

Procedure. The stimulus list of 320 items wasdivided in half so that the permutations of 40 ofthe words were in one list and the permutations ofthe other 40 words were in the other. List 1 had norepeated letters in 37 of the strings and onerepeated letter in 3 of the strings. List 2 had atleast one repeated letter in each of the 40 strings(see Appendix B). In the experiment proper, eachsubject was tested on List 1 in the first sessionand List 2 in the second session each day. List 1was always given first on Day 1 since this listhad only 3 items with repeated letters and it alsoallowed a direct comparison between the adult'sresults on Day 1 and the results from the sixth-grade readers in Experiment 2. Each letter stringappeared once as a target trial and once as a catchtrial. On target trials, the target letter was selectedrandomly with replacement from the six letters inthe test string. On catch trials, the target letter wasselected from the remainder of the alphabet. Theprobability of selecting a given target letter oncatch trials was weighted by the frequency ofoccurrence of that letter in the 320 test strings.

On the first day, the subject was introduced tothe visual display and the response panel. Toacquaint subjects with the response procedure,they were first given SO trials of responding "yes"and "no" to the words YES and NO presented onthe visual display. All subjects pushed the yesbutton with their preferred hand. The word re-mained on until the subject pressed one of the twobuttons. Next, the subjects received SO practicetrials with random letter strings. The subject wasasked to indicate whether or not a target letterwas present in these six-letter strings. Each trialbegan with a 400-msec presentation of the targetletter followed by a 400-msec blank period and thenthe test letter string. The letter string remainedon until the subject pressed one of the two buttons.The interval from the response to the onset of thenext trial was 1.5 sec. The subject was instructedto respond "yes" if the target letter appeared at

least once in the letter string and "no" otherwise.The subject was also instructed to respond asaccurately and quickly as possible. Feedback wasgiven at the end of each practice sequence in anattempt to keep error rates below 3%. This practicesession was followed by 320 trials of the experi-ment proper. After a rest of about 10 min thesubject was given a second testing session consist-ing of 50 practice trials followed by 320 experi-mental trials. On Day 2 the subject also had twosessions, each with practice trials followed by 320experimental trials. Each subject was tested fora total of 1,280 trials in the experiment proper,giving roughly 320 observations per subject in eachof the four experimental conditions. A 10-min restbreak was given between sessions.

Apparatus. The displays were presented on aBeehive video computer terminal under the controlof a Harris DC6024/5 computer. A hardware mod-ification permitted the terminal's video to be turnedon and off with program-generated control signals.The display strings were loaded into the memorybuffer of the terminal with video off and then thevideo was turned on for the appropriate exposureduration. The Beehive's cathode ray tube (CRT)employs a P4, blue-white phosphor which decaysto .1% of maximum luminance in 32 msec. Theexperiment was conducted in a partially darkenedroom to enhance image contrast.

All single-letter and letter-string displays werepresented in lowercase letters. The Beehive ter-minal uses a 5 X 7 dot matrix, 2.S mm wide and5 mm high. At the average viewing distance ofapproximately 38 cm, the six-letter strings sub-tended a horizontal visual angle of 2.25° and avertical visual angle of .60°. The displays werepresented in the upper center of the screen. Thesingle-letter target was positioned to appear twolines above Serial Position 3 of the string display,a vertical visual angle separation of 1.65°. The RTswere measured from the time the video was turnedon for the letter-string displays. A 1-kHz clockprovided RT measurements accurate to milli-seconds.

Results

An analysis of variance was carried out onthe mean RTs for correct responses for the11 subjects, with the four sessions, the twolevels of positional frequency, orthographicregularity, and test type (target vs. catchtrial) as factors. Table 1 presents the meanRTs for target and catch trials as a functionof positional frequency and orthographicregularity. The RTs averaged 16 mseclonger for low than for high positional fre-quency, F(\, 10) = 16.64, p < .005. Re-sponses were 9 msec faster for orthograph-ically regular than for orthographically


Table 1Mean Reaction Times (in msec) for Targetand Catch Trials in Experiment 1 as aFunction of Positional Frequency (High orLow) and Orthographic Regularity

Orthographicregularity

RegularError %

IrregularError %

Target

High

6222.5

6312.5

Low

6393.0

6394.0

Catch

High

6962.0

7061.8

Low

7131.8

7292.0

irregular strings, F(l , 10) =6.91, p <.05. Catch trials produced RTs that were78 msec slower than RTs on target trials,F(l, 10) = 15.95, p < .005. The RTsdecreased from 708 msec in Session 1 to 639msec in Session 4, but this effect was notstatistically significant, F(3, 30) = 1.72, p> .10. Also, sessions did not interact withany of the other variables. No other sourceof variance approached significance in theanalysis.

