Title: An ERP study for investigating the semantic combinability effect and phonetic
consistency effect in reading Chinese
Names: Chun-Hsien Hsu1, Jie-Li Tsai1, Chia-Ying Lee1, 2, Daisy L. Hung1 and Ovid J.L.
Tzeng1, 2
Affiliation:
1. Laboratory of Cognitive Neuroscience, National Yang-Ming University, Taiwan
2. The Institute of Linguistics, Academia Sinica, Taiwan
Abstract
The majority of Chinese characters are phonograms, which usually contain a phonetic
radical on the right and a semantic radical on the left. According to the split-fovea
theory, the semantic and phonetic radicals of a central fixated phonogram may be
initially projected to and processed in right and left hemispheres, respectively.
Recently, a repetitive transcranial magnetic stimulation (rTMS) study by Hsiao et al.
(2006) showed that rTMS over the left occipital cortex impaired the facilitation of
semantic radicals with large combinability, whereas right occipital rTMS did not.
Their results suggested that for a character with a large combinability semantic radical,
the reliance is more skewed to the information on the right of the character. The
presented event-related potential (ERPs) study aimed to examine this hypothesis by
manipulating character consistency and semantic combinability in a homophone
judgment task. Character consistency was defined as whether a group of characters
containing the same phonetic radical have same pronunciation. Semantic
combinability was defined as the number of characters sharing the same semantic
radical. If the hypothesis is true, the consistency effect shall be more salient in reading
characters with high semantic combinability. The results showed significant semantic
combinability effect and consistency effect on P200. Meanwhile, there was a
significant consistency-by-semantic combinability interaction in N400. High
consistency characters revealed greater negative of N400 than low consistency
characters for reading characters with high semantic combinability, but not for those
with low semantic combinability. These findings support the split-fovea theory for
lexical processing.
Laboratory of Cognitive Neuroscience, Library and Information Building, National
Yang-Ming University
N0. 155, Sec. 2, Linong st.
Pei-Tou District, Taipei City, Taiwan
Introduction
In Chinese, approximately 80% of the characters are phonetic compounds that
are made up of a semantic radical (usually on the left) and a phonetic radical (usually
on the right). Furthermore, these sub-character units could contribute in recognizing
Chinese phonograms by means of phonological consistency and radical combinability.
The phonological relationship between phonogram and phonetic radical can be
addressed by consistency. This refers to whether the pronunciation of a character
agrees with those of its orthographic neighbors which, by definition, contain the same
phonetic radical. While manipulating the character frequency and consistency, robust
previous studies have demonstrated the frequency by consistent interaction in the
naming of Chinese phonograms (Fang et al., 1986; Lee et al., 2004; Lee et al., 2005).
That is, naming low consistency characters revealed longer reaction time and lower
accuracy than naming high consistency characters, especially when those are low
frequency characters. The radical combinability refers to the number of characters
sharing same phonetic or semantic radical, therefore it can be further divided into the
semantic combinability and the semantic combinability. By manipulating the radical
combinability (for phonetic and semantic radical respectively) and the radical position
in the character decision task, Feldman and Siok (1997) found facilitative
combinability effects for both semantic and phonetic radicals, but not reliably within
their positions. For phonetic radicals, the combinability effect was significant on both
left and right positions. For semantic radicals, the combinability effect was significant
only when the semantic radical was on the left. These findings suggested that radical
function should be considered while investigating the radical processing.
Recently, some studies suggested the anatomical constrain of the split-fovea
phenomena, which assumes a vertical meridian split in the foveal representation and
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the consequent contralateral projection of information in the two hemifields to the two
hemispheres, should be considered in reading (see Lavidor and Walsh, 2004 for
review). Therefore, the presented study aimed to further verify the rising question that
how the two radicals interact in retrieving phonological and semantic knowledge.
