effet du nombre des graphèmes en Anglais - Aix Marseille Université

Appendice I 235Table 3 : Parameter set used in our simulation studies. "Alpha" refers to the weight of excitatory connections and"gamma" refers to the weight of inhibitory connections. FL = Feature Level ; LL = Letter Level ; PUL =Phonological Unit Level ; OL = Orthographic Lexicon ; PL = Phonological Lexicon.CONNECTIONS Alpha GammaFeature - Letter .005 .15Letter - Feature 0 0Feature - Feature 0 0Letter - Letter 0 0Letter - Lexical Orth. .035 .02Lexical Orth. - Letter 0 0Lexical Orth. - Lexical Orth. 0 .2Letter - Sublex. Phono. .1 .004Sublex. Phon. - Letter 0 0Sublex. Phon. - Sublex. Phon. 0 .05Sublex. Phon. - Lexical Phon. .1 .02Lexical Phon. - Sublex. Phon. .3 .2Lexical Phon. - Lexical Phon. 0 .2Lexical Orth. - Lexical Phon. .1 0Lexical Phon. - Lexical Orth. .2 0MODEL PREDICTIONS AND TESTSTESTING STRATEGY. The literature provides no generally accepted testing policy for complex A-typemodels. The situation can even be assimilated to something like anarchy. As an example, consider PDP(parallel distributed processing) or ANN (artificial neural network) "learning" models. Whereas classicalmathematical learning theory provided a wealth of testing principles for M-type modelers (Tack, 1976 ;Myung & Pitt, present volume), ANN or A-type modelers today seem little concerned with this issue(Simon & Kaplan, 1989 ; Prechelt, 1996). Surely, the "fit-or-die" strategy of classical learning theory hasboth statistical, inferential pitfalls (Collyer, 1985 ; 1986), and epistemological drawbacks (Greenwald, Pratkanis,Leippe, & Baumgardner, 1986 ; Lakatos, 1970). On the other hand the question arises whether thecurrently visible anarchical, or laissez-faire testing strategy for computational, A-type models will eventuallygive the positive results anticipated by laissez-faire anarcho-epistemologists like Feyerabend (1975).Without any claims that the testing strategy adopted here is the right or optimal one, we neverthelessprefer a critical-rational, (mildly) Popperian approach. At least, such an explicit approach can be constructivelycriticized and thus potentially advances our enterprise. Basically, our testing approach comprises thefollowing 5 steps, inspired by testing procedures in psychometrics and mathematical psychology.Step 1 . Parameter tuning studies. During the initial phase of model construction, these tests check theglobal appropriateness of the architectural and parametric model assumptions in a simple way to see whetherthe model is not fundamentally flawed (e.g., does not include parameter configurations that produce catastrophicmodel behavior). The meaning of "in a simple way" depends very much on each model builder'simplicit assumptions and preferences. We see no such thing as a systematic, explicit approach for parametertuning in complex A-type models in the literature (cf. McClelland & Rumelhart, 1988). We can only providemotivated examples for how we proceeded.Step 2 . Estimator set studies. In analogy with classical procedures of cross-validation in psychometrics, anestimator study provides the data set from which model parameters are estimated (cf. Collyer, 1986). For thepresent purposes the difficult questions of how parameters of M-type and A-type models are best estimatedand how such model's identifiability can best be determined must be put aside (see Grainger & Jacobs, presentvolume). Once a new model has stood the test of step 1, it is economical to run it against some alreadyavailable data from the literature in step 2, before carrying out time-intensive new experiments. This is themethod we adopt here.Step 3 . Criterion set studies. In analogy with the procedures of psychometrics, a criterion set study providesdata with which the model predictions are compared once the parameters have been fixed after the estimatorset study. Using a criterion of descriptive or behavioral accuracy (for a discussion of this criterion see Jacobs& Grainger, 1994), the criterion set study provides the first serious cross-validation test of the model. In thepresent paper we provide two such tests. The first uses data concerning the same effect as the one used in the