effet du nombre des graphÃ¨mes en Anglais - Aix Marseille UniversitÃ©

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258Appendice IIIHowever, an unsolved question remains with respect of both models : how does the reading system parsethe letter sequence into graphemes ? The dual-route model suggests that a set of rule correspondances is usedfor the graphemic segmentation. The remaining question here is : which representational format or cognitivecode does the reading system use to implement this set of rules given that rules belong mainly to high-levelcognitive processes and seem less plausible for fast and automatic perception processes ? Similarly, Plaut etal. (1996) did not specify how does the reading system perform the graphemic segmentation. In their model,input letter strings are parsed into onset-nucleus-coda graphemes by turning on the corresponding graphemenodes. Thus, it appears that these authors implicitly assume that graphemes are basic units of the readingsystem which leads paradoxically to the conclusion that graphemes are considered in their model as localrepresentations (Grainger &Jacobs, 1998a).A solution to the graphemic segmentation problem can be found in a boxological model proposed byLaberge and Samuel (1974). These authors suggested that during the acquisition of reading, intermediate readingunits (units situated between the letter and word levels) are developped within the reading system. Thefunctional role of these units would be to increase the automaticity and rapidity of skilled reading and to allowprogressively the processing of letter strings as wholes. The establishment of these intermediate units wouldlikewise be a manner to push the reading system in the direction of an increased parallel processing of writtenwords. The idea that graphemes could be such intermediate units was already suggested by Gibson, Pick,Osser, & Hammond (1962). Another argument in favour of this assumption comes from the fact that graphemeshave a functional role with respect to phonological units. Graphemes could thus be viewed in suchcontext as phonographic reading units (Peereman & Content, 1997).How does the « reading unit » hypothesis solve the graphemic segmentation problem? If one assumesthat the reading system develops representations of intermediate reading units, then the functional segmentationof a letter sequence could be done by activating a set of matching intermediate reading units. This solutionis implicitly used in the Plaut et al. model (1996) despite that these authors do not explicitly assume thatgraphemes are reading units or localist representations of the reading system. Also, this « reading unit »solution could be a more functionally plausible alternative in order to perform the grapheme-to-phonemeconversion in the Coltheart et al. model (1993).The purpose of the present study is to test experimentally the assumption that graphemes are processed asperceptual units by the reading system. Indeed, the psychological validity of graphemes has been assumed bymany models of word recognition despite only few empirical data are available to support such assumption.As a window to explore unitization processes, a large number of studies used the letter detection procedure (fora review, see Healy, 1994). The preliminary idea in these manipulations is that the reading system processparticular groups of letters as wholes or reading units. Therefore, searching for a letter embedded in a readingunit appears costly since subjects have to split the unit into its constituent letters in order to perform thetask. Letter detection studies reported, for example, missing detections for letters embedded in high frequencyshort words such as THE (the target letter being E). Since these words are probably processed as wholes bythe reading system, they consequently seem to mask the identification of their constituent letters (e.g., Corcoran,1966 ; Drewnowski & Healy, 1977).We followed the same logic in the present study. Indeed, if graphemes are processed as units by the readingsystem, then detecting a letter embedded in a multi-letter grapheme should be harder than detecting a letterbeing a single-letter grapheme itself. As an illustration, searching for the letter A in the word BEACH (Abeing embedded in the multi-letter grapheme EA -> /i/) should be harder than searching for A in the wordGRASS (A being here a single-letter grapheme).We tested this hypothesis in two experiments done in English (Experiment 1) and French (Experiment 2).In both experiments, we used the same experimental design. Subjects were presented a target letter followedby a word and had to decide whether or not the letter was in the word. In present trials, letters were eitherembedded in a multi-letter grapheme (e.g., A in BEACH) or were equivalent to a single-letter grapheme (e.g.,A in GRASS). In addition, we performed a frequency manipulation of the words. In half of the graphemeconditions, words were of high frequency and for the other half, words were of low frequency. This manipulationwas intended to test whether unitization is pre-lexical as suggested by the reading unit assumption. Theprediction in this case is that if there is any evidence of a pre-lexical unitization procedure, then letter detectionlatencies should be independant of the lexical status of the target word.EXPERIMENT 1ParticipantsParticipants were 21 psychology students from Macquarie University. All were native Australian-Englishspeakers and had normal or corrected to normal vision.Stimuli and ApparatusThe stimulus set was composed of 60 target-present trials and 60 target-absent trials. The 60 target-presenttrials were divided into four lists of 15 monosyllabic, 5-letter English words (see Appendix A). Each list wasmatched between items for the target letter and for its position in the word. Two lists were composed of lowfrequencywords (F < 10 occurences per million) and two lists of high frequency words (F > 50 occurences permillion). Each frequency list was matched between items for word frequency. Frequency was estimated usingthe CELEX frequency count (Baayen, Piepenbrock, & van Rijn, 1993). For one list of words in each fre-
