3.1 ERP data
Table 2 illustrates the amplitudes and latencies of N400 and P600 components elicited by the FL-R, L-FR, FL-FR, and NL-NR stimuli in three separate runs of recording. As N400 and P600 components were typically distributed in the centroparietal areas and reached the maximum at the midline electrode sites (Brouwer et al., 2012; Brouwer & Hoeks, 2013; Swaab et al., 2012; van Herten et al., 2005), analyses of the amplitudes and latencies of N400 and P600 are carried out at the midline sites of Fz, Cz, and Pz. The environmental sounds (running water, train noises, and birds singing) as novel stimuli in three runs of ERP recording did not elicit any language-related N400 and P600 effects, thus confirming the connection between N400 and P600 with language.
Table 2. N400 and P600 amplitudes and latencies in FL-R, L-FR, and FL-FR configurations
|
|
|
N400
|
P600
|
Configuration
|
Electrode
|
Stimulus
|
Amplitude
(μV)
|
Latency
(ms)
|
Amplitude
(μV)
|
Latency
(ms)
|
FL-R
|
Fz
|
NL-NR
|
-8.36
|
568
|
9.06
|
655
|
FL-R
|
-12.61
|
584
|
6.26
|
705
|
Cz
|
NL-NR
|
-6.23
|
568
|
8.98
|
638
|
FL-R
|
-11.41
|
580
|
4.99
|
720
|
Pz
|
NL-NR
|
-3.20
|
569
|
7.27
|
635
|
FL-R
|
-10.71
|
564
|
3.64
|
721
|
L-FR
|
Fz
|
NL-NR
|
-2.53
|
450
|
3.10
|
531
|
L-FR
|
-6.10
|
418
|
4.86
|
613
|
Cz
|
NL-NR
|
-2.52
|
434
|
5.07
|
482
|
L-FR
|
-6.26
|
424
|
5.50
|
633
|
Pz
|
NL-NR
|
-3.14
|
419
|
3.64
|
500
|
L-FR
|
-5.35
|
397
|
8.90
|
823
|
FL-FR
|
Fz
|
NL-NR
|
-6.67
|
444
|
6.66
|
546
|
FL-FR
|
-6.30
|
461
|
6.09
|
728
|
Cz
|
NL-NR
|
-7.22
|
444
|
7.46
|
534
|
FL-FR
|
-5.28
|
467
|
2.73
|
731
|
Pz
|
NL-NR
|
-5.98
|
442
|
2.95
|
532
|
FL-FR
|
-4.07
|
433
|
3.03
|
750
|
In the first run of the experiment regarding the FL-R configuration, the amplitudes of the N400 component induced by the unfiltered NL-NR stimuli were smaller than the FL-R signals elicited at the midline sites. But for the P600 component, the FL-R stimuli elicited smaller amplitudes than the NL-NR stimuli did. The latencies of the N400 and P600 components showed that FL-R elicited longer response time compared to the latencies induced by the NL-NR signals, except for the N400 latency at the Pz site elicited by FL-R was slightly shorter. As for the L-FR configuration, the L-FR stimuli induced larger amplitudes of both N400 and P600 components in the centroparietal areas compared with NL-NR. In addition, the L-FR signals elicited shorter latencies of the N400 component but longer P600 latencies at the midline sites relative to the NL-NR stimuli. Compared to the NL-NR signals, FL-FR elicited smaller N400 amplitudes, and smaller P600 amplitudes were also induced by the FL-FR signals except for the Pz site that showed a slightly larger amplitude. Longer latencies of N400 induced by FL-FR occurred at the Fz and Cz sites but shorter latency at the Pz site relative to NL-NR. P600 latencies reflected that FL-FR elicited a longer response time than NL-NR did.
The absolute voltage values of ERPs elicited by the FL-R, L-FR, FL-FR, and NL-NR stimuli at the midline sites (Fz, Cz, and Pz sites) are plotted in Figure 5. ERP waves are plotted in the FL-R, L-FR, and FL-FR configurations respectively corresponding to three separate runs of ERP recording. Green waves refer to the deviant stimuli (i.e., the FL-R, L-FR, and FL-FR stimuli in three runs of the experiment respectively) and blue waves represent the standard NL-NR stimuli in three runs. Since the difference waves between deviant and standard stimuli provide more information on the impact of linguistic manipulations on brain responses rather than the absolute voltage values (Morgan-Short & Tanner, 2014), the difference waves between FL-R/L-FR/FL-FR and NL-NR are displayed by red waves to identify the N400 and P600 effects.
