Neural division of labor in reading is constrained by culture: A training study of reading Chinese...

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Neural division of labor in reading is constrainedby culture: A training study of reading Chinesecharacters

Jingjing Zhao a,b,*, Xiaoyi Wang c, Stephen J. Frost b, Wan Sun c,Shin-Yi Fang a,b, W. Einar Mencl b, Kenneth R. Pugh a,b,d,e, Hua Shu c,1 andJay G. Rueckl a,b,1

aDepartment of Psychology, University of Connecticut, Storrs, CT, USAbHaskins Laboratories, New Haven, CT, USAcState Key Laboratory for Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, ChinadDepartment of Linguistics, Yale University, New Haven, CT, USAeDepartment of Diagnostic Radiology, Yale University School of Medicine, New Haven, CT, USA

a r t i c l e i n f o

Article history:

Received 27 February 2013

Reviewed 13 August 2013

Revised 11 November 2013

Accepted 8 January 2014

Action editor Roberto Cubelli

Published online 15 January 2014

Keywords:

Reading

Chinese

Division of labor

fMRI

Learning

a b s t r a c t

Word reading in alphabetic language involves a cortical system with multiple components

whose division of labor depends on the transparency of the writing system. To gain insight

about the neural division of labor between phonology and semantics subserving word

reading in Chinese, a deep non-alphabetic writing system, functional magnetic resonance

imaging (fMRI) was used to investigate the effects of phonological and semantic training on

the cortical circuitry for oral naming of Chinese characters. In a training study, we

examined whether a training task that differentially focused readers’ attention on the

phonological or semantic properties of a Chinese character changes the patterns of cortical

activation that was evoked by that character in a subsequent naming task. Our imaging

results corroborate that the cortical regions underlying reading in Chinese largely

overlap the left-hemisphere reading system responsible for reading in alphabetic lan-

guages, with some cortical regions in the left-hemisphere uniquely recruited for reading in

Chinese. However, in contrast to findings from studies of English word naming, we

observed considerable overlap in the neural activation patterns associated with phono-

logical and semantic training on naming Chinese characters, which we suggest may reflect

a balanced neural division of labor between phonology and semantics in Chinese character

reading. The equitable division of labor for Chinese reading might be driven by the special

statistical structure of the writing system, which includes equally systematic mappings in

the correspondences between written forms and their pronunciations and meanings.

ª 2014 Elsevier Ltd. All rights reserved.

* Corresponding author. Laboratoire de Sciences Cognitives et Psycholinguistique, Departement d’Etudes Cognitives, Ecole NormaleSuperieure, 29 rue d’Ulm, 75005 Paris Cedex 05, France.

E-mail addresses: [email protected] (J. Zhao), [email protected] (H. Shu), [email protected] (J.G. Rueckl).1 Address correspondence also to Jay G. Rueckl, Department of Psychology, University of Connecticut, Storrs, CT 06269, USA, and Hua

Shu, State Key Laboratory for Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.

Available online at www.sciencedirect.com

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1. Introduction

Extant evidence indicates that in skilled readers of alphabetic

writing systems, word reading involves a highly organized

and specialized multi-component cortical system distributed

primarily in left-hemisphere (LH) language areas [inferior

frontal gyrus, superior, middle and inferior temporal gyri,

superior and inferior parietal lobule, and fusiform gyrus

(hereafter, IFG, STG, MTG, ITG, SPL, IPL, and FG, respectively)].

These cortical regions are differentiated by their contribution

to orthographic, phonological, and lexical-semantic process-

ing (Cattinelli, Borghese, Gallucci, & Paulesu, 2013; Pugh et al.,

2000; Taylor, Rastle, & Davis, 2013). The relative contribution

of these cortical regions to printed word recognition also ap-

pears to vary systematically as a function of the properties of

the writing system. For example, Paulesu et al. (2000, 2001)

found that readers of Italian, an orthography with a trans-

parent or largely univalent mapping between letters and

phonemes, show greater activation in left STG (phonological

processing areas) than English readers. In contrast, readers of

English, an opaque orthography with multivalent mappings

between letters and phonemes (e.g., ‘I’ is pronounced differ-

ently in ‘PINT’ and ‘MINT’), showed greater activations in the

left posterior ITG and anterior IFG (lexical-semantic process-

ing areas) than Italian readers. In other words, within a

common reading network, the division of labor among its

component processes is differently weighted depending on

specific characteristics of the orthography (Seidenberg, 1992,

2011).

Although alphabetic systems vary in the structure of the

mapping from spelling to sound, differences in this mapping

are particularly pronounced in the contrast between alpha-

betic writing systems and Chinese, in which the mapping

from spelling to sound is syllable-based with no constituent

parts of a character corresponding to phonemes. In addition,

the statistical structure of the mapping from spelling to

meaning also differs substantially between Chinese and

alphabetic languages. Chinese, as one of the oldest writing

systems in the world, is commonly described as an ‘ideo-

graphic’ or morphosyllabic writing system with an extremely

deep orthography. It is true that logographic characters, as the

basic units of Chinese, are typically corresponded to mor-

phemes. In fact, however, only a small percentage of Chinese

characters (those that are most ancient, dating back more

than 3000 years) are aptly termed ideographs (DeFrancis,

1989). A large percentage (80e90%) of modern Chinese char-

acters are “phonograms”, semanticephonetic compounds

with one element (phonetic radical) suggesting its

pronunciation and the other element (semantic radical) indi-

cating the general category of its meaning, e.g.,湖 (/hu2/, lake)

which contains a phonetic radical胡 pronounced as /hu2/ and

a semantic radical 氵meaning water. For example, Chinese

regulareconsistent phonograms1 have exactly (39%) or

approximately the same (26%) pronunciations as their pho-

netic radicals as evaluated by Shu, Chen, Anderson, Wu, and

Xuan (2003) from a total of 2570 Chinese characters taught

in Chinese elementary school. Similarly, the meanings of a

large percentage (88%) of Chinese phonograms are trans-

parently (58%) or semi-transparently (30%) related to the

meanings of their semantic radicals.2 Thus, the structure of

Chinese phonograms is not “absolute-ideographic”, but in-

cludes substantial regularities (although not perfectly pre-

dictable) in the correspondences between written forms and

both their pronunciations and their meanings.

In other words, although the differences between Chinese

and alphabetic writing systems are illustrated remarkably in

the structure of written forms (logographs vs alphabets),

substantial differences between the two writing systems can

also be understood in terms of the statistical properties of

orthography-to-phonology (OeP) and orthography-to-

semantics (OeS) mappings. The statistical structure of the

Chinese writing system and that of alphabetic systems might

differ in two important ways. On the one hand, the OeP

mapping is less systematic in Chinese than in alphabetic

systems. In alphabetic systems, an alphabet of letters can

correspond to individual speech sounds, although English is

somewhat an “outlier” in alphabetic systems but letters or

combinations of letters in English still roughly correspond to

phonemes or combinations of phonemes. In contrast, in

Chinese, although phonetic radicals can provide cues for the

pronunciations of the characters, phonetic radicals are also

logographs, per se, and the pronunciations of phonetic radi-

cals correspond to syllables, the global phonological units for

the pronunciations of the characters, not the constituent parts

of the syllables. Thus, computation of pronunciation of a

Chinese character is not a process of sound-by-sound

assembling as in alphabetic systems in essence, but is a pro-

cess of addressed direct access from logographic forms to

phonology in syllables. In addition, although about two-thirds

of the phonograms have the same or approximately the same

pronunciations as their phonetic radicals, this is far from

consistent as in alphabetic systems. All together, the relations

between orthography and phonology in Chinese are more

arbitrary than in alphabetic scripts. One the other hand, the

OeS mapping is more systematic in Chinese than in alpha-

betic systems. Other than the morphosyllabic characteristics

of simple Chinese characters, semantic radicals in Chinese

phonograms indicate general semantic categories of the

characters and aid in the computation from orthography to

semantics. In contrast, alphabetic systems rarely contain se-

mantic information in the way that Chinese do by grouping

characters into different semantic categories. Although there

1 A regulareconsistent (ReC) phonogram has the same pro-nunciation to its phonetic radical and all other phonogramscontaining the same phonetic radical. An irregulareinconsistent(IReIC) phonogram has different pronunciation to its phoneticradical and other phonograms containing the same phoneticradical. Naming latency and accuracy were found longer and lessaccurate for naming IReIC phonograms than for ReC phono-grams, which termed as regularityeconsistency effects (e.g., Lee,Tsai, Su, Tzeng, & Hung, 2005), similar to the regular-ityeconsistency effects found in reading alphabetic languagessuch as English (e.g., Jared, 2002).