Appendice III 259quency condition, the target letter was embedded into a multi-letter grapheme. For the other list of words, thetarget letter had the status of a single-letter grapheme. That is, as an example, let A be the target letter. Thefour words in each experimental condition were : 1. HOARD in the low frequency - multi-letter graphemecondition ; 2. BRASH in the low frequency - single-letter grapheme condition ; 3. BOARD in the high frequency- multi-letter grapheme condition ; 4. STAFF in the high frequency - single-letter grapheme condition.The mean frequency values of each word list was respectively : 5.1, 5.1, 138.7 and 139.8 (occurences permillion). The experiment was controlled by a Macintosh SE computer. The stimuli were typed with fontCourrier size 24.ProcedureParticipants were seated at 50 cm in front of the computer screen and were given written instructions.They started with a training session composed of 10 trial (5 target-present and 5 target-absent trials). After abreak during which any questions concerning the task were answered, the experiment started with the 120experimental trials (60 target-present and 60 target-absent) presented in a randomized order for each participant.Each trial began with a 700 ms presentation of the target letter in the middle of the screen. Then a fixationmark (« : ») was presented for 1000 ms. The fixation mark was replaced by the stimulus word, which remainedon the screen for 33 ms. The stimulus word was followed by a blank that was presented for 70 ms.Then a mask consisting of superimposed X’s and O’s, appeared on the screen until participant’s response.Afterward, the mask was replaced by a blank for 1000 ms that was followed by the next trial. The target letterwas presented in uppercase and the stimulus word in lowercase. The entire procedure took about 15 minutes tocomplete.RESULTSMean correct response times and error rates for the four experimental conditions are reported in Table 1(upper panel). The trimming procedure excluded scores smaller than 200 ms and greater than 3 SDs above theparticipant’s overall response time. Analyses of variance (ANOVAs) were conducted using both participants(F 1 ) and items (F 2 ) as random factors, treating frequency and grapheme category as within-participant factors.Response times were affected by the grapheme manipulation but not by word frequency. Letter detectiontimes were longer for letters embedded in a multi-letter grapheme compared to letters having the status ofsingle-letter graphemes, F1(1,20) = 7.38, p < .05 ; F2(1,56) = 4.21, p < .05. No difference was observedbetween the high and low frequency conditions, all Fs < 1. The error data did not show any effect of graphemecategory or word frequency, all Fs < 1.EXPERIMENT 2ParticipantsParticipants were 19 students from the Center for Cognitive Neuroscience. All were native French speakersand had normal or corrected to normal vision.Stimuli and ApparatusThe stimulus set was elaborated in the same way as in Experiment 1 except that word stimuli were monosyllabic,5-letter French words. Frequency was estimated using the BRULEX frequency count (Content,Mousty, & Radeau, 1990). Low-frequency words had less than 10 occurences per million and high-frequencywords had more than 50 occurences per million. As an example, let A be the target letter. The four words ineach experimental condition were : 1. TAUPE in the low frequency - multi-letter grapheme condition ; 2.BAGNE in the low frequency - single-letter grapheme condition ; 3. CAUSE in the high frequency - multilettergrapheme condition ; 4. SALLE in the high frequency - single-letter grapheme condition. The meanfrequency values of each word list was respectively : 4, 4, 95.5 and 88.5 (occurences per million). The experimentwas controlled by a Macintosh Quadra computer.ProcedureThe experimental procedure was the same as in Experiment 1.RESULTSMean correct response times and error rates for the four experimental conditions are reported in Table 1(lower panel). The trimming procedure excluded scores smaller than 200 ms and greater than 3 SDs above theparticipant’s overall response time. Analyses of variance (ANOVAs) were conducted using both participants(F 1 ) and items (F 2 ) as random factors, treating frequency and grapheme category as within-participant factors.As in Experiment 1, response times were affected by the grapheme manipulation but not by word frequency.Letter detection times were longer for letters embedded in a multi-letter grapheme compared to lettershaving the status of single-letter graphemes, F1(1,18) = 27.75, p < .0001 ; F2(1,56) = 4.53, p < .05. Nodifference was observed between the high and low frequency conditions, all Fs < 1. The error data showed amarginal effect of grapheme category, that is, subjects did more errors in detecting a letter into a multi-letter
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UNIVERSITÉ DE PROVENCE, AIX-MARSEI
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Ce travail clôt un cycle de format
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CHAPITRE 7 : LE FUM . . . . . . . .