N400 and P600 effects were elicited due to linguistic (semantic and syntactic) anomalies relative to well-formed linguistic signals (Kutas & Federmeier, 2011; Swaab et al., 2012). From the waveforms in the FL-R configuration, FL-R elicited larger N400 amplitudes compared to NL-NR, which indicates a higher processing load of semantic manipulation elicited by the FL-R signals. The N400 effect was elicited by FL-R at around 500 ms after the stimulus onset in the Pz site. But for P600, FL-R did not elicit any obvious P600 effect since smaller amplitudes were elicited at the midline sites relative to NL-NR. It indicated that a lower load of syntactic manipulation was elicited by FL-R than NL-NR. The FL-R configuration shows that the brain actively manipulates semantic processing which occupies a higher processing load compared to NL-NR, meanwhile, the FL-R signals are syntactically easier to process.
Regarding the L-FR configuration, both N400 and P600 effects were elicited by the L-FR signals at the midline sites. Different from the other configurations of FL-R and FL-FR, difference waves between L-FR and FL-FR fluctuated considerably at three midline sites. It indicates that the L-FR signals are unusual and unexpected for the brain to process, as a result, the brain struggles with the L-FR stimuli.
The waveforms in the FL-FR configuration showed that smaller N400 amplitudes were elicited by FL-FR compared to NL-NR, and no N400 effect was found in the midline sites. But the P600 effect induced by the FL-FR signals was observed at the Pz site. It indicated that the FL-FR stimuli were semantically easier for the brain to process than NL-NR since the FL-FR signals were prosodic signals without identifiable lexical information, however, the FL-FR stimuli with only intonation and rhythm information were unusual for the participant to manipulate syntactic processing.
Topographic maps of the ERP components N400 and P600 that were elicited by the standard and deviant stimuli in three runs of the experiment were plotted in Figure 6. Since the novel stimuli of environmental sounds did not elicit the N400 and P600 components during three runs of recording, the scalp topographies of ERP components elicited by the environmental sounds were not plotted in Figure 6. In the topographic maps, the N400 component with negative voltage values was presented on the left, and the positive P600 component was shown on the right.
As standard stimuli in the experiment, the unfiltered NL-NR stimuli maintained all frequencies of the auditory signals so that each word in the sentences could be identified. Thus, a smaller processing load and shorter response time for semantic manipulation were elicited by the NL-NR signals relative to the FL-R stimuli. But it showed a quite small mental workload for syntactic processing of the FL-R signals though response time to FL-R was longer compared to NL-NR. Topographic maps showed that the N400 and P600 components elicited by NL-NR were distributed in central and frontal areas symmetrically, but FL-R induced left-lateralized patterns for semantic and syntactic processing in the frontal area peaking at 584 ms and 719 ms respectively.
The L-FR signals elicited larger amplitudes of N400 and P600 components, which indicated that a heavier mental load was required for semantic and syntactic manipulations relative to the NL-NR signals. This may result from the L-FR signals violating the left ear advantage for prosodic information and the right ear advantage for linguistic signals (Meyer et al., 2002; Sammler et al., 2015; Tervaniemi & Hugdahl, 2003; Vigneau et al., 2006). L-FR sent prosodic signals to the right ear and linguistic information to the left ear, which is presumably a non-optimal signal for the brain to process. As to latencies, a shorter response time elicited by L-FR was observed for semantic processing, but a longer response time was detected for syntactic manipulation compared to the NL-NR signals. Topographic maps indicated that NL-NR in the second run of the experiment elicited N400 and P600 with general symmetrical distributions in the central areas, similar to the distributions in the first run of the experiment. In addition, the N400 component elicited by the L-FR signals was distributed in the central and frontal areas, and the P600 component was detected in the centroparietal area. It is not obvious that L-FR elicited lateralization during semantic and syntactic processing.