2 Phonograms also vary in semantic transparencydthe degreeto which the meaning of a phonogram is related to the ‘core’meaning of the radical. The more semantic information a se-mantic radical contributes to the meaning of the phonogram thatcontains it, the more transparent is the phonogram.

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are morphological words in English (e.g., final -s and -ed in

English), they are somewhat encoded phonologically too and

hence not orthogonal to orthography-to-semantics mapping.

To the end, the correspondence between orthography and

semantics in Chinese is certainly less arbitrary than in

alphabetic systems. In such away, the Chinesewriting system

conveys a relatively symmetric statistical structure between

OeP mapping and OeS mapping comparing to that of the

alphabetic systems, which is substantially asymmetrical be-

tween the twomappings with highly consistent OeP mapping

and arbitrary OeS mapping.

The relatively symmetric statistical structure of thewriting

system of Chinese compared to the alphabetic systems im-

plies that the division of labor between phonology and se-

mantics may be different for readers of Chinese than for

readers of alphabetic systems, with readers of Chinese relying

more on semantics than alphabetic readers (Harm &

Seidenberg, 2004; Seidenberg, 2011; also see Frost, 2012). For

example, the arbitrariness in spelling-to-sound mapping of

Chinese characters may make the computation from orthog-

raphy to phonology much less efficient in Chinese and the

consistency in spelling-to-meaning mapping of Chinese

characters may make the computation from orthography to

semantics more efficient in Chinese compared to alphabetic

systems. Thus, semantics should contribute more in naming

(a typical task for examining computation from orthography

to phonology) Chinese characters than in naming alphabetic

words. In fact, extant behavioral work has supported this

hypothesis and shown that shallow alphabetic orthographies

(e.g., Italian) demonstrated negligible effects of semantic

variables (e.g., imageability and concreteness) in skilled

reading (Barca, Burani, & Arduino, 2002; Bates, Burani,

D’Amico, & Barca, 2001) and only in deep orthographies such

as English, semantic variables play some role in naming aloud

words with atypical pronunciations especially if those words

are relatively infrequent (Shibahara, Zorzi, Hill, Wydell, &

Butterworth, 2003; Strain, Patterson, & Seidenberg, 1995,

2002; Woollams, 2005), a finding that is also borne out by

computational modeling (Harm & Seidenberg, 2004; Plaut,

McClelland, Seidenberg, & Patterson, 1996) and neuro-

imaging (Frost et al., 2005; Graves, Desai, Humphries,

Seidenberg, & Binder, 2010) measures.

In contrast, a large corpus analysis of the naming responses

of 2423 Chinese characters indicates that semantic variables

seem to play substantial role in reading aloud all Chinese

characters regardless of types (Liu, Shu, & Li, 2007). Yang, Shu,

McCandliss, and Zevin (2012) further provided quantitative

evidence for semantic contribution in naming different types of

Chinese characters in their connectionist modeling work. They

specifically compared semantic and phonological effects in

naming different types of characters via impairments of se-

mantic and phonological units of the naming model. The au-

thors found that semantics not only played substantial role in

naming Chinese irregulareinconsistent (IReIC) phonograms

but also made as much contribution as phonology in naming

regulareconsistent (ReC) phonograms. They additionallymade

direct comparison of semantic and phonological effects in

naming Chinese phonograms with those of naming English

words using the same model architecture and training pro-

cedures. The naming results between the two writing systems

contrast dramatically. For ReC words, semantics showed

relatively little effects in English reading, but made substantial

effects as phonology in naming Chinese ReC phonograms. For

IReICwords, semantics showed some effects in English reading

but the effects were weaker than phonology and much more

modest than the effects in naming Chinese IReIC phonograms.

In summary, previous studies coherently suggest that se-

mantics plays limited effects in alphabetic systems and only

contributes in naming IReIC words in relatively deeper

alphabetic orthography such as English, whereas in the

reading of a non-alphabetic script such as Chinese, semantics

plays important role in naming all types of Chinese charac-

ters, including both IReIC phonograms and ReC phonograms.

Thus, investigating reading of Chinese, especially for ReC

phonograms,might offer an important approach to examine if

the division of labor between phonology and semantics in

reading Chinese characters is different than in reading English

words. Although extant behavioral and computational

modeling findings have examined this issue and suggest dif-

ferences in division of labor in reading between the two

writing systems, few studies have explored this issue in the

neural level.

The purpose of the present study was to provide neuro-

imaging evidence for the division of labor between phonology

and semantics for Chinese readers. We used an adaptive

learning paradigm developed by Sandak et al. (2004) that has

been shown to provide a sensitive measure of the division of

labor between the neural circuits underlying OeP and OeS

processes in readers of English. By using a paradigm and

scanning parameters previously used in this study of reading

in English, the results of the present studywould also allow us

to compare the division of labor of Chinese reading with the

findings in English reading. In the present Chinese adaptive

learning experiment, native Mandarin speakers were first

trained on Chinese low-frequency phonograms with regu-

lareconsistent pronunciations and transparent semantic

radicals outside the functional magnetic resonance imaging

(fMRI) scanner, half in a training condition that emphasized

attention to phonological form and half in a training condition

that emphasized semantics. Immediately following training,

participants named aloud the trained phonograms and a

comparable set of untrained phonograms in an event-related

fMRI paradigm. This design allowed us to observe how the

neural activation associated with naming a phonogram is

modulated by recent prior exposures to that phonogram by

contrasting the response to trained and untrained phono-

gram. Critically, the training-task manipulation allowed us to

further determine whether the effects of prior exposure differ

depending on the focus (phonological or semantic) of the

training task.

2. Methods

2.1. Participants

A total of 18 right-handed native Mandarin speakers (seven

males and 11 females) participated in the fMRI study after

giving informed consent. The subjects ranged in age from 19

to 27 years with a mean of 22.8 years. All participants were

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undergraduate or graduate students from Beijing Normal

University. None of the subjects reported a history of neuro-

logical disorders or reading impairments. All experimental

protocols were approved by State Key Laboratory of Cognitive

Neuroscience and Learning at Beijing Normal University.

Becausewewere not able to obtain behavioral naming data

during fMRI scanning, we further recruited a separate cohort

(hereafter, the behavioral cohort) of 37 right-handed partici-

pants (13 males and 24 females, 19e28 years of age with a

mean of 22.1 years) from undergraduate and graduate stu-

dents of Beijing Normal University in order to collect naming

latencies and accuracy outside scanner. The participants were

all native Mandarin speakers. None of the participants re-

ported a history of neurological disorders or reading impair-

ments. Training was identical for this cohort; however,

instead of receiving an fMRI scan we collected their naming

latencies and accuracy for the Chinese phonograms outside

scanner.

2.2. Materials

The critical items in our adaptive learning paradigm included

a total of 126 Chinese low-frequency phonograms that ranged

in frequency from 1 to 30 occurrences per million (mean

(M) ¼ 10.99) according to the Modern Chinese frequency dic-

tionary (Wang, 1986). All critical phonograms were semantic-

transparent regulareconsistent (ReC) phonograms, whose con-

sistency ratios3 ranged from .5 to 1 and averaged .91 (Liu, Shu,

et al., 2007). For regularity of pronunciation of a phonogram,

tone difference between the pronunciation of a phonogram

and the pronunciation of its phonetic radical was not

controlled due to the limited number of possible stimuli, but

each phonogram and its phonetic radical shared the same

onset and rime. All phonograms were semantically trans-

parent with explicit meaning cues from their semantic

radicals.2

For counterbalancing purposes, the critical phonograms

were partitioned into three lists of 42 items each. The three

lists were matched on frequency (M ¼ 10.78, 11.74, 10.46,

standard deviation (SD) ¼ 7.24, 8.99, 7.30), consistency ratio

(M ¼ .92, .90, .90, SD ¼ .15, .14, .13), number of strokes (M ¼ 11,

12, 11, SD¼ 3, 3, 3), frequency of phonetic radical (M¼ 629, 596,

606, SD ¼ 856, 923, 825), and number of characters that had

different toneswith their phonetic radicals (13, 12, 12). Each list

was assigned to the phonological training, semantic training,

and untrained conditions equally across subjects.