A second analysis was carried out to eval-uate whether the RTs on catch trials weresensitive to the similarity between the targetletters and the letters in the catch string.The analysis was directed at finding outwhether RT was a function of the numberof letters in the catch string that werevisually similar to the target letter. To dothis, the letters of the alphabet were groupedinto six sets based on the similarity resultsof Bouma (1971). These six sets are asfollows: (m, n, r, u, v, w), (a, s, x, z),(c, e, o), (b, d, h, k), (f, i, j, 1, t), and(g, p, q, y). The relationship between eachtarget letter and catch string was definedin terms of the number of letters in thestring that were members of the same set asthe target letter. For example, the test stringlisver would have two letters from the sameset as the letter t and two from the same setas in. For each subject and for each of thesix types of target letters, an RT was com-puted as a function of the number of lettersin the catch string similar to the targetletter. If any subject had fewer than four

observations at a particular condition, thiscondition was eliminated from the analysisfor all subjects. The analysis indicated thatRT was a direct function of the number ofletters in the catch string that were similarin shape to the target letter. With no similarletters in the test string the RT was 685msec, whereas the RTs were 724, 736, and755 msec for catch strings with one, two,and three similar letters, respectively. Thiseffect of number of similar letters was highlysignificant, F(3, 30) = 18.86, p < .001.

A similar analysis was carried out on themean RTs for target trials as a function ofthe number of letters in the test stringsimilar to the target letter. In contrast to thecatch trials, there was not a consistent in-crease in the RTs with increases in thenumber of letters of the test string similarto the target letter. The RTs for targetstrings that had one, two, three, and fourletters similar to the target letter averaged641, 624, 635, and 633 msec, respectively,F(3, 30) < 1. In contrast to the orderlyincreases on catch trials, there appears to beno systematic effect of letter class similarityon target trials.

Experiment 2

Method

Subjects. Sixteen sixth-grade students, 5 maleand 11 female, participated in individual H-hrtesting sessions. Two additional students' data werelost because of equipment failures. Chronologicalages of the subjects ranged from age 11-0 to age12-1. Reading comprehension was evaluated byadministering the comprehension subtest of theGates-MacGinitie Reading Test (1965, Survey D,Form 1). Comprehension grade level scores variedfrom 5.3 to 12+ with a mean grade level of 9.5.All subjects participated in response to advertise-ments in local newspapers and were paid $2 fortheir participation.

Stimuli and apparatus. The stimuli and appa-ratus were the same as for Experiment 1 with theexception that only List 1 was used.

Procedure. Each subject's session began withadministration of the 25-min written comprehensiontest. After completing the reading test, the subjectwas seated before the computer terminal and thetask explained. The remainder of the session wasconducted in the same fashion as the first sessionof Test Day 1 in Experiment 1. Accordingly,each subject was tested for 50 yes-no trials, 50


practice trials, and 320 trials from List 1 in theexperiment proper, giving roughly 80 observationsper subject at each of the four experimental condi-tions (less error trials). As in Experiment 1, yesresponses were given with the preferred hand.

Results

The mean correct RTs and their associatederror percentages are presented in Table 2.Although the RTs were about 12 msec fasterfor regular than for irregular strings andabout 12 msec faster for high than for lowpositional frequency, an analysis of variancerevealed that neither result was significant.These results replicate those found withadult subjects, except that the small differ-ences were statistically significant for theadults but not for the sixth-grade readers.The only statistically significant result wasthe 65-msec mean difference between targettrials and the slower catch trials, F(l, 15)= 16.11, p < .005. However, this advantagein RTs on target trials appears to be at leastpartially due to higher error rates on thosetrials. Fourteen of the 16 subjects (signifi-cant by a sign test, p < .02) were moreaccurate in responding to catch trials thanto target trials.

Analyses of similarity between catch stringletters and target letters were made as inExperiment 1. The children's results exactlyreplicate the adult data. On catch trials, RTsaverage 876, 935, 977, and 1,080 msec forzero, one, two, and three similar letters,respectively, F(3, 45) = 10.50, p < .001.A similar analysis of the target trials revealedthat RTs did not increase as a function ofincreasing numbers of letters of the sameclass as the target. Average RTs for targettrials were 868, 840, 891, and 874 msec,for one, two, three, and four similar letters,respectively, F(3, 45) = 1.77, p > .20.These results contrast with those found forcatch trials, and together the results providean exact replication of the adult perform-ance in Experiment 1.

