Pmcassme WORDS BY SIGN ANDIOR 35mm FACTORS BY DEAF SUBJECTS Thesis for the Degree of Ph. D. MECHTGAN STATE UNW‘ERSITY ROBERT D. MOULTON 1974 ’ This is to certify that the thesis entitled PROCESSING WORDS BY SIGN AND/bR SEMANTIC FACTORS BY DEAF SUBJECTS presented by Robert D. Moulton has been accepted towards fulfillment of the requirements for JAIL—degree in Speech Pathology 4 * ”— r.- ajor professor Date _M_8.~3L 17 . 1974 0-7639 Vl-Viw.‘nfflflfrc:' 9 1’ ‘ u. . 3 E '3 ‘ ‘_ “by C... k... -'«"‘""::.V -:‘ ‘3‘ 0.3.‘8 " y'k " " ’ 1'? ‘9. . .guxv.“1—v"m‘ ‘ I k ‘Q—"w‘ ' . . .s, 3 3 '1 'I ‘\ $7 ABSTRACT PROCESSING WORDS BY SIGN AND/OR SEMANTIC FACTORS BY DEAF SUBJECTS By Robert D. Moulton This study tests the hypothesis that deaf subjects who consistently use sign language can use sign formation factors and/or semantic relationships as learning strat- egies during a paired-associate verbal learning task involving words. In addition, the study compares and contrasts the relative efficiency of coding by either a sign system, a semantic system, or a combination of the two. Research dealing with verbal learning tasks sug- gests that language-related material is first perceived at the level of sensory impulses and is then converted into a code which facilitates rehearsal and recall. Such research has indicated that acoustic and/Or speech- motor coding will be used by hearing subjects if they are required to recall lists of words or letters within seconds after presentation. Semantic processing of words has also been demonstrated in hearing subjects when the IQ Robert D. Moulton \U {V ‘3) temporal patterns of the verbal learning task have been long enough to permit such coding. Because the acoustic and speech-motor coding models might not be readily applicable to deaf subjects, investigators have tried to discover the coding strat- egies used by the hearing impaired. It has been hypo- thesized that deaf subjects might encode language- related material in a form directly related to the type of communication used by individual subjects. This theory has been given support by recent research which has indicated that deaf subjects who have relatively good speech can code words and letters on a speech- motor basis. The suggestion of a relationship between communication systems and encoding modes leads to the prediction that deaf subjects who are dependent on sign language could code words and letters by some dactyl form. Evidence of manual coding of single letters has been found, but a relationship between word coding and sign language factors has not been clearly specified. Twenty-six deaf teenage subjects who were profie oient in the use of signs participated in a paired- associate learning task. The stimuli consisted of 5 lists of word pairs. The 5 lists were so constructed that they differed from each other on the basis of the sign and/or semantic relationship between the word Robert D. Moulton pairs. List 1 contained word pairs which shared a simi- lar meaning and a similar sign. List 2 contained word pairs which had different signs but similar meanings. List 3 contained word pairs which shared a similar sign but had different meanings. Lists h and 5 were control lists and the word pairs in these lists contained no ob- vious sign or semantic relationships. The subjects were randomly divided into thirteen groups of two indi- viduals each and administered the paired-associate lists in a repeated measures design with random ordering of list order presentation. The presentation procedures used to examine subjects' performance on each of the 5 lists followed standard paired-associate study-test research techniques. The measured variable was the total number of word pairs learned during 6 learning trials for each of the 5 lists. The results of this study indicate that during the initial phases of the paired-associate learning situation. deaf subjects who use sign language can code words on either a sign or a semantic basis. In addi- tion, the findings indicate that for the paired-associate learning of words, semantic relationships offer a more efficient coding strategy than do sign formation factors. The indication of coding by sign factors found in this study offers some support to the contention that .1 Robert D. Moulton the physiological components of communication produc- tion will be reflected in the processing of language- related material. The findings showing that semantic coding occurs in a paired-associate task is consistent with learning models which predict a reciprocal rela- tionship between the motoric component of short-term memory coding and the semantic aspects of long-term memory storage processes. The findings of this study indicate that when given a choice between semantic and sign coding strat« egies, the deaf tend to select the semantic strategy rather than use the sign code or a combination of sign and semantic cues. Based on the results of this study and related investigations, suggestions are offered for educational Iflanning of deaf students. Some current methods of educating the deaf are discussed relative to possible relationships between educational practices, coding systems, speech, reading, language and speechreading. PROCESSING WORDS BY SIGN AND/OR SEMANTIC FACTORS BY DEAF SUBJECTS By Robert D. Moulton A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Audiology and Speech Sciences 197G Accepted by the faculty of the Department of Audiology and Speech Sciences, College of Communication Arts, Michigan State University, in partial fulfillment of the requirements for the degree of Doctor of Philosophy. C} ' ‘2 _, ’ 7 e- Thesis ComitteewaM/fiirector Danie S. Beasley, Ph.D. géi;Zgéfiv;g;:;;://;E:;V€;jé::dwwnw’r William F. Rinterhann, Ph.D. )I'Ohn Simpkins , PX. D . ¥,/’ SiLmeSKCK; ng :E;;(nog§§\4 Linda Lou Smith, Ph.D. ACKNOWLEDGMENTS The author wishes to express his appreciation to Dr. Daniel S. Beasley, whose guidance, concern, encourage- ment and friendship over the years have contributed so much to the completion of this work. Appreciation is also expressed to the members of the thesis committee (Dr. Wil- liam F. Rintelmann, Dr. Herbert J. Oyer, Dr. John D. Simp- kins, and Dr. Linda Lou Smith). All members of this com- mittee have demonstrated their superior professional abili- ties through their contributions to this dissertation. Special acknowledgment is also extended to Mr. Tony Christopulos, Principal of the Utah School for the Deaf, and to the faculty and students of that institution for their unselfish cooperation. Finally, warm recognition must be given to my wife Anne and to our four children who have willingly sacri- ficed so much so that my dissertation and graduate studies could be completed. iii TABLE OF CONTENTS Page LIST OF TABI‘ES O O O O O O O O O O O O O O O O O O 0 Vi LIST OF FIGURES O O O O O O O O O O O O O O O I O O Vii INTRODUCTION 0 O O O O O O O O O O O O O O O O O O O l Verbal Learning in Deaf and in Hearing subjeCtS o o e e e c c o e o o 1 Language, Thought and Educational Methodologies . . . . . . . . . . . 5 Verbal Learning, Coding and Short-Term Memory. . . . . . . 8 Acoustic and/or Speech- -Motor Coding . . . . . . l6 Coding for Letters in Hearing Subjects . . . . 18 Coding for Words by Hearing Subjects . . . . . 22 Verbal Learning by Deaf Subjects . . . . . . . 28 COding for Letters by Deaf SUbjeCtS o o e o o o 30 Coding for Words by Deaf Subjects . . . . . . . 4 Summary and Statement of Problem . . . . . . . 6 EXPERIMENTAL PROCEDURES . . . . . . . . . . . . . . 50 SUbjeCtS O O O C I O O O O O O O O O O O O O O 50 StimUJ-iococoooooooocooooooo 51 Presentation Procedures . . . . . . . . . . . . 5C Analij-S O O O O O O O O O O O O O O O O O O O 58 RESULTS I O O O 0 O O O O O O 0 O O O O O O O O O O 60 Main Effect of Trials . . . . . . . . . . . . . 61 Main EffeCt 0f 1118123 0 e c o o o o o o o o c o 63 LiSt by Trial InteraCtiCn o s o o o o o o o c o 63 smary O C O O O O O O O O O O O O O O O O O O 69 iv sol \ no. DISCUSSION I C I O C O O O O O O O O O O O O Coding by Sign Formation Factors . Coding by Semantic Factors . . . . Learning Theories and Models . . . Thought, Language and Coding . . . . Educational Implications and Suggestions for Further ResearCh o o o e e o 0 LIST OF REFERENCES 0 O O O O 0 O O O O O I O .APPENDICES Appendix A. PA Word Lists With Their Respective Frequencies and Word Lengths . Bo ResponseFomceoooooooee C. Instructions to Subjects . . . . . Page 71 72 80 88 9O 99 109 114 115 Table l. 2. 3. 5. LIST OF TABLES Page Confusion Matrices Used in the Analysis Of Errors by Conrad (1964) o o o o o o 20 Comparison of the Word Frequencies of the Signable and Unsignable Word Lists Used by Odom, et a1. (1971). . . an Summary of an Analysis of Variance Performed on Individual Scores for All Subjects (N=26) at Six Levels of Factor A (Trials) and Four Levels of Factor B (Lists) . . . 61 Summary of the Simple Effects ANOVA Performed on the Individual Means of the u Lists Found Within Each Trial. 0 O O O O O O O O O O O O O O O 67 Confidence Intervals Around the Dif- ferences Between List Mean Co - trasts Found Using the Scheffe Post Hoc Procedure . . . . . . . . . . 68 Summary of Research Into Coding Systems for Words and for Letters in Hearing Subjects, Deaf Subjects with Good Speech, and Deaf Sub- jects Who Sign . . . . . . . . . . . . 73 vi Figure l. A Schematic Representation of the Rela- tionship Between Stage 1 (Precate- Mean Mean Mean LIST OF FIGURES gorical storage), Stage 2 (Encoding), rehearsal, and LTM. Adapted from Waugh and Norman (1969, page 90). Number of Correct Responses Per Trial with Lists Averaged . . . Number of Correct Responses Per List with Trials Averaged . . . Number of Correct Responses Per LiSt for EaCh Trial 0 o o o o o A Flow-Diagram of Information in the Logogen Model. The Dotted Line Indicates the Boundaries of Man. Adapted from Morton (I970, page 205). vii Page 15 62 64 65 83 INTRODUCTION Verbal Learning in Deaf and in HearingSubjects Educators and researchers have long been dissat- isfied with the ability of deaf subjects to learn language-related material. The deaf have been found . to have a limited vocabulary (Vernon and Koh, 1970; and Stuckless and Birch, 1966), depressed reading ability (Wrightstoce, Aronow and Moskowitz, 1962) and "abnormal" expressive language structure (Meadow, 1968; McClure, 1966; and Boatner, 1965). These language-related problems are thought by many to be related to the type of communication system used in teaching the deaf. Traditionally, the deaf in the United States have been taught in either oral or manual communication systems. The oral system has stressed speechreading. speech prod- uction, and auditory training; while the manual method has included the use of the fingerspelled manual alpha- bet and/or formally recognized signs from the American Sign Language or other sign systems. Research designed to specify the relative effects of the two communication systems has been equivocal. Critics of manualism have carried out investigations which seem to show that a reliance on signs and fingerspelling results in a myriad of language-related problems (Streng, 1960; Rupp and Mikulas. 1973: and Dale, D. M. C., 1967). However, other research seems to indicate that the oral method also results in deviant language patterns (Vernon and Koh, 1970; and Mindel and Vernon, 1971). While investi- gators agree that each system of communication has an effect on language skills, they are not in agreement as to the relative effects of the two systems. To date, it is not known what specific aspects of the language- learning problems of the deaf are peculiar to each Communication system. Recently, investigators have produced evidence dealing with coding during verbal learning tasks which may give some insight into language and learning proces- ses used by deaf individuals. These investigators are as"liling if the type of communication system used will affect the way in which language-related material is coded during the language reception and production pro- QeSS. That is, it may be that the two communication S3’8‘tems used by the deaf can result in distinctively different coding processes. If this is the case, then the relative efficiency of different coding strategies U a 1 mi than 3!. ac: H5“ CU" A )9- U Vic‘s- . a ii! ‘ y :5 a: . \ Cy u. a. La ‘4 n .1» w l u .. . 