Simulating Language-specific and Language-general Effects in a Statistical Learning Model of Chinese...

Journal of Memory and Language 61 (2009) 238–257

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Journal of Memory and Language

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Simulating language-specific and language-general effects in a statisticallearning model of Chinese reading

Jianfeng Yang a,b, Bruce D. McCandliss a, Hua Shu b, Jason D. Zevin a,*

a Sackler Institute, Weill Cornell Medical College, USAb State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, China

a r t i c l e i n f o

Article history:Received 31 December 2008Revision received 23 April 2009Available online 7 June 2009

Keywords:ReadingComputer simulationCross cultural differences

0749-596X/$ - see front matter � 2009 Elsevier Incdoi:10.1016/j.jml.2009.05.001

* Corresponding author.E-mail address: [email protected] (J.D. Z

a b s t r a c t

Many theoretical models of reading assume that different writing systems require differentprocessing assumptions. For example, it is often claimed that print-to-sound mappings inChinese are not represented or processed sub-lexically. We present a connectionist modelthat learns the print-to-sound mappings of Chinese characters using the same functionalarchitecture and learning rules that have been applied to English. The model predicts aninteraction between item frequency and print-to-sound consistency analogous to whathas been found for English, as well as a language-specific regularity effect particular to Chi-nese. Behavioral naming experiments using the same test items as the model confirmedthese predictions. Corpus properties and the analyses of internal representations thatevolved over training revealed that the model was able to capitalize on information in‘‘phonetic components” – sub-lexical structures of variable size that convey probabilisticinformation about pronunciation. The results suggest that adult reading performanceacross very different writing systems may be explained as the result of applying the samelearning mechanisms to the particular input statistics of writing systems shaped by bothculture and the exigencies of communicating spoken language in a visual medium.

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Introduction

Over the past three decades, computational modelshave become increasingly sophisticated in accountingfor a broad range of phenomena and specifying themechanisms underlying skilled reading and its acquisi-tion (see reviews in, e.g., Plaut, 2005; Rayner, Foorman,Perfetti, Pesetsky, & Seidenberg, 2001). The vast majorityof this work has been done in English, and has thusfocused on issues arising from the particularities of itswriting system (Share, 2008). This has led to the con-struction of models that implement relatively writing-system specific assumptions, such as the inclusion ofdistinct processing mechanisms for ‘‘sub-lexical” and

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evin).

‘‘lexical” translation from spelling to sound (Coltheart,Curtis, Atkins, & Haller, 1993; Coltheart, Rastle, Perry,Langdon, & Ziegler, 2001). An alternative approach hasbeen to assume that reading skill is acquired by way ofdomain-general learning mechanisms that operate overdistributed representations of basic levels of information,such as orthography, phonology and semantics (Plaut,McClelland, Seidenberg, & Patterson, 1996; Seidenberg& McClelland, 1989). Here we present a general connec-tionist model of Chinese print-to-sound translation thatimplements the same functional architecture and learn-ing rules as models that have been previously appliedto English (Harm & Seidenberg, 1999, 2004; Treiman,Kessler, Zevin, Bick, & Davis, 2006; Zevin & Seidenberg,2002, 2006), The model provides a computationallyexplicit theoretical account of the role of sublexical pho-nology and the emergence of functional units in Chinesereading.

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J. Yang et al. / Journal of Memory and Language 61 (2009) 238–257 239

‘‘Orthographic depth” and print-to-sound translation inChinese

The notion of ‘‘orthographic depth” (Bentin & Frost,1987) provides a descriptive framework that capturesimportant differences among writing systems. In ‘‘shallow”orthographies, each character or group of characters corre-sponds with a high degree of consistency to a single speechsound. An extreme example of this is the Hangul system inKorean, in which each syllable is transcribed as a set of oneto four jamo, each of which in turn comprises a set ofstrokes that each indicate a specific phonetic feature,assuring that words with the same pronunciation are iden-tical to one another in the script, and that words with sim-ilar pronunciations are written similarly (Lee, 2000).Although English spelling is famously complicated(Malone, 1925; Venezky, 1999), at its core is an alphabetof letters that correspond roughly to individual speechsounds (Venezky, 1970) which can be described in termsof sub-lexical mapping ‘‘rules”. In the dual-route frame-work, these rules are supplemented by a lexical route thatcontains the correct pronunciations for known words(Coltheart et al., 2001; Zorzi, Houghton, & Butterworth,1998).

Chinese, in contrast, is an example of an extremely‘‘deep” orthography (DeFrancis, 1989) in that the pronun-ciation of a character cannot be computed sound-by-soundfrom its constituent parts. Chinese orthography has aroughly hierarchical organization, with five types ofstrokes combined to form radicals, orthographic units thatmay carry probabilistic information about meaning andsound. Unlike letters, radicals do not contain componentialinformation about pronunciation (Mattingly, 1987). Thereis no relationship between, e.g., the first radical in a char-acter and the first phoneme in the spoken word itrepresents.

Further, radicals are often organized into larger unitssuch as phonetic and semantic components. The majority(85%) of characters in Chinese are phonograms (Zhu,1988), which consist of a semantic component that pro-vides information about the meaning of the character,and a phonetic component that provides informationabout the character’s pronunciation (Li & Kang, 1993).Although the pronunciation of each character can beprobabilistically determined by its phonetic component,it is sometimes entirely arbitrary, so that very similarlywritten words often have completely different pronunci-ations, and many homophones share no orthographicfeatures.

The effect of orthographic depth on the organizationof the reading system is often described in terms of abias toward using one or another of the component pro-cesses hypothesized to be necessary for English (Frost,Katz, & Bentin, 1987). Visual word recognition in shalloworthographies is thought to involve relatively limitedengagement of lexical processes, because sub-lexicaltranslation from spelling to sound is so automatic andconsistently accurate. For example Korean Hangul char-acters are thought to be read predominately via sub-lex-ical processing unless task demands create a strong biastoward lexical processing (Kang & Simpson, 2001, also

see Raman, Baluch, & Besner, 2004 for Turkish; Havelka& Rastle, 2005 for Serbian).

Many existing models of Chinese character reading re-flect the converse assumption, i.e., that spelling-to-soundtranslation is accomplished entirely via a ‘‘lexical” mecha-nism (Perfetti, Liu, & Tan, 2005; Zhou & Marslen-Wilson,1999a). The lexical mechanism for print-to-sound transla-tion is most often described as relying on hierarchically or-ganized levels of representation, with localistrepresentations at each level, in the vein of the interac-tive-activation model of English word recognition (McClel-land & Rumelhart, 1981).

The architecture of the lexical mechanism in thesemodels also reflects a number of assumptions aboutthe organization of the reading system that are fairlyspecific to Chinese. For example, Taft and colleagues(Taft, 2006; Taft & Zhu, 1997; Taft, Zhu, & Peng, 1999)have proposed a multilevel Interactive-activation modelof word recognition in Chinese that assumes characters,radicals and strokes each have their own separate levelof representation, and that these are organized serially.The assumption that radicals form a basic orthographicunit is well supported by a number of priming studies(Ding, Peng, & Taft, 2004), but as we shall argue below,additional considerations suggest that radicals are notnecessarily apt functional units for print-to-soundconversion.

Rethinking the assumptions of Chinese character reading

A number of empirical findings appear to conflict withthe notion that print-to-sound translation is purely lexicalin Chinese. First, some properties of the writing systemsuggest that a sub-lexical mechanism is plausible afterall. Phonetic components contain probabilistic sub-lexicalinformation about how the whole characters are pro-nounced. There is a great deal of evidence that this sub-lex-ical information is involved in reading aloud. When aphonogram’s pronunciation matches its phonetic compo-nent exactly, it is called ‘‘regular” (Seidenberg, 1985; Shu& Zhang, 1987). Large effects of regularity – regular charac-ters are named faster and more accurately – have beenfound in studies of both adults (Fang, Horng, & Tzeng,1986; Hue, 1992; Lee, Tsai, Su, Tzeng, & Hung, 2005; Peng& Yang, 1997) and children (Shu, Anderson, & Wu, 2000;Shu & Wu, 2006; Xing, Shu, & Li, 2004; Yang & Peng,1997). Although regularity effects are prima facie evidencefor sub-lexical mappings from print-to-sound, they arealso potentially consistent with models that assume purelylexical activation of phonology. It could be possible, forexample, to construct a model such that when a characterthat has a lexical representation is presented — even aspart of another character – it becomes activated, thus lead-ing to a regularity effect.

Phonetic components also vary with respect to howreliably the words they appear in match their canonicalpronunciation, so that even items that are ‘‘regular”, asdescribed above, can vary in ‘‘consistency” in a mannerdirectly analogous to English (Jared, McRae, & Seiden-berg, 1990). Just as a completely regular pronunciationof a rime in English can be difficult to compute if there

240 J. Yang et al. / Journal of Memory and Language 61 (2009) 238–257

are many irregular neighbors (e.g., DOLL vs. ROLL, POLL,TOLL), a completely regular character can be difficult topronounce if many characters containing the same pho-netic component have irregular pronunciations (Fanget al., 1986; Hue, 1992). These effects argue morestrongly for some form of sub-lexical print-to-soundtranslation, because they reflect distributional informa-tion related to shared sub-lexical units (phonetic compo-nents) among characters. Although it has been suggestedthat consistency effects in English might arise from acti-vation of similar words in the lexical route, for examplein the Dual-Route Cascade model (Coltheart et al., 2001),subsequent work with this model has shown that wherethis model correctly simulates consistency effects, it isdue to sub-lexical processing (Seidenberg & Plaut,2006). It is thus an open question whether a purely lex-ical model of print-to-sound translation for Chinesecould correctly simulate consistency effects.