To construct the materials for the training tasks, each

target phonogram (e.g., 筏 /fa2/, raft) was paired with three

different higher frequency comparison characters: a phono-

logically related character (e.g., 乏 /fa2/, tired), a semantically

related character (e.g., 舟 /zhou1/, boat), and a phonologically

and semantically unrelated character (e.g., 斗 /dou4/, fight).

The phonologically related characters were homophones of

their corresponding targets. The semantic relationship be-

tween the targets and their semantically related and unre-

lated characters was rated on a 1e7 Likert scale (1 e least

relevant; 7 e most relevant) by a group of 20 native Chinese

students from Beijing Normal University, none of whom

participated in the main experiments. Themeans of semantic

relatedness scores for semantically related and unrelated

characters were respectively 5.82 (SD¼ .72) and 1.54 (SD¼ .33).

No obvious orthographic clues from the paired characters

were able to be used to perform the training tasks.

In addition to the 126 ReC phonograms, the stimuli pre-

sented during the naming task also included 42 low-

frequency, semantic-transparent, irregulareinconsistent

(IReIC) phonograms. The mean consistency ratio for these

items was .23 (SD ¼ .39). They were matched with the critical

phonograms in frequency (M ¼ 11.97, SD ¼ 5.83) and number

of strokes (M¼ 11, SD¼ 4). These IReIC phonograms served for

two purposes: (a) equating the number of trained versus un-

trained items in the naming task, and (b) allowing us to

examine neural effects of orthographicephonological consis-

tency,1 which can also be used to validate the credibility of the

present fMRI results by comparing with extant neuroimaging

results of regularity and consistency effects (Lee et al., 2004;

Tan, Feng, Fox, & Gao, 2001).

Finally, in order to directly compare the neural circuits

underlying phonological and semantic processes in reading

Chinese phonograms, participants were also required tomake

in-scanner homophone judgments and semantic association

judgments on a new set of phonograms after they completed

the naming task. The stimuli included 72 low-frequency ReC

phonograms. The means of frequency and number of strokes

were 21.72 (SD ¼ 14.64) and 11 (SD ¼ 3), respectively. Each

phonogram was assigned to the semantic task for half the

participants and to the phonological task for the remaining

participants and counterbalanced across participants be-

tween the two tasks. As in the training task, the phonograms

were paired with one-character comparison words such that

half of the trials in each task required a ‘yes’ response.

2.3. Procedure

The experiment was performed in two phases: a behavioral

training session followed by an fMRI session. During behav-

ioral training, participants were reinforced their attention to

different features for the low-frequency Chinese phonograms

through repeated presentationsdhalf in a training condition

that emphasized attention to the phonological form and the

other half in a training condition that emphasized attending

to semantics. Immediately following behavioral training, we

examined transfer of training effects in an event-related fMRI

session in which participants named aloud the trained pho-

nograms as well as an additional set of untrained phono-

grams. Following the naming scans, participants were

scanned in a block design paradigm while making phonolog-

ical and semantic judgments on a new set of Chinese pho-

nograms. A computer with Windows XP and Eprime 1.1

software (Psychology Software Tools Inc., Pittsburgh, PA, USA)

was used for all stimulus presentation and response

collection.

2.3.1. Behavioral trainingEach participant was trained on two sets of phonograms with

feedback. In the phonological training condition, participants

3 A consistency ratio is the number of phonograms with thesame pronunciation divided by the total number of phonogramswith the same phonetic radical.

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made homophone judgments on targets and corresponding

comparison items [e.g., “Do筏 and乏 sound the same?”, where

筏 (/fa2/, raft) is the target item and 乏 (/fa2/, tired) is the

comparison item]. In the semantic training condition, partic-

ipantswere asked to indicatewhether targets and comparison

items were semantically related [e.g., “Are鞍 and马 related in

meaning?”, where 鞍 (/an1/, saddle) is the target item and 马

(/ma3/, horse) is the comparison item].

During each trial, a target item was presented in parallel

with a comparison item in themiddle of the computer screen.

Subjects were asked to judge whether or not the target item

matched the comparison item on the targeted feature by

pressing one of two buttons on the keyboard. The items stayed

on the screen until a response was made or elapsed after

2000 msec, followed by an inter-trial interval of 2000 msec.

During the training, response feedback (“Correct” or “Incor-

rect”) was provided on each trial in order to encourage

attention to the targeted feature. Each subject was trained on

sixteen blocks with eight blocks in each training condition.

Training alternated between phonological and semantic

conditions every four blocks and the initial training condition

was counterbalanced across participants. Each phonogram

was presented only once in each block andwas repeated eight

times through the whole training session with either phono-

logical training or semantic training. Within each block,

phonograms were presented randomly. Across all repetitions

within each condition, the number of trials with correct yes

and no responses was equated and balanced across blocks.

Total time for the training session was approximately 2 h.

Both reaction time (RT) and accuracy were recorded during

behavioral training.

2.3.2. fMRI sessionImmediately after the training session, participants per-

formed an overt naming task in an event-related fMRI design

in which they named the phonologically and semantically

trained ReC phonograms, along with untrained ReC phono-

grams, and untrained IReIC phonograms. We employed an

event-related design with interleaved acquisition and “jit-

tered” trial durations (4, 5, 6, 7 sec) with occasional longer trial

durations (i.e., null trials) for the naming task. The target

stimulus in each trial was presented for 1000 msec. Data were

collected from seven functional runs (3 min plus 10 sec for

image stabilization) consisting of 24 items each. Three

different pseudorandom orders of stimuli were created and

rotated equally across subjects and training sets. Due to

scanning environment constrains, we were not able to obtain

behavioral naming responses from this group of subjects

during fMRI scanning.

After performing the overt naming tasks in scanner, par-

ticipants were scanned in a block design with blocks of

phonological judgments and blocks of semantic judgments

alternating with a baseline condition consisting of a line-

orientation judgment, in which participants judged whether

two sets of lines with left or right orientations had the same

pattern of left/right alternation (e.g., Are / / \ / and / / \ / the

same?). The phonological and semantic judgment tasks were

identical to those used during training, but without response

feedback and with a new set of stimuli.

Neuroimaging data for the judgment tasks were collected

from three fMRI runs. Each functional run consisted of four

22 sec activation blocks (two phonological, two semantic) and

three 22 sec baseline (line judgment) blocks. The order of

activation block types was counterbalanced across runs. Each

block consisted of six trials and was preceded by task in-

structions and a letter cue for 4000 msec at the bottom of the

screen to indicate the upcoming task [The English letter “S”

(abbreviation for Sound) was used as a cue for the homophone

judgment; “M” (abbreviation for Meaning) for the semantic

association judgment; “L” for the line-orientation judgment].

The cues remained on the lower part of the screen throughout

the entire block as a reminder of the task. The comparison pair

in each trial was presented for 1500 msec. Behavioral re-

sponses for the judgment tasks (RT and accuracy) were

recorded during fMRI scanning.

2.4. Image acquisition

Functional images were collected on a 3 T Siemens MR im-

aging system (Siemens MAGNETOMTrio, a TIM System) in the

Imaging Center for Brain Research of Beijing Normal Univer-

sity. Prior to functional imaging, 32 axial-oblique anatomic

images (echo time, 2.47 msec; repetition time, 300 msec; field

of view, 220 mm2; 4-mm-thick contiguous slices; matrix size,

256 � 256 � 2 number of excitations) were prescribed parallel

to the intercommissural line based on sagittal localizer im-

ages (echo time, 6.83 msec; repetition time, 20 msec; field of

view, 240/256 mm2; 4-mm-thick contiguous slices; matrix

size, 240 � 256 � 1 number of excitations). The axial-oblique

images were obtained at the same relative slice locations in

each subject, extending from the inferior aspect of the tem-

poral lobes to the parietal convexity. A single shot, gradient

echo, echo planar imaging (EPI) sequence (flip angle, 80�; echotime, 30 msec; repetition time, 2000 msec; field of view,

220 mm2; 4-mm-thick contiguous slices; matrix size,

64 � 64 � 1 number of excitations) was used to acquire acti-

vation images in the same slice locations as those used for

axial-oblique anatomic images, resulting in 90 images ob-

tained per functional run. After functional images were ac-

quired, an additional high-resolution whole brain anatomic

scan was obtained with the following parameters (flip angle,

7�; echo time, 3.66 msec; repetition time, 2530 msec; field of

view, 256 mm2; 1-mm-thick contiguous slices; matrix size,

256� 256� 1 number of excitations). The entire scanning took

about 40 min.