Recently, Hsiao, Shillcock and Lavidor (in press) suggested while recognizing high
semantic combinability characters the impact of the phonetic radical on the semantic
judgment would be increased due to a less informative semantic radical. However,
their investigation was relied on the reaction time measurement that reflects the
summation of all the steps prior to the behavioral response. By measuring the
event-related potentials (ERPs), different cognitive operations in the brain could be
separated at each millisecond from the onset of the language stimuli. For example,
N170 has been associated with word form analysis; P200 has been used to index
mechanisms related to feature detection (Luck and Hillyard, 1994), and the N400 is
well known for being associated with lexical semantic processing (Curran et al., 1993).
Current studies have found that low frequency words elicit greater negativity of N400
in comparison with high frequency words. In contrast, frequency of the first syllables
of a word elicits a reversed pattern (Barber et al., 2004; Barnea and Breznitz, 1998).
Furthermore, they found the onset of syllable frequency effect (around 150mse, words
containing high frequency syllables produced less positive P200) was earlier than that
of lexical frequency effect (around 350mse). Moreover, Lee et al (in press) and Hsu et
al. (in preparation) also suggested these three components could reflect radical
processing in reading Chinese also.
Both consistency and semantic combinability were manipulated in the presented
experiment. According to the hypothesis of split-fovea theory, the usage of phonetic
radical would depend on the combinability of the semantic radical; hence there shall
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be a consistency-by-semantic combinability interaction. Specifically, the consistency
effect shall be more salient in reading characters with high semantic combinability
than in reading those with low semantic combinability.
Experimental Procedure
Participants
Thirty-one native Chinese speakers were paid for NT500 for their participation.
All participants were native Chinese speakere and right-handed with normal or
corrected vision.
Experimental Design and materials
A list of 120 Chinese phonograms, configured horizontally with a semantic
radical on the left and a phonetic radical on the right, were selected from the
Academia Sinica balanced corpus (1998). These phonograms were divided into four
conditions by manipulating their phonological consistency (high versus low) and
semantic combinability (large versus small). The index for semantic combinability
and consistency was calculated based on 3697 phonograms. The semantic
combinability is defined as the number of phonograms that shared a semantic radical.
On the other hand, within a group of phonograms which share a same phonetic radical,
let’s say characters with the same pronunciation were classified as ‘friends’. Character
consistency was indexed by the relative proportion of the numbers of friends and
semantic combinability. Table 1 illustrates the examples of the material and the
characteristics of each condition. Each target character was paired by a homophone
for the homophone judgment task. This task was aimed to ensure that the phonology
of target was being processed without an overt naming response which might cause
muscular noise for the EEG recording. To ensure that participants knew the correct
pronunciation of the target character, every target character was followed by a
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homophone as a “yes” trial. For the balance of response, another 120 non-homophone
pairs were created for “no” trials.
Procedure
Participants were seated in front of a monitor at a distance of approximately
60cm in an acoustically shielded room. Each participant received 35 trials for practice
and 240 randomized experimental trials in three test sessions. Participants could take a
break between test sessions for as long as they needed. Total time for the experiment
took about thirty minutes. Each trial started with two short vertical lines for 500msec
and it was replaced by a target character for 150msec. Participants were asked to
fixate the middle of the space between the two short lines all the time during the
experiment and name the target character silently. The target character was replaced
by a cross for 850 ms and then a probe character was presented. Participants were
asked to decide whether the target character and probe character were homophones by
pressing a button on the mouse as quickly and as accurately as possible. The index
finger indicated “yes” and the middle finger “no”. The correctness and reaction time
were recorded. After the disappearance of the probe character, a blank screen was
presented for 1800 ms the next trial.