The role of phonetic components in print-to-soundtranslation also poses a problem for the notion of radicalsas the basic unit of character reading, because phoneticcomponents can comprise one or more individual radicals.Some single radicals can also function as phonetic compo-nents. For example, the radical ‘‘ ” is pronounced 1

when it appears as a word, and is also the phonetic compo-nent of a number of words that share the same pronuncia-tion ( ). The same radicals may participate inmany different phonetic components, however. For exam-ple, the ‘‘ ” radical also occurs in characters that do notshare its pronunciation at all ( ), which in turnserve as phonetic components of larger characters( ). Complicating matters further, thesame simple radical occurs in many contexts( ) without any relation to the whole char-acter’s pronunciation. Thus, although radicals clearly func-tion as a unit at the level of orthographic organization,(Ding et al., 2004; Taft, 2006; Taft & Zhu, 1997; Zhang, Per-fetti, & Yang, 1999), they are not usually the most relevantfunctional unit for print-to-sound translation. This raisesan important challenge for models of reading in Chinese –how can models explain the emergence of phonetic compo-nents in print-to-sound mappings when they are not readilyidentifiable as ‘‘basic units” from the surface features of theorthography?

A general framework across orthographic depth from aconnectionist perspective

Many theorists assume that different scripts necessitatedifferent processing assumptions. For example, in discuss-ing the possibility of extending their model of Englishreading to other languages, Coltheart et al. (2001) assertthat ‘‘the Chinese, Japanese, and Korean writing systemsare structurally so different from the English writing sys-tem that a model like the DRC model would simply notbe applicable.” (p. 236). An alternative approach is to

1 Here and throughout, transcriptions are in International PhoneticAlphabet. Note that in the Pinyin romanization system, the voiceless,unaspirated stop /p/ is written as a ‘‘b.”

assume that the basic processes underlying reading areessentially the same across writing systems, and that dif-ferences in how the reading system becomes organizedare due to the influence of statistical learning mechanismsoperating over distributed orthographic, phonological andsemantic representations (Seidenberg, 1992; Seidenberg& McClelland, 1989). Thus, rather than assuming that Chi-nese and English require entirely different processingmechanisms, this approach seeks to explain processing dif-ferences between the two languages in terms of propertiesof the orthography that lead to different learning out-comes. The most important of these in the current caseare the grain size (Ziegler & Goswami, 2005) and degreeof arbitrariness (Seidenberg, 1992) of mappings acrosswriting systems.

English can be described as having regularities at multi-ple grain sizes (Ziegler & Goswami, 2005). For example,each spoken consonant in the English word BREED is rep-resented by a single letter, whereas the vowel is writtenas a two-letter combination. These written forms of singlephonemes – or ‘‘graphemes” – are all completely regular,in the sense that they are each assigned their most typicalpronunciation in this word. In contrast, the word BREADcontains an atypical pronunciation for the grapheme EA.Words that contain such mappings are called ‘‘irregular”in some models (Coltheart et al., 2001). Note, however, thatthe spelling-to-sound mapping for BREAD is supported bymany other examples, e.g., HEAD, THREAD, and TREAD.Statistical regularities at this level of detail give rise to con-sistency effects (Jared et al., 1990). Thus, subsyllabic map-pings from spelling to sound are dominant in the Englishwriting system, with multiple grain sizes contributing tothe spelling-to-sound mapping, sometimes in conflictingways.

Note that describing English in this manner reveals animportant parallel between English and Chinese. In bothsystems, a basic orthographic unit (the letter in English,the radical in Chinese) can be recombined into largerfunctional units for print-to-sound translation. Indeed,regularities at multiple grain sizes are clearly used pro-ductively in generalization, as demonstrated by studiesof nonword reading in adults (Treiman, Kessler, & Bick,2002) and children (Treiman & Kessler, 2006)—effectsthat are accurately simulated in connectionist modelsthat explicitly capitalize on multiple grain sizes simulta-neously during learning (Treiman et al., 2006; Zevin &Seidenberg, 2006).

The key difference between English and Chinese, then,is that whereas functional print-to-sound units in Englishcan exist at a number of different sizes, in Chinese, reg-ularities exist almost entirely in the mapping of phoneticcomponents to whole syllables (Leong, 1997; Mattingly,1987). The difference in the grain size at which regular-ities exist drives a difference in the degree of arbitrari-ness between the two writing systems. English spelling,while highly inconsistent, is never entirely arbitrary.Even a very strange word such as YACHT has some pre-dictability to it (the Y, A and T are assigned pronuncia-tions common in other contexts). In Chinese this is notnecessarily the case. The many pronunciations forcharacters containing the ‘‘ ” component, for

Fig. 1. (A) Two orthographic ‘‘neighborhoods” in Chinese. Examples onthe left are from a perfectly consistent neighborhood: Every wordcontaining the phonetic component is pronounced the same way. Onthe right, an inconsistent neighborhood is shown. The top two items are‘‘regular,” in that they share the pronunciation of the phonetic componentwhen it appears as a simple character. The bottom three items havedifferent pronunciations from the component as a simple character andare therefore irregular. (B) Two radicals in phonetic component of Panel A(left) also can be phonetic components to form their families.


example, are a highly heterogeneous set (see Fig. 1A)2.Whereas in English, the spelling of an unfamiliar wordprovides partial cues to its pronunciation, in Chinese char-acters, the distribution of pronunciations for any givenphonetic component may contain a number of highly dis-similar forms. Thus, the differences between the two writ-ing systems can be described in terms of factors that havewell-characterized effects on models that employ domain-and language-general statistical learning mechanisms.

Previous computational models of Chinese reading

The Lexical Constituency Model (LCM, Perfetti et al.,2005) is an implemented model based loosely on the inter-active-activation model of reading in English. The focus ofresearch in the LCM framework is establishing a role forphonological processing in reading, despite what is de-scribed as a lack of reliable sub-lexical phonological infor-mation in the writing system. In the authors’ view, thisreflects a general principle about reading across languages,i.e. that the natural medium for language is speech, andtherefore reading necessarily involves accessing phonolog-ical representations. However, the processes by whichprint-to-sound translation is accomplished are thought tobe defined by the language. Unlike alphabetic writing sys-

2 Fully 48% of irregular words share neither onset nor rime with theirregular counterparts, making them dramatically less predictable than even‘‘strange” words in English. Interestingly, however, there is some subsyl-labic regularity, such that 18% of irregular forms share their onset withregular family members, and 35% share a rime. These are substantiallygreater than would be predicted if pronunciations were uniformly distrib-uted (spoken Standard Mandarin has 23 onsets and 39 rimes). Note,however, that due to the non-componential nature of the mapping, it is notpossible to know whether a particular phonetic component predicts theonset or the rime (or the vowel, or the tone, which we have not explored).

tems, which the same authors describe as having ‘‘assem-bled phonology as a sub-lexical mechanism” (Perfettiet al., 2005, p. 43) in addition to a lexical look-up mecha-nism (Coltheart et al., 1993), Chinese is described asdepending on only the lexical look-up mechanism. The in-put to this model comprises localist representations of rad-icals. These feed forward to a lexicon of localistrepresentations of whole characters, which in turn are con-nected to their pronunciations in a phonological layer. Rep-resentations of character meaning are activated jointly byinput from the phonological and lexical orthographiclayers. Critically, there is no direct input from the radicallayer to the phonological layer (although many radicalsare also characters in their own right, and have redundantrepresentations in the lexical layer which are connected totheir pronunciation). In this model, then, access to pronun-ciation is strictly lexical, in the sense that sub-charactercomponents cannot directly activate their pronunciations.Thus, although the modeling framework is fairly general,the implementation embodies a number of writing-sys-tem-specific assumptions about how the reading systemis organized for Chinese.

In addition to the LCM (Perfetti et al., 2005), there havebeen a number of connectionist models of Chinese reading.These have tended to focus on language-specific phenom-ena. For example, Hsiao and Shillcock (2005, 2006) focusedon the interaction between regularity and the position ofphonetic and semantic radicals, but didn’t explore fre-quency by consistency interactions. Regularity in this mod-el was defined in terms of the canonical pronunciation ofthe phonetic radical This model also includes some inter-esting details about the potential role of hemisphericasymmetry in reading aloud that makes specific predic-tions about experiments in which stimuli are presentedto either the left or right visual field. This model uses local-ist representations for phonetic and semantic components,and therefore can say little about the emergence of func-tional units at multiple grain sizes – the functional unitof analysis for print-to-sound translation is presupposedby the selection of the training corpus, and pre-coded intothe network’s architecture.

Another model, developed by Xing et al. (2002, 2004)focused on simulating how children acquire reading skillin Chinese based on Self-Organized Feature Mapping(SOFM) between orthographic and phonological similar-ity. The SOFM model captured the development of regu-larity, consistency and their interaction with frequency,correctly simulating some aspects of children’s acquisi-tion of characters during the elementary school years.One limitation of this model was that training corpuswas quite small (about 300 words), and it is unclear thatthe particular formalism employed would generalize toother training sets, or scale up to a larger lexicon. Amore serious limitation of the model was the way regu-larity effects were simulated: Each time a word was pre-sented, the pronunciations both of the whole characterand its phonetic component were input simultaneouslyto the model. Thus, the model shows that statisticallearning mechanisms can integrate sub-lexical and lexi-cal information about pronunciation, but it assumes thatthe phonetic component is an a priori functional unit in


print-to-sound mapping, and that its most frequent pro-nunciation is explicitly related to whole character’s pro-nunciation each time it is encountered, instead oflearning the mapping from print to sound.