51. A... u AIV 1‘ a a v I H. Vt nxy Kun- \ t h s s v luv In. I v A..- Iv \1 used by the deaf might be directly related to such language-related skills as speech, reading, writing, and the learning of language-related material (Allen, 1970; Conrad, 1970; and Conrad and Rush, 1965). Analysis of the errors made during verbal learning tasks using hearing subjects has shown that the errors committed are consistent rather than random. This consistency of errors is thought by many researchers to be a reflection of the type of coding used in the learning process. The errors made by hearing subjects when learning a list of letters have been shown to have an acoustic (Conrad, 1964: Conrad and Hull, 1961+; and Wickelgren, 1965) or a speech-motor relationship (Hintzman, 1967). This acoustic or speech-motor coding is also evident when words are used instead of letters in the verbal learning task (Baddeley, 1966 and 1972). The use of words has also been shown to make semantic coding possible (Craik and Levy, 1970; Dale, 1967; Dale and Gregory, 1966; and Shulman, 1970 and 1971). Because the acoustic and speech-motor coding moSiels do not readily apply to deaf subjects, inves- tigators have tried to specify the verbal learning QC><3~Iing systems used by the hearing impaired. It would a‘Zpipear that the type of coding used by the deaf depends on several factors such as the amount of hearing loss, age at onset'of the loss, type of training received and the type and use of sound amplification. Conrad (1970) has shown that orally trained deaf subjects with rela- tively good speech appear to code lgttgrs on an artic- ulatory basis during verbal learning tasks, while Locke and Locke (1971) have indicated that manual deaf subjects with poor speech code letters on the basis of kines- thetic cues from fingerspelling or from visual cues associated with the shape of the printed letter. Research designed to distinguish between possible 'tyres of coding systems used by the deaf when perform- iJLg verbal learning tasks involving words has been Partially inconclusive. It is generally agreed that ailcnistic coding is not used by the profoundly deaf (JXJJIen, 1970; and Blanton and Nunnally, 1967), and speech- 1nc>1nar coding is not used by deaf subjects with rela- 'tii‘vtely poor speech (Blanton and Nunna113 1967). Conrad (Tl-Sr70) has found that deaf subjects with relatively €§<>1lreased vocabulary and other language-related tasks. Researchers are now calling for objective evi- (16311<3e on the effects that various methodologies might }IEt‘r€3 on the language, speech and thought processes c>i? 1ihe deaf. Myklebust (1960) called for such action when he stated: Methods utilized for developing language in children with deafness are based on theory and experience rather than on scientific evidence... Many claims and counterclaims have been made regarding the effectiveness of a particular methodology. Only through objective study can such claims be evaluated. (page 2&0) o‘vo \ .— (1v V ) c f I 'An’ evil. a” a... ’I- ..-II . , ‘Ino ‘e... y ’l ' i w h, \ ’1! 'di. g.“ .- ‘Un. :‘r V'y. An attempt to answer this need for objective investi- gation has led some researchers to look at the coding processes used by deaf subjects when learning verbal material in a short-term memory task. This search for coding strategies can be viewed as a method of ob- jectively studying the issues raised by the early founders of oral and sign language systems. That is, can it be shown that deaf children who sign will code language-related material on the basis of signs and that deaf children who are taught orally will code in a manner related to spoken language? Investigators are also asking what effects these different coding Strategies, if they can be shown to exist and to dif- fer. might have on important educational objectives Suoh as speechreading. speech, written language, reEELding, and etc. This research into verbal learning S"Wrategies has been carried out using both hearing and deaf subjects and attempts have been made to correlate the findings from the two pOpulations. Ve— . \I‘b_______a.___s___ Sho al Learnin Codin and “-~._£:trTe;m Memory ”Verbal learning" refers to a broad classific- ation of behavior investigated by learning theorists, 13syehologists, philos0phers, educators, speech scien- t 9 13138 and others. In general, "verbal learning" is “t: he term used to designate any learning situation in i. e u #- ' ‘\ (I) ‘ uh la, 'obo 53w vvaéh 1! AA‘ I VV.. ' i n p p A i A “In“ :12" .v“ 1'? 7“ 5.: It) ..‘ 91.. 9 which the task requires the learner to respond to verbal material, such as words or individual letters (Ellis, 1972). The task to be performed by the subé jects might include the learning of lists of words or letters or the forming of associations between pairs of letters or words. The types of verbal learning tasks available have been limited only by the imagin- ation of the investigator, and the literature con- cerning verbal learning constitutes one of the largest colJections of systematic investigations in the behavioral sciences (Hall, 1971). Verbal learning tasks usually involve several learning trials in which it is assumed that any change irz loehavior can be attributed to learning. Melton (1963) makes note of this in his definition of learning: Learning may be defined as the modification of behavior as a function of experience. Operationally, this is translated into the question of whether (and, if so, how much) there has been a change in behavior from trial n to trial n + 1. Any attribute of be- havior that can be subjected to counting or measuring operations can be an index of change from trial n to trial n + 13 and therefore an index of learning. (page 3 Even with a broad concept of learning such as is u*Sed by Melton (1963), it has sometimes been diffi- 'C:113ut to determine if the results of verbal learning tasks have not been coynfounded by perceptual or mem- orial factors rather than learning. For example, it 9 V ' p. an. M p i 5gb“ a ‘1” no t .0...“ I I...A‘ [x3 (I. :10. (1 '3 I 1)- ‘g. "L U 7:117 'Q 10 might be possible to attribute the correct responses in a verbal learning task to the "remembering" of the stimulus items by a system which can only be defined as something less permanent than is usually found in true learning. Hall (1971) has acknowledged that it is often difficult to determine how permanent the learning aspect of the verbal learning procedure is but holds that the problem is probably largely seman- tic. Hall has reviewed the literature on the problem (xf distinguishing between learning and memory and con- claides that clearly separating the two factors would ncrt alter the concepts or conclusions reached from Verbal learning research. Hall (1971) noted: It does not seem possible that we can resolve this problem of how permanent the behavioral change must be in order for the learning pro- cess to be inferred, since it appears that the nature of the controversy is primarily a seman- tic one. As we shall subsequently note, the distinction between a learning process and a retention or memorial one is arbitrary since the learning of new material obviously involves the remembering of that which has been previously learned. The placing of emphasis upon a memor- ial process, however, does emphasize the position that a continuum of behavior change exists in which at the one end we have changes that are extremely temporary and at the other end changes that are quite permanent. (pages 9-5) In developing a verbal learning task, the invest- lga‘tor generally has not been too concerned with ‘9'}lerther or not the results will be the product of Ipltre learning". Instead, the investigator has design— es<1 ‘the task so that the performance on the verbal task 11 can improve over trials and in turn this improvement has been operationally defined as learning. Hall (1971) has stated this aspect of verbal learning designs well: Generally, there is little concern on the part of the experimenter with looking at a given be- havior pattern and attempting to decide whether or not it has come about as a result of learning or as a result of some other process, i.e., mat- uration, fatigue, etc. Rather, and this has been particularly true in the investigation of verbal learning, the general procedure has been to provide the subject with a task in which his change in behavior leads logically to what must be the Operation of the learning process. Thus, when a subject is asked to learn a list of words, it is obvious he could not have produced these words before observing them; it is obvious that after a number of practice trials, he is capable of doing so. It is generally assumed, then, that the process that accounts for such a behavior change is learning. (pages 5-6) Verbal learning tasks are closely allied to short- tnerm memory (STM) processes. In fact, when the items used in a STM task involve words or letters, "STM" 81nd "verbal learning" are simply two terms for the same Iprwacess. Note, however that the two terms appear to foster the problem of distinguishing between memory and learning. Hall (1971) has attempted to lessen this IPIWJblem somewhat by referring to STM as short-term retention, The point is, research into STM and verbal learning is very closely related, and when the items uSed in a STM task involve verbal material, little or “‘3 clifference exists between the two constructs. This is an important point, since the research into coding 81ui‘éitegies used in the processing of verbal material 12 has usually been classified as STM. The concept of short-term memory was introduced by Jacobs in 1887 (Hall, 1971) and given further at- tention by James in 1890 (Norman, 1969). These early writers noted that man has both a short-term memory and a long-term memory. James distinguished betweeen an immediate grasp of the past which he characterized as short-term memory, and "properly recollected ob- jects" which he stated were peculiar to long-term memory (LTM). Norman (1969), in a more current dis- cussion of STM and LTM, noted that short-term memory has a relatively small capacity of a few items or “chunks”of information, whereas LTM has a relatively large storage capacity. Norman also stated that items are held in STM in some form that facilitates re- hearsal”, and that items are retrieved from LTM through semantic associations. The time element associated Wifbh.STM§ according to Norman, is usually not con- sidered to extend beyond several seconds, but items may remain in LTM for an indefinite period. Shulman (1970) has pointed out that the boundaries between STM and LTM are not always distinct, and it is Possible for these two memory systems to affect each “flier. Paivio (1971) has noted that, while theoreti- cs:]_ (isfinitions and distinctions regarding LTM and STM are possible, it is usually desirable to establish 13 operational definitions based on temporal factors. Aaronson (1967) has reviewed the literature on this issue and designated the time factors relevant to studies of coding and learning during STM. Recent research in STM has found evidence of the operation of two stages or systems (Sperling, 1960 and 1963; and Broadbent, 1957 and 1958). During the first stage; relatively large amounts of incoming perceptual information can be stored for a very brief period. Sperling (1960) noted that the visual impression during Stage 1 can decay in a matter of milliseconds, while Murdock and Walker (1969) and Craik (1970) suggested that precategorical auditory storage is longer than Visual storage ,‘ perhaps up to 5 seconds. During this iJlitial