To provide a direct comparison betweenthe sixth-grade and adult readers, an analy-sis of variance was carried out on the resultsfrom both groups. For direct comparability,only Session 1 on the adult data was includedand the five slowest sixth-graders were elim-

Table 2Mean Reaction Times (in msec) for Targetand Catch Trials in Experiment 2 as aFunction of Positional Frequency (High orLow) and Orthographic Regularity


RegularError %

IrregularError %

Target

High

8508.4

8587.7

Low

8487.0

8786.9

Catch

High

9074.8

9224.1

Low

9343.6

9292.7

inated to provide an equal number of sub-jects in the two groups to be compared. Theanalysis indicated that both summed posi-tional frequency and orthographic regularitywere significant and that these effects didnot interact with reader group. The RTsto irregular strings averaged 19 msec slowerthan RTs to regular strings, F(l, 20) =13.48, p < .005; RTs to high positional fre-quency strings were 18 msec faster than RTsto low positional frequency strings, F(l, 20)= 13.73, p < .005. The RTs on target trialswere 65 msec faster than on catch trials,F(l, 20) = 29.49, p < .001. Finally, thesixth-grade readers were 123 msec slowerthan the adult readers, F(l, 20) = 6.90,p < .025. This analysis shows completeagreement between the two groups of read-ers with respect to the variables manipulatedin the experiments.

Discussion

Experiments 1 and 2 demonstrate thatthe target search task is relatively insensitiveto the orthographic structure of the testletter strings. Only very small effects oforthographic structure were apparent, where-as target search times on catch trials werecritically dependent on the number of lettersin the test string similar to the target letter.In terms of our model, this means that theobserver was usually capable of respondingat the level of feature detection before recog-nition of the letters in the test string wascomplete.

One observation appears to be inconsistentwith the idea of early responses on catch


trials in the target search task. Across thetwo groups of readers, average RTs on catchtrials were about 72 msec slower thanaverage RTs on target trials. If subjectscould terminate their search before all letterswere recognized on some catch trials, thenthese RTs might be expected to be shorterthan RTs on target trials. However, longerRTs on catch trials might result from a dif-ferent stage of processing than that respon-sible for the similarity effect. The commonresults in a wide variety of tasks is that neg-ative responses take longer than positiveresponses. Subjects may tend to hesitate ordouble check their decision on catch trials,adding to the overall RT. Some evidence forthis possibility comes from the significantlyhigher error rates on target than on catchtrials. A second reason for longer RTs oncatch trials might be that all subjects re-sponded "yes" with their preferred hand.Accordingly, absolute differences in RTsbetween target and catch trials are not validindexes of a feature detection strategy in thetarget search task.

The feature detection strategy in targetsearch appears to neutralize utilization of theorthographic structure of letter strings. Ac-cording to our model, what is required toassess the use of orthographic structure is atask that requires primary recognition of theletters of the test string. Consider the taskin which the test string is presented underrestricted viewing conditions followed bypresentation of the target letter. In this task,the subject still indicates whether or not thetarget letter was contained in the test string,but must resolve the letter string to the deep-est level possible (Juola et al., 1974; Thomp-son & Massaro, 1973). Given that thedisplay test must be processed through thestage of primary recognition, we should beable to assess the psychological validity ofvarious descriptions of orthographic struc-ture.

Experiment 3

Method

Subjects. Ten introductory psychology students,S male and 5 female, participated for 1 hr a day on2 consecutive days and received extra point credit

toward their psychology grades. The data from 3additional subjects were lost, in two cases becauseof failure to perform the task as instructed and inthe third case because of equipment malfunctions.

Apparatus and materials. The stimulus materialsand apparatus were the same as in Experiment 2.Because the task was changed from a precuedtarget search to a postcued target search, theexperimental display sequence was reprogrammedas follows. A cardboard mask with a rectangularaperture was affixed to the face of the CRT screen.This window defined the area where the displayswere presented and served as a fixation box. Atrial began with the presentation of the six-letterstimulus string for a brief, controlled durationfollowed immediately by the masking stimulus, sixuppercase Xs. The masking stimulus was presentedfor 250 msec and followed by the target letter,which remained in view until the subject responded.The target letter was presented on the same lineas the stimulus and mask strings but three char-acter positions to the left of the initial letter posi-tion of the stimulus string.