Finally, there is a preliminary report of a model thatemploys a similar architecture to models that have beenapplied to English (Chen & Peng, 1994). This model hasorthographic and phonological representations that donot build in any assumptions about the role of phoneticcomponents in computing pronunciations from print,and yet it correctly simulates the interaction between fre-quency and regularity as observed in a number of con-temporary studies (Fang et al., 1986; Hue, 1992).Although these are encouraging preliminary results, themodel did not address consistency effects, nor were anyanalyses conducted on the acquisition of reading skillover the course of development. Thus, existing modelsof reading in Chinese have either been implemented withChinese-specific assumptions about the processes (Perfet-ti et al., 2005), and functional units (Hsiao & Shillcock,2005, 2006; Xing, Shu, & Li, 2002; Xing et al., 2004), orhave addressed a relatively limited range of phenomena(Chen & Peng, 1994).

The current model

Here we adapt a computational model that has beensuccessful in explaining a range of English reading phe-nomena to simulating skilled reading aloud in Chinese. Inthis framework, aspects that are common to the two read-ing systems, such as the frequency by consistency interac-tion, can be accounted for in terms of the learningmechanisms that underlie acquisition: Frequency effectsare simply practice effects on particular items, whereasconsistency effects reflect the influence of statistical pat-terns across many similar items, and emerge from a gener-ic tendency to preserve similarity: i.e., most statisticallearning mechanisms are predisposed to map similar in-puts onto similar outputs. Thus, the most difficult itemsboth in acquisition and skilled performance are those withstatistically rare print to sound correspondences that arealso encountered infrequently (Jared, 2002; Lee et al.,2005). At the same time, the adaptation of these mecha-nisms to different properties of the writing systems (grainsize, arbitrariness) explain language-specific effects asemerging from statistical patterns shaped by historicaland linguistic forces.

The model presented here has the same functionalarchitecture and learning rules that have been used ina number of English studies (Harm & Seidenberg,1999; Treiman et al., 2006; Zevin & Seidenberg, 2002,2006), but with input and output representations modi-fied to represent Chinese orthography and phonology. InStudy 1, we demonstrate that the model correctly simu-lates both the effects of regularity and consistency, andtheir interactions with frequency. In Study 2, we explorethe frequency by consistency interaction in detail, inlight of the statistical properties of the lexicon andpotential biases in the stimulus set designed for Study1. Here again, the model correctly predicts human per-formance in a word naming task. Finally, a series of

analyses of the training corpus and the internal repre-sentations that support the model’s performance –including how these develop over time were conducted.These analyses provide new insights into how the pho-netic component emerges as a functional unit in learn-ing to read Chinese.

Study 1

Connectionist model

Model architectureEvery unit in the orthographic input layer was con-

nected to the 200 hidden units, which in turn were con-nected to every unit in the phonological output layer(See Fig. 2A). In addition, every unit in the output layerwas connected back to the output layer (including auto-connections) both directly and via a set of 50 ‘‘cleanup”hidden units, forming an attractor structure (Hinton &Shallice, 1991; Plaut & Shallice, 1993). This architecturediffers from models used in English (e.g., Treiman et al.,2006; Zevin & Seidenberg, 2002, 2006) only in the numberof units used in each layer, and the fact that the ortho-graphic and phonological representations are based onChinese instead of English. Prior to training, all connectionsin the model were randomized to weights between �0.1and 0.1 with a mean of 0.0 and a Gaussian distribution ofvalues over the network.

Phonological representationThe phonological representation was based on a pho-

netic description of Standard Mandarin (Huang & Liao,2002) and comprised five slots, one for the onset (in Chi-nese, the onset unit, or ‘‘shengmu” contains only the initialconsonant), three for the rime (the ‘‘yunmu”, which in thiscase includes any semivowels, the nuclear vowel and co-das) and a fifth slot composed of four units to representlexical tone (See Fig. 2B). The representations were cen-tered so that the nuclear vowel always occurred in the sec-ond slot of the rime.

Each phoneme was encoded as a set of abstract pho-netic features using a distributed representation withgraded similarity. This is equivalent to representations thathave been used in a number of English models (Harm &Seidenberg, 2004; Treiman et al., 2006; Zevin & Seiden-berg, 2006). The four slots used to represent phonemescomprised 3 groups: (1) 8 units encoded manner (e.g., stop,affricate, fricative, semivowel/liquid, vowel), (2) 6 units en-coded place (e.g., dental, retroflex, palatal, velar), (3) 8units encoded impressionistic vowel quality, with 1 unitto code retroflexing, 2 to represent backness, 3 for heightand 2 for lip rounding. Thus, each of the four phonemeslots had 22 units, which, taken together with the 4 unitsfor lexical tone, makes 92 output units. Note that moreunits were used in some cases than was mathematicallysufficient to represent the number of possible values in afeature space. This was done to encode the relative similar-ity of different features, for example, stop is coded as‘‘1 0 0”, affricate as ‘‘1 1 0” and fricative as ‘‘1 1 1” on theunits that encode degree of closure in the ‘‘manner” group.

Fig. 2. Architecture and representational scheme. (A) The architecture of the model; arrows indicate connections between every unit in each layer. (B) Therepresentation of a Chinese syllable example on the phonological output layer. (C) The representation of a Chinese character including 21 units for overallstructure and 249 units to represent seven radical slots.


Orthographic representationThe orthographic layer of the model was organized into

a set of 9 slots, each of which was further organized into avariable number of groups containing a variable number ofunits that could take on values of either 0 or 1 – comprising270 binary units in all (See Fig. 2C). Each character wasrepresented by a unique pattern of activity over theseunits, based on a linguistic description (Xing et al., 2004)of Chinese orthography, adapted into a distributed repre-sentation that included features for both the hierarchicalstructure of each character (overall shape, relative positionof radicals) and the orthographic details of which radicalsappear in the character, and a finer-grained descriptionof the radicals in terms of strokes.

Two slots encoded the overall structure of each charac-ter. One slot, containing seven groups of between 2 and 4units each, represented overall character shape, with eachencoding one type: left–right, top–bottom, left–middle–right, top–middle–bottom, cross, round and single. All to-gether, 27 different sub-types of character structure wereencoded using 18 units. The second slot comprised a singlegroup of three units for the number of radicals in the char-acter (1–7).

Another seven slots were used to represent the radicalsin the character, with each slot corresponding to a singleradical according the definition of 560 unique radicals in

the Chinese Character Component Standard of GB13000.1Chinese National Language Affairs Commission (1997).Radicals are roughly analogous to letters in an alphabeticscript, except that they can be arranged in a variety of con-figurations within a character and do not necessarily en-code for phonology. The variety of positions in whichradicals can appear poses a version of the ‘‘dispersion prob-lem” – i.e., that because the input representations are posi-tion-specific, what is learned about a letter in one positiondoes not transfer to the same letter in a different position –that has been noted in English (Plaut et al., 1996). In Chi-nese, the problem is more complex because of the greaterpossible number of spatial arrangements a character cantake. For example, phonetic components typically appearon the right in a character with an overall left–right struc-ture, and on the bottom in top–bottom characters. In themodel, radicals were aligned in slots to reflect this similar-ity, as shown in Table 1. For left–right characters, the rad-icals from the left component were left–aligned into slots 1through 3, and the radicals from the right component wereright-aligned into slots 5 through 7, with the same overallscheme for top–bottom characters (with the top compo-nent left-aligned and the bottom component right-aligned). For left–middle–right and top–middle–bottomcharacters, the ‘‘middle” radicals were central-aligned inslot 4. When a phonetic component appeared alone in a

Table 1Arrangement of radicals in complex characters.

Radical sequence

1st 2nd 3rd 4th 5th 6th 7th

Right only

Left–right

Left–right

Top–bottom

Center


character, it was represented in its most frequent positionaccording its family members. This encoding was adoptedin order to represent the similarity of characters containingthe same components. It maintains the overlap amongcharacters with very different shapes that share a phoneticcomponent, which is critical for simulating both consis-tency and regularity effects.

All radical slots comprised at least nine groups, three ofwhich encoded overall structure, five of which encoded the‘‘strokes” used to write the character, and a final group of 6units used to disambiguate the 53 remaining radicals thatcould not be uniquely identified based on the first twogroups. For six slots, a tenth group of 3 units encoded therelationship between the radical in that slot and adjacentradicals in terms of six different possible relations (e.g.,left–right, surrounding, single). Groups for overall struc-ture comprised (1) a group of 4 units to encode ten typesof radical structure, – e.g., left–right ( ), top–bottom ( ),surrounding ( ), (2) a group of 3 units to encode six pos-sible stroke relations – e.g., crossing ( ), separate( ), connecting ( ), and (3) a group of 4 units to en-code eleven possible modal positions. Five groups encodedfor strokes, including 4 units to encode number of strokesfrom 1–10, and 3 units to encode five possible stroke types(horizontal, vertical, slanted, pointed and crooked) for thefirst, second, third and last stroke in the radical,respectively.