or "buffer" stage, very little mental proces- SiJng of the information occurs --the information held 111 storage is more nearly a direct representation of the: physical attributes of the stimulus and no catego- I'ization or encoding takes place. Stage 1 is relative- ly’Ilarge in capacity and more than one item can enter 'th£! system simultaneously (Aaronson, 1967). Thus Stage 1 aPpears to be a brief store of sensory information “that does not require attentional or coding mechanisms. It is at Stage 2 of STM that individuals incorp- orate mental processes to select and code the sensory itlfkagpmation found in Stage 1. In her description of (I) .u 4. t“ A A‘ '§ ,ID [\‘u (I) 14 Stage 2, Aaronson (1967) has noted: Stage 2 differs from Stage 1 in several res- pectsx (a) Processing at this stage is at a higher level than at Stage 1. Items are identi- fied or encoded on the basis of meaning or a name of some sort. (b) Representations are more permanent after Stage 2 than during Stage 1. Their rate of decay has decreased even though some of the initial information was sacrificed in the abstraction of roperties that occurred in identification. (c At Stage 2, representa- tions are handled by a limited-capacity system. This system can receive items only in series, that is, only one item can enter the system, additional items being delayed until the "single- channel" is free. (page 130) Once material from Stage 1 has been encoded in Stage 2, it is repeated in its code as a form of rehearsal which will maintain the material in Stage 2 for a limited ‘time (Brown, 1958; and Broadbent, 1958). Figure l is a schematic representation of the re- lxationship between Stage 1 (precategorical storage), Stage 2 (encoding), rehearsal and LTM. While it is generally well accepted that this cod- irug and rehearsal take place, the exact form of the ccnie has been a much discussed theoretical subject. Paivio (1971) has reviewed the literature on the issue 100 > 592 2 ,008 307 Unsignable Words steam A 341 340 73 harvest 48 217 112 27 modern AA 250 731 40 energy 41 116 1,190 194 material AA 376 651 32 special AA 360 1,192 167 engineer 40 126 167 28 condition AA 200 123 2 (Mean) > 72 311 572 71 Where: G = Thorndike-Lorge (1944) 'G-count” based on word occurrence per one million wordS. J - Thorndike-Lorge count from 120 juvenile books based on word occurrence per 4% million words. F = Total word frequency, Carroll, et a1. (1971) based on word occurrence per 5 million words from text- books from grades 3 through 9. C = Total word frequency from Carroll, et al., grade 3. And Where: AA - Word occurs more than 100 times per million words. A 8 Word occurs more than 50 times per million words. H = Word occurred 1,000 times or more in the count of 120 juvenile books checked by Thorndike and Lorge (191:4). 45 word frequency count which would seem to be more ap- plicable to the subjects in the Odom, et al. (1970) study. The Carroll, et al. frequency count is the result of a computerized analysis of the words contained in texts used in public schools for grades 3 through 9. Since the maj- ority of the books used in schools for the deaf were de- signed for use with hearing students, the Carroll, et al. frequency count should give some indication of how often a deaf child is meaningfully exposed to particular words in their printed form. While it would be best to take fre- quency counts from direct translations of the sign lan- guage, Bornstein (1973) has shown that theoretical and practical problems have so far precluded this possibility. Further, although Stokoe, Casterline, and Croneberg (1965) have developed a notation system to represent sign lan- guage, the system has not been used enough to make word frequency counts feasible. It would appear that until an accurate frequency count can be made of words used in the American Sign Language, the Carroll, et a1. (1971) compilation will have to suffice. Column "F", Table 2, represents the word frequencies found in the texts used in grades 3 through 9 according to Carroll, et a1. Column "C", Table 2, is a listing of word frequencies taken from grade 3 reading level only. Grade 3 was chosen to match the low reading ability of deaf children. In column "F" and in column "C", the signable words are shown to occur 46 more frequently than the unsignable. The results of the Odom, et al. (1970) study can be explained in terms of list familiarity rather than a dif- ference in coding efficiency between signing and finger- spelling. Neither Putnam, et al. (1962) nor Odom, et al. have adequately explained the effects of signing on coding for words in deaf subjects who depend on manual forms of communication. Even if the theoretical and methodologi- cal problems of the two studies are disregarded, the question of coding systems used by the deaf remains un- solved since the results of the two studies indicate both the existence of a sign code (Odom, et al., 1971) and the absence of a sign code (Putnam, et a1, 1962). A review of the literature has revealed no further studies on this issue and the question remains a fertile topic for research. Summary and Statement of Problem The performance of deaf subjects on language-related material used in verbal learning tasks has been shown to be significantly different from the performance of hearing subjects. One possible explanation for this difference in performance is that different communication systems used by the deaf could produce distinctively different cod- ing strategies which affect learning. These coding systems have been examined with verbal learning techniques. 47 A review of the literature revealed that language- related material in a verbal learning situation is con- verted into a code which facilitates rehearsal and recall (Paivio, 1971). This coding process is affected by temporal factors (Aaronson, 1967). It appears that acous- tic or speech-motor coding will be used by hearing sub- jects if they are required to recall serial order material within seconds after presentation (Shulman, 1970 and 1971). Acoustic and/or speech-motor coding has been found to oc- cur in both letters and words during STM tasks with hearing subjects (Conrad, 1964; and Hintzman, 1967) and some indication of semantic coding for words has also been found (Shulman, 1970). The loss of the auditory channel in the deaf essen- tially precludes the use of the acoustic coding mode (Con- rad, 1970). Speech-motor coding has been found in learn- ing lists of letters and also in learning word lists for deaf subjects with relatively good speech (Conrad, 1970), but not in subjects with poor speech skills (Blanton and Nunnally,l967). The search for coding processes other than that of a speech-motor nature in deaf subjects has been successful with letters but not with words. Locke and Locke (1971) have shown that subjects who depend on signing and fingerspelling code letters on the basis of kinesthetic cues from the fingers as well as on the basis of visual cues related to the shape of the printed letter. Only two 48 studies have been found dealing with coding for words on a manual basis and they have produced equivocal results. A study by Putnam et al. (1962) failed to find evidence of coding based on sign language, whereas a study by Odom, et al. (1970) found that coding by signs did occur and was an efficient coding strategy. The results of both the Odom, et al. and the Putnam, et al. studies must be ques- tioned, however, because: (a) the results of the two studies are contradictory, (b) the results of the Put- nam, et a1. study do not conform to established learning and coding theory, (c) Putnam, et al. failed to distinguish between sign and semantic similarity between the word pairs used, (d) the Odom, et al. study did not properly control for word frequency differences between the two lists used, and (e) the Putnam, et a1. study has reported no attempt to control for word frequency. Currently, there appears to be a paucity of empirical information concerning the effects of manual forms of com- munication on coding of words during verbal learning tasks with deaf subjects. The purpose of the present investiga- tion, then, is to further delineate the verbal learning coding strategies used by deaf subjects. Specifically, the following questions will be investigated: 1. Does coding by sign and/or semantic factors occur during a PA learning task using words as stimuli? 2. Do semantic and sign cues interact during the 5. 3. 5. 49 coding process? If semantic and sign coding can be shown to oc- cur, how will these two coding systems compare in relative efficiency? If deaf subjects are given a choice between two coding systems, will the subjects use one code, both codes simultaneously, or will a switching process be used between the two codes? If the PA task involves several trials, how will the learning curves for the different coding modes compare? asb- :1 EXPERIMENTAL PROCEDURES For this study, 26 deaf teenagers participated in a paired-associate learning task. Each subject attempted to learn five lists of word pairs over a period of six learning trials per list. The five lists represented several possible combinations of sign and/or semantic cod- ing factors. Subjects The 26 deaf subjects used in this study were students from the Total Communication Department of the Utah School for the Deaf. The Total Communication Department uses, teaches and encourages sign language and fingerspelling as possible modes of communication. All subjects were re- quired to meet the following criteria: (a) Be not younger than 12 nor older than 20, (b) have a hearing loss of at least 70 dB (re ANSI, 1969) in the better ear for the freq- uencies 500, 1,000, and 2,000 Hz (AAOO, 1970), (0) have a hearing loss which was discovered before the child's first birthday, and (d) have satisfied the visual screening crit- erion of 20/20 acuity in both eyes (with or without 50 51 correction). The mean age of the subjects selected was 18 years. To satisfy the hearing and visual criteria, reference was made to the school records for each subject. The school employs a full-time Ph.D. Audiologist (CCC-A) who keeps the hearing evaluations current. Yearly visual screenings and necessary referrals are performed by the school's Registered Nurse. Stimuli The stimuli for the study consisted of five lists of 14 word pairs. The five lists differed in the sign and/or semantic relationship of the word pairs. The sign-semantic relationship was arranged as follows: List 1 - The two words making up each word pair were similar to each other on both a sign and a semantic basis. That is, the words within a pair shared a common sign formation as well as a close semantic association (e. g., PRET- TY-BEAUTIFUL is a word pair with close seman- tic association and the sign formation is the same for each word). This list was desig- nated as "similar sign, similar meaning". List 2 - For this list the two words within each pair were related to each other on a semantic basis only.. The words making up a pair shared a common semantic association but had 52 distinctively different sign formations (e.g., LAUGH-SMILE is a word pair which has a close semantic association but does not share a similar sign). This list was desig- nated "similar meaning, different sign". List 3 - For this list, the two words within each pair were related to each other on the basis of a similar or common sign. The words make ing up a pair shared a similar sign but were not closely related semantically (e.g., HAMBURGER-MARRY is a pair of words which have a similar sign but do not share a similar meaning). This list was designated as "similar sign, different meaning". Lists 4 and 5 - These two lists served as control lists in the testing situation. The two words mak- ing up each pair in these lists were not related to each other by any obvious sign or semantic relationship (e.g., TOMORROW-KEY is a word pair with no close semantic or sign associations). These lists