The stimulus string exposure duration was indi-vidually determined for each subject during thepractice trials. Because of the 60-Hz refresh ratefor the CRT, 1 refresh cycle (16.7 msec) waschosen as the basic time unit. The number ofcycles needed to achieve an overall accuracy levelof 75% was determined on line by a modifiedversion of the PEST algorithm (Taylor & Creel-man, 1967). An adjustment of 1 or 2 cycles wasmade after each block of trials, if necessary, tomaintain accuracy at 75%. Exposure times re-mained equivalent across conditions, since eachblock contained equal numbers of strings in eachstimulus category. The mean exposure durationfor the 10 subjects was 8.5 cycles or 142 msec.The range of average exposure durations acrosssubjects varied between 2.1 and 14.8 cycles.

Procedure. The subjects were informed thatthe exposure duration would be brief and wouldbe adjusted during the session to maintain anaccuracy of 75%. It was emphasized that the mostaccurate judgments possible were desired on everytrial. The subjects were also told that half thetrials would be target trials and half would becatch trials.

To acquaint the subjects with the response pro-cedure and visual display, each day's session beganwith 10 trials of responding to the words yes andno. On the first day the yes-no trials were followedby four 50-trial blocks of practice trials. For thepractice trials the stimulus strings were six ran-domly selected letters. For these trials the initialexposure duration was set at 20 refresh cycles(333 msec) and adjusted, if necessary, every eighthtrial. The practice trials were followed by four80-trial experimental blocks. The procedure for thesecond day was similar except that only two 50-trial practice blocks were administered and theinitial exposure duration for the day was set atthe final exposure duration reached the preceding


day. On both days the subjects were given shortrest breaks between blocks of trials.

Results

The average percentages of correct re-sponses on the target and catch trials at eachof the four experimental conditions are pre-sented in Table 3. Regular strings wererecognized 4% more accurately than irreg-ular strings, F(l, 9) = 9.67, p < .025.Strings high in positional frequency wererecognized 5% more accurately than stringslow in positional frequency, F(l, 9) = 38.61,p < .001. The interaction of regularity andpositional frequency was not significant,F(l, 9) = 1. No other sources of variancewere significant.

Performance was analyzed as a functionof the similarity of the target letters to theletters in the test items on catch trials.There was no significant effect of the num-ber of similar letters in the catch strings.Performance averaged 84%, 81%, 82%,and 76% correct for zero, one, two, andthree similar letters, respectively, F(3, 27)= 1.97, p > .10. A similar analysis showedno significant effects of similarity on targettrials. Performance averaged 79%, 80%,81%, and 86% correct for one, two, three,and four similar letters in the target string,F(3,27) =2.64, p > .06.

Discussion

In agreement with our hypothesis, theaccuracy task with limited stimulus infor-mation and with the target letter presentedafter the test display appears to be moresensitive to the orthographic structure of thetest strings. An orthographically regularstring high in positional frequency wasrecognized 9% better than an irregular lowpositional frequency string. This result con-trasts with the 22-msec difference in the RTtask when the target letter was given beforethe test string. Although it might be arguedthat these results are not directly compar-able, the difference in similarity effects inthe two tasks supports the idea of a greaterutilization of .orthographic structure in theaccuracy task. In contrast to the catch strings

Table 3Average Percentage of Correct Responses forTarget and Catch Trials in Experiment 3as a Function of Positional Frequency(High or Low) and Orthographic Regularity


RegularIrregular

Target

High

86.080.2

Low

78.975.2

Catch

High

85.583.0

Low

81.578.2

Note. Each cell is based on 80 observations foreach of 10 subjects.

in the RT task, no consistent effect wasobserved in the accuracy task. The criticalvariable determining large effects of similar-ity is the degree to which the subject termi-nates processing before recognition iscomplete. If the subject is given the targetbefore the test string and speed of responseis stressed, large effects of similarity shouldbe observed. If presentation of the targetis delayed until after the test string has beenresolved, similarity should not have mucheffect. According to this analysis, tasks thateliminate effects of similarity are more likelyto show positive effects of orthographicstructure.

The orthographic structure effects alsoprovide some evidence against the positionaluncertainty hypothesis of Estes (1975). Inhis view, orthographic structure does notfacilitate resolution of the letters in a stringbut simply reduces uncertainty about theirrelative spatial positions. In our experiment,identification of the letters was critical toperformance whereas their spatial positionwas irrelevant to the task. Accordingly, ifsubjects identified the letters equally for boththe regular and irregular strings, as Esteswould predict, no differences should havebeen observed.