TrainingA set of 4468 items from the Modern Chinese Frequency

Dictionary (1986) was used to train the model. Duringtraining, the probability of using any character on a giventrial was proportional to the square root of its frequency(Plaut et al., 1996), with raw frequencies capped at 1000.This ensured that low-frequency characters would be se-lected a reasonable number of times over the 3 milliontraining trials.

Table 2Materials for Study 1.

High frequency

R-C R-I

Frequency 466.25 470.03Consistency level 1.00 0.47Family size 5.80 9.35Number of radicals 3.10 2.80Number of strokes 9.60 8.45

Note: frequency expressed as occurrences per million.

Following Harm and Seidenberg (1999), we first pre-trained the phonological attractor model, and then trainedthe full reading model on the mapping from orthographyto phonology. The continuous recurrent back-propagationalgorithm (Pearlmutter, 1995) was used, with online learn-ing, a learning rate of 0.005 and momentum of 0.9. On eachtrial, a character was selected and the orthographic unitswere clamped with the pattern corresponding to the writ-ing of the character for 12 time ticks. Error computed at thephonological layer was computed after the first time tickactivation was propagated forward to the output layerfrom time ticks 5–12 and a gradient based on this errorwas back propagated to update the connection weights.

TestingA set of 120 items was chosen to test the frequency,

consistency and regularity effects from the model on hu-man participants: 20 from each cell resulting when threelevels of regularity/consistency (regular-consistent, R-C;regular-inconsistent, R-I; irregular-inconsistent, I-I) werecrossed with two levels of frequency (high, HF; low, LF).The mean frequency of occurrence was 475.35 per millionfor HF characters and 12.56 per million for LF characters,yielding a frequency manipulation of equivalent size acrossall three levels of regularity/consistency. Regular-consis-tent items contain phonetic components that appear onlyin characters that share the same onset and rime, regard-less of tone. By definition, regular-inconsistent items havethe same pronunciation as their phonetic component,although other characters sharing the same phonetic com-ponent are pronounced differently. Conversely, irregular-inconsistent items are pronounced differently from theirphonetic components. The number of characters that sharea phonetic component is called ‘‘family size,” a propertythat was matched across the various frequency and regu-larity/consistency conditions, with the exception that theR-C items have a smaller family size than the others. Notethat throughout the lexicon, R-C items have a much smal-ler number of neighbors than the other stimulus types; be-cause larger family size have a facilitative effect on earlyvisual recognition for naming R-C items (Feldman & Siok,1999; Hsu, Tsai, Lee & Tzeng, 2009), we matched the familysize for inconsistent items only and included R-C itemswith a smaller family size than R-I or I-I items. Consistencyis defined by the ratio of the frequency and number of‘‘friends” (characters with the same pronunciation) and‘‘enemies” (characters with different pronunciations, Peng& Yang, 1997). We therefore matched frequency weightedconsistency value for inconsistent items. The consistency

Low frequency

I-I R-C R-I I-I

489.78 12.36 13.03 12.310.40 1.00 0.31 0.259.45 5.60 9.25 8.103.05 3.50 3.30 3.259.85 11.60 11.65 10.85


value is equal to summed frequency of friends divided bysummed frequency of all family members (including thecharacter itself, as in Shu, Chen, Anderson, Wu, & Xuan,2003). Characters were also matched across all conditionsfor structure type, typical position of phonetic compo-nents, number of strokes and number of radicals.

Testing was carried out by presenting each character tothe model, i.e., clamping the appropriate pattern on the in-put layer. We allowed the model to compute a pronuncia-tion over 12 time ticks and the last tick was counted as thefinal output. Naming accuracy and sum squared error (SSE)were computed to test the model’s performance. Accuracywas determined by applying a winner-take-all scoring sys-tem: for each slot on the output layer, we determinedwhich phoneme was closest to the pattern on the outputat the final time tick and reported this as the model’s pro-nunciation; responses that did not match the correct pro-nunciation were scored as errors. SSE, a stand-in forresponse latency, was computed from the model’s outputat the second to last time tick by adding together thesquare of the difference between the model’s output andthe target for each unit. Because the model was run 40times with different starting weights and random seeds,we were able to conduct both ‘‘by subjects” analyses thattreated each run as a subject, and analyses with items asa random factor.

ResultsAt the end of training, 95% of the test items were named

correctly. A 2 (high vs. low frequency) � 3 (type: R-C vs. R-Ivs. I-I) ANOVA was conducted to examine the effects of fre-quency, regularity and consistency, and their interaction(See Table 3). The overall correlation between SSE and re-sponse latency for all items in the behavioral experimentwas 0.617 (p < .01).

This analysis revealed a significant frequency effect forboth naming accuracy and SSE. The main effect of consis-tency/regularity was also significant for naming accuracyand SSE. A strong interaction between frequency and con-sistency/regularity was found for both accuracy and SSE,such that the effect of print-to-sound regularity and con-sistency were both larger for low than for high-frequencywords. Performance was perfect for high-frequency regularwords, whereas high-frequency irregulars elicited 1%

Table 3Results of the ANOVA of SSE and accuracy of connectionist model in Study 1.

F1 F2

df f p df

SSEFrequency 1.39 928.39 <.01 1.114Type 2.78 253.61 <.01 2.114Freq � type 2.78 158.44 <.01 2.114High freq: type 2.78 179.72 <.01 2.115Low freq: type 2.78 210.06 <.01 2.115

AccuracyFrequency 1.39 238.00 <.01 1.114Type 2.78 58.27 <.01 2.114Freq � type 2.78 58.27 <.01 2.114High freq: type 2.78 3.16 .048 2.115Low freq: type 2.78 59.26 <.01 2.115

errors. For low-frequency items, a strong effect of the con-sistency/regularity factor was observed in naming accu-racy and SSE.

Large effects of stimulus type were observed in onewayANOVAs for each level of frequency (Table 3). To furtherexplore the effects of regularity and consistency for low-frequency items independently, Tukey tests (one-tailed)were conducted for both SSE and accuracy. The effect ofconsistency (R-I vs. R-C) was reliable for SSE t1 (39) =12.26, p < .01; t2 (38) = 2.29, p < .05, but reliable only byparticipants for accuracy, t1 (39) = 6.83, p < .01; t2 (38) =1.68, p = .11. A similar pattern was observed for regularityeffects (R-I vs. I-I) among inconsistent words, with regular-ity effects reliable for SSE, t1 (39) = 9.29, p < .01; t2 (38) =4.57, p < .05, and reliable by participants only for accuracy,t1 (39) = 4.65, p < .01; t2 (38) = 1.65, p = .12.

Behavioral experiment

MethodsTo test predictions of the model, a character naming

experiment was conducted with 39 undergraduates (19male, 20 female) from Beijing Normal University. All par-ticipants were native speakers of Mandarin Chinese withnormal or corrected-to-normal vision, aged between 17and 25. They provided written informed consent and werepaid for their participation. Participants sat a comfortabledistance from the screen (about 60 cm) and were in-structed to read aloud single characters into a microphoneas quickly and accurately as possible. On each trial, a fixa-tion cross appeared for 500 ms, after which the screen wascleared for 120 ms and a single character was presented forup to 2000 ms (or until a response was made). Stimuliwere presented centrally, in white against a black back-ground using 28pt Songti font. Stimulus presentation andresponse latency collection was controlled using DMASTRsoftware (Forster & Forster, 2003). Stimuli were presentedin a different, randomized order for each participant.

ResultsThree participants’ data were removed from the analy-

sis because of error rates above 25%. Overall naming accu-racy for the remaining 36 participants was 92.8%. Responselatency and accuracy for each condition are summarized in

minF

f p df f p

63.13 <.01 1.128 59.11 <.0113.04 <.01 2.126 12.40 <.01

7.35 <.01 2.124 7.024 <.010.28 .757 2.115 0.27 .757

12.96 <.01 2.129 12.20 <.01

27.88 <.01 1.137 24.95 <.015.55 <.01 2.135 5.067 <.015.54 <.01 2.135 5.059 <.010.01 .992 2.116 0.00 .9918.98 <.01 2.148 7.798 <.01


Fig. 3, revealing effects that confirm each of the model’spredictions.

A 2 (high vs. low frequency) � 3 (R-C vs. R-I vs. I-I) AN-OVA was conducted both for naming latency and accuracy(See Table 4). The main effect of frequency was observedboth for naming accuracy and response latency. Thehigh-frequency items were named faster and more accu-rately than low frequent items. Consistency/regularityinfluenced participants both on accuracy and response la-tency. Most critically, the predicted interaction betweenfrequency and the consistency/regularity factor was ob-served for both accuracy and response latency.

For low frequency items, the effect of consistency (R-Ivs. R-C) was reliable for both response latency, t1

(35) = 6.55, p < .01; t2 (38) = 2.75, p < .01, and for accuracy,t1 (35) = 4.08, p < .01; t2 (38) = 2.37, p < .05. A regularity ef-fect (R-I vs. I-I) was also found for these items on responselatency, t1 (35) = 6.54, p < .01; t2 (38) = 3.56, p < .01, andaccuracy, t1 (1, 35) = 5.39, p < .01; t2 (38) = 3.05, p < .01.

Discussion

A computational model of reading aloud in Chinesebased on connectionist principles correctly simulated ef-fects of regularity, consistency, frequency and their inter-action, as observed in a behavioral study of readingaloud. In both the human and simulation data, regularityand consistency effects were each larger for low- than forhigh-frequency words. A regularity effect was found forinconsistent items, and was correctly simulated, demon-strating that regularity effects do not depend on categori-cal rules, but can emerge from the statistics of thewriting system.