were desig- nated as "different meaning, different sign". The word pairs for Lists 1, 2, and 3 were developed With the cooperation of a panel of six teachers of the deaf subjects. Four of the panel members were deaf and two had normal hearing. All of the panel participants 53 were proficient in the use of manual communication. The characteristics of the three lists were eXplained to each panel member and the members were then asked to generate as many word pairs as possible for each list, with the re- striction that all words selected should, in the teacher's Opinion, be part of the general vocabulary of the students in question. If a word could be represented by more than one sign, the panel was instructed to use the sign which was more commonly used by the teachers and students at the Utah School for the Deaf. From the pool of word pairs submitted by the panel, the three test lists were produced. The three lists were matched on word length by considering the total number of letters making up each list. The lists were also matched on word frequency counts for third.grade reading material compiled by Carroll, et al. (1971). This was accomplished by keeping the sum of the word frequencies equivalent be- tween lists. Lists 4 and 5, the two control lists, were generated from a pool of words selected from the third grade reading vocabulary of Carroll, et al. Referrence was made to Watson (1964) to make certain that the words in the control lists were part of the American Sign Lan- guage. The two control lists were so constructed as to make them equivalent with the other three lists on word length and word frequency. Finally, the two control lists were referred back to the panel of six teachers who were W0! m; l ..n... 3. FF. - 54 asked to delete any words which were not within the stu- dentS' semantic and sign vocabulary. The five lists of word pairs and their respective word lengths and frequen- cy counts can be found in Appendix A. A 35mm slide was made for each word pair and for each stimulus word. To produce these slides, a word pair or a stimulus word was typed on a white 3" by 5” index card with a Royal Model 470 typewriter with bulletin type style. A picture of each card was then taken with a Mamiya/sekor 1,000TL camera with a 1:1.8 lens fitted with a Spiralite Proxivar 52mm close-up lens (No. 665222). The camera was mounted on a Honeywell Copy Stand Model 7101 fitted with photographic tungsten lamps. Presentation Procedures 13 groups of 2 subjects each were randomly assigned to individual testing sessions. Each group of 2 subjects participated in one training session and 5 testing sessions over a three week period. In each testing session, subjects were allowed a maximum of six trials to learn one list of paired-associate words. Subjects were tested on one list per session and sessions were separated from each other by a minimum of two days and a maximum of four. Testing sessions varied in length from 25 to 45 minutes depending on how soon both subjects reached the required performance criteria. The performance criteria was set as two 55 consecutive errorless trials or a maximum of 6 trials per list. The training and testing sessions occurred within the same classroom at the School for the Deaf. ’The slides used in the PA task were projected on a 4 x 4 foot Da- Lite screen by a Kodak Carosel 750 projector. A distance of ten feet separated the front of the viewing screen from the two chair-desks provided for the subjects. The sub- jects' chair-desks were separated from each other by 5 feet to help insure individual work. The projector and the testor were located at a table behind the subjects. A distance of 15 feet separated the projector from the screen. Blackout drapes were used over all windows during the testing. A General Electric Model 8DW58Y4 Exposure Meter calibrated at 0 foot-candles in the darkened room was used to measure the reflected light from the screen. The re- flected light readings were made with and without slides in the projector. Reflected light 6 inches in front of the screen reached 30 foot-candles without a slide and 18 foot-candles when a slide was used. At the subjects' chair-desks, the readings were 5 foot-candles without a slide and 3 foot-candles with a slide. The study-test paired-associate task procedure as described by Hall (1971) was used in the training and test sessions. By this procedure, the subjects were first given a study session which exposed them to all of the word . G 7.5. 1a- :0- 9n. Aid ,1: Vi 1‘ 1 r \K u. ‘\N ‘k 56 pairs of a particular list. Following this study segment, the first word (stimulus) of each pair was presented alone and the subjects attempted to supply the missing second word (response) on an answer sheet (see Appendix B). The study-test procedure was repeated after the subjects had attempted to respond to all 14 stimulus words. Each cycle of the 14 stimulus-response pair study session fol- lowed by the test segment of the 14 stimulus components was considered one trial. Subjects were allowed a maximum of 6 of these study-test trials for each list but the pro- cess was terminated for a subject if the criterion of 2 errorless trials was reached. The temporal patterns in the presentation of the slides during the study and test sessions were controlled by a Tiffen Show/border Model 7100 which automatically advanced the Kodak projector. During the study session of each trial, subjects viewed each pair of words for 3 sec- onds. A one second period was used by the Kodak pro- jector to change each slide. The entire study session of each trial consisted of 56 seconds (4 seconds for each of the 14 pairs). Fifteen seconds elapsed between the last word pair in the study sequence and the first stimulus word in the test sequence. Each test sequence slide ex- posed a stimulus word for 9 seconds and one additional second was used to change slides. This allowed the subjects 10 seconds in which to insert their response on the answer 5? sheet. The entire test sequence lasted for 140 seconds. Following the last test word, a new trial was begun after the lapse of one minute. These presentation times follow the suggestions of Hall (1971) and Calfee and Anderson (1971). In order to control for a possible learning effect as well as a possible fatigue factor, both list presentation order between sessions and the serial order of the word pairs and stimulus words between trials were randomized. This meant that no two groups received the lists, the stim- ulus-response pairs, or the stimulus words in the same order. This randomization also meant that no single group ever recieved the same word pair or stimulus word order between trials in the same session. If the serial order of the stimulus words corres- ponds to the serial order of the stimulus-response pairs (e.g., a PA task with HOUSE-BLUE, MONKEY-BLACK, TREE- YELLOW, and CHAIR-GREEN followed by HOUSE ? , MONKEY ? , TREE ? , and CHAIR ? ), the subjects could conceivably simplify the PA task by learn- ing only the serial order of the response words and dis- regarding the stimulus words (McGeoch and Underwood, 1943). In this study, subjects were forced to attend to the stim- ulus words because the serial order of the stimulus words was independent of the serial order of the stimulus- response pairs. This independent reordering of both the .sU A‘- R‘- ..u h'i .‘u ‘(1 ‘Q to ~ u‘ ‘r H. 58 study (stimulus-response pairs) and the test (stimulus only) lists occurred between each of the 6 trials in each session. During the training session, the nature of the test was explained to the subjects. The instructions for the test were given in a combination of signs, fingerspelling, speech and writing by a trained, experienced teacher of the deaf (see Appendix C for the complete set of instructions). Following the instructions, the subjects participated in a training session in which they performed a PA task which paralleled the task which would later be used in the test situation. The training task differed from the test sit— uation in that the list of words used did not contain any of the test words found in the five test lists. In addi- tion, the training list did not contain any obvious or systematic sign-semantic associations. Each subject was given repeated trials during the training session until the criterion of the two errorless trials was reached. Analysis The data was hand-scored by the experimenter. The number of correct responses per trial for each list was re- corded for each subject. If a subject had reached the criterion of 2 errorless trials before being tested on all 6 trials, he was given full credit (14 points) for each trial for which he was exempt. Since the two control lists 59 (Lists 4 and 5) were developed from the same parent pool of words equated on reading level and word frequency counts, the results from these two lists were averaged and considered as one list. During the analysis and discus- sion stages of the study, the list which resulted from the averaging of the two control lists (Lists 4 and 5) was re- ferred to simply as "List 4". Following the suggestions of Kirk (1968), the data was placed into a two factor (6 x 4 ) analysis of variance with repeated measures design, and suitable F-tests were performed (computerized). Two computerized post hoc pro- cedures were also employed. A simple effects ANOVA was used to test for AB (trials by list) interactions occur- ring within the 6 trials. Where appropriate, Scheffe post hoc comparisons were then used to locate differences be- tween the lists within trials. RESULTS The findings of this study support the thesis that profoundly deaf subjects who communicate manually code words on a semantic basis. Performance on the two lists devised according to semantic criteria (Lists 1 and 2) was superior to the performance on the other two lists during the early stages of learning. Further, the results give qualified support to the thesis that these deaf subjects can also code words by sign formation. Coding by signs was indicated by the higher scores on List 3 (similar sign, different meaning) relative to the scores obtained on List 4 (different sign, different meaning). However, coding by sign formation was found to be less efficient that semantic coding during the first trial. The results also indicate that, given a choice between coding by sign and/or coding by semantic factors, the semantic coding will take precedence over coding by signs. This prefer- rence for semantic coding was indicated by the fact that the learning curve for List 1 (similar meaning, similar sign) followed the learning curve for List 2 (similar 60 ..L - :5. n as 61 meaning, different sign) rather than the learning curve for List 3 (similar sign, different meaning). Main Effect of Trials Table 3 depicts the results of the two factor (6 x 4) analysis of variance with repeated measures on all factors for each subject which was performed on the data. Table 3.--Summary of an analysis of variance performed on individual scores for all subjects (N = 26) at six levels of factor A (trials) and four levels of factor B (lists). t Scurce SE? df MS F A 2,588.12 5 517.62 223.58* B 603.33 3 201.11 28.28* AB 328.97 15 21.93 13.67* * Significant at a level greater than .001. Table 3 reveals that the main effect of trials was significant at an alpha level of 0.001. Thus the over-all means of all lists for each of the six trials (7.35, 10.63, 11.90, 12.61, 13.07 and 13.25, respectively) contain sig- nificant differences. Figure 2 shows these differences were most pronounced during the first three learning trials. 62 7”?’?’