2.5. Image analysis

The imaging data were preprocessed and statistically

analyzed using in-house routines written in MATLAB (Math-

Works, Natick, MA). The first five images of each functional

run were excluded from data processing due to image stabi-

lization. Functional images were first sinc-interpolated to

correct for slice acquisition time, corrected formotion (Friston

et al., 1995), and spatially smoothed with a Gaussian filter of

size 3.125 mm full width at half maximum. For each subject,

an affine transformation to the standardized space defined by

the Montreal Neurological Institute (MNI) was obtained using

Bio-ImageSuite (Papademetris, Jackowski, Schultz, Staib, &

c o r t e x 5 3 ( 2 0 1 4 ) 9 0e1 0 694


Duncan, 2003; www.bioimagesuite.org), mapping between the

subject-space T1 anatomic and the MNI space “Colin” brain

(available at www.bic.mni.mcgill.ca). Prior to across-subjects

analysis, this transformation was applied to the single-

subject activation maps, with trilinear interpolation, into 2-

mm isotropic MNI space.

For single-subject event-related analysis, a regression-

based method was used that allows direct estimation of the

hemodynamic response for each trial type at each voxel

separately (Miezin, Maccotta, Ollinger, Petersen, & Buckner,

2000). Time course estimates were made for 1-sec intervals

from �5 to þ15 sec relative to trial onset. To create subject

activation maps of the evoked response for each task condi-

tion, we obtained regression estimates of the mean difference

in these time course estimates for an activation period

(4e8 sec after trial onset) relative to a baseline period (0e3 sec

prior to trial onset). For single-subject block design analysis,

linear regression was used to generate contrast images of the

mean signal differences among the experimental and baseline

conditions. Stimulus blocks were entered into a separate

regressionmodel as a simple boxcar function, with a 4-sec lag

to account for the hemodynamic delay. Contrasts for effects of

interest were applied to these regression estimates to obtain

contrast images for each subject. In order to test effects across

subjects for both event-related and block design sections,

each voxel in these contrast images was tested versus zero

with a t-test, implementing a repeated measures analysis of

variance (ANOVA; Holmes & Friston, 1998; Woods, 1996).

3. Results

3.1. Adaptive learning effects

3.1.1. Behavioral training tasksMean RT and accuracy for the behavioral training outside fMRI

scanner (the fMRI group) are presented in Fig. 1. Two

2 � 8 � 2 � 3 (training condition � repetition � block

order � stimulus list) repeated measures ANOVAs were

computed with RT for correct responses and accuracy as

dependent measures separately. Training condition and

repetitionwere entered as within-subjects factors; block order

and stimulus list were entered as between-subjects factors.

Because block order and stimulus list were counterbalanced

across subjects and not theoretically interesting; interactions

involving these two factors are not reported. The main effects

of training condition [F(1,12) ¼ 10.985, p < .01] and repetition

[F(7,84) ¼ 20.009, p < .001] were both significant for RT, such

that RT decreased reliably across repetitions in both phono-

logical and semantic training conditions, and RT was signifi-

cantly faster in the semantic training condition than

phonological training condition. Planned paired-sample t-

tests revealed no RT differences in the first three pre-

sentations and shorter RTs for semantic training condition

than phonological training condition beginning at the fourth

presentation [t(17) ¼ 3.019, p < .01] and continuing to the end

of training [t(17) ¼ 3.941, p < .01]. For accuracy, only the main

effect of repetition [F(7,84) ¼ 10.487, p < .001] was significant,

indicating the accuracy increased comparably in both training

conditions. Neither RT nor accuracy showed significant in-

teractions between training condition and repetition. In sum,

the behavioral performance from training tasks suggests

successful training from both training tasks.

3.1.2. Training effects on behavioral naming responseAlthough we were not able to collect behavioral naming re-

sponses due to scanning constrains from the participants in

the fMRI group, we collected the naming responses from a

comparable behavioral cohort group. Since the two groups

showed similar response pattern in the training tasks, we

expected the fMRI groupwould show similar response pattern

in the naming task as the behavioral cohort group. Naming

latencies less than 200 msec and greater than 1750 msec were

discarded as outliers (2.8% of all the responses). Data from one

male subject were excluded due to excessive naming errors,

microphone failures and naming time outliers. The mean

naming latencies and accuracies for correct responses to

phonologically trained ReC phonograms, semantically

trained ReC phonograms, and untrained ReC phonograms for

the remaining 36 subjects in the behavioral cohort group were

shown in Fig. 2. A repeated measures ANOVA revealed a sig-

nificant training transfer effect on naming latencies

[F(2,70) ¼ 54.250, p < .001]. Post-hoc t-tests showed that both

phonologically and semantically trained ReC phonograms

yielded faster naming latencies relative to untrained ReC

phonograms [t(35) ¼ �9.170, p < .001 and t(35) ¼ �7.731,

p < .001, respectively], indicating that phonological and se-

mantic training facilitated subsequent naming of the trained

Fig. 1 e Mean reaction time (RT) and accuracy by training conditions and presentation numbers in the behavioral training

tasks from the fMRI group before fMRI scanning.

c o r t e x 5 3 ( 2 0 1 4 ) 9 0e1 0 6 95


phonograms. No difference was observed between naming

latencies for phonologically and semantically trained ReC

phonograms, indicating that semantic and phonological

training produced equivalent levels of behavioral training ef-

fects to naming. The observed differences between trained

and untrained ReC phonograms in naming time did not

reflect a speedeaccuracy tradeoff. Naming accuracies of ReC

phonogramswere high in all conditions, with a slightly higher

accuracy for phonologically trained ReC phonograms than the

other two types of ReC phonograms.

3.1.3. Training effects on neural circuits of namingTo examine whether there were differential effects of the

phonological and semantic training on brain activation, we

directly contrasted the naming maps for phonologically and

semantically trained phonograms (Fig. 3A and Table 1). Only

the inferior aspect of LH IFG Brodmann area 47, or (BA47) in the

well-documented reading networks was less activated for

semantically trained phonograms than for phonologically

trainedphonograms (the regionofyellow/red inFig. 3A). Fig. 4A

presents the mean activation (B-weights in the regression

function) across all the voxels in this regionwhen participants

named aloud the phonologically trained, semantically trained,

and untrained phonograms, suggesting more neural facilita-

tion effect in this region for semantically trained phonograms

than for phonologically trained phonograms. Beside the infe-

rior aspect of LH IFG (BA47), no other regions in the major

identified LH reading systems showed any differences be-

tween phonologically trained and semantically trained pho-

nograms. Several regions outside the standard reading

networks were less activated for phonologically trained than

for semantically trained ReC phonograms, including LH pos-

terior cingulate gyrus, RH cingulate gyrus, andRH superior and

medial frontal gyrus (regions of blue/purple in Fig. 3A).

Interestingly, when phonologically and semantically

trained ReC phonograms were combined as trained ReC

phonograms and compared with the activations of untrained

ReC phonograms, many differences were found (Fig. 3B and

Table 1). Most importantly, trained phonograms showed

reduced activation at bilateral speech-articulatory areas

compared with untrained phonograms (regions of blue/purple

in Fig. 3B), including LH insula/IFG (BA13/45) and right-

hemisphere (RH) IFG (BA45). The reduction in activation in

these regions indicates general neural training-facilitation

effects. In addition, trained phonograms were more active at

bilateral IPL than untrained phonograms (regions of yellow/

red in Fig. 3B). Fig. 4BeE presents the mean activations (B-

weights in the regression function) across all the voxels in

each of the following regions respectively, LH insula/IFG, RH

IFG, LH IPL, and RH IPL, when participants named aloud

phonologically trained, semantically trained, and untrained

phonograms.

3.2. Regularityeconsistency effects

Both naming latency and accuracy in the behavioral

cohort group confirmed the empirical behavioral regular-

ityeconsistency effects.1 Naming responses to untrained

IReIC phonograms were significantly slower [latency:

770 msec (SD ¼ 109), t(35) ¼ �6.058, p < .001] and less accurate

[accuracy: 87% (SD ¼ 7), t(35) ¼ 4.310, p < .001] than for un-

trained ReC phonograms.

Neurobiologically, the contrast between the two types of

phonograms from the fMRI group revealed many differences

in the well identified distributed LH Chinese reading circuitry.

IReIC phonograms showed higher activation than ReC pho-

nograms at LH IFG/middle frontal gyrus (MFG) (including

BA45, 46, 47, and 9), FG, MTG, and SPL extending to precuneus,

IPL, and supramarginal gyrus (SMG) (see Fig. 5 and Table 2).