The interaction between frequency and consistency ob-served in Study 1 was quite large, and no consistency effectwas found for high-frequency items. While this is broadlyconsistent with many studies of reading in English (Balota& Ferraro, 1993; Taraban & McClelland, 1987; Treiman,Mullennix, Bijeljac-Babic, & Richmond-Welty, 1995), thecurrent results are in apparent conflict with some findingsin the literature, particularly with respect to the strength ofthe frequency by consistency interaction and the lack of aconsistency effect for high-frequency items. In a series ofexperiments focusing on the neighborhood statistics thatgive rise to consistency effects, Jared (1997) found a rela-

Fig. 3. Predictions from the model for all three item types: Regular-consistent (Rof frequency (high and low) are shown in the graph on the left. On the right areThe model correctly predicts that both consistency and regularity interact with

tively weak frequency by consistency interaction whenneighborhood properties were tightly controlled betweenhigh- and low-frequency items. Although there was a trendtoward an frequency by consistency interaction for bothresponse latency (non-significant) and accuracy (signifi-cant by subjects, but not by items), this failure to replicatethe ‘‘standard” effect under tight controls raised questionsabout the real basis of this interaction, which may be rele-vant to the current study.

The focus of the Jared (1997) study was on the consis-tency effect for high-frequency items. In a series of exper-iments, neighborhood properties (described in terms of‘‘friends” and ‘‘enemies” as above) were shown to havestrong and reliable effects on high-frequency items. Moreimmediately relevant to the current study is an experimentby Lee et al. (2005, Experiment 3) in which a reliable con-sistency effect was observed for high-frequency words.

Thus, the current findings leave some open questionsregarding the strong frequency by consistency interactionand the null consistency effect for high-frequency items.One possibility is that these effects arise from the typesof confounds identified by Jared (1997). A later study(Jared, 2002) examined complex interactions among fre-quency, regularity and neighborhood properties and foundresults consistent with a significant frequency by consis-tency interaction with similar controls, suggesting againstsuch an explanation. Another possibility is that the fre-quency manipulation in Study 1 was larger than those em-ployed in studies reporting consistency effects for high-frequency items. Consistency effects have been found mostreliably for items with frequencies closer to 100 per mil-lion (Jared, 1997, 2002; Lee et al., 2005), yet in the currentstudy, high-frequency items appeared on average about500 times per million words in the relevant corpora.

To extend and confirm the results of Study 1, it is criti-cal to establish whether the model correctly predicts theinteraction (or lack thereof) between frequency and con-sistency when stricter controls on neighborhood structureand a weaker manipulation of frequency are employed.

Study 2

It is an open empirical question whether the interac-tion between frequency and consistency in Chinese is anartifact of the same stimulus properties that appear to

-C), Regular-inconsistent (R-I) and Irregular-inconsistent (I-I) at two levelsresponse latency data from Chinese adult readers naming the same items.frequency, and a regularity effect for inconsistent items.

Table 4Results of the ANOVA of RTs and accuracy of behavioral experiment in Study 1.

F1 F2 minF

df f p df f p df f p

RTsFrequency 1.35 174.15 <.01 1.114 116.26 <.01 1.129 69.71 <.01Type 2.70 52.45 <.01 2.114 16.29 <.01 2.169 12.42 <.01Freq � type 2.70 59.82 <.01 2.114 12.55 <.01 2.156 10.37 <.01High freq: type 2.70 2.53 .087 2.115 0.13 .876 2.127 0.12 .884Low freq: type 2.70 74.74 <.01 2.115 14.27 <.01 2.154 11.90 <.01

AccuracyFrequency 1.35 67.01 <.01 1.114 30.39 <.01 1.144 20.90 <.01Type 2.70 30.74 <.01 2.114 15.96 <.01 2.183 10.50 <.01Freq � type 2.70 23.78 <.01 2.114 8.39 <.01 2.173 6.201 <.01High freq: type 2.70 2.18 .121 2.115 0.50 .609 2.160 0.40 .667Low freq: type 2.70 40.63 <.01 2.115 18.90 <.01 2.182 12.80 <.01


enhance this interaction in English. As a first step inexploring whether there are potential confoundsbetween frequency and consistency in the lexicon atlarge, we conducted a corpus analysis. Each item in thetraining corpus (4468 items) from Modern Chinese Fre-quency Dictionary (1986) was tagged with respect tothe following dimensions: whether it is a phonogram,which phonetic component it contains, the pronunciationof the phonetic components, and the phonological rela-tionship between phonetic component and character (interms of regularity and consistency). Phonograms wereidentified based on the XianDaiHanZiXingShengZiZiHui(Ni, 1982). The number of characters and phonogramswere counted as well as three types of phonograms: reg-ular-consistent (R-C), regular-inconsistent (R-I) and irreg-ular-inconsistent (I-I). As in Experiment 1, consistencylevel was calculated as the ratio of the summed fre-quency of friends to the summed frequency of the char-acters in a family, with family defined as all characterssharing the same phonetic component.

As summarized in Table 5, this corpus analysis revealedthat consistency level is related to frequency, such thathigher frequency items in general tend to be more consis-tent than low-frequency items. Taken together with thefact that there are many fewer high-frequency items thanlow-frequency items, and that a smaller proportion ofthese are phonograms than is the case for the low and mid-dle frequency bands, it does seem plausible to suggest thatthe interaction between consistency and frequency is duein part to a confound with some additional statistical prop-erties of the input corpus.

The comparisons of the consistency effect for high andlow-frequency items may be confounded with differencesin consistency level. Although Study 1 controlled for thispossibility, as shown in Table 2, it did not control for otherproperties, such as family size or family frequency, nor forfrequency of friends/enemies across levels of frequency forthe consistency manipulation. Study 2 directly addressesthese shortcomings by contrasting middle and low fre-quency items, thereby avoiding several of the confoundspresent in the high frequency stimuli, and enabling anexamination of the interaction between consistency andfrequency in more detail.

Connectionist model

MethodA set of 120 regular characters were selected from the

training corpus, with 30 characters in each cell createdby crossing frequency (middle/low) with consistency (con-sistent/inconsistent). Details of the stimulus properties areshown in Table 6. The simpler design permitted better con-trol over family size across levels of consistency, in addi-tion to a number of other stimulus properties such asfamily frequency, phonetic component frequency, andsummed frequency of friends – which were matchedacross levels of frequency. Finally, frequency levels wereselected to be similar to Lee’s (2005) and Jared’s (1997)experiments 1&2 which showed consistency effect forhigh/medial frequency items and the weak or null interac-tions between frequency and consistency. Frequencycounts were confirmed by drawing estimates from twocorpora: Modern Chinese Frequency Dictionary (1986)and Balanced Corpus of Modern Chinese (Sun, 2006).

All 120 characters were tested on 40 models fromSimulation 1. The testing method was the same as in theprevious simulation. Both the SSE and naming accuracywere computed at 3 million training trials.

ResultBecause of the lower overall frequency, the average SSE

was nominally higher than Simulation 1 and overall accuracywas lower (91% vs. 95% in Simulation 1. The 2 (middle vs. lowfrequency)� 2 (consistent vs. inconsistent) ANOVA was con-ducted both for SSE and naming accuracy (Table 7). The over-all correlation between SSE and response latency for all itemsin the behavioral experiment was 0.320 (p < .01).

A main effect of frequency was found both for SSE andnaming accuracy. Middle frequency characters had lowerSSE and fewer errors than low-frequency characters. Consis-tent characters were read more easily than inconsistent char-acters. Finally, a significant interaction between frequencyand consistency was found for both SSE and accuracy.

For both accuracy and SSE, the simple consistency effectfor middle frequency characters was significant in SSE byparticipants’ analysis (treating each run of the model as aparticipant), but not in the item analysis, whereas the con-sistency effect was strongly significant for low–frequency

Table 5The interaction between frequency (times/million) and consistency in training corpus.

Frequency All

Low(610) Middle(10–100) High(P100)

Characters 1750 1537 1182 4468Phonogram 1525 (87%) 1239 (81%) 651 (55%) 3415(76%)Number of phonogramR-C 392 (26%) 258 (21%) 100 (15%) 750 (22%)R-I 685 (45%) 553 (45%) 314 (48%) 1552 (46%)I-I 422 (28%) 414 (33%) 229 (35%) 1065 (32%)

Consistency levelR-C 1.00 1.00 1.00 1.00R-I 0.48 0.48 0.60 0.50I-I 0.15 0.22 0.43 0.24

Table 6Materials for Study 2.

Middle frequency Low frequency

Consistent Inconsistent Consistent Inconsistent

Frequency 1 45.06 38.72 3.48 3.13Frequency 2 42.64 45.98 5.21 3.75Consistent level 1.00 0.35 1.00 0.32Number of Friends 6.03 3.17 5.97 3.4Number of Enemies 0 3.03 0 2.93Friends frequency 1646 593 1420 427Enemies frequency 0 1125 0 1098Number of radicals 3.27 3.03 3.33 3.2Number of strokes 10.53 10.23 11.23 10.83Phonetic frequency 692 627 728 657

Note: frequency 1 = frequency (per million) from Modern Chinese Frequency Dictionary (1986); frequency 2 = frequency (per million) from Balanced Corpusof Modern Chinese (Sun, 2006); the value of frequency was times per million.

Table 7Results of the ANOVA of SSE and accuracy of connectionist model in Study 2.

