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236Appendice Iestimator set study, but from a different empirical study . The second test uses data concerning a different e f -fect and coming from a different study .Step 4 . Strong inference studies. In our introductory paper to the present volume we discuss the stratagem ofstrong scientific inference in detail. Suffice it to say here that this relatively costful but worthwhile testingphase involves formal, criterion-guided comparisons of alternative models against the same data sets. Forreasons given below, we cannot provide such testing in the present paper (for a typical approach, see Massaro& Friedman, 1990).Step 5 . Model refinement or replacement. As theoretically firm believers, but practically mild (nondogmatic,non-naive) users of a theory building approach adhering to Popper's (1934/94) and Platt's (1964)principles (see Grainger & Jacobs, present volume), we acknowledge that we would continue with a processof model refinement (after which one reiterates back to Step 1) as long as the model is only mildly discreditedand no better alternative is available. Given the current state of the art in modeling visual word recognition(Jacobs & Grainger, 1994), within a pluralistic perspective of canonical modeling (Stone & Van Orden,1993 ; 1994 ; see also Grainger & Jacobs, present volume), such an adoption of a hybrid between "falsificationism"and "refinementism" seems in order. We are nevertheless aware of the dangers of confirmation bias(Greenwald et al., 1986) and believe that eventually IA-type models -including the present one- will nolonger be refined but replaced by better models. However, at present, we have reasons to believe that IA-typemodels have still a lot to offer (Jacobs & Grainger, 1994). The present volume is perhaps the nicest expressionof and justification for this belief.THE MROM AS NULL-MODEL. In the following model tests, we use the MROM as a "null-model" ofphonological effects. That is, since the MROM has no explicit phonological processing units, it should notpredict any difference between stimuli having phonological properties, such as pseudohomophones, and supposedlycontrol stimuli that lack these properties. Note that the MROM can very well produce "pseudophonological"effects when the pseudohomophones differ on other dimensions than phonological ones from thecontrols, e.g., when they were badly matched for orthographic neighborhood properties. To the extent thatthe MROM provides a successful model of orthographic neighborhood effects in word recognition (Grainger& Jacobs, 1996), it can also be used as a tool for precisely selecting one's stimuli, e.g., for avoiding pseudophonologicaleffects.In contrast, if we included adequate phonological processing units into the MROM-P, it should predictclear differences between control stimuli and pseudohomophones, for example. In the present MROM simulations,the parameters governing the phonological parts of MROM-P were simply set to zero.STEP 1. PARAMETER TUNING STUDIES : THE PSEUDOHOMOPHONE TEST. Once the initialparameter tuning procedure gave satisfactory results, a first simple test of the ability of MROM-P to accountfor phonological effects consists in presenting the model with "watertight" pseudohomophone stimuli,i.e. stimuli whose correct pronunciation is empirically confirmed (Van Orden, Johnston, & Hale, 1988).As an example, we presented both MROM and MROM-P with stimulus triples, such as FEEL (base word),FEAL (pseudohomophone), and FEEP (control). Figure 7 shows activation functions for both MROM andMROM-P at the level of orthographic word units, which we take to be the critical level for assessing interactivephonological effects in the LDT (cf. Ferrand & Grainger, 1996). The simulation results are clear-cut.Whereas stimuli like FEEL generate sufficient lexical activity in both MROM and MROM-P to be correctlyrecognized, FEAL and FEEP generate the same lexical activity in MROM, but not in MROM-P. Here,pseudohomophones like FEAL generate activity that is intermediate between real words, like FEEL, andcontrol pseudowords like FEEP. Thus, in stochastic simulations under data-limited conditions (i.e., brief,backward-masked stimulus exposure), MROM-P will occasionally (i.e., depending on the noise level) identifyFEAL as FEEL (Ziegler & Jacobs, 1995 ; Ziegler et al., in press c). We took this result as suggestingthat MROM-P's architectural-parametric assumptions are adequate, and fixed the parameters to the valuesyielding this result (see Table 3 above).STEP 2. ESTIMATOR SET STUDY : COLTHEART ET AL. (1977) TEST. While the previous studyhinted at the appropriateness of MROM-P's structural-parametrical assumptions, it was no serious estimatorset study. For this, we chose the stimuli and data of the classical study of Coltheart et al. (1977, Experiment1, Table 1, and appendix A), which provided one of the first falsifications of serial search models of wordrecognition (Forster, 1976). This study had already given good service in this respect during the constructionphase of SIAM (Jacobs & Grainger, 1992).The crucial result of Coltheart et al. for the present purposes concerns the longer mean latencies for correct"No" responses to pseudohomophones than to control pseudowords in the LDT. Coltheart et al. observeda 62 ms difference in the subject analysis, and a 35 ms difference in the item analysis. Instead of usingColtheart's data for a full-blown parameter-fitting study, as one could have done with an M-type model,here we simply checked whether the MROM-P, as structurally-parametrically defined during the previoustest phase, could simulate the data from Coltheart et al. If not, we would have gone through another phaseof parameter tuning or model restructuring.