////‘glg/lg/ 6 V’////////////////////////////////// o r/l/l/l/l/l/l/V/l/l’l/l/l/l/l/ll/ 4 TRIALS Figure 2. Mean number of correct response: per trial with lists averaged. Vl/lll/lllll/////////////////// a 7/////////////////////// 2 rl//////// : ...I. 2 I O 9 8 7 6 mmmzommwc hommcoo no cmmzaz 24m! 63 Main Effect of Lists A significant main effect of lists (p< 0.001) is shown in Table 3. This indicates that significant differ- ences can be found in the means for the 4 lists (12.45, 12.22, 11.26 and 9.95, respectively) when the mean perform- ance for all trials within each list is considered. Fig- ure 3 shows that over-all performance on the 4 lists can be considered to be on three levels. The greatest number of correct responses occurred in Lists 1 (similar meaning, similar sign) and 2 (similar meaning, different sign). Performance on List 3 (similar sign, different meaning), while not as high as Lists 1 and 2, was above that of List 4 (different meaning, different sign). Finally, over- all performance on the three codable lists (Lists 1, 2, and 3) was greater than the performance on List 4 (control list, coding association between pairs unknown). List by Trial Interaction Table 3 also shows the significant interaction effect which was found between lists and trials (p (0.001). That is, there was a greater increase in scores between Trials 1, 2, and 3 than between Trials 4, 5, and 6 (see Figure 4). Figure 4 indicates that List 1 (similar meaning, similar sign) and List 2 (similar meaning, different sign) differ- ed minimally from each other between trials. Figure 4 also suggests that the differences between List 3 (similar sign, 64 7’”////‘//////////// 4 V////////////I’ll/[I’l/ll/l/ a 7///////////////////’ll/I/I/I/V/ 2 .V/l/l/l/égéllllllll .. LISTS Figure 3. Mean number of comet reeponeee per liet with trials averaged. 3 Ms. M nlv 9 8 7 6 I mmmzoammc 59550 no mum‘s: z Liet4. <> 4 3 2 I I 2 3 4 5 6 TRIALS Figure 4. Mean number of correct responcee per Iiet for each trial. 66 different meaning) and Lists 1 and 2 considered togeth- er, were most evident in the earlier learning trials. The learning curve for List 4 (different meaning, differ- ent sign) shows that the lag in performance found in the earlier trials may have been overcome during the last few trials (see Figure 4). The significant interaction effect between lists and trials was further investigated using post hoc statistical procedures. The first of these post hoc tests consisted of an analysis of variance of the four list means found in each trial. This was done with a simple effects ANOVA (see Table 4). Table 4 shows that significant differences between the means of the four lists may only be found within the first three trials. As can be seen in Figures 2 and 4, the differences between list means during the last three trials tended to become smaller relative to the differences found during the first three trials. A Scheffe post hoc procedure was used to further locate the differences indicated by the simple effects ANOVA. The Scheffe technique was used to make simpleand complex contrasts between the 4 list means within trials. Only the list means of the first three trials were con- sidered since the simple effects ANOVA had indicated that significant differences between list means did not occur within any of the last three trials. The results of the comparisons made by the Scheffe procedure can be found in Table 5. 67 Table 4.--Summary of the simple effects ANOVA performed on the individual means of the 4 lists found within each trial. Pooled error term Source SS df MS F A 497.85 3 165.95 65.80* B 294.18 3 98.06 38.88* C 88.35 3 29.45 11.68* D 27.26 3 9.09 3.60 E 12.55 3 4.18 1.66 F 12.11 3 4.04 1.60 G 1,135.01 450 2.52 Where: A = List means within Trial 1 B = List means within Trial 2 C = List means within Trial a D = List means within Trial E = List means within Trial 5 F = List means within Trial 6 G: *2 68 Table 5.-Confidence intervals around the differences between list mean contrasts feund using the Scheffé post hoc procedure. Trial Contrasts 1. List 1 vs. List 2 -l.36 to 1.67 List 1 vs. List 3 1.71 to 4.75 * List 1 vs. List 4 3.69 to 6.73 * List 2 vs. List 3 1.56 to 4.60 * List 2 vs. List 4 3.54 to 6.58 * List 3 vs. List 4 0.46 to 3.50 * 1 List 1 + List 2; vs. List 3 1.84 to 4.47 * List 1 + List 2 vs. List 4 3.82 to 6.45 * 1 3(List 1 + List 2 + List 3) vs. List 4 2.82 to 5.30 * 2. List 1 vs. List 2 (-1.06 to 1.98 List 1 vs. List 3' 0.25 to 3.29 * List 1 vs. List 4 2.81 to 8.85 * List 2 vs. List 3 -O.21 to 2.83 List 2 vs. List 4 2.35 to 5.38 * List 3 vs. List 4 1.04 to 4.08 * iEList 1 + List 2; vs. List 3 0.22 to 2.85 * List 1 + List 2 vs. List 4 2.78 to 5.41 * l/3(List 1 + List 21+ List 3) vs. List 4 2.33 to 4.81 t 3. List 1 vs. List 2 -1.71 to 1.33 List 1 vs. List 3 -0.94 to 2.09 List 1 vs. List 4 0.63 to 3.67 * List 2 vs. List 3 -0.75 to 2.29 List 2 vs. List 4 0.83 to 3.86 * List 3 vs. List 4 0.06 to 3.10 * igList 1 + List 2; vs. List 3 -0.64 to 1.99 List 1 + List 2 vs. List 4 . 0.93 to 3.56 * 1 3(List 1 + List 2 + List 3) vs. List 4 0.78 to 3.26 * * Significant at. p (0.008 99.2% Confidence Interval 69 In general, the findings from the Scheffé give credence to the trends noted in the original two way (6 x 4) analysis of variance. That is, Lists 1 (similar sign, similar meaning) and 2 (similar meaning, different sign) did not differ from each other and together exhibit- ed higher mean scores than the other lists during the initial stages of the learning curve. Mean performance on List 1 was better than the mean performance on List 3 (similar sign, different meaning) during Trials 1 and 2 only. Mean scores for List 2 were higher than the mean scores for List 3 during the first trial but no signifi- cant differences between these two lists were found during later trials. Lists 1 and 2, considered together, had a higher mean score than List 3 during Trials 1 and 2 but significant differences were not found during the later trials. Performance on List 4 (different meaning, differ— ent sign) was lower than on any other list or combination of lists during the first three trials. Summary The results of this study revealed that, in the first three learning trials, significant differences exist be- tween the relative efficiency of the paired-associate coding strategies investigated. As evidenced by the re- latively high mean scores achieved on Lists 1 and 2, the deaf subjects performed best when the word pairs within a 70 list shared a similar meaning or a similar meaning as well as a similar sign. The results also indicate that as- sociations between signs may also be used as a coding strategy. However, coding by sign formation was not found to be as efficient as was coding by meaning during the first and second learning trial as indicated by the de- pressed scores obtained on List 3 relative to Lists 1 and 2. Performance on the control list (List 4), where no consistent coding strategy was readily available, was lower than the performance on the other three lists. DISCUSSION The findings of this study are in general agree- ment with the direction of previous research into coding strategies for verbal learning tasks. Studies dealing with hearing subjects had indicated that coding strate- gies could be expected to be related to speech-motor and/or acoustic factors of speech as well as semantic factors when the learning task involved words. Corollaries to this process had been sought in deaf subjects and some evidence had been found which indicated that no one coding strategy was used by all deaf subjects. Research indica- ted that when coding for letters in a STM task, deaf subjects would usually code by speech-motor or manual alphabet configuration factors. However, when the complex- ity of the learning task was increased by using words instead of letters, investigators were not able to complet- ely Specify the possible coding strategies. Research had indicated that subjects with good speech coded words by speech-motor and/or semantic factors, and several writers had speculated that deaf subjects who communicated with 71 72 the language of signs would probably code words on the basis of sign formation and/or semantic factors. The findings of this study give support to this assumption and also give some indication of the relative efficiency of coding by sign or semantic factors. In addition, the re- sults of this study provide significant insight into theories and models applied to learning and language pro- cessing. Coding by Sign Formation Factors The results of this study indicating a coding strat- egy related to signs in deaf subjects who use the sign language are consistent with predictions which can be made from parallel studies on hearning subjects and on deaf subjects who have relatively good speech. This can be seen in Table 6. Table 6 shows that hearing subjects as well as deaf subjects with relatively good speech can encode words in a manner related to the coding strategy used for letters. That is, when acoustic and/or speech-motor processing has been evidenced in research using letters, similar pro- cessing systems have also been found in investigations using words. This relationship between the coding of let- ters and the coding of words would lead to the prediction that the dactyl processing for letters found by Locke and Locke (1971) would be followed by some form of manual 73 coding for words in deaf subjects who sign. Indication of this predicted manual coding for words has been found by Odom, et al. (1970) and by the present study. Table 6.--Summary of research into coding systems for words and for letters in hearing subjects. deaf subjects with relatively good speech, and deaf subjects who sign. Hearipg Deaf-Good Speech _peaf Sigpers Let- Acoustic and/or Speech-motor, Dactyl, ters speech-motor (Conrad, 1970) (Locke and (Conrad, 1964) Locke, 1971) Acoustic and/or Speech-motor, sign forma- speech-motor, (Conrad, 1970) tion, (Odom, semantic, semantic, et al., 1971) Words (Shulman, 1970) (probable but semantic, not specified) (specified by the present study) Semantic coding has been found to occur in coding for words by hearing hearing subjects. It is probable that semantic coding can be shown to be used by deaf subjects with relatively good speech but this has not yet been reported in the literature. Table 6 shows that coding by sign formation factors and coding by semantic relationship on the part of deaf subjects who sign is consistent with existing research findings. It should not be considered that the processing strategies indicated in Figure 6 are mutually exclusive. For instance, it may be possible for 74 a hearing subject to code words on a sign basis if he is well versed in signs and if the verbal learning task permits efficient coding by signs. As will be seen later, it does not necessarily follow that evidence of the exist- ence of a particular coding system automatically rules out the use of other systems. The findings of this study are an extension of the findings of Conrad(l970). That is, while Conrad was not able to specify the nature of the coding system used by his deaf subjects who did not code on an articulatory basis, Conrad did speculate on possible coding mechanisms for such subjects. Conrad considered that it might be possible for deaf subjects to code words by storing visual images of printed words but he was not able to find empirical evidence of this process. Conrad (1972) also speculated on the possibility of coding by signs when he stated that "There is no reason at all why a deaf person should not mentally plan an activity by means of imaged signs-again they might be visualized or experienced in imagination kinesthetically” (page 149). The results of this study are in accord with Conrad's prediction that coding can occur on the basis of signs. The present find- ings, however, can not be used to determine the exact nature of this coding. Like Conrad (1972), the present writer can only state that the coding was probably related to either visual or kinesthetic factors or possibly to a 75 combination of the two. Since the hypothesis that deaf subjects who use sign language as their form of communication will code language by sign factors is a logical extension of existing re- search, it is not surprising that attempts have been made to identify such