F1 F2 minF


SSEFrequency 1.39 1313.56 <.01 1.116 36.38 <.01 1.122 35.3 <.01Consistency 1.39 120.37 <.01 1.116 6.62 .011 1.128 6.36 .013Freq � con 1.39 89.48 <.01 1.116 4.27 .041 1.126 4.07 .046Mid-freq: con 1.39 48.34 <.01 1.117 0.10 .754 1.117 0.09 .753Low-freq: con 1.39 107.48 <.01 1.117 8.27 <.01 1.133 7.67 <.01

AccuracyFrequency 1.39 703.44 <.01 1.116 40.63 <.01 1.129 38.4 <.01Consistency 1.39 66.78 <.01 1.116 4.19 .043 1.129 3.94 .049Freq � con 1.39 41.53 <.01 1.116 3.16 .078 1.132 2.93 .089Mid-freq: Con 1.39 5.27 .027 1.117 0.03 .869 1.118 0.02 .863Low-freq: Con 1.39 55.97 <.01 1.117 5.44 .021 1.137 4.95 .028


items in both analyses. This replicates Lee’s (2005) Exper-iment 3 results. The simple frequency effects for consistentand inconsistent characters were significant both for SSEand naming accuracy (Ps < 0.01). The frequency effectwas larger for inconsistent characters (SSE = 0.59) thanfor consistent characters (SSE = 0.29).

Behavioral experiment

MethodForty participants (none had participated in Experiment

1) were recruited from Beijing Normal University to name

the new set of 120 characters used in Simulation 2. Theprocedure was the same as in Experiment 1.

ResultAll participants’ data were included and the overall

naming accuracy was 92.19%. The reaction time for errorresponses was replaced with the participant’s conditionmean for correct trials. The average reaction time is pre-sented in Fig. 4. The ANOVA analysis for naming latencyand accuracy is shown in Table 8.

The ANOVA results revealed that participants namedmiddle–frequency characters significantly faster and


accurately than low-frequency characters. Naming laten-cies were significantly longer for inconsistent charactersthan for consistent characters, and participants were sig-nificantly more accurate in naming consistent charactersthan inconsistent characters. As in Experiment 1, we founda significant interaction between frequency and consis-tency, both in the latency and accuracy data. The simplefrequency effects both for consistent and inconsistentcharacters were significant (Ps < 0.01); the frequency effectwas larger for inconsistent (124 ms) than consistent char-acters (66 ms). The consistency effect was 27 ms for mid-dle frequency characters and 84 ms for low-frequencycharacters.

As predicted by Simulation 2, both for reaction time andnaming accuracy, medium-frequency characters produceda significant consistency effect in the analysis by partici-pants, but not by items. The consistency effect in low-fre-quency characters was reliable both for participant anditem analyses.

Analysis of training corpus and hidden units in the model

In the following analysis, we examine the training cor-pus and the internal workings of the model to shed lighton how these phenomena might emerge from the applica-tion of statistical learning mechanisms to the problem ofmapping from print to sound in different writing systems.

Corpus analysisTo examine the statistical properties that might give

rise to functional units in reading Chinese characters,descriptive statistics for phonetic components and radicalswere calculated in the training corpus: Pronunciations forall characters containing each radical and phonetic compo-nent were identified and counted in order to determine thereliability of these two sub-lexical structures in print-to-sound mappings.

For the 4468 characters in the training corpus, 298 con-stituent radicals and 1021 phonetic components wereidentified. Some radicals are also phonetic components,which creates some ambiguity with respect to what thefunctional units of the print-to-sound mapping might be.For example, as shown in Fig. 1B, the radicals ‘‘ ” and‘‘ ” can function as phonetic components on their own,they also can be combined to form the phonetic compo-

Fig. 4. Predictions from the model for interaction between consistency (consisgraph on the left. On the right are response latency data from Chinese adult readfor low frequency and the weak consistency effect was found in high frequency

nent ‘‘ ”. Most phonetic components are composed ofmore than two radicals, on average 2.4 radicals, with arange of one to six (Fig. 5A). Fig. 5B shows a histogram ofthe number of characters and syllables (including and notincluding tone) that can be formed by radicals and pho-netic components. Radicals appear, on average, in about39 characters with 27 different pronunciations each. Incontrast, more than 70% of phonetic components aremapped to no more than four characters (on average aboutthree), with an average of two distinct pronunciations.Thus, phonetic components, if they can be identified, aremuch stronger cues to pronunciation than radicals.

Emergent functional units in Chinese print-to-soundIn our model, no explicit coding was included in the

representation to identify phonetic components, rather,the influence of these units emerged in the course of learn-ing the mapping from orthographic to phonological repre-sentations. Our model did, however, replicate the empiricalfinding of a regularity/consistency effect, which is definedin terms of phonetic components. In order to explore theemergence of functional units for print-to-sound transla-tion in the model, we analyzed the similarity space in hid-den layer activations over the course of learning for asubset of items from the training set.

These analyses were carried out on a small subset of thetraining items, selected in order to probe whether the simi-larity space defined by hidden unit activations showed evi-dence of being shaped by phonetic components. A set ofitems that shared a phonetic component was selected, alongwith controls for orthographic and phonological similarity.In all, forty-four items were selected, of which 7 shared aphonetic component. In addition to the simple character,‘‘ ” ( , three regular ( ) and threeirregular ( ) items from the samefamily were included. As a comparison group, a set of 27orthographically similar items was selected to have thesame degree of orthographic similarity to the critical itemsas they would if they shared a phonetic component. Thiswas done by finding items that shared two radicals thatdid not comprise a phonetic. For example, shares the rad-icals and with the target item (comprising , and

). In addition, we selected phonologically similar charac-ters, which were 10 high frequency homophones of twoirregular characters ( ), included in order

tent and inconsistent) and frequency (middle and low) are shown in theers naming on the same characters. A strong consistency effect was foundfor both participants and models.

Table 8Results of the ANOVA of RTs and accuracy of behavioral experiment in Study 2.

F1 F2 minF


RTsFrequency 1.39 139.61 <.01 1.116 35.96 <.01 1.153 28.5 <.01Consistency 1.39 78.55 <.01 1.116 15.37 <.01 1.149 12.8 <.01Freq � con 1.39 33.94 <.01 1.116 4.74 .031 1.142 4.15 .043Mid-freq: con 1.39 14.29 <.01 1.117 1.17 .282 1.134 1.08 .300Low-freq: con 1.39 90.33 <.01 1.117 14.32 <.01 1.146 12.3 <.01

AccuracyFrequency 1.39 76.91 <.01 1.116 6.47 .012 1.134 5.96 .016Consistency 1.39 237.89 <.01 1.116 9.93 <.01 1.125 9.53 .003Freq � con 1.39 87.19 <.01 1.116 6.47 .012 1.132 6.02 .016Mid-freq: con 1.39 5.21 .028 1.117 0.18 .675 1.125 0.17 .678Low-freq: con 1.39 216.68 <.01 1.117 15.49 <.01 1.132 14.4 <.01


to address the role of phonology by itself in organizing thehidden layer.

Each character was clamped on the input layer for 12time ticks and the activation values of 200 hidden unitswere computed at the last time tick. Multidimensionalscaling was computed to characterize the similarity spaceboth on the orthographic and hidden layers. Tests for sim-ilarity of groups of items (shared phonetic component,orthographic controls, and phonological controls) wereconducted on the Euclidean distances within and betweengroups in this similarity space.

Results from the Euclidean distance measures based onthe similarity space of characters derived from hidden unitactivations are plotted over the course of training inFig. 6A. At the beginning of training, overall activity levelson the hidden units were quite small (because of the small,random weights to which the model was initialized),

Fig. 5. Panel A is a histogram showing how many phonetic components are comtwo. Panels B, C, and D are histograms showing the number of characters (B) syllacomponents occur. Phonetic components have a much more tightly constrained

resulting in relatively small distances driven entirely bythe input. As the model learned to map from print tosound, however, differences emerged, such that by theend of training, items that share a phonetic componentwere on average more similar to one another than theywere to either orthographic or phonological controls. Thiswas supported by a 2 (training period: initial weights, after3 million trials) by 4 (stimulus comparison: within pho-netic component’s family, orthographic control, phonolog-ical control, between phonetic component’s family)ANOVA, which demonstrated a main effect of training, F(1, 313) = 553.68, MSE = 50.58, p < .01; a main effect ofstimulus, F (3, 313) = 22.17, MSE = 3.95, p < .01, and a sig-nificant training by stimulus interaction, F (3, 313) =10.78, MSE = 0.99, p < .01. Planned comparisons at 3 mil-lion training trials revealed that within-phonetic compo-nent’s family comparisons had smaller distances (1.41)

posed of different numbers of radicals – most are composed of more thanbles (C) and syllables not counting tone (D) in which radicals and phoneticdistribution than radicals in all cases.

Fig. 6. Analyses of the internal representations that develop as the model learns to read. Panel A depicts change in the mean Euclidean distance forcomparisons among item types over training. Between P. Family = comparisons between all items that share the critical phonetic component ( ) andcontrol phonograms; Within P. Family = comparisons among all items that share the critical phonetic component; Orth. Control = comparisons between allitems with the critical phonetic component and control items selected to share the same amount of orthographic information; Phon. Control = comparisonsbetween all items with the critical phonetic component and their homophones that do not overlap orthographically. Panel B depicts the similarity spacebased on orthographic inputs for the test items. Grey patches indicate clusters of items that share orthographic structure; black circles indicate items thatshare a phonetic component. In Panel C, the similarity space based on hidden unit activations before training is shown. Grey patches and black circles as inPanel B. Panel D shows the similarity space based on hidden unit activations after 3 million trials of training on spelling-to-sound translation. The graypatch indicates items that share the same phonetic component, and cluster together only after training on spelling-to-sound correspondences. Black circlesencompass items that share the same pronunciation in addition to sharing a phonetic component.


than the other three conditions: Orthographic controls(1.90, t (46) = 3.1, p < .01), phonological controls (2.03, t(29) = 2.49, p < .01) or between-family comparisons (2.13,t (278) = 6.58, p < .01).