Appendice I 237Figure 8a gives the mean overall lexical (orthographic) activity over time, which according to MROM isthe critical information dimension determining No-responses (Grainger & Jacobs, 1996), generated by the 30base words, 30 pseudohomophones, and 30 control pseudowords of the Coltheart et al. study which are containedin our lexicon (18 of the stimuli had to be excluded because their base word was bi-syllabic or absentin our model lexicon). In MROM, both pseudohomophones and control pseudowords generate the same activity,which yields identical mean No-RTs. In contrast, in MROM-P, overall lexical activity nicely reflectsthe differences between pseudohomophones and controls. Since pseudohomophones generate more activitythan controls, No-responses to them will take longer to reject. This is shown in the bar charts of Figure 8bthat capture the pattern of results obtained by Coltheart and collaborators.ACTIVATION0.80.70.60.50.40.30.20.10- 0.1MROMf eel (Base Wor d)f eal (Pseudohomophone)f eep (Or thogr aphic Contr ol)0 5 10 15 20 25CYCLESACTIVATION0.80.70.60.50.40.30.20.10- 0.1MROM-P0 5 10 15 20 25CYCLESFigure 7 : Activation functions for the most activated word unit (local activity) with the MROM and the MROM-P for a pseudohomophone (feal), an orthographic control (feep) and their base word (feel).PseudohomophonesOr thogr aphi c Contr ol sBase Wor ds1M R OM1M R OM - P0. 80. 80. 50. 50. 20. 200 10 20 30 4000 10 20 30 408 aCy cl esCy cl es650Col thear t et al . ( 1 9 7 7 )6256005752120. 820. 5550( ms) (M R OMCy cl es) ( Cy cles)P HContr ol2120. 820. 5M R OM - P20. 220. 28 b20P HContr ol20P HContr olFigure 8 : Simulations run with Coltheart et al.'s (1977) stimuli. Figure 8a shows the mean overall orthographicactivity over time for pseudohomophones, orthographic controls and base words, obtained with the MROM andMROM-P. Figure 8b gives obtained and predicted effects on response times to pseudohomophones and orthographiccontrols.
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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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Page 188 and 189: 188BibliographieFerrand, L., Segui,
Page 190 and 191: 190BibliographieGrainger, J., & Jac
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Page 194 and 195: 194BibliographieMewhort, D. J. K.,
Page 196 and 197: 196BibliographiePerea, M., & Pollat
Page 198 and 199: 198BibliographieSeidenberg, M. S.,
Page 200 and 201: 200BibliographieTreiman, R., & Zuko
Page 202 and 203: 202AnnexesAnnexes
Page 204 and 205: 204AnnexesAnnexe II : Temps de rép
Page 206 and 207: 206AnnexesAnnexe IV : Temps de rép
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Page 212 and 213: 212AnnexesAnnexe VI : Liste des 120
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Page 216 and 217: 216AnnexesSujets Stim. Erreur Sujet
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Page 220 and 221: 220AnnexesAnnexe X : Stimuli employ
Page 222 and 223: 222AnnexesAnnexe XI : Stimuli emplo
Page 224 and 225: 224Appendice IMROM-P : An interacti
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Page 228 and 229: 228Appendice Isummed frequency of f
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Page 232 and 233: 232Appendice Ito an orthographic le
Page 234 and 235: 234Appendice IFigure 6 gives an ill
Page 238 and 239: 238Appendice ISTEP 3. CRITERION SET
Page 240 and 241: 240Appendice Iteractive processes o
Page 242 and 243: 242Appendice INotwithstanding, a no
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,
Page 254 and 255: 254Appendice IImay depend on their
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Page 258 and 259: 258Appendice IIIHowever, an unsolve
Page 260 and 261: 260Appendice IIIgrapheme compared t
Page 262 and 263: 262Appendice IIIRead, J. D. (1983).
Page 264: 264Appendice IIIAppendix AMean resp
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