The small effects in the RT task mightbe claimed to be due to a poor selection oftest items in the present experiments. Itmight be argued, for example, that theregular items are not all that regular and,therefore, the regular strings do not differvery much from the irregular strings. Al-though the positive results in the accuracy


task argue against this interpretation, itseemed worthwhile to provide an indepen-dent assessment of the regularity of the teststrings, To provide this assessment, collegestudents were asked to rate each string interms of how much it looked like a realEnglish word. These ratings also provide aconverging measure of the degree to whichorthographic regularity and summed posi-tional frequency are psychologically realdescriptions of the reader's knowledge oforthographic structure.

Experiment 4

Method

Subjects. Fifty-eight college students were re-cruited as in the previous experiments. One sub-ject was eliminated from the analysis because heused the scale backwards.

Stimulus lists. The 320 strings of Lists 1 and2 were randomized and typed, 40 items per page.The order of the items on each page was constantacross subjects, although each subject received adifferent random order of the eight pages. Eachword was typed in lowercase letters with an under-lined blank area next to it.

Procedure. Subjects were tested in groups of8 to 10. The subjects were told the general natureof the experiment and the rating scale to beused. The scale was graphically represented onthe blackboard for reference purposes as a line with10 even graduations. All subjects completed theratings in 30 to 45 min. They were instructedto rate each letter string on a scale of 1 to 10according to how much it looked like a realEnglish word. Strings that were extremely closewere to be given high ratings, while strings thatwere not very close were to be given low ratings.Subjects were encouraged to skim over the stringsbefore they began in order to obtain an idea oftheir range of variation. It was emphasized thatthe full scale was to be used. The "best" itemswere to be given a rating of 10 and the "worst"a rating of 1.

Table 4Average Ratings in Experiment 4 as aFunction of Summed Positional Frequencyand Orthographic Regularity

Positionalfrequency

HighLow

M

Regularity

Regular

6.825.206.01

Irregular

3.302.913.11

M

5.064.05

Results

Both positional frequency and ortho-graphic regularity had a significant effect onthe ratings. Regular strings received anaverage rating of 6.06, which was twice theaverage rating of 3.02 of the irregularstrings, F(\, 56) = 337, p < .001. Stringshigh in positional frequency were rated asmore like English words than strings lowin positional frequency (5.06 vs. 4.02), F(l,56) = 338, p < .001; but the effect of posi-tional frequency was almost entirely due tothe regular strings, F(l, 56) = 249, p <.001. Table 4 shows the interaction of posi-tional frequency and regularity.

Discussion

The ratings of the test strings revealedthat subjects could easily discriminate itemshigh in orthographic structure from thoselow in orthographic structure. Rule-governedregularity was about three times as effectivein discriminating the test strings than wassummed positional frequency. However,either summed positional frequency didinfluence the subjects' rating responses or,contrary to our rule-governed assignment,the regular strings were not equally regularfor the high and low positional frequencies.If the latter was the case, some or all of theadvantage enjoyed by the high positionalfrequency strings could have been due toregularity.

General Discussion

The present research addressed the ques-tion of whether or not the target searchlatencies are very sensitive to the ortho-graphic structure of the test letter strings.Only a small, but sometimes significant,effect of orthographic structure was ob-served. On the other hand, target searchtimes for catch trials were critically depen-dent on the number of letters in the testword that belonged to the same letter classas the target letter. The RTs on catch trialswere slower to the degree that the catchstrings contained letters of the same classas the target. In terms of our stage model,


this means that the observer was sometimescapable of responding at the level of featuredetection before recognition of the lettersin the test string was complete.

If we assume that (a) a sequence ofletters can be processed in parallel, (b)features of individual letters are extractedcontinuously, (c) feature extraction pro-cesses operate on all letter positions inde-pendently and with unlimited capacity, and(d) comparisons between extracted featuresin the target letter can occur before percep-tion is complete, then a relatively par-simonious description of the results can begiven. Backward masking and partial reportexperiments (Averbach & Coriell, 1961;Massaro, 1975a; Sperling, 1960) haveshown that the resolution of the visualdisplay occurs gradually over time. Massaroand Klitzke (1977) have previously con-ceptualized the perception of letters asoccurring in a two-stage process. The overallshape of each letter is assumed to be resolvedin the first stage; the details of the letterare resolved in the second stage. The readerdoes not have to complete both stages ofprocessing for each letter, but can in manycases identify the letters unambiguouslyafter the first stage of processing. In targetsearch tasks, the subject can select a responseafter either stage, depending on the con-fidence the subject has in the decision atthe end of the first stage. The subject main-tains a fixed criterion to keep the false alarmrate minimal. If the information available tothe subject at the end of the first stage issufficient to respond correctly with prob-ability PC, and 1—Pc < Pf where Pf is themaximum false alarm rate the subject willtolerate, then the subject outputs theresponse. Otherwise the second stage ofprocessing is completed. This model can beextended to account for the results of thepresent experiments. It is assumed that letterfeatures are continuously extracted inparallel across all of the letters in the teststring. A subject can initiate a response atany point in this processing sequence. If thesubject is given the target letter before thetest display presentation, each letter in thestring does not have to be completely pro-

cessed. On catch trials, the subject does nothave to recognize all of the letters in the teststring, but only has to process enough ofeach letter to insure that it is not the sameas the target letter. On target trials, enoughfeatures from the appropriate letter must beextracted in order to identify it unambig-uously.

