effet du nombre des graphèmes en Anglais - Aix Marseille Université
effet du nombre des graphèmes en Anglais - Aix Marseille Université
effet du nombre des graphèmes en Anglais - Aix Marseille Université
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App<strong>en</strong>dice I 241wh<strong>en</strong> perc<strong>en</strong>tage correct is the dep<strong>en</strong>d<strong>en</strong>t variable. As concerns the more complex issue of predicting RTmeans and distributions with A-type models, first steps towards progress in this direction have be<strong>en</strong> made insome rec<strong>en</strong>t studies (Grainger & Jacobs, 1996 ; Jacobs & Grainger, 1992).Like its precursor, the MROM, the pres<strong>en</strong>t MROM-P has pot<strong>en</strong>tial <strong>des</strong>criptive accuracy for a variety ofdep<strong>en</strong>d<strong>en</strong>t variables, including RT means and distributions for both correct and incorrect responses, as wellas hit or false alarm rate for the LDT, and perc<strong>en</strong>t correct for perceptual id<strong>en</strong>tification tasks (see Grainger &Jacobs, 1996).As concerns the actual <strong>des</strong>criptive accuracy of the MROM-P, the above tests are <strong>en</strong>couraging but notconclusive. To be conclusive, we would need to test the model in both a deeper and broader fashion, similarlyto our ext<strong>en</strong>sive tests concerning the MROM. Deeper tests would imply providing graphs showinglinear regression betwe<strong>en</strong> predicted and observed mean RTs for item- and/or participant analyses, as well asdistributional and error analyses (Grainger & Jacobs, 1996). Broader tests imply running simulations ofother tasks than the LDT (e.g., perceptual id<strong>en</strong>tification tasks).Additionally, in accord with step 4 of the testing strategy proposed above, the MROM-P should be testedin competition with comparable, alternative models on the same broad range of tasks and dep<strong>en</strong>d<strong>en</strong>t variables,before any interesting conclusions with regard to its actual <strong>des</strong>criptive accuracy can be made. At pres<strong>en</strong>t,the time is not yet ripe for this, but we hope that easily comparable, broadly testable variants of theMROM-P and, for example, the <strong>du</strong>al-route cascaded model (Coltheart & Rastle, 1994) are soon available.It is useful to note, however, that ev<strong>en</strong> if quantitative, strong infer<strong>en</strong>ce comparisons betwe<strong>en</strong> models ofphonological processing in visual word recognition become possible in the near future, a problem remains.The problem is that finding that one model fits the data better than competing models does not establish thebest-fitting model as the probable source of the data (Collyer, 1985 ; 1986). Developing methods to overcomeCollyer's almost totally neglected problem repres<strong>en</strong>ts one of the interesting chall<strong>en</strong>ges for A-type (andM-type) model builders in the future.HORIZONTAL AND VERTICAL GENERALITY. In Jacobs and Grainger (1994), we distinguished betwe<strong>en</strong>horizontal and vertical g<strong>en</strong>erality. Horizontal g<strong>en</strong>erality refers to a model's ability to g<strong>en</strong>eralize acrossdiffer<strong>en</strong>t stimulus sets and/or configurations (stimulus g<strong>en</strong>erality), differ<strong>en</strong>t tasks (task g<strong>en</strong>erality), or responsetypes / measures (response g<strong>en</strong>erality). Vertical g<strong>en</strong>erality refers to a model's ability to g<strong>en</strong>eralizeacross differ<strong>en</strong>t scales of the modeled process, e. g. (macrostructural) static-asymptotic behavior vs. microstructuraldynamics, or differ<strong>en</strong>t types or sizes of a processing structure, such as the number of <strong>en</strong>tries inthe lexicon of a simulation model. Vertical g<strong>en</strong>erality has received little att<strong>en</strong>tion in comparison with horizontalg<strong>en</strong>erality but it might become an important issue in a field that provi<strong>des</strong> more and more complexalgorithmic models, some of which may have severe limitations for scaling-up, e. g. distributedconnectionist models (Feldman-Stewart & Mewhort, 1994 ; Jacobs & Grainger, 1994).We have discussed the vertical g<strong>en</strong>erality of the SIAM elsewhere (Jacobs & Grainger, 1994). Suffice it tosay that by virtue of nested modeling (i.e., SIAM is an integral part of MROM, which is an integral part ofMROM-P), and giv<strong>en</strong> the fact that MROM-P inclu<strong>des</strong> a richer lexicon than MROM, MROM-P has highervertical g<strong>en</strong>erality. Concerning horizontal g<strong>en</strong>erality, again thanks to our application of the nested modelingstratagem, we can say the following : Since MROM has stood an ext<strong>en</strong>sive series of tests in differ<strong>en</strong>t tasksand languages (and thus has reasonable horizontal g<strong>en</strong>erality), and to the ext<strong>en</strong>t that we can show that theMROM-P behaves at least qualitatively like the MROM (e.g., as for the simulations of the nonword data inFigure 11), the MROM-P has a higher degree of horizontal g<strong>en</strong>erality than the MROM. This is because wehave shown that it allows adequate simulation of the processing of stimuli that the MROM cannot accountfor, i.e. pseudohomophones and inconsist<strong>en</strong>t words. Apart from this verified higher stimulus-g<strong>en</strong>erality, itremains to be se<strong>en</strong> to what ext<strong>en</strong>t the MROM-P also inclu<strong>des</strong> the promise of higher task-g<strong>en</strong>erality, i.e. thecapacity to simulate data from other tasks than the LDT or the perceptual id<strong>en</strong>tification task, e.g. the namingtask.SIMPLICITY AND FALSIFIABILITY. This is one of the trickiest criteria of model evaluation (Jacobs andGrainger, 1994). However, giv<strong>en</strong> the curr<strong>en</strong>t state of affairs, and our adoption of a moderate "Popperianism",things are relatively easy : It is simply premature to make any s<strong>en</strong>sible statem<strong>en</strong>t regarding the simplicityand falsifiability of the MROM-P. Popper (1934/94) linked the criterion for simplicity to that of falsifiability(i.e., a model's ability to g<strong>en</strong>erate predictions that can be falsified), in proposing that, giv<strong>en</strong> two modelsin the same domain with equal success, we should prefer the simpler. He defined simplicity as a propertythat places the greatest restrictions on the world, that is, on how the empirical data can turn out to be 8 .Thus, we should prefer the model that is more easily falsified (cf. Estes, 1975 ; Massaro & Cowan, 1993).However, that implies that we already have two viable models (or model variants), for which equal successhas be<strong>en</strong> established in the same domain. Clearly, as concerns the pres<strong>en</strong>t subject area, this is not the case,and we can only postpone evaluation with respect to this criterion.8 For Popper's simplicity/falsifiability criterion to work, certain rules have to be respected in this game ofnon-naive, undogmatic falsificationism and strong infer<strong>en</strong>ce (e.g., constraints on the use of auxiliary assumptions; see Popper, 1934/94 ; 1966 ; 1972). Otherwise, the str<strong>en</strong>gth of Popper's approach can easily beturned into weakness (cf. Feyerab<strong>en</strong>d, 1975 ; Lakatos, 1970).