coding. Putnam, Iscoe and Young (1962) used a paired-associate design similar to that used in the present study. However, the findings of the present investigation and the Putnam et al. study are not in accord with each other. Putnam, et al. were not able to find indication of coding by sign formation, while the findings of the present study do indicate such coding. The differences between the results of the two studies might be eXplained by the different temporal patterns used. Also, Putnam, et a1. failed to control for the confounding effects of semantic associations between their word pairs which shared a similar sign. In addition, no attempt was made in the Putnam, et al. study to control for word frequency differences between the word lists used. Another attempt to identify coding by signs was made by Odom, Blanton and McIntyre (1970). The Odom, et al. in- vestigation was designed to examine the hypothesis that coding by a single sign would be more efficient than coding by multiple fingerspelling codes. The findings of the Odom, et al. study appear to indicate that a sign coding system was used by some deaf subjects and that such coding was 76 relatively efficient. Such a finding was consistent with and predictable from previous research of such coding systems. However, further research such as the present study was deemed necessary because of the failure of Odom, et al. to properly control for frequency count differences between the word lists used in the experiment. As will be seen later, an imbalance in frequency counts can be an indication of a serious confounding of word lists by semantic factor differences. Coding by Semantic Factorg Table 6 shows that the evidence of semantic coding found in this study is consistent with the findings of related studies conducted on hearing subjects. The findings of the present study, however, differ from previous studies in that the performance of deaf subjects on the semanti- cally related lists (Lists 1 and 2) was better than would have been expected. Parallel research has shown that when semantic coding occurs in hearing subjects during a PA learning task, its efficiency will be equal to or lggg than the efficiency of coding by acoustic and/hr speech- motor cues (Shulman, 1970). In this study, performance on the semantically related lists was equal to or better than the performance on the other lists. This difference in performance between deaf and hearing subjects may have been caused by differences in vocabulary size between the two 77 populations. For hearing subjects, semantic associations in the PA task may be weakened by the fact that semantic relationships between words are made practically limitless by the size of the hearing person's vocabulary. The smal- ler vocabulary of the deaf, however, would make semantic coding relatively efficient because the field of possible words which could be used in each association is limited, and the Opportunity for confusion is minimized. For ex- ample, consider the case where the word pair ”PRETTY- BEAUTIFUL” is used. Assume that a hearing subjeCt had noted the semantic relationship of the similar meaning during the study session of a study-test PA task. Then, if, during the test session, the hearing subject forgot the correct response but remembered the semantic cue, the possibility of a correct response by chance selection from all words which have the same meaning as the word ”PRETTY" would not be high. The smaller vocabulary of a deaf sub- ject placed in the same situation could narrow the field of possible words from which to select, and therefore the mathematical probability of selecting the correct response would be greater with deaf than with hearing subjects. While this explanation is a plausible cause of the relatively high performance on the semantically codable lists, it could also be suggested that semantic coding is a more ef- ficient means of coding for the deaf subjects in question. Due to the derth of research in this area, it can not be 78 assumed that coding by the deaf will parallel coding by the hearing in all areas. That is, it does not necessarily follow that the coding efficiency hierarchy found in the hearing population will be mirrored in all research design- ed to investigate coding strategies used by the deaf. The importance of this semantic aspect can be seen when the implications of semantic coding found in the pres- ent study are used to interpret the Odom, et al. (1970) and the Putnam, et al. (1962) studies which were designed to investigate coding by sign formations by deaf subjects. In these studies, the frequency of occurrence of the words within lists were either not controlled at all or were not pr0perly controlled. Hall (1971) has found that word fre- quency counts are closely correlated with measures of mean- ingfulness. Since the present study has shown that deaf subjects can use meaning as a relatively efficient coding factor, it can be seen that failure to adequately control for word frequency could have affected the results of the Odom, et. and the Putnam, et al. studies. The indication that the performance on List 1 (similar meaning, similar sign) more closely resembled the perform- ance on List 2 (similar meaning, different sign) more than List 3 (similar sign, different meaning) has several implic- ations. In ListIL the subjects could have elected to code by signs, by meaning, or by a combination of the two factors. The relationship. between Lists 1 and 2 indicates int W miner associi helice tent 0| iii nc sign) interl finger the u .119 w These 79 that when given such a choice, the semantic component rather than the sign component will be used as the coding association strategy. This gives added emphasis to the implication that semantic factors were relatively impor- tant coding elements for the deaf subjects in this study. The indication that the deaf subjects' performance did not differ between List 1 (similar meaning, similar sign) and List 2 (similar meaning, different sign) is interesting in light of some conclusions reached by Schles- inger and Meadow (1972). These writers state that one of the unique prOperties of the American Sign Language is the way in which words sharing similar meanings are formed. These authors stated that words with similar meanings often shared similar signs. As examples, Schlesinger and Meadow noted that male signs are characteristically made on or near the forehead, while female signs are made on or near the lower check. This characteristic of the sign language could be considered to be represented by List 1. It would seem plausible that the similar sign-similar meaning phenomenon which, according to Schlesinger and Meadow, occurs in the sign language should have made List 1 relatively easy to learn because deaf subjects would be very familiar with the consistency of the sign-meaning relationship. By the same reasoning, List 2 would have been predicted to be relatiéay difficult to learn because the signs were distinctly different. The fact that performance 80 between these two lists (Lists 1 and 2) did not differ indicates that the deaf subjects were capable of discrim- inating between sign and semantic factors of each word pair without dependence on the similar sign-similar meaning relationship reported by Schlesinger and Meadow. List 3 (similar sign, different meaning) would represent a rela— tionship opposite to that described by Schlesinger and Meadow and it may be that the similar sign-different mean- ing relationship occurs often enought to discourage the deaf from depending on the similar meaning-similar sign as- sociations as a coding strategy. LearningTheopies and Models The results of this study indicated that coding by semantic factors occurred in deaf subjects and was more efficient, at least during the very early stages of the PA learning task, than was coding by sign formations. These findings can not be explained by learning models which make a definite separation between short-term memory (STM) and long-term memory (LTM). Such models usually assume that acoustic and/or speech-motor coding is restricted to STM and that semantic coding takes place only in LTM (Norman, 1969). Such models can be used to explain the results of experiments which produce a clearly definable coding strategy using either semantic or acoustic and/or speech-motor factors, but not both. For example, when 81 Baddeley (1966) found acoustic coding evidence in his verb- al learning task using acoustically similar words, he was able to state that his results were a reflection of acoustic coding which is traditionally restricted to STM. Also, when Baddeley and Levy (1971) extended their presentation time periods in a PA task, they began to see the effects of sem- antic coding, and therefore concluded that their time inter- vals were long enough to allow their subjects to use seman- tic rules and associations previously stored in LTM. In the present study, however, the results would seem to imply that coding originated in both STM and LTM. Coding in STM was evidenced by the higher performance on List 3 (similar sign, different meaning) relative to)the control list, and coding from LTM was indicated by the high performance on the two lists containing semantically related words (Lists 1 and 2) relative to the other lists. To make this statement, it should be observed that the assumption was made that a correlation exists between speech-motor coding in STM by the hearing and sign forma- tion coding in STM by the deaf. Such an assumption is justified by the widely held tenet that coding in STM takes a form which is closely related to sensory input (Shulman, 1971). A learning model, then, which would best fit the results of the present study must be able to explain the existence of coding strategies from both STM and LTM. Iormal disenl LTA, : betwel also 82 Norman (1969) noted that many researchers are becoming disenchanted with the traditional separation of STM and LTM, and many writers are postulating that the boundaries between the two memory systems are not distinct. Norman also found that some researchers are finding it increas- ingly useful to alter their learning models to include a means of reciprocity between STM and LTM. A good example of such a system is given by Morton (1970), (see Figure 5). To apply Morton's model to the findings of the present study, a few modifications in labeling would be necessary. The Cognitive system in Morton's model corresponds to the semantic coding factors of LTM. The Logogen system and The Acoustic analysis are related to the reception of a word and its subsequent coding in some form related to sensory input (speech-motor and/or acoustic factors in hearing subjects, and sign factors in the deaf subjects used in this study). Note that this model is uniquely suited to application to the deaf due to the inclusion of a visual input system. The Morton model can be used to explain the occurrence of coding by semantic factors in the present study. The model depicts a give-and-take between the Cognitive (LTM with its stored semantic rules and associations) and the Logogen system (which will be identified as encoded words in STM, neglecting serious problems of detailed definition). 83 Verbal stimuli 1F 1— Visual Acoustic l""""'"' “““““ '-""""""""l I VIIUOI ACOUSIIO | VOCOI eheorsal analysis analysis (including PAS) l I Cognitive ’ Logogen syst . System Resp. I r I I I I l I I I I I I I I I I I I I buffer : r._.._..-_.._. .._.. ..-...