The processing time for each letter in thetest string on catch trials will be a directfunction of the number of features it shareswith the target. To the extent the letter doesnot share features with the target, it canquickly be rejected. Accordingly, test stringsthat contain nothing but highly dissimilarletters from the target should be respondedto very quickly, whereas RTs should belarge for test strings with a large number ofitems similar to the target letter. In contrastto catch trials, no effects of letter similarityshould be observed on target trials. Regard-less of the similarity of the nontarget lettersin the test string, the target letter must stillbe resolved sufficiently in order to executea positive response. Since it is assumed thatprocessing occurs in parallel, only the timeto identify the target letter is critical onpositive target trials.

The support for this model of letter recog-nition comes from the similarity analysis car-ried out in Experiments 1 and 2. We assumethat letters of the same letter class share morefeatures than those not of the same class.Accordingly, RTs on catch trials shouldincrease with increases in the number ofletters of the same class as the target letter.In agreement with this prediction, averageRTs increased steadily as the number ofletters of the same class increased from zeroto three. Further support for the modelcomes from the negative findings on positivetarget trials. RTs showed no consistentchanges as the number of letters of the sameclass as the target was increased from oneto four.

Other models of the target search taskhave been proposed, most notably Gardner's(1973) independent channels confusionmodel and Estes' (1972) interactive chan-nels model. Both of these models assume thatfeatures are extracted in parallel from all


letter positions in a visual display. Gardner'smodel assumes that feature extraction pro-cesses are independent at all letter positionsfor displays in foveal vision, but that post-perceptual decision processes permit con-fusions between the target letter and confus-able noise elements in the display. Althoughthis model could be modified to predict theincrease in RT on catch trials with morenumerous noise letters similar to the targetletter, Gardner assumes that decision pro-cessing begins only after perceptual recog-nition is complete and that decision latencyis unaffected by the extent of confusabilitybetween target and noise elements. There-fore, the current version of Gardner's modelpredicts no effect of target letter similarityon RTs to target or catch trials.

Estes' (1972) model assumes that targetand nontarget (noise) letters compete forfeature extraction resources. Thus, thegreater the similarity between target andnoise letters, the greater the competition. Ifa target letter is accurately detected, a pri-mary detection response is evoked andprocessing stops which results in a rapid yesresponse. If no primary detection processoccurs, then processing continues and sec-ondary decision processes must select a yesor no response. This model correctly predictsthat catch trial RTs would increase withincreasingly similar noise letters because ofthe competition during feature extraction.Without further assumptions, however, thismodel would incorrectly predict that RT totarget trials would also be an increasingfunction of similarity between the targetletter and the noise letters in the display,although the magnitude of this effect neednot be as great as on catch trials. Thus,neither Gardner's nor Estes' model ade-quately predicts the results of Experiments1 and 2 without additional assumptions.

It is necessary to assume that the featuredetection strategy that we have proposed isinconsistent with the active utilization oforthographic structure in the target searchtask. Only small effects of orthographic reg-ularity will be found in this task to theextent that subjects actively utilize the fea-ture detection strategy. We predicted, how-

ever, that the utilization of orthographicstructure would occur when the readers wererequired to resolve all of the letters in thetest string to the deepest level possible. Ifthe subjects are no longer able to reject apartially resolved letter as they can in thetarget search task, then it is to their advan-tage to utilize what they know about thestructure of letter strings in order to identifyall of the letters that make it up. We en-couraged the readers in Experiment 3 tooperate along these lines by presenting thetest string first, followed by a single targetletter. Since the subjects did not know thetarget letter in advance, they had to resolveall of the letters in the test string in order toperform the task correctly.

Similar trade-offs between the featuredetection strategy and the utilization oforthographic structure have been reportedin other studies. Johnston and McClelland(1974) demonstrated the importance of pro-cessing strategy on the perception of wordstrings. Subjects either were told to attemptto see a whole word or were told the criticalletter position in a Reicher-Wheeler task(see Reicher, 1969; Wheeler, 1970). Sub-jects were about 7% more accurate with thewhole-word than with the single-letter pro-cessing strategy. In terms of our model,subjects utilized the orthographic structureavailable in the word in the whole-wordprocessing strategy but did not take advan-tage of the letter context when attention wasdirected to the critical spatial position.

