Item performance in visual word recognition

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Item performance in visual word recognition Powered By Docstoc
					Psychonomic Bulletin & Review
2009, 16 (3), 600-608
doi:10.3758/PBR.16.3.600



  Item performance in visual word recognition                        Clelland, 1989). The results were somewhat surprising,
                                                                     since both of these models accounted for only a small
               ArnAud rey And Pierre Courrieu                        amount of the item variance (3.3% for Plaut et al.’s model,
      CNRS and Université de Provence, Marseille, France             10.1% for Seidenberg and McClelland’s). Spieler and Ba­
                                                                     lota also noticed that the models explained the amount of
                    FloriAn SChmidt-WeigAnd                          variance less well than did the linear combination of three
                 Universität Kassel, Kassel, Germany                 simple linguistic predictors: log frequency, word length,
                                 And                                 and neighborhood density (which accounted for 21.7%
                                                                     of the variance). Finally, when variables related to onset
                           Arthur m. JACobS
              Freie Universität Berlin, Berlin, Germany
                                                                     phonemes were added to the analysis, the simple predic­
                                                                     tors were able to account for 42% of the item variance.
   Standard factorial designs in psycholinguistics have been         Item­level data therefore seem to provide a critical test for
complemented recently by large-scale databases providing em-         computational models of reading.
pirical constraints at the level of item performance. At the same       Seidenberg and Plaut (1998) claimed, however, that
time, the development of precise computational architectures         two reasons might explain the relatively low item vari­
has led modelers to compare item-level performance with item-        ance accounted for by these models. First, item means
level predictions. It has been suggested, however, that item per-    are affected by several factors that are not addressed by
formance includes a large amount of undesirable error variance       these models. For example, they do not specify the pro­
that should be quantified to determine the amount of reproducible    cesses involved in letter recognition or in the production
variance that models should account for. In the present study, we    of articulatory output. Balota and Spieler (1998) noticed,
provide a simple and tractable statistical analysis of this issue.   however, that the performance of these models remains
We also report practical solutions for estimating the amount of      surprisingly weak, since they fail to explain more vari­
reproducible variance for any database that conforms to the ad-      ance than do three simple predictors (i.e., log frequency,
ditive decomposition of the variance. A new empirical database       word length, and neighborhood density) that are, in prin­
consisting of the word identification times of 140 participants on   ciple, captured by these models. Their second, and prob­
120 words is then used to test these practical solutions. Finally,   ably more critical, argument is based on the fact that item
we show that increases in the amount of reproducible variance        data include a substantial amount of error variance. The
are accompanied by the detection of new sources of variance.
                                                                     question is how substantial this amount of error variance
                                                                     is. Comparing Spieler and Balota’s database with a simi­
                                                                     lar database recorded by Seidenberg and Waters (1989),1
   The precision of theoretical accounts in the field of             they found a .54 correlation between item latencies in the
visual word recognition has significantly increased over             two databases. This relatively low correlation indicates
recent years. Indeed, cognitive modelers have proposed               that a large amount of the variance in one database is
several detailed descriptions of the structure and dynam­            absent from the other.
ics of the reading system (e.g., Ans, Carbonnel, & Valdois,             In the present study, in line with Seidenberg and Plaut’s
1998; Coltheart, Rastle, Perry, Langdon, & Ziegler, 2001;            (1998) criticism, we address the issue of error variance
Grainger & Jacobs, 1996; Harm & Seidenberg, 2004;                    in item databases (for a similar a
				
DOCUMENT INFO
Description: [...] when variables related to onset phonemes were added to the analysis, the simple predictors were able to account for 42% of the item variance. [...] we present a new empirical database consisting of the word identification scores of 140 participants on 120 words, and we use it to quantitatively estimate the amount of variance that should be accounted for as a function of the number of participants.
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