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8IntroductionPour cela, notre domai
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10Introduction• au niveau lexical
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12Introduction• sa forme visuelle
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14IntroductionAprès avoir posé le
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16Méthodologiespulations sur les i
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18Méthodologies2.1. Protocoles exp
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20Méthodologiessi le stimulus se t
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22MéthodologiesCertaines études t
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24Méthodologiestes, on obtient des
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26Méthodologies1996 ; Peter & Turv
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28Méthodologiesles performances da
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30Méthodologies6 %8%10%15%30%50%80
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32MéthodologiesMatériel expérime
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34Méthodologiesentraîne le masqua
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36MéthodologiesLe même résultat
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38Méthodologies120100Situation Sta
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Chapitre 3Orthographe et phonologie
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42Orthographe et Phonologie3.1. Var
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44Orthographe et PhonologieLa Figur
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46Orthographe et PhonologieJacobs,
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48Orthographe et Phonologiedans la
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50Orthographe et PhonologieDans l
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52Orthographe et Phonologieteurs du
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54Orthographe et PhonologieGoldstei
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56Orthographe et Phonologietion est
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58Orthographe et Phonologierand, 19
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60Orthographe et Phonologieplus ad
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62Orthographe et Phonologie3.2.3.1.
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64Orthographe et PhonologiePlus ré
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66Orthographe et PhonologieUne autr
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68Orthographe et Phonologiedeux var
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Chapitre 4Modèles de la perception
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72Modèles de la perception visuell
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98MROM-pspécifier leur lien avec l
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100MROM-pphonèmes reliés par un r
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102MROM-pLorsque le modèle génèr
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104MROM-pque ce système artificiel
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106Unités de la lecturelinguistiqu
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108Unités de la lecture22606TR (ms
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110Unités de la lecturemes. Aussi
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112Unités de la lecturephonologiqu
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114Unités de la lectureelle-même
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116Unités de la lecture6.3. Expér
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118Unités de la lectureRead est qu
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120Unités de la lectureces modèle
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122Unités de la lecturechapitre su
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124FUMmultiples existant au sein de
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126FUMpar Berndt, Lynne D'Autrechy
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128FUMcessus de compétition et du
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130FUMgène et suit les principes c
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132FUMPseudohomophonesContrôles Or
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134FUM61023TR (ms) Seidenberg et al
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136FUMportementaux et les résultat
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138FUMà une entité extérieure au
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Chapitre 8Des prédictionsau niveau
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142Des prédictions au niveau des m
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166Les mots polysyllabiquesmots mon
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168Les mots polysyllabiquesTableau
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170Les mots polysyllabiques9.2. Exp
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172Les mots polysyllabiques19001890
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174Les mots polysyllabiquesnexe XI
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176Les mots polysyllabiques9.4. Dis
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178ConclusionConclusion« La grande
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180Conclusionplutôt un système o
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182Conclusiontester les prédiction
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184BibliographieAderman, D., & Smit
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186BibliographieBrysbaert, M., Vitu
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188BibliographieFerrand, L., Segui,
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190BibliographieGrainger, J., & Jac
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192BibliographieKay, J., & Bishop,
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194BibliographieMewhort, D. J. K.,
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196BibliographiePerea, M., & Pollat
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198BibliographieSeidenberg, M. S.,
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200BibliographieTreiman, R., & Zuko
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202AnnexesAnnexes
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204AnnexesAnnexe II : Temps de rép
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206AnnexesAnnexe IV : Temps de rép
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Page 224 and 225: 224Appendice IMROM-P : An interacti
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Page 232 and 233: 232Appendice Ito an orthographic le
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Page 244 and 245: 244Appendice I1994). Our stratagem
Page 246 and 247: 246Appendice ILass, U. (1995). Einf
Page 248 and 249: 248Appendice IAPPENDIXA1. Cleaning
Page 250 and 251: 250Appendice IIA phoneme effect in
Page 252 and 253: 252Appendice IIAs shown in Table 1,
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Page 262 and 263: 262Appendice IIIRead, J. D. (1983).
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