In a study by Appelman (1976), subjectswere presented two words or two nonwordsin a same-different task. Subjects were 8%more accurate on word than on nonwordtrials. In a second experiment, only a singletest string was presented with a target letterabove or below the critical letter in the teststring. Subjects indicated whether the targetletter was the same as or different from thecritical letter in the test string. The wordadvantage was eliminated in this task. Interms of our model, subjects processed theletter strings through primary recognitionin the first case but not in the second. In thesecond case, the target letter would encour-age subjects to process only the critical letter


in the test string. Therefore, orthographicstructure would not be utilized and no wordadvantage would be observed.

We have interpreted the processing advan-tages with highly structured strings in termsof a facilitation of letter recognition. Analternative hypothesis would claim thathighly structured strings allow the directrecognition of higher-order units such asspelling patterns or words, which wouldresult in more efficient processing of thesestrings. The higher-order unit model, how-ever, does not necessarily predict that atarget letter will be found more quickly in astructured string than in an unstructuredstring. In fact, recent higher-order unitmodels have been developed to make just theopposite prediction: it should take longerto find a target letter in a word than in anonword string (Johnson, 1975). Extendingthis class of models to the current RT taskwould predict a disadvantage of the regulartest strings; but the opposite results wereobserved. Until the letter and higher-orderunit models can be differentiated, we preferto interpret the results in terms of the facil-itative effect of orthographic structure onletter recognition.

In summary, our results and analyses ofthe target search task lead to the conclusionthat the target search RT task is relativelyinsensitive to the presence of orthographicstructure in the letter strings to be searched.The task promotes utilization of a featuredetection strategy, which appears to be in-consistent with the active utilization of ortho-graphic structure in processing letter strings.However, despite this insensitivity, an effectof orthographic structure was found for bothtarget search RT and accuracy tasks. Theseeffects could not be attributed solely to theeffects of either a rule-governed or a single-letter positional frequency description oforthographic structure. Either both thesedescriptions have psychological reality, orsome as yet unexplicated description thatencompasses the descriptive power of bothmeasures is necessary to understand theeffects of orthographic structure on the visualprocessing of letter strings.

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Received August 23, 1977

(Appendixes follow)


Appendix A

Selection of Orthographically Regular and Irregular Strings

From the list of permutations of a real word for a particular summed positional frequencyclass (high or low), a string was selected for the Orthographically irregular list if it contained anyof the following spellings:

1. An unpronounceable initial or final cluster (e.g., tproer, aorlld)2. A pronounceable, but Orthographically illegal, spelling for an initial or final consonant or

consonant cluster (e.g., ecapet, aerrgd)3. An illegal vowel spelling (e.g., greeed, frwwet)4. An unpronounceable medial cluster (e.g., e/c/oi, ettrbe).

Rules for pronounceability and orthographic legality were derived by extending the rulesgiven in Venezky and Massaro (in press). These in turn are based on the letter-sound patternsfound in approximately 20,000 English word types. When these criteria failed to yield an illegalstring, implausible spellings were selected. These usually included marginally pronounceablemedial consonants that would occur only in compound words (e.g., erptro), or a rare vowelspelling (e.g., ylelra).

Regular strings were selected (a) to be pronounceable, (b) to contain common vowel andconsonant spellings, and (c) to have not more than three letters for a medial consonant cluster,if one occurred. No control was placed on number of syllables, however. When these criteriafailed to yield a regular string, the rules were relaxed to allow uncommon doubled consonants(e.g., cotaww) and uncommon final vowels (e.g., inalmo, tressi).

Appendix B

Table BlThe Words, the Four Permutations Used in the Experiments, and the Summed PositionalFrequency i of Each for List 1 (First 40 Words) and List 2 (Last 40 Words)

Words

modernminuteitselfmasterislandhappengroundgoldengardenfriendfingerfarmerduringdoubledirectbrokenbelongbehindaroundamountalmostactionyellowwonderwinter

High-/ Regular /

10101167751

17271000142012641417156813241530171911641171107314671356165714111048955988825

15261584

remondmuinetsifletmatsersinaldphanepgonurdgenoldnagredfirdenfirgenramferrigundboudeltecirdronkebgolbenhibnednaroudmanoutsamoltacointlewolywronedwerint