-...._ _. .. __-__ --- ..................... I I # Response Figure 5. A flow-diagram of Information In the Logogen Sys- tem Model. The dotted line indicates the boundaries of man. Adapted from Morton (I970; page 205). strate and 2 of se1 and 1‘1 into nece 84 This interaction between the Cognitive and Logogen sys- tems would allow a subject to discover the semantic coding strategy available in Lists 1 (same meaning, same sign) and 2 (same meaning, different sign). This application of semantic associations found in LTM to material held and rehearsed in STM is consistent with the model and predictions of Baddeley (1971). Baddeley has noted that as a subject searches for a possible coding strategy in a PA learning task involving words, he may be able to dis- cover a semantic retrieval rule which is usually stored in LTM. This semantic retrieval rule is then used to simplify the task called for in STM. The high scores obtained on Lists 1 and 2 are an indication that the semantic associ- ations between the word pairs of these lists were recognized through reference to semantic rules obtained from the Cognitive system. This does not mean that the words entered the Logogen system as visual impressions and were then coded directly into a semantic code with no mediating strategy. The necessity of using a mediating system such as speech- motor and/or acoustic factors for increased efficiency and rehearsal is well documented (Conrad, 1970: Norman, 1969: Hall, 1971: and Frijda, 1972). Ervin-Tripp (1973) has stated that mediation systems are necessary for the pro- cessing of language-related material. She has noted: But if it were the case that only semantic use the the 85 information is retained, language learning could turtoccur. There must be some storage of the phonological markers and semantic features in- ferred from the milieu of a new item for it to become part of the dictionary. (page 280) Though it is probable that the deaf subjects in this study used some form of encoding as a mediating system between the printed form of the words and their semantic aspects, the exact nature of this encoding can not be ascertained from the data.at hand. All that can be stated at this point is that the semantic associations between the word pairs of List 1 and 2 were used by the subjects as a re- latively efficient learning strategy. Morton's model can also be used to describe the occurrence of coding by sign formation. Such coding is indicated by the high level of performance on List 3 (similar sign, different meaning) relative to the perfor- mance on List 4 (different sign, different meaning). According to the model, the word pairs entered the Logogen system via the Visual Analysis component. In the Logogen system, the words were encoded and an attempt was made to find an association between the encoded words. Here again the Cognitive system was brought into play. At some point, rules of association which were stored in the Cognitive system were used to discover the associative link between the encoded pairs. The nature of this encoding can be deduced from the structure of the word pairs and the subsequent performance of the subjects on List 3. Giv sig thi ima int prc Ker. med al jet Ind Sho Iii-CW 86 Given that the word pairs in this list shared a common sign, it follows that the subjects could not have discovered this sign association without first encoding the visual image of the printed words into their sign equivalents. The specification of coding by sign factors might be interpreted as complimenting a motor theory of speech production/perception (Liberman, Shankweiler, and Studdert- Kennedy, 1967). The motor theory would predict that the mediating system used in processing language—related materi- al would be directly related to the muscular processes used in the production of speech, or at least the stored neuro- logical patterns associated with such processes. In the hearingpopulation, this implies that a linguistic unit such as,a word or phoneme is recognized when it is paired with the particular neural impulses from the articulators which would have been used if the phoneme or word were actually produced. This does not imply that hearing sub- jects actually repeat linguistic material before it is understood, but rather that neural impulses occur in a short-circuited manner within the brain without ever actually moving the articulators. One obvious problem with the motor theory is the fact that the motor aspects of speech production are closely associated with the acoustic factors of the speech pro- duct. In trying to specify the motor components of speech perception, researchers have had difficulty in separating 87 the motoric and the acoustic aspect (Delattre, 1941) resulting in a great deal of controversy (Wickelgren, 1969). It might appear that using a deaf population could be one way to eliminate the acoustic factor and objectively test the motor theory. However, as will be seen, using deaf subjects might eliminate the acoustic variable, but the new variable of visual images of the sign formations is introduced and no real progress in solving the motor theory controversy has been made. If the production of signs could be equated with the production of speech, and if the neural impulses from the hands could be considered to be the equivalent of the neural impulses from the articulators, evidence of coding by signs might be considered to be supportive of the motor theory of speech perception. It is not difficult to see a correlation between speech and signing since both are used as a means of communication (Bornstein, 1973). It.might be a little more difficult to equate the neural impulses of the hands with the neural impulses from the articulators. However, a close anatomical relationship between the motor area for speech and motor area for the hand has been noted ‘by Penfield and Roberts (1959), and Ranson and Clark (1959). With but few qualifications, the results of the present study could be considered to be in support of the motor theory. However, such a conclusion is not without an inherent 88 problem. Just as the acoustic aspects Of Speech closely follow production, the visual aspects Of the Sign formation are closely related to the kinesthetic factors in Sign production. The present study has found indication of encoding words by signs, but it is not known at this point in what form these signs are encoded. That is, a subject might use the visual form of the Sign or he could use the kinesthetic or movement patterns of the sign for coding. Coding by signs, then, could be considered to be supportive of the motor theory of perception only if it could be shown that kinesthetic factors, rather than visual images, were in fact used in the coding strategy. Thought, Langgage and Codggg The indication of coding by sign formation found in this study has some implications regarding theories of thought and language. Evidence of coding by Sign is not consistent with the empirical and behavioral schools' stand on the importance of coding by speech-motor factors. Historically, these schools held that thinking was language dependent and that language could only occur through the use of speech. The present findings would indicate that coding can take place in forms other than speech-motor. This finding is in direct contrast with the Opinions of early writers who shared Sapir's (1921) view that ". . . 1. ,. :Itdfi 89 auditory imagery and the correlated motor imagery leading to articulation, are, to whatever devious ways we follow the process, the historic fountain-head of all speech and of all thinking.” (Markowicz, 1972, pages 33-34). It should not be considered that the indication of coding by signs found in this study gives support to the "mother tongue-natural language” concept proposed by Furth (1966), Giangreco and Giangreco (1970) and Markowicz (1972). These authors have stated that the Sign language is the ”natural" form of language processing used by the deaf. Though their references are vague and unsubstantiated by research, the prOponents of this theory hold that deafness somehow alters the human language processing system so that only manual forms Of communication will correspond to the "natural language" Of the deaf. For example, Furth (1966) has written that ”The true 'language' of the deaf is the sign language, as one can readily Observe" (page 15), and Markowicz (1972) has stated that "The deaf turn to Sign language as their 'natural' language in the same way that a hearing individual acquires and uses the language of the community in which he grows up.” (page 23). Statements such as these must be questioned in light of the results of Conrad's (1970) study and the present investigation. Conrad has shown that some deaf subjects code on a speech- motor basis and do not show any evidence Of Furth's and 9O Markowicz's "natural" coding by signs. The present study indicates that even when coding by sign.is evidenced, such coding is less efficient than semantic coding during initial learning trials and is never more efficient than is coding by semantic factors. In addition, neither Conrad's study nor any other investigation has been designed to contrast coding by signs with coding by speech-motor factors. Until this is done, speculation as to whether the sign cue or the speech-motor factor is the ”most natural” or most efficient coding mechanism for the deaf will remain tenu- ODS. Edgcational Implications and Suggestions for Further Research The present study has been limited in scope as well as highly theoretical. Nevertheless, there are several implications for educational applications and future re- search which have become apparent. One might hypothesize, for example, that a deaf child will make Optimal educational progress when the manner in which he codes language "matches” the system in which he is taught. This would suggest that a deaf child might process linguistic materi- al at an Optimal level only when input and processing coincide. Both White (1972) and Conrad (1972) have dis- cussed such a possibility but neither have provided objective evidence to support this concept. Such evidence could 91 result from research which first specified the coding strategies used by the deaf subjects. This specification could be achieved through a simple verbal learning task such as that described in the present study or by Conrad (1970). Following this, research could be conducted to investigate the relationship between coding efficiency relative to different modes of information input. It is tempting to assume direct relationships between educational methodology, coding systems and speaking ability in the deaf. Such an asSumption might seem warranted because Conrad (1970) has indicated that deaf subjects with relatively good speech code words on an articulatory basis, and the present study has shown that deaf subjects who consistently use signs can code words on a sign basis. These findings could conceivably lead to the conclusion that deaf children who are taught orally will develop articulatory coding and good speech, while deaf children who are taught in a system allowing the use of a Sign language system will develop Sign coding strategies which, in turn, will be reflected in relatively poor speech skills. Such conclusions, however, are not justified by the data currently available. The subjects in Conrad's study were selected from an oral school but not all of the subjects exhibited articulatory coding. Conrad (1973) reported that he tried unsuccessfully to determine why 92 some deaf children code on an articulatory basis while others do not. Related to this, Conrad noted that while it is probable that relatively good speech in the deaf and articulatory coding are highly correlated, variables leading to or discouraging such coding have not yet been specified. The results Of the present study must not be interpreted as an indication that coding by sign forma- tion precludes coding on an articulatory basis. This study was designed to investigate evidence Of coding by Sign and/Or semantic factors, and no attempt was made to correlate these coding strategies with speaking ability. Further research is therefore needed to delineate the effects which different coding systems have on speech and to indicate what variables contribute to the development of a particular coding method. A frequent criticism of the sign language is that it is such an efficient communication tool