However, no regions were found with higher activation for

ReC phonograms than IReIC phonograms. The activation

pattern at LH IFG/MFG, FG, SPL, IPL, and SMG in the present

data was similar to a previous study for consistency effect in

reading Chinese phonograms (Lee et al., 2004); however, we

also found higher activation for naming IReIC phonograms

than ReC phonograms at LHMTGwhereas Lee et al. (2004) did

not. Our finding of the differential activation at LH MTG as a

function of consistency squares findings in English with

similar manipulations, which has typically been taken as an

indication that visual word recognition involves the coopera-

tive interaction of phonological and semantic processes, with

stronger recruitment of semantic processes when the

computation of phonology is difficult (Frost et al., 2005; Graves

et al., 2010).

3.3. Judgment tasks

Behaviorally, mean RT and accuracy of the (in-scanner)

phonological and semantic judgment tasks were similar.

Mean RTs in the phonological judgment task, semantic judg-

ment task, and line-orientation judgment task were 951

(132) msec, 950 (134) msec, and 1155 (120) msec, respectively.

Fig. 2 eNaming latency and accuracy for phonologically trained, semantically trained, and untrained ReC phonograms from

the behavioral cohort group.

c o r t e x 5 3 ( 2 0 1 4 ) 9 0e1 0 696


Mean accuracies in the phonological judgment task, semantic

judgment task, and line-orientation judgment task were .95

(.1), .93 (.1), and .88 (.1), respectively. Repeated measures

ANOVA revealed significant main effects of task for both RT

[F(2,34) ¼ 38.223, p < .001] and accuracy [F(2,34) ¼ 10.405,

p < .001]. Post-hoc t-test revealed differences between

phonological task and line-orientation task, as well as se-

mantic task versus line-orientation task [RT: t(17) ¼ �6.221,

p < .001 and t(17) ¼ �8.994, p < .001, respectively; accuracy:

t(17) ¼ 4.106, p < .001 and t(17) ¼ 2.652, p < .05, respectively],

but no difference between phonological task and semantic

task.

Surprisingly, direct comparison of neural activation levels

during the semantic task versus the phonological task also

revealed few differences (Fig. 6A and Table 3). Only one region

of LH IFG extending to MFG was activated higher for the se-

mantic task than for the phonological task (regions of yellow/

red in Fig. 6A). The only region that showed higher activation

for the phonological task was precuneus extending into the

posterior cingulate gyrus (regions of blue/purple in Fig. 6A). In

contrast, combining phonological and semantic tasks

revealed a number of regions that were significantly more

activated for the language (phonological and semantic) tasks

than for the non-language control (line-orientation) task,

including LH IFG (BA47, 44, and 45) extending to MFG, MTG,

middle occipital gyrus (MOG) extending to lingual and FG, and

a focal activation in and along left collateral sulcus (regions of

yellow/red in Fig. 6B and Table 3). It is worth noting that an

area at RH FG, which was suggested to play an important role

in Chinese reading by previous studies (Bolger, Perfetti, &

Schneider, 2005; Liu, Dunlap, Fiez, & Perfetti, 2007; Nelson,

Liu, Fiez, & Perfetti, 2009; Tan, Laird, Li, & Fox, 2005), was

found activated to a higher degree for the non-language con-

trol task compared with the language tasks. In general, the

neuroimaging responses from judgment tasks indicate a

multi-components neural network for reading of Chinese in

widespread regions of the LH, which subserves phonology and

semantics by-and-large equally.

4. Discussion

In this study, we examined the neural system of Chinese

reading, with a particular focus on the division of labor be-

tween phonological and semantic processes of reading Chi-

nese ReC phonograms. We employed an adaptive learning

Fig. 3 e Brain activations where semantic-trained

phonogramswere less activated than phonological-trained

phonograms (yellow/red) and where phonological-trained

phonograms were less activated than semantic-trained

phonograms (blue/purple) (A). Brain activations where

trained phonograms were less activated than untrained

phonograms (blue/purple) and where trained phonograms

were more active than untrained phonograms (yellow/red)

(B). Images were presented at a univariate threshold of

p < .01, corrected for mapwise false discovery rate (FDR;

Genovese, Lazar, & Nichols, 2002). Images from top to

bottom correspond to the following position along the z-

axis in MNI space: D52, D42, D22, D8, D2, and L6,

respectively, with the LH on the right side of the images.

c o r t e x 5 3 ( 2 0 1 4 ) 9 0e1 0 6 97


paradigm, in which participants were required to focus on

different properties (phonology vs semantics) of Chinese ReC

phonograms during training. This paradigm allowed us to

examine the transfer of phonological and semantic training to

a subsequent naming task. Our training effects showed that

prior exposure had similar effects on the naming of Chinese

phonograms regardless of whether the prior exposure took

place while participants were engaged in a phonological or a

semantic task. Behaviorally, naming latencies were facilitated

to the same extent by phonological and semantic training.

Neurobiologically, the transfer effects of phonological and

semantic training on subsequent naming circuits were by-

and-large rather similar in the major identified LH reading

system for Chinese reading. Our data suggest that the char-

acteristics of the Chinese writing system produce a relatively

equitable division of labor between phonological and seman-

tic processes, specifically when reading Chinese ReC

phonograms.

Beyond our main finding of division of labor between

phonology and semantics in reading of Chinese ReC phono-

grams, our neuroimaging results from judgment tasks also

shed light on the neural circuitry of the Chinese reading. In

general, neural activation patterns in judgment tasks corrob-

orated a multi-componential reading system for Chinese

reading primarily in left-sided reading areas (see Tan et al.,

2005, for reviews), including LH areas that also subserve

alphabetic reading (IFG, MTG, IPL/SPL, and FG), but also MFG

that is thought to play a specific role in the reading of Chinese.

These regions were all engaged in component (both phono-

logical and semantic) judgment tasks and were also activated

higher in naming IReIC phonograms compared to naming

ReC phonograms, again suggesting that semantics and

phonology are somewhat sharing the same neural network.

Our results provided the first-order neurobiological

empirical evidence for cooperation of phonology and seman-

tics to reading of Chinese ReC phonograms. More impor-

tantly, we directly contrasted phonological and semantic

effects in reading of Chinese ReC phonograms in fMRI, which

was rarely done in previous studies. Surprisingly, phonology

and semantics not only illustrated relatively comparable

behavioral effects in naming aloud, but also induced quite

similar effects neurobiologically. All together, the results

imply that Chinese reading might require a combination of

semantic and phonological strategies that are hardly differ-

entiated from each other. The equitable division of labor be-

tween phonological and semantic contribution to reading of

Chinese ReC phonograms that we found in the current fMRI

study corroborated recent computational modeling work

within connectionist framework, which showed that seman-

tics played as much important role as phonology in naming

Chinese ReC phonograms (Yang et al., 2012).

From a broader theoretical background, the current results

are also consistent with findings of reading development and

impairments in Chinese. Although a numerous studies have

reported a strong link between phonological processing and

character recognition in Chinese children (Chow, McBride-

Chang, & Burgess, 2005; Ho, Law, & Ng, 2000; Siok & Fletcher,

2001), the contribution of semantics to the development of

Chinese reading is also critically important (Chen, Hao, Geva,

Zhu, & Shu, 2009; McBride-Chang, Shu, Zhou, Wat, & Wagner,

2003; Shu, McBride-Chang, Wu, & Liu, 2006). In particular, Li,

Shu, McBride-Chang, Liu, and Peng (2012) administrated 184

kindergarteners at age 5e6 and 273 primary school students at

age 7e9 from Beijing with a comprehensive battery of tasks

and found both phonological and semantic skills significantly

explained the performance of character recognition of these

children, which underscores the importance of dimensions of

both phonological and semantic processing for very early and

intermediate acquisition of Chinese reading. Developmental

dyslexic work by Shu, Meng, Chen, Luan, and Cao (2005) also

implies that semantic and phonological impairments have

widespread effects for Chinese character reading. They re-

ported three cases of developmental dyslexia in Chinese, and

found that one case with impairment in semantic processing

(surface dyslexia) and two cases with deficit of phonological

processing (deep dyslexia). However, both children with se-

mantic and phonological deficits were impaired on naming of

all characters relative to their age-matched controls. These

Table 1 e Brain activation regions for training effects.Specific training effects: direct comparison betweenphonologically trained ReC phonograms and semanticallytrained ReC phonograms. General training effects: directcomparison between trained phonograms (collapsed ofphonologically trained and semantically trained ReCphonograms) and untrained ReC phonograms.