For comparison to the MDS solutions shown for the hid-den units, Panel B of Fig. 6 shows a solution for the similar-ity space represented on the orthographic input. Note that,although the members of the same family form a loosecluster, for many items, there are non-family membersthat are much closer in similarity space than other familymembers. In Panel C, it is clear that in the initially random-ized state, the hidden unit activations essentially recapitu-late the input representations. Indeed, correlationsbetween pairwise distances on the input and hidden unitsbefore training are nearly perfect (r = 0.99, p < .001). After 3million trials of training (Panel D) items with the samephonetic component ( ) form a relatively tightcluster. Within this cluster, there are also sub-clusters ofitems that share a pronunciation, so that, for example,the items with the same pronunciation ( )and the items with different pronunciations( ) occupy distinct regions ofthe similarity space. The correlation between input and

hidden unit representations (r = 0.48, p < .001) is smallerthan what was observed initially (t (943) = 54.35, p < .001).

The relatively high degree of similarity among theseitems is not simply a reflection of their output similarity.A number of orthographically related homophones( ) are quitedistant from this cluster in the similarity space. The dis-tance between homophones (mean distance = 2.03,STD = 0.52, N = 10) and irregular items ( ) wassignificantly larger (t (20) = 3.11, p < .01) than the distanceto other phonetic component’s family members (mean dis-tance = 1.37, STD = 0.48, N = 12).

Fig. 7 shows hidden unit activations for a set of itemsfrom the similarity space analyses. The overlap amongitems that share the phonetic component ( ) and are pro-nounced is strikingly compact. Two hidden unitsare near their maximum activation for all three of theseitems. One of these same hidden units is active for all itemsthat contain the phonetic component ( ), independent ofhow they are pronounced. In contrast, among items thatare equivalently similar in terms of their orthographic rep-resentations, but do not share a phonetic component, thereis no such overlap in the hidden layer representation. Thus,


we can see that functional sub-lexical units are repre-sented in a relatively compact manner in the model.

It is interesting to contrast the patterns of hidden unitrepresentation to what has been observed in similar anal-yses of English reading models (Harm, McCandliss, &Seidenberg, 2003; Harm & Seidenberg, 1999). In thosemodels, individual words evoke much more diffuse andwidespread activity over the hidden layer, likely reflectingboth the denser representations permitted by the muchsmaller range of possible inputs, and the contribution ofsmaller sub-lexical units (e.g., single letters mapping withsome consistency onto single phonemes). In Chinese, therelatively large grain size and high degree of arbitrarinessrequire a much sparser representation. This may also ex-plain why a larger number of hidden units are needed forChinese than for English in these models.

Items that share the same phonetic component are trea-ted as more similar than items that share the same amountof orthographic information, even when these map on tohighly distinct phonological outputs. For example, thecharacters and have essentially no overlap in theirpronunciations ( ), and yet they share similarrepresentations (in this case, largely defined by a singlehidden unit). This example clearly illustrates that the rep-resentations arrived at in the acquisition of print-to-soundmappings are organized by both the information-bearingstructures in the input and the similarity of their mappingto the output, reflecting the model’s extraction of sub-lex-ical regularities in the translation from print to sound. Themodel thus demonstrates how compact and superposition-al representations of shared orthographic structure may belearned and play a role in reading aloud, even when thegrain size of mappings from print to sound is fairly coarse,and the mappings themselves are probabilistic with a highdegree of arbitrariness.

Discussion

This study explored the interactions between consis-tency and frequency under tight stimulus control andmanipulation of ‘‘medium” vs. ‘‘low” frequency, in orderto determine the robustness of this finding. As in previous

Fig. 7. Hidden unit activations for seven of the characters from the similari( ) have overlapping representations, whereas control items, matched

studies with similar manipulations, the interaction be-tween frequency and consistency was much weaker thanin Study 1 (but nonetheless reliable), and a consistency ef-fect was found for the medium-frequency items (althoughthis was only significant by subjects). Thus, the interactionbetween frequency and consistency in Study 1 was not theresult of stimulus factors that could not be controlled dueto the complexity of the design. The predictions of themodel were confirmed by behavioral experiments on thesame testing items; further analyses revealed that thefunctional units on which the frequency by consistencyinteraction depends are emergent properties of the appli-cation of the statistical learning rules of the model to thecorpus.

Analyses of the hidden units revealed that these effectswere driven by the organization of representations that en-code probabilistic information about the functional unitsthat support mappings from orthography to phonology.Further, they demonstrate how these representationsmight emerge over development as a result of statisticallearning, and without assumptions about the level atwhich regularities exist in the writing system. Whereasat first, the similarity space defined by hidden unit activa-tions is organized by orthographic similarity, relativelyearly in training, words that share a phonetic componentbegin to be represented as more similar to one anotherthan orthographic or phonological controls.

There is some empirical evidence to support the mod-el’s account of learning to read in Chinese as well: Shenand Bear (2000) collected invented spellings from school-children and classified them according to whether theycontained orthographic, phonological or semantic errors.They found that the earliest invented spellings involvedmostly orthographic confusions, whereas by fourth grade,the majority of invented spellings involved insertion of aphonetic radical that was consistent with the intendedcharacter’s pronunciation.

The emergence of regularity and consistency effects inchildren is also broadly consistent with this analysis. Earlyreaders (second graders) show a strong regularity effect,tending to misread phonograms according to the pronunci-ation of their phonetic component when it appears by itself

ty analyses. Items from the same phonetic family as the sample itemfor the degree of orthographic similarity, do not.


(Shu et al., 2000). By sixth grade, however, subtler effectsof phonetic family (i.e., consistency effects) are also found(Chen, Shu, Wu, & Anderson, 2003; Yang & Peng, 1997).Analyses of children’s reading materials (Shu et al., 2003)suggest that this transition is driven in part by changesin the statistics of children’s reading materials. Phoneticcomponents are learned as individual words in the earlygrades, whereas in later grades, families of words thatshare a phonetic component are learned as the writtenvocabulary expands to include a larger number of phono-grams. A more detailed simulation of this developmentalpattern could be achieved with incremental training (Pow-ell, Plaut, & Funnell, 2006).

General discussion

A computational model of reading aloud in Chinesebased on connectionist principles correctly simulated ef-fects of regularity, consistency, frequency and their inter-action, as observed in a behavioral study of readingaloud. In both the human and simulation data, regularityand consistency effects were each larger for low- than forhigh-frequency words. A regularity effect was found forinconsistent items, a finding that would seem on its faceinconsistent with this modeling framework. The simula-tion correctly captured this effect, however, demonstratingthat it can be explained without rule-based processing orthe inclusion of phonetic components as a special ‘‘level”of the representation. A more in-depth exploration of thefrequency by consistency interaction demonstrated thatit was robust to strict controls of neighborhood character-istics, and could be found even under a relatively weakmanipulation of frequency. The model correctly simulatedthese findings as well. Finally, analyses of the training setand internal representations in the model revealed thatits sensitivity to consistency and regularity results fromthe emergence of phonetic components as functional unitsover the course of training.

Simulating language-general and language-specific effects inreading Chinese

The current simulations applied the same basic archi-tecture and learning rules used to simulate a variety ofphenomena in English reading (Harm & Seidenberg, 1999,2004; Seidenberg & Zevin, 2006), to capture both effectsthat are common to both English and Chinese – i.e., fre-quency, consistency and their interaction – and effects thatare specific to a particular writing system – i.e., regularityas defined with reference to phonetic components in Chi-nese. In both cases, the explanation rests on the operationof domain-general statistical learning mechanisms.

Frequency and consistency effects are explained bycomplementary features of the learning algorithm appliedin the current simulation. On the one hand, learning in themodels is local, so that practice with a particular item hasthe greatest impact on performance for that item itself. Onthe other hand, because representation in the model issuperpositional – i.e. many different items are representedas patterns of activation over the same units – the model

has an inherent tendency to map similar inputs onto sim-ilar outputs. Thus, whereas frequency effects reflect previ-ous experience with a particular word, consistency effectsreflect experience with all similar words. The effects ofthese properties interact because sufficient practice witha particular word can override ambiguity arising from sim-ilarly spelled words. These same principles hold, even asthe definition of consistency itself (Jared, 2002; Treimanet al., 2006) is further refined, because the critical assump-tion is the role of statistics at multiple grain sizes, not theparticular unit (letter, grapheme, word body or wholeword) over which the statistics are relevant.

Studies of the interaction between consistency and fre-quency – including those presented here – typically takeadvantage of the power of factorial designs. Nonetheless,it is clear that both consistency and frequency are essen-tially continuous variables. Thus it is fair to suggest thatthe strength of the interaction between them will dependon the details of how each is manipulated. Indeed, someof the contradictory findings across studies reviewed byJared (1997, 2002) are apparently the result of quantitativedifferences in the neighborhood structure of inconsistentwords. Words like TINT, that have many similarly pro-nounced, relatively high-frequency ‘‘friends” (HINT, MINT,LINT, etc.) and few, relatively low-frequency ‘‘enemies”(PINT) show relatively weak or null consistency effects,even when they are relatively low frequency.