that its use tends to discourage the use Of other perhaps more ”desirable" methods Of communication, such as speechreading. speech and residual hearing (Dale, 1967 and Ewing and Ewing, 1961). The results of this study should not be considered as proof of such a contention. This study indicates only that coding by sign or semantic factors can be used by the deaf subjects in question. The data gives no indication that coding by these two systems occurs exclusively. It is 93 possible that deaf subjects may be capable Of switching codes and that an efficient coding system could be sought for different situations. The present study does indicate that at least two systems maybe used (sign or semantic), but the semantic coding, not the sign coding appears to be the more efficient method in the initial stages Of learning. Here, too, further research is needed. It would be very useful in educational planning, for example, to determine how the three coding systems identified by this study and by Conrad (1970 and 1973) compare in rela- tive efficiency in different learning and communication situations. The findings Of this study indicate that when given a choice between semantic and Sign coding strategies, the subjects tended to select the semantic strategy rather than use the Sign code or a combination of Sign and semantic cues. This finding is consistent with the findings of Gaeth (1966). Gaeth reported that the deaf subjects in his verbal learning study, when presented with two modes of information input (visual and auditory), tended to disregard the less meaningful auditory channel and attend to the visual channel. Further, Gaeth found no evidence of additivity of cues. That is, performance on a verbal task containing cues from both channels was not superior to the performance on tasks using a single channel. He also found that the hard-of—hearing subjects did not 94 consistently attend to one information transfer mode, but instead shifted back and forth between modes. This shift- ing strategy used by the hard-of—hearing subjects might mean that they are capable of coding by either of the systems investigated by Gaeth. This could indicate that the hard-of—hearing may code using any of several systems, depending on which system is carrying the most information at any given time. At this point, such a conclusion is merely speculation. The present study used no hard-Of- hearing subjects and no data is currently available on the coding systems used by subjects whose hearing loss is less than the 70 dB criteria used in the present study. Conrad (1973) has noted that a correlation exists be- tween articulatory coding and hearing loss. He noted that articulatory coding is more likely in subjects with relatively better hearing. Conrad's study was designed to find evidence of articulatory coding and he was not con- cerned with coding by Sign formations or with a possible relationship between hearing levels and coding by signs. It would seem important to ask what effects different levels Of hearing loss might have on coding systems of deaf children who are in an educational setting which permits and/Or encourages signing. Will all children in a school system which uses signs develop Sign and semantic codes or will other codes be possible? Conrad's findings could lead to the hypothesis that children with relatively 95 better hearing might develop an articulatory code instead of, or in additon to, sign and semantic codes: but again, further research is needed to determine this. Recently an educational methodology which claims to promote all forms of communication in deaf children has been receiving wide interest in the United States. This educational philOSOphy has been termed ”total communi- cation" and has been defined by Denton (1971) as: By total communication is meant the right Of a deaf child to learn to use all forms of communi- cation available to develOp language competence at the earliest possible age. This implies intro- duction to a reliable receptive-expressive symbol system in the preschool years between the ages Of one and five. Total communication includes the full spectrum Of language modes: child devised gestures, formal Sign language, speech, speech- reading fingerspelling, reading and writing. (page 35 Proponents of total communication have not couched their theories in the terminology of the learning theorists. However, it is apparent that the philOSOphy behind total communication is directly related to coding theory. As can be seen in Denton's (1971) definition, total communi- cation attempts to incorporate both speech and Sign systems in preschool age children. This would imply that the total communication methodology somehow fosters, or at least allows, the development of multiple encoding and decoding systems in young deaf children. The concept of total communication could provide several areas of research. For example, the concept of 96 multiple encoding and decoding systems, on which the total communication philosophy is partly based, has no empirical backing to date. NO research has been devised to investi- gate the possibility of coding by both articulatory and sign formation systems. Also, no research has directly considered the coding systems of preschool age deaf children, and it is not known what affects multiple coding systems will have on deaf children during their very early years when language systems are being formu- lated. The relationship between age, educational planning, and coding systems may provide new areas Of research in deaf education. Traditionally, deaf children in an oral program begin their oral training during their preschool years. Many of these children remain in the oral educa- tional system throught their elementary and secondary schooling. However, a child may be transferred into an educational system using sign language if it is determined that he will not succeed orally. Knauf (1972) has written that "For those deaf children who were identified late and show no aptitude for oral language and for those, who after reasonable exposure in a totally committed oral program still can not understand and use oral language, manual communication is recommended." (page 752). Educa- tors Of the deaf in oral systems, then, are frequently faced with the question of the Optimal placement for a 97 child who does not appear to be developing adequate verbal language, speech, and speechreading skills. In such a situation, these educators must decide: (a) if the child has had a "reasonable exposure" to verbal language: (b) will the student benefit from continued oral education; or (0) would the student be better placed in a system using signs. It may be that research into coding systems used by the deaf could aid in answering such questions. It is a plausible assumption that a deaf child who is failing in an oral system might be processing linguistic information in some code other than articulation. If this is the case, educators could use data concerning a child's coding system or systems in decisions regarding the placement of deaf children. It would also be well to ask at what age or developmental level decisions concerning coding systems can or should be made. Is it ever ”too late" for a child to develOp coding systems which will benefit speech, speechreading, and verbal language? 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APPENDICES APPENDIX A PA WORD LISTS WITH THEIR RESPECTIVE FREQUENCIES AND WORD LENGTHS APPENDIX A PA WORD LISTS WITH THEIR RESPECTIVE FREQUENCIES AND WORD LENGTHS 22§p_2 e uenc of Occurrence* Combined Length pretty - beautiful 147 + 288 = 435 15 coat - sweater 90 + 12 = 102 11 happy - glad 229 + 160 = 389 9 strong - power 227 + 71 = 298 11 clothes - dress 178 + 124 = 302 12 sugar - candy 107 + 87 = 194 10 hear - listen 597 + 230 = 687 8 fire - burn 281 + 35 s 316 8 bring - carry 202 + 213 = 415 10 mad - angry 31 + 116 = 147 8 true - real 258 + 604 = 862 8 begin - start 180 + 246 = 426 10 woman - lady 212 + 176 = _3_88_ _2_ Total 5.788 139 Mean 206.7 4.96 *Word frequency determined by referrence to Carrol, et al. (1971), based on 5 million words from textbooks from grades 3 through 9. The frequencies shown are from the third grade reading level. 109 llO 22§3_g e enc of Occurrence* Qppp2ppg_2ppgph steal - robber 22 + l = 23 8 floor - ground 186 + 372 = 558 11 finish - end 98 + 591 = 689 9 clean - wash 118 + 64 = 182 9 enough - full 433 + 192 = 625 10 house - building 785 + 165 = 950 13 sad - cry 99 + 77 = 176 6 wrong - mistake 107 + 31 = 138 12 between - middle 315 + 11m = 459 13 laugh - smile 59 + 32 = 91 10 hurry - fast 87 + 354 = 441 9 cold - freeze 361 + 14 = 375 10 baby - young 288 + 269 = 557 wait - stay 181 + 270 = _452_ __§_ Total 5:715 139 Mean 204.1 4.96 *Word frequency determined by referrence to Carrol, et a1. (1971), based on 5 million words from textsbooks from grades 3 through 9. The frequencies shown are from the third grade reading level compilation. 111 22§j_3. Fregpency of Occurrence* Combined Length fork - mean 18 + 201 = 219 8 egg - short 114 + 314 = 428 8 farm - dry 189 + 226 = 415 7 soft - wet 145 + 133 = 278 7 hamburger - marry 11 + 36 = 47 14 hungry - wish 109 + 163 = 272 10 black - summer 30? + 253 = 560 11 salt - chair 103 + 78 = 181 9 voice - stuck 182 + 45 = 227 10 kind - world 430+ 399 = 829 9 almost - easy 372 + 141 = 513 10 family - important 309 + 278 s 587 15 paper - school 663 + 488 = 1151 11 pig - dirt 47 + 53 = .129. _7_ Total 56807 136 Mean 207.4 4.46 *Word frequency determined by referrence to Carrol, et a1. (1971), based on 5 million words from textbooks from grades 3 through 9. The frequencies shown are from the third grade reading level compilation. 112 22§p_4 Ezeguency of Occurpence* Comb2ped Length money - bear 367 + 116 = 483 9 rain - stand 240 + 210 = 450 9 hand - best 345 + 347 = 692 8 heart - class 97 + 216 = 313 10 train - spell 173 + 274 = 447 10 pencil - bread 74 + 140 = 214 11 morning - girl 484 + 285 = 769 11 basketball - milk 12 + 284 = 296 14 apple - learn 94 + 335 = 429 10 draw - letter 426 + 411 = 837 10 broke - funny 60 + 100 = 160 10 math - ugly 1 + 51 = 52 8 doctor - green 60 + 344 = 404 11 bed - coke 214 + 3 = _g;z_ __2_ Total 5.763 138 Mean 205.8 4.93 *Word frequency determined by referrence to Carrol, et al. (1971), based on 5 million words from textbooks from grades 3 through 9. The frequencies shown are from the third grade reading level compilation. List 5 rain - coffee win - lazy smell - live shoes - answer drOp - taste add - yesterday silly - hard tomorrow - key meat - problem home - test year - idea nothing - both eight - class hate - telephone Total Mean 113 Fre uenc of 240 73 80 140 71 378 60 83 135 786 412 235 88 12 28 30 689 353 +~ -+ + + + 269 460 91 149 64 171 331 216 + 4- -+ + +- + + ccurrence* 268 103 769 493 109 647 520 173 284 820 583 566 304 _13_ 5.712 204.0 Combined Lepgth 10 7 9 ll 9 12 9 11 11 8 8 11 10 _13__ 139 4.96 *Word frequency determined by referrence to (1971), based on 5 million Carrol, et al. words from textbooks from grades 3 through 9. The frequencies shown are from the third grade reading level compilation. APPENDIX B RESPONSE FORM APPENDIX B RESPONSE FORM Name Group List Trial 1. " 30 " 50 " 6. - 7o ' 9o ‘" 10. '- 11. ‘- 12. - 13. - 14. .- 114 APPENDIX C INSTRUCTIONS TO SUBJECTS APPENDIX C INSTRUCTIONS TO SUBJECTS* I want to see if you can remember words. First you will see two words, and then two more words, and then two more. You will see many of these words two at a time. Try to remember the words but do not write or capy the words when you.see two of them. Later you will see one word and then I want you to write the missing word. Any Questions? Let's practice and learn a list of words just for fun. *These instructions were given to the subjects by a trained, experienced teacher of the deaf. During the instructional session, the dir- ections were administered via a combination of signs, fingerspelling, writing, and speech- reading. 115