Region BA x y z Volume(mm3)

p Value(at peak)

Semantic-trained < Phonological-trained

L. IFG 47 �26 34 �6 528 .0006

Phonological-trained < Semantic-trained

R. MFG 32 22 6 51 2824 .0001

L. Posterior

cingulate gyrus

23 �4 �32 22 1472 .0004

R. Cingulate gyrus 24 6 �18 44 1296 .0001

R. STG 9 22 48 42 960 .0002

R. Cingulate gyrus 24 8 �2 34 832 .0002

R. Cerebellum 35 20 �36 �44 320 .0006

L. Cerebellum 19 �18 �64 �42 248 .0018

L. Cerebellum 19 �24 �70 �46 240 .0031

L. Thalamus e �16 �26 16 216 .0024

L. STG 6 �16 0 74 216 .0014

Trained < Untrained

R. IFG 45 46 22 8 656 .0008

L. Insula 13 �42 16 2 312 .0034

Trained > Untrained

R. IPL 19 44 �72 42 2288 <.0001

L. IPL 19 �42 �76 42 2240 .0010

L. Putamen e �10 4 �8 640 .0007

L. Cuneus 7 �8 �68 32 632 .0033

R. Lingual gyrus 18 4 �72 �1 448 .0049

L. Cerebellum 37 �32 �48 �44 432 .0007

L. STG 6 �34 12 60 432 .0016

R. MFG 6 14 �16 56 352 .0007

R. MFG 10 20 60 6 296 .0038

For clusters with multiple anatomical locations, only the peaks of

activations are labeled. Volumes bigger than 200 mm3 are reported.

Thresholds for the training effects in the naming task are set at

p < .01 FDR corrected. Coordinates are MNI space at activation

peaks. IFG: inferior frontal gyrus, MFG: middle frontal gyrus, STG:

superior temporal gyrus, IPL: inferior parietal lobule.

c o r t e x 5 3 ( 2 0 1 4 ) 9 0e1 0 698


cases were successfully simulated and confirmed by con-

nectionist models recently (Yang et al., 2012). It is also worth

noting that a recent neuroimaging study observed similar al-

terations in brain activations for phonological and semantic

processing to visual characters in a group of Chinese devel-

opmental dyslexic children (Liu et al., 2012), again suggesting

that access to phonology and semantics from the visual

orthography for Chinese readers might use the same neural

network.

Even though our neurobiological results have shown that

the neural network that underlies reading in Chinese largely

overlapped the network responsible for reading in English, the

neural division of labor between phonology and semantics in

Chinese reading contrasted tremendously from English find-

ings. Althoughwe did not conduct comparable experiments in

an alphabetic writing system in the present study, it is still

worth doing a qualitative comparison between the current

study and those studies from the literature of alphabetic

writing systems to illuminate how structure of the writing

system drives the differences in division of labor between

phonology and semantics in reading.

First, for the adaptive learning experiments, the most

comparable study that has been done in alphabetic writing

systems is the work conducted by Sandak et al. (2004) in En-

glish reading. Although the two studies employed slightly

different training procedures (phonological training: rhyming

judgment in English vs homophone judgment in Chinese; se-

mantic training: category judgment in English vs semantic

association judgment in Chinese) necessitated by the differ-

ences in training materials (English pseudowords vs Chinese

ReC phonograms), in general both studies employed the same

adaptive learning paradigm and both aimed to examine how

different strategies of training change the responses of

naming words. In addition, the behavioral pattern of adaptive

learning in the present study was similar to that observed in

readers of English. In both studies, phonological and semantic

training resulted in faster naming latencies than control

condition and the facilitation effects in naming latencies were

comparable between phonological training and semantic

training, which again suggests the comparability of the two

studies.

However, the neurobiological patterns associated with di-

vision of labor between phonological and semantic processes

in reading Chinese and English seemed quite different. In the

results of Sandak et al. (2004) with readers of English, the

neurobiological effects of phonological and semantic training

differed substantially. Reinforcing different dimensions

(phonological vs semantic) facilitated naming via different

neural components: phonological training resulted in de-

creases in left FG, SMG, and posterior IFG activation, whereas

semantic training produced an increase at left MTG. In

contrast, in the present study phonological and semantic

training had largely similar neurobiological effects on the

neural correlates of reading in Chinese characters, including

increased activations in bilateral IPL and reduced activations

in the well-known speech output areas at bilateral IFG (BA45),

Fig. 4 e Activation values for naming phonologically trained, semantically trained, and untrained ReC phonograms in the

regions showed specific training effects: LH IFG (A) and general training effects: LH insula (B), RH IFG (C), LH IPL (D), and RH

IPL (E).

c o r t e x 5 3 ( 2 0 1 4 ) 9 0e1 0 6 99


which may reflect training-related increased processing effi-

ciency to naming at articulatory-motor circuits (Katz et al.,

2005; Poldrack & Gabrieli, 2001; Pugh et al., 2008; Sandak

et al., 2004). Perhaps the only meaningful difference between

the two training conditions was in one region of the anterior

part of left IFG (BA47), which was reducedmore when naming

semantically trained phonograms than naming phonologi-

cally trained phonograms, suggesting that semantic training

slightly induced more efficient retrieval of semantics than

phonology in naming Chinese phonograms. Although our

findings of IFG (BA47) seem new compared to Sandak et al.

(2004)’s study, in which the authors didn’t observe neural

changes of left IFG responding to semantic training, our result

did corroborate findings from our own component judgment

tasks, in which we observed that the anterior part of IFG

(BA47) was responsible more in semantic processing, paral-

leling various findings from English studies (Gough, Nobre, &

Devlin, 2005; Poldrack et al., 1999). In sum, our results

showed that phonological and semantic training have rather

similar effects on the patterns of brain activation associated

with naming aloud Chinese charactersdin contrast to the

differential effects of phonological and semantic training in

English. In other words, neural OeP and OeS pathways for

reading of Chinese are not as easily to differentiate by focused

learning as in English.

Second, the effects of the task manipulation (phonological

vs semantic) observed in the judgment tasks appear to differ

substantially from those previously observed in English too.

We found that phonological and semantic tasks induced quite

similar neural activation patterns in the reading system for

Chinese, indicating that the weights for the multi-

components of Chinese reading system do not change sub-

stantially according to the task demands. Although semantic

judgment task did induce higher activation than phonological

judgment in left IFG (BA47), most of the components in the LH

(BA45 of IFG, MFG, MTG, FG, and IPL/SPL) were activated

equivalently between phonological judgment and semantic

judgment. In contrast, previous studies in readers of English

revealed that phonological and semantic tasks result in sub-

stantially different patterns of neural activation. Relative to

phonological tasks, semantic tasks are associatedwith greater

activation in LH anterior IFG, angular gyrus, MTG, and anterior

FG; phonological tasks evoke greater activation in LH poste-

rior/dorsal IFG, insula, SMG, and posterior FG (Booth et al.,

2002a, 2002b; Demonet et al., 1992; Devlin, Matthews, &

Rushworth, 2003; McDermott, Petersen, Watson, & Ojemann,

2003; Poldrack et al., 1999; Price, Moore, Humphreys, & Wise,

1997; Pugh et al., 1996). The comparison between results

from our component judgment tasks and previous English

findings again supports our hypothesis that Chinese character

reading has more equitable division of labor than English

reading. The similar network for OeP and OeS processes in

Fig. 5 e Brain activations where untrained IReIC

phonograms were greater than untrained ReC

phonograms. Images were presented at a univariate

threshold of p < .01, corrected FDR. Images from top to

bottom correspond to the following position along the z-

axis in MNI space: D52, D42, D32, D22, D8, D2, L6, and

L12, respectively, with the LH on the right side of the

images.

c o r t e x 5 3 ( 2 0 1 4 ) 9 0e1 0 6100


reading of Chinese characters suggests that even though

subjects were asked to focus on phonological information,

semantic processes are recruited to facilitate the computation

of phonology and vice versa. In sum, the cortical regions

subserving phonological and semantic processing seem to be

more interdependent when reading Chinese characters than

when reading English words.