EEG Recording and ERPs pre-processing
The electroencephalogram (EEG) was recorded from 64 Ag/AgCl electrodes
(QuickCap, Neuromedical Supplies, Sterling, USA) with a common vertex reference
located between Cz and CPz. The data was re-referenced off-line to the average of the
right and the left mastoids for further analysis. Vertical eye movements (VEOG) were
recorded by a pair of electrodes placed on the supra- and infra-orbital ridges of the left
eye. Horizontal eye movements (HEOG) were recorded by a pair of electrodes placed
lateral to the outer canthus of the right and left eyes. A ground electrode was placed on
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the forehead anterior to Fz. Electrode impedances was kept below 5 KΩ. EEG signal
was continuously recorded and digitized at a rate of 1000Hz. Signal was amplified by
SYNAMPS2 ® (Neuroscan, Inc.) amplifiers with the low-pass filter at DC-100Hz for
off-line analysis. For the off-line analysis, the continuous wave was epoched with
100msec pre-stimulus interval and 922msec post-stimulus interval. The pre-stimulus
interval was used for baseline correction. Trials contaminated by eye movement or
with voltage variations larger than 60 V were rejected. The data was then band-pass
filtered at 0.01~30 Hz (zero phase shift mode, 12 dB). ERPs of four conditions were
computed for every participant at every electrode site by averaging over
corresponding trials with correct response.
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Result
Figure 1 shows the grand averaged ERPs of four conditions at 9 representative
electrodes. As shown in the figure, two major components were identified for further
analysis. The first component is P200, which is a positive going wave distributed at
frontal-central sites, reached its peak around 220msec. The third one is N400, which
is a slowly negative going wave, peaked around 350msecand appeared at almost every
electrode. Effects of consistency and combinability were assessed via comparisons of
mean amplitudes in two temporal time windows of interest: P200 (210-240 ms) and
N400 (300-450 ms).
Differently repeated measure ANOVA was performed on these components,
including factors such as consistency, semantic combinability, and the electrode in the
region of interest. Both P200 and N400 were divided into the midline analysis and the
lateral analysis since these components were distributed on the entire scalp. For
midline analysis, the dependant variable was the mean amplitude during the latency
from 300msec to 450msec; and five electrodes (FZ, FCZ, CZ, CPZ, PZ) were chosen
as the electrode variable. For lateral analysis, the same dependant variable of midline
analysis was used and eight electrodes (F3/4, FC3/4, C3/4, CP3/4) were chosen as the
electrode variable. Accordingly, midline analysis were tested by a three-way ANOVA,
which included the character consistency, the semantic combinability, and electrode as
within-subject factors; and lateral analysis was tested by a four-way ANOVA, which
included the character consistency, the semantic combinability, hemisphere and
electrode as within-subject factors. For each ANONA, the Greenhouse-Geisser
adjustment to the degrees of freedom was applied to correct for violations of
sphericity associated with repeated measures. Accordingly, for all F tests with more
than 1 degree of freedom in the numerator, the corrected p-value is reported. Post hoc
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comparisons were carried out by using Tukey’s procedure.
P200
The data showed a non-significant consistency effect (midline: F(1,25) = 2.821,
p > 0.1; lateral: F(1,25) = 2.735, p > 0.1) and a significant semantic combinability
effect (midline: F(1,25) = 9.211, p < 0.01; lateral: F(1,25) = 12.219, p < 0.01). Low
combinability characters revealed more positive P200 than high combinability
characters. The interaction between consistency and semantic combinability effect
was not significant (midline: F(1,25) = 0.117, p > 0.1; lateral: F(1,25) = 0.188, p >
0.1). None of the interaction effect was significant (p > 0.1). The result of consistency
effect here didn’t replicate previous studies (Lee et al, in press; Hsu et al., in
preparation). According to these studies, the consistency effect may be localized at
frontal site. Therefore, an additional ANOVA was performed by including mean
amplitude of three frontal electrodes only (F3, FZ and F4). The results showed both
main effect of consistency (F(1,25) = 4.815, p < 0.05) and semantic combinability
(F(1,25) = 11.813, p < 0.01) were significant. Low consistency characters revealed
more positive P200 than high consistency characters. However, the interaction
between consistency and semantic combinability effect was still not significant
(F(1,25) = 0.131, p > 0.1).