Less attention has been paid to the role of the strengthof the frequency manipulation in modulating this interac-tion. The studies reviewed and presented here suggest thatthe interaction between frequency and consistency isstrongest for relatively large frequency manipulations. Thisis correctly predicted by the model because, as a statisticallearning model, it naturally takes into account the contin-uous nature of both frequency and consistency. Thus themodel’s prediction of a frequency by consistency interac-tion for a particular set of items is not the same as a blan-ket prediction from a verbal model that these variablesshould interact under all conditions. That the model fitsboth strong and weak interactions of frequency and consis-tency revealed across human studies (and could conceiv-ably fit null effects with the appropriate stimulusmanipulations) suggests that humans are sensitive to thesame statistical properties that drive its performance.

Regularity effects based on phonetic component pro-nunciation are unique to the Chinese writing system, andyet they can be explained as resulting from the same prop-erties of statistical learning. Most phonetic componentscan appear on their own as individual characters (A fewbound phonetics are archaic characters whose pronuncia-tion are lost in modern Chinese – see Shu et al., 2003 –however all phonetic components of the characters usedin the current study were unbound). This creates a veryeffective learning situation for mapping from a phoneticcomponent to its canonical pronunciation. In terms of thelearning algorithm used here, all of the error on learningtrials when a phonetic component is presented in isolationaccrues to strengthening the connections between thatcomponent and its pronunciation. In this way, the canoni-cal pronunciation of each phonetic component attainssomething of a ‘‘special” status, such that characters whose


pronunciations deviate from it are more difficult to read.Critically, the special status of phonetic components inthese models is driven by statistics of the input rather thanby an a priori representational scheme imposed by themodelers.

The emergence of functional units and ‘‘sub-lexical”processing in print-to-sound conversion for Chinese

Previous models of Chinese reading have included sep-arate representational ‘‘layers” for radicals and characters(Perfetti et al., 2005) or designed input representationsexplicitly around the radical as an unanalyzed, holistic unit(Hsiao & Shillcock, 2005, 2006) and in one case (Xing et al.,2002, 2004) included phonetic components along withtheir pronunciations as part of the input to the model in or-der to capture regularity and consistency effects (but seeChen & Peng, 1994). In contrast, the current model uses adistributed representation of Chinese characters that es-chews any a priori assumptions about the functional unitsthat underlie spelling-to-sound translation.

In some sense, the current coding scheme for the inputlayer contains some language-specific features – a moreelaborate model that learned to identify contrastive fea-tures from an input that incorporated features of the visualsystem would be truly language-general, in the sense thatit could take either English or Chinese as its input. Never-theless, the current model is language-general in the sensethat its orthographic representation is based on the small-est contrastive unit in the writing system (strokes), and or-ganized to capture the visual similarity of characters,rather than fixing a specific level of representation suchas the radical or phonetic component as a functional unit.This is illustrated in Fig. 6B, which shows that the similar-ity space on the orthographic input layer is loosely orga-nized by orthographic units at multiple levels ofrepresentation. For example, the cluster of characters inthe bottom left hand corner mostly share the (‘‘water”)radical, but some also contain a (‘‘heart”) radical (whichalso has three strokes and appears on the left). In responseto the demands of learning to map from print-to-sound,the model’s representations are increasingly organized bybehaviorally relevant structures, i.e., phonetic components(Fig. 6C and 6D). Thus, it is not necessary to assume a fixedlevel of orthographic representation in order to simulateprint-to-sound conversion in Chinese.

Previous studies have used priming paradigms to ad-dress the issue of whether radicals have special status inthe organization of the adult reading system (Ding et al.,2004; Taft & Zhu, 1997; Taft, Zhu, & Ding, 2000; Taftet al., 1999). The results of these studies have shown thatradicals influence lexical access, and may be a basic unitof Chinese orthographic representation (Ding et al.,2004). The analyses presented in Study 2, however, suggestthat radicals are an unlikely candidate for a functional unitin mapping from spelling to sound. It may be that they aremore consistently related to semantics, or that they areimportant in some early stage of character identification.

Although it is beyond the scope of the current work tosimulate the priming effects that are taken as evidencefor radicals as functional units, the model provides an

alternative perspective on how functional units emergeover the development of reading, instead of assuming afixed functional unit for all reading processing. A topicfor future research is the emergence of similar structurein mappings from orthography to semantics. Just as thephonetic component of a phonogram provides cues to itspronunciation, the semantic component provides cues tomeaning. For example, the radical for water ( ) appearsin many phonograms that are related to water ( , lake,

, river, , thirsty, , swim), but it can also appear inphonograms that are not ( , law, , negotiate). This radicalmay thus be viewed as encoding probabilistic informationabout semantics at the sub-lexical level.

The role of phonology in Chinese reading

A number of studies have focused on the role of phonol-ogy in computing meaning from sound (e.g., Perfetti & Tan,1998; Perfetti & Zhang, 1995). This work led to the LexicalConstituency Model (LCM, Perfetti et al., 2005), which simu-lates the time-course of effects of graphical, phonologicaland semantic primes. The central theoretical point of theLCM is that phonology is rapidly and obligatorily computedduring reading (even reading for meaning). While there aresome data inconsistent with this claim (Zhou & Marslen-Wilson, 2000), there are a large number of phenomena thatappear difficult to explain in any other way (Chen, Floresd’Arcais, & Cheung, 1995; Chen & Shu, 2001; Xu, Pollatsek,& Potter, 1999; Zhou & Marslen-Wilson, 1999b).

Because we present a model of the print-to-sound, wecannot directly address the role of phonology in the com-putation of semantics. A model of English reading with asimilar architecture for print-to-sound that also includedsemantics (Harm & Seidenberg, 2004), was able to cor-rectly simulate a wide range of phonological effects inthe computing semantics from print. Given the gross dif-ferences in the ease of print-to-sound mapping, and theprobabilistic cues to meaning that distinguish Chinesefrom alphabetic writing systems, we expect the divisionof labor between direct and phonologically-mediated map-pings from print to semantics to differ between the twolanguages. The current model, however, demonstrates thatprint-to-sound mappings can be learned and processedefficiently independently of semantics, and thus couldplausibly contribute to semantic activation in a model thatincluded semantics.

In contrast to the LCM, however, we have specified asub-lexical mechanism for print-to-sound conversion. Inthe LCM, it is assumed that each character has a stored pro-nunciation, and these are simulated in practice by hand-coding the pronunciations into the model. The currentmodel learns print-to-sound mappings over distributedrepresentations, and thus is capable of discovering regular-ities among sub-lexical units of varying size and consis-tency. The behavioral results presented here (and Hsu,Tsai, Lee, & Tzeng, 2009; Fang et al., 1986; Hue, 1992;Lee et al., 2005; Peng & Yang, 1997) demonstrate that thesesub-lexical regularities play an important role in a task thatdirectly taps print-to-sound conversion, and their simula-tion in the model demonstrates that they can be explainedin terms of sub-lexical processing.


Conclusion

We have adapted a statistical learning model of readingaloud initially developed to study an alphabetic writingsystem (English) to an ideographic one (Chinese). Thiswas possible because the central assumptions of the modelare simple and readily generalize across writing systems.The success of this adaptation suggests that the sameframework can be applied to understanding reading skillacross a wide variety of languages, despite gross differ-ences in their surface properties. This is a critical first stepin providing a mechanistic explanation of many cross-lin-guistic phenomena, including the differential impact offactors that predict reading success (McBride-Changet al., 2005; Shu, Peng, & McBride-Chang, 2008), differ-ences in the prevalence of developmental disorders ofreading (Johansson, 2006; Shu, McBride-Chang, Wu, &Liu, 2006; Shu, Meng, Chen, Luan, & Cao, 2005) and pat-terns of reading disorder subsequent to brain injury (Bi,Han, Weekes, & Shu, 2007; Jefferies, Sage, & Ralph, 2007;Woollams, Lambon Ralph, Plaut, & Patterson, 2007).

The central explanatory principles of this frameworkmay be usefully expanded in future research to examinethe unique aspects of semantic encoding in Chinese. Simi-lar types of probabilistic mappings are present in print tomeaning in Chinese, and future research should focus onincorporating this very unusual property of the writingsystem. Preliminary studies suggests that it may be possi-ble to capture the differential division of labor betweensemantically mediated and direct translation from printto sound, and the differential contributions of phonologicaland semantic processing across languages in this sameframework (Yang, McCandliss, Shu, & Zevin, 2008; Yang,Zevin, Shu, McCandliss, & Li, 2006). Thus, this frameworkhas the potential to explain how the same functional archi-tecture can give rise to both strikingly similar (e.g., the caseof regularity and consistency effects) and highly distinct(e.g., differential contributions of phonological abilities toreading acquisition) outcomes across writing systems.

Acknowledgments

The authors would like to thank Ping Li and HongbingXing for contribution on the orthographic representation,Haiyan Zhou, Youyi Li and Xiaojuan Wang’s work on theempirical data, and Mike Harm for technical assistance,the Mikenet software and interesting discussions. This re-search was supported by NSF of China 30870758,60534080 and NSF of Beijing 7092051 (HS) grants, NSFREC 0337765 (BDM).

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Simulating Language-specific and Language-general Effects in a Statistical Learning Model of Chinese...

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