The different neurobiological effects of training from En-

glish and Chinese adaptive learning experiments suggest that

reading system of Chinese might illustrate a more equitable

division of labor between phonology and semantics than that

of English. The differences in the neural division of labor be-

tween readers from the two writing systems might reflect the

differences in the structure of the writing systems and might

be explained in the framework of connectionist models, with

the assumption that different statistical regularities from

print to sound andmeaning drive the differences of division of

labor between phonology and semantics across different

writing systems (Harm & Seidenberg, 2004; Seidenberg, 1992,

2011; Yang et al., 2012). As we described in Introduction, for

Chinese, the OeP and OeS mappings are relatively equally

systematic, although both are not as much consistent as OeP

mapping in English. In contrast, although the OeP mapping in

English is largely systematic (albeit not to the extent of

‘shallower’ orthographies such as Italian or Spanish), the OeS

mapping is largely arbitrarydwith the exception of morpho-

logical regularities (Rueckl, 2010; Seidenberg & Gonnerman,

2000) that are for the most part redundant with the structure

of the phonologicalesemantic mapping [and hence, available

to the reader via the phonologically mediated (orthogra-

phicephonologicalesemantic) pathway]. Thus, it is reason-

able to conjecture that reading in Chinese should involve a

more equitable division of labor between phonological and

semantic processes than reading in English (Harm &

Seidenberg, 2004; Yang et al., 2012). The comparison of the

division of labor between OeP and OeS processes in the

reading process of computing phonology from orthography is

deciphered in Fig. 7. As shown in the figure, the two processes

(direct mapping from orthography to phonology and indirect

mapping from orthography to semantics then to phonology)

both contribute to the computation of phonology in the two

writing systems. However, the weighting for the two map-

pings differ substantially between the two writing systems. In

English, there is greater reliance on OeP mapping than OeS

mapping, whereas in Chinese, the twomappings by-and-large

equally contribute to the computation of phonology.

Although we have discussed in general the differences

between Chinese and English reading in terms of division of

labor between OeP and OeS processes under the framework

of connectionist models, these differences may also partly

depend on the structure of written and phonological forms

themselves. The greater orthographic complexity and simpler

monosyllabic phonological structure of Chinese may also

contribute to the differences in reading processes between the

two writing systems.

In terms of orthographic structure, although Chinese in-

cludes both phonological radicals and semantic radicals, the

radicals may be further divided into about 600 sub-

components, which have fixed internal structures. The com-

plex visuospatial organization of these subcomponents may

make relatively greater demands on basic visual or ortho-

graphic analysis in Chinese reading than in alphabetic

reading. Also, many of the radicals or components have their

legal positions, thus awareness of these orthographic legal-

ities is important in character recognition (e.g., Li et al., 2012).

Studies have also found that orthographic processing deficit

may also directly relate to reading disability (Ho, Chan, Chung,

Lee, & Tsang, 2007; Ho, Chan, Lee, Tsang, & Luan, 2004).

With regard to phonological structure, Chinese has a

relatively simpler phonological structure than English does.

Chinesemorphemes always correspond tomonosyllables and

the syllable only allows structures such as CV, CVN, V, and VN

(C ¼ Consonant, V ¼ Vowel, and N ¼ Nasal). Indeed, syllable

awareness was found more important than phonemic

awareness in spoken word processing in Chinese, a finding

contrasted substantially with English studies (Zhao, Guo,

Zhou, & Shu, 2011). Siok, Jin, Fletcher, and Tan (2003) also

observed that distinct brain regions associate with syllable

and phoneme processing for Chinese readers. Thus, it is

plausible to infer that the specialty of the phonological

structure of Chinese might also produce some effects in Chi-

nese reading compared to English reading too (Siok, Perfetti,

Jin, & Tan, 2004; Tan et al., 2005).

Further, some brain regions have also been argued to be

specifically involved in identifying Chinese characters for the

increased demand on visuospatial processing (LHMFG and RH

FG in Bolger et al., 2005; Liu, Dunlap, et al., 2007; Nelson et al.,

2009; Tan et al., 2005) and “addressed” phonological process-

ing at the syllable level (LH MFG in Siok et al., 2003, 2004; Tan

et al., 2005). In our study, we observed that LH MFG was more

engaged for the linguistic judgment task as comparedwith the

non-linguistic judgment task, indicating its role for general

visuospatial analysis of Chinese characters (Tan, Liu, et al.,

2001; Tan et al., 2003). However, LH MFG was also recruited

more by the semantic judgment task than the phonological

judgment task as illustrated by our direct comparison map

between the two tasks and more for naming IReIC

Table 2 e Brain activation regions forregularityeconsistency effects: direct comparison betweennaming untrained IReIC phonograms and untrained ReCphonograms.

Region BA x y z Volume(mm3)

p Value(at peak)

Irregulareinconsistent > Regulareconsistent

L. IFG 9 �56 14 30 13,296 <.0001

R. Cerebellum 37 38 �70 �48 5216 .0002

L. SPL 7 �30 �56 50 4928 .0001

L. FG 19 �52 �72 �12 4248 .0003

L. Cingulate gyrus 8 �8 16 56 1448 .0001

R. Cerebellum 19 34 �82 �21 1120 .0004

L. MFG 6 �32 �4 62 720 .0005

L. MFG 6 �20 �4 52 472 .0007

R. IFG 47 34 26 �2 312 .0014

For clusters with multiple anatomical locations, only the peaks of

activations are labeled. Volumes bigger than 200mm3 are reported.

Thresholds for the regularity-consistency effects in the naming

task are set at p < .01 FDR corrected. Coordinates are MNI space at

activation peaks. IFG: inferior frontal gyrus, SPL: superior parietal

lobule, FG: fusiform gyrus, MFG: middle frontal gyrus.

c o r t e x 5 3 ( 2 0 1 4 ) 9 0e1 0 6 101


phonograms than ReC phonograms. Hence, we tend to sug-

gest that LH MFG may broadly serve roles for orthographic,

phonological, and semantic processing. In terms of RH FG, it

should be noted that our results do not support a reading-

specific role of it for Chinese reading as proposed by some

previous studies (Bolger et al., 2005; Liu, Dunlap, et al., 2007;

Nelson et al., 2009; Tan et al., 2005), because in our study,

the activation of this region was higher in a non-language

control task (line-orientation) than in the language (seman-

tic association and homophone judgment) tasks. Thus, we

suggest that RH FG may not be overly specialized for Chinese

character processing. Our claim is consistent with a recent

Chinese and English reading comparison study by Hu et al.

(2010) with the most tightly matched participants and ana-

lyses, which did not show any RH FG activation more for

Chinese readers than English readers. To sum, our data sug-

gest that the fundamental differences in orthographic and

phonological representation between Chinese and alphabetic

systems seem drive differences in neural activation patterns

in LH MFG but not in RH FG for the reading network. However,

additional workwill be required for a complete understanding

of whether these fundamental representational differences

have a comparable, bigger, or smaller impact than the differ-

ential statistical structure of weighting between OeP and OeS

mappings in driving differences in division of labor between

the two writing systems.

5. Limitations

In the current study, we have made our primary claim (equi-

table division of labor between phonology and semantics for

Chinese reading) based on relatively similar responses for

phonological and semantic strategies. However, we believe

that the results that we found in this paper were not null re-

sults but rather reflect important and specific characteristics

of Chinese reading. It might be argued that the lack of large

differences produced by phonological training and semantic

training could likely be due to the basics of the adaptive

learning paradigm and the naming task that we used in the

study, which may not be sensitive to detect the differences

across the two training conditions. This argument is not

tenable, because of the following reasons. Firstly, we found

many differences in cortical activation in the naming task

between trained and untrained conditions regardless of

training types. The most important neural facilitation effect

Fig. 6 e Brain activations greater in the semantic judgment

task than in the phonological judgment task (yellow/red)

and greater in the phonological judgment task than in the

semantic judgment task (blue/purple) (A). Brain activations

greater in the linguistic judgment task (collapsed across

the phonological judgment and the semantic judgment)

than in the line-orientation judgment task (yellow/red) and

greater in the line-orientation judgment task than in the

linguistic judgment task (blue/purple) (B). Images were

presented at a univariate threshold of p < .0001, corrected

FDR. Images from top to bottom correspond to the

following position along the z-axis in MNI space: D42,

D28, D20, D12, D4, L4, L12, and L18, respectively, with

the LH on the right side of the images.

c o r t e x 5 3 ( 2 0 1 4 ) 9 0e1 0 6102


for trained ReC phonograms compared to untrained ReC

phonograms was reduced activation at bilateral IFG/insula,

which was consistent with many previous training-related

studies in English (Katz et al., 2005; Poldrack & Gabrieli, 2001;

Pugh et al., 2008; Sandak et al., 2004). This suggests that

neural response of the task of naming aloud is actually sen-

sitive to training and the reason that it is not sensitive to

different training conditions might be mainly driven by the

inherent characteristics of the training focus rather than the

naming task.