1600144616641671150513251278153017081546154415731293144816091372137716551313105414801223127817191513

High-Irregular /

rmnoedmntieutlfiesmrateslanisdpahpneruogndolngedarngedirfnedfrniegafmreruirgndIbuoedtericdbeoknrbenolghnbieduoarnduaotnmmaotlscoaitnolwleywnroedwtnier

1651140616741693149012711266153917211560158713721284159916151364137816541304106114791223121517371525

Low-Regular /

endromitenumestfilestramindsalenpaphodgrunengdoledgranendrifefgrinermrafidgrunodelubedtricenbrokenglobendhiboduranotunamotslamincatoelylowedwronetwrin

426383429465372375342458416372428538328286447391415332 ,441440454455470457476

Low-Irregular /

rdenmoinemtueflstiemtrsainlsdaehpnpaondrgueldngoednrgaefnrdiinerfgerarfmundrgiebdluoetrcdierknboonelgbinedhbndoraunmutaoosltmantiacoeylwoledwnroetwnri

431384428471372393346459402371442533324271447392414325441440447445455444503

(table continued)

124

Table Bl

D. MASSARO, R,

(continued)

High-Words

traveltowardstreamspreadsinglesimplesilversecondremainresultreasonprinceperiodnumberbridge

acceptspiritreportreallyofficereturnrefuseregardseasonschoolsisterstreetsuddensummervalleyacrossafraidaffairanimalappeararrivebattlebecamebecomebeforebesidebettercannotchancedecidedegreedemanddesiredollarefforteitherescapefellowfutureheaven

/ Regular /

13211205918

128714131411165717251013135710811008167913101001

810818

12141348902

1247129313421227

7721780175414091555154110931067690590769

1275159212441335145814561931114810721344173014511495929778

1500696959

11921597

vartlewatordtasmersardepnigleslimpeslisversocendnaimersurtelsaronecipnerpodiermurbenribged

paccetpiristtroperleralycofiferutnersufeerdragerasonessolochsirsetrettesdesundmerumsvalelycasorsafriadfaraiflamianpaparevairertabtelceabemboceemboreefbideesbetretcotanncachenciededgedeernamdedreisedloraldfofretheitercaseepwollefrufuethaveen

146614471608168714911643149216211528165117121531173513901660

15801256150813901244153816621439157710671772198614981303121513591265922

1148122515161518157415601676173218301166156917561663179419121513154317521596136314661555

. VENEZKY, AND G. TAYLOR

High-Irregular /

avrletwaotrdmrtaesprsaedsgnielpmliesserilvcdnoesaeminrIrtuessraoenpcnierpeoirdbnruemberigd

ccapetpitisrtproerIrlaeyfcfieotnruersrfueeaerrgdseoanshcoolssrtiessrtteedsnuedsrmmeullvaeysaocrsaraifdafaifraaimlnaapprerraievbeatltbcmaeembcoeebrfoeebsieedbrtteeaoctnnccahencdeiedgreeedanmdedsrdieeaorlldffroethtrieespcaeellfoewfruuetvhanee

147514581603169215281649153815591526168916771583171414071678

14381208153114571299154617821468159711371967191715681355132213741282927

1144123815041502157215341713170118181192134017431665163918811564152317891627128314721554

Low-Regular /

trelvaotdrawestramedrsapengsilempsilevsrilendsocineramestruloseranencripediropembrunegdrib

ectcapisprittreroplyeraleficofertruneserufedgraresosanoscholtressietertsesddunemsrumvyelalosscaridarafariffainalmaeparapevirarettlabebecamebecomebeforedesiberbettontcanechcanedediceredgeeddmanedesiroldraletorffhteeriecesapellfowuferutevehan

528359465417398342430395390464458426413364285

408475605506365657312497564442560771371339398492414480452457567522291338516316812536385342855393530449464666364476472441

Low-Irregular /

tleravrtdwaomtersaesrdpangelsiesplmirselivescndormenaielsrtuesnroaenrcpierdpiornembuigerdb

ctecpatpsriierptroylelraefcfoirrentuesrefurdgeransseoaIsochoesrtsirtestednesdursemmuylelavocssrardfaairfaafiilnmaapperaarreaivetlatbebeamcemeobcoreefbiseedbettrbencntaoenhccaedeedceeerdgednadmiseedrollrdaefrftoeheritaseepcIwefolertfuueneavh

516378496429379282444389461519445417433385270

396455658521332649548607546437620882373357399485418470427453569516332396519409810534280415865393623449464666555461438429

orthographic regularity, positional frequency, and visual ... · letter items. subjects searched...

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