N400
The data showed a non-significant consistency effect (midline: F(1,25) = 0.244,
p > 0.1; lateral: F(1,25) = 0.946, p > 0.1) and a non-significant semantic combinability
effect (midline: F(1,25) = 0.396, p > 0.1; lateral: F(1,25) = 1.326, p > 0.1).
Interestingly, the interaction between consistency and semantic combinability effect
was significant (midline: F(1,25) = 6.858, p < 0.05; lateral: F(1,25) = 6.575, p < 0.05).
Post hoc test revealed that high consistency characters revealed more negative N400
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than low consistency characters only in characters with high semantic combinability
(midline: F(1,50) = 5.509, p < 0.05; lateral: F(1,50) = 6.699, p < 0.05). However, the
consistency effect in low semantic combinability characters was not significant
(midline: F(1,25) = 2.977, p = 0.08; lateral: F(1,25) = 1.785, p > 0.1).
Discussion
This study showed that low combinability characters produce more positive
amplitude at the P200 time window and less negative amplitude at the N400 time
window than high combinability characters. This finding is congruent with Hsu et al’s
(in preparation) finding and supports that the two-stage framework for lexical access
use P200 and N400 to index the early and late stage of lexical processing. According
to this framework, characters with high combinability (larger orthographic neighbors)
will facilitate their processing at orthographic level due to the larger orthographic
activation and show less positivity in P200. On the other hand, the larger
neighborhood also led to greater semantic competition and thus shows greater N400
(Holcomb et al., 2002). This is also congruent with the syllable frequency effect found
in P200 and N400 (Barber et al., 2004; Barnea and Breznitz, 1998), which showed
words containing high frequency syllables produced less positive P200 and more
negative N400 than words containing low frequency syllables.
This study also demonstrates that low consistency characters produce more
positive amplitude at the P200 time window and less negative amplitude at the N400
time window than high consistency characters. This finding is congruent with our
previous studies (Lee et al., in press; Hsu et al., in preparation). Characters associate
with more phonological alternatives and show greater P200 positivity. The N400
indexes the semantic competition at lexical level. Given the semantic combinability
was matched between high and low consistency conditions, there were more
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homophones within a selected phonological subgroup for high consistency characters
than there were for low consistency characters. Therefore, characters with high
consistency would involve greater semantic competition and thus showed greater
N400 than low consistency characters. Furthermore, the consistency effect was
especially true of those with high semantic combinability characters. This pattern of
interaction was congruent with the prediction by the theory of fovea-splitting, which
is the processing on characters with less informative semantic radicals (high semantic
combinability) may rely on the phonetic radicals more than those characters whose
semantic combinability are small.
In conclusion, this study suggests that reading Chinese phonograms involve an
early selection of the phonological alternatives associated with a given phonetic
radical and the late competition among the candidate within a selected phonological
group. Most importantly, the lexical processing of character recognition is
accomplished by the interaction of statistical mapping among orthography, phonology,
and semantics. Moreover, these mapping may induce different type of
cross-hemisphere processing in reading Chinese characters. Future studies may
further examine the relationship between semantic and phonetic radicals and also how
their function interacts with the neighborhood properties in the domain of orthography,
phonology, and semantics.
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Table 1. Table 1.—Mean consistency index, semantic combinability, character
frequency, number of strokes, semantic combinability and represented
characters of four conditions.
High Consistency
Conditions
Low Consistency
High
Low
High
Low
Combinability Combinability Combinability Combinability
Number of Strokes
13.37
15.10
13.30
13.20
Frequency
29.03
25.53
25.23
22.67
Consistency
0.87
0.86
0.29
0.27
Semantic Combinability
82.17
11.70
90.53
9.17
Phonetic combinability
7.17
6.87
7.87
8.40
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Figure 1. Grand average ERPs of four conditions across 26 participants at 9
represented electrodes.
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