Secondly, we also observed robust results with regard to

the regularityeconsistency effects in the naming task: The

less consistent (IReIC) phonograms induced higher activation

at distributed LH reading circuits than the phonograms that

are more consistent (ReC phonograms). Themain locus of the

regularityeconsistency effect at left IFG in naming Chinese

phonograms was similar to the region associated with

phonological consistency effects in English (Fiez, Balota,

Raichle, & Petersen, 1999; Frost et al., 2005; Graves et al.,

2010) and overlapped with the loci reported in two previous

studies respectively for regularity effects (Tan, Feng, et al.,

2001) and consistency effects (Lee et al., 2004) in Chinese

reading. We also observed the differential activation of pre-

dominately semantic regions (LH MTG) as a function of the

orthographicephonological consistency in Chinese reading, a

similar neuroimaging finding as parallel manipulations in

English (Frost et al., 2005; Graves et al., 2010). The

Table 3 e Brain activation regions for judgment tasks. Specific linguistic effects: direct comparison between phonologicaljudgment and semantic judgment. General linguistic effects: direct comparison between linguistic judgment (collapsed ofphonological and semantic judgments) and line-orientation judgment.

Region BA x y z Volume (mm3) p Value (at peak)

Semantic judgment > Phonological judgment

L. IFG 47 �40 41 �6 4232 <.0001

Phonological judgment > Semantic judgment

R. Cingulate gyrus 31 20 �52 24 7128 <.0001

Linguistic judgment > Line judgment

L. IFG 47 �38 38 �4 12,984 <.0001

R. Cerebellum 18 14 �88 �30 6816 <.0001

L. MTG 21 �58 �42 0 3496 <.0001

L. MOG 18 �26 �84 �6 3120 <.0001

L. Cuneus 18 �10 �98 18 2840 <.0001

L. Parahippocampus 20 �38 �13 �22 1880 <.0001

L. Collateral sulcus 37 �30 �36 �18 872 <.0001

R. Cuneus 18 38 �96 0 720 <.0001

L. MFG 6 �38 10 52 712 <.0001

L. STG 8 �8 34 47 600 <.0001

R. MFG 13 36 �18 22 520 <.0001

R. Cuneus 18 16 �100 18 384 <.0001

L. MFG 13 �36 �26 24 304 <.0001

Line judgment > Linguistic judgment

R. IPL 7 26 �64 34 24,200 <.0001

R. Precentral gyrus 6 32 �2 50 7504 <.0001

L. SPL 7 �22 �60 56 5528 <.0001

L. Cerebellum 37 �36 �46 �28 3136 <.0001

R. FG 37 54 �62 �14 3072 <.0001

L. Cerebellum 37 �30 �60 �42 1536 <.0001

L. Precentral gyrus 6 �22 �6 50 1336 <.0001

R. MTG 19 38 �78 18 1320 <.0001

R. MTG 9 51 6 28 912 <.0001

R. Cerebellum 37 40 �46 �32 568 <.0001

L. SPL 19 �36 �80 14 328 <.0001

For clusters with multiple anatomical locations, only the peaks of activations are labeled. Volumes bigger than 200 mm3 are reported.

Thresholds for the specific and general linguistic effects in the judgment task are set at p < .0001 FDR corrected. Coordinates are MNI space at

activation peaks. IFG: inferior frontal gyrus, MTG:middle temporal gyrus, MOG:middle occipital gyrus, MFG:middle frontal gyrus, STG: superior

temporal gyrus, IPL: inferior parietal lobule, SPL: superior parietal lobule, FG: fusiform gyrus.

Fig. 7 e Division of labor between OeP and OeS processes

in the reading process of computing phonology from

orthography in English and Chinese.

c o r t e x 5 3 ( 2 0 1 4 ) 9 0e1 0 6 103


regularityeconsistency effects in the current results came

from the same imaging runs as the training comparison data,

suggesting a lack of fundamental problems with the data and

the less positive results from the training comparison might

not reflect anything about statistical power of the fMRI design.

In addition, both Frost et al. (2005) and Graves et al. (2010)

have used naming aloud as an experimental task and the

neural OeP andOeS pathwayswerewell separated by amulti-

parametric design of the stimuli (e.g., consistency by image-

ability) in both studies of English readers. Indeed, in the pre-

vious study most closely related to ours, Sandak et al. (2004)

also used the naming task to reveal differential effects of

phonological and semantic training in English. Thus, naming

aloud is certainly a task that has been proved to be sensitive to

examine division of labor between phonology and semantics

and our results should not be due to a problem from the task

thatweused but rather because of theChinesewriting system.

Finally, our judgment tasks also revealed that the phono-

logical and semantic processing for ReC phonograms pro-

duced relatively little differences in ensembles of LH reading

areas, which coincides with an earlier finding by Tan, Liu,

et al. (2001) (although in that study the tasks were not

directly compared and the stimuli included various types of

Chinese characters not specific to ReC phonograms). Using an

explicit task, which specifically tapped into OeP and OeS

processing of Chinese, we still failed to find any differences

between phonological and semantic processes. This result

again suggests that the little differences in our training com-

parison between phonological and semantic reinforcements

may not be due to the paradigm problem, but rather reflect

interdependence of phonological and semantic processes in

Chinese reading, per se. It should be noted that some of the

previous studies (if not all) have reported some differentia-

tions in the neural circuits between Chinese semantic pro-

cessing and phonological processing. However, the subtle

differences might only be able to detect in some special cir-

cumstances, such as increasing task demands and not

restricting the tasks to reading (e.g., Wu, Ho, & Chen, 2012) or

using two-character words as stimuli for reading (e.g., Booth

et al., 2006). Whether the findings from Wu et al. and Booth

et al. can be generalized into single character reading in Chi-

nese seems inconclusive.

Nevertheless, we acknowledged that the comparison in

adaptive learning that we made between Chinese and English

perhaps should be taken conservative, because of the differ-

ences in terms of stimuli and procedure between the two

studies. It might be valuable to conduct a future study on

adaptive learning of English ReC words with similar training

procedure as the current Chinese study, so that the division of

labor between the twowriting systems could be compared in a

more matched way. It might also be interested to look at

cooperative learning effects in reading Chinese pseudo-

characters. Actually, Zhao (2012) have addressed this issue

and found cooperation between phonology and semantics in

learning to read Chinese pseudo-characters, which may

deserve a follow-up neuroimaging study to examine the

neural division of labor effects between phonology and se-

mantics in learning Chinese pseudo-characters.

It is also worth highlighting that the current paper only ad-

dresses the issue related to the division of labor between

phonology and semantics in reading single Chinese characters

and whether division of labor between phonology and seman-

tics in reading two or multi-character words or even sentences

or texts in Chinese is beyond the scope of this paper and is still

an open question which might be valuable to investigate in

future studies. In addition, we have examined the division of

labor between phonology and semantics to a task of reading

aloud in Chinese, but a full account of the division of labor be-

tween phonology and semantics will require consideration of

other tasks (e.g., silent readingwith computation ofmeaning as

the ultimate goal). Indeed, Harm and Seidenberg (2004) have

modeled the division of labor between phonology and seman-

tics in Englishwith silent reading formeaning as the task. None

of previous studies have conducted similar modeling or

empirical research in Chinese reading yet. Future studiesmight

be worth pursuing more in this direction too.

6. Conclusions

In sum, the present study suggests that the division of labor

between phonology and semantics in Chinese reading is equi-

table, which may be driven by similar systematicity between

OeP and OeS mappings in the writing system of Chinese. Even

though the neural network that underlies reading in Chinese

largely overlaps the network responsible for reading in English,

the neural division of labor between phonology and semantics

in Chinese reading contrasts tremendously from English find-

ings. Phonological and semantic processes for English readers

induced great neural differentiations in brain circuits, whereas

the two processes for Chinese readers have largely similar

neurobiological effects. This culture-constrained difference

may be caused by language-specific statistical regularities

embodied in the writing system.

Acknowledgments

This work was supported by the National Institute of Child

Health andHumanDevelopment (grant number P01HD001994)

to Haskins Laboratories, the Natural Science Foundation of

Beijing (grant number 7132119) to Hua Shu, and the Natural

Science Foundation of China (grant number 31271082) to Hua

Shu.

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