For both-ears-filtered FL-FR stimuli, smaller amplitudes of N400 were elicited by FL-FR, indicating a small processing load for semantic manipulation. Further, smaller P600 amplitudes elicited by FL-FR were also found in the central and frontal areas, which suggested a lower mental workload for syntactic processing. This result supports the assumptions of the authors that 320 Hz low-pass filtering reduces the processing load for language signals since the filtered sounds only contain intonation and rhythm information that lightens the load for processing meanings of the signals (Asp et al., 2012; Guberina, 1972; He et al., 2015; Lian, 1980; Yang, 2016). As to response time, FL-FR with only prosodic information sounded unusual and was an unexpected language signal for the participant, and thus, required a longer response time to process both for semantic and syntactic information. Only one exception occurred in the centroparietal area where FL-FR induced a shorter response time regarding semantic processing relative to the NL-NR signals. Topographic maps suggest that N400 and P600 elicited by NL-NR presented a basically symmetrical distribution in the central and frontal areas, which was consistent with the results of distributions in the other two runs of the experiment. But the N400 component elicited by FL-FR was distributed in the occipital area and the P600 showed a slightly left-lateralized distribution in the central area.
Additionally, the environmental sounds of running water, train noises, and birds singing, used as novel stimuli in three runs of the experiment, did not elicit any language-related N400 or P600 component. It indicates that the brain distinguishes language and non-language signals successfully and processes them differently.
3.2 fMRI data
To investigate brain regions for processing low-pass filtered and unfiltered stimuli under dichotic listening conditions, four runs of fMRI scanning with a rest-stimulus block design were performed for the FL-FR, FL-R, L-FR, and NL-NR signals respectively. Brain activations and activation maps for FL-FR, FL-R, L-FR, and NL-NR stimuli were presented in Table 3 and Figure 7.
Table 3. Brain activations for FL-FR, FL-R, L-FR, and NL-NR stimuli
Stimulus
|
Region
|
BA
|
k
|
z-value
|
T
|
MNI coordinates
|
x
|
y
|
z
|
FL-FRa
|
L-MFG
|
-
|
158
|
3.69
|
3.92
|
-40
|
48
|
-6
|
|
R-IFG
|
-
|
113
|
3.38
|
3.56
|
50
|
42
|
4
|
|
|
46
|
|
3.33
|
3.50
|
54
|
44
|
18
|
|
|
9
|
|
3.61
|
3.82
|
62
|
16
|
32
|
|
L-Putamen
|
-
|
111
|
3.14
|
3.29
|
-14
|
16
|
6
|
|
L-STG
|
-
|
110
|
3.61
|
3.83
|
-68
|
-22
|
12
|
|
|
42
|
|
3.60
|
3.82
|
-70
|
-32
|
20
|
|
R-SFG
|
-
|
101
|
3.69
|
3.92
|
-40
|
48
|
-6
|
|
|
10
|
|
3.49
|
3.69
|
24
|
62
|
-6
|
|
R-STG
|
22
|
97
|
3.71
|
3.94
|
54
|
-12
|
8
|
|
R-Cun
|
-
|
93
|
3.42
|
3.60
|
12
|
-88
|
36
|
|
R-MTG
|
-
|
55
|
3.27
|
3.43
|
40
|
-70
|
26
|
|
|
|
|
|
|
|
|
|
FL-Ra
|
L-Inferior parietal lobule
|
-
|
871
|
4.2
|
4.54
|
-34
|
-60
|
64
|
|
|
40
|
|
4.17
|
4.51
|
-44
|
-52
|
-60
|
|
|
42
|
|
3.90
|
4.17
|
-70
|
-22
|
14
|
|
L-MFG
|
8
|
195
|
3.73
|
3.97
|
-26
|
24
|
62
|
|
|
10
|
80
|
3.60
|
3.82
|
-39
|
45
|
30
|
|
R-Inferior parietal lobule
|
40
|
180
|
3.37
|
3.55
|
60
|
-50
|
42
|
|
R-Precuneus
|
7
|
74
|
3.45
|
3.65
|
2
|
-62
|
52
|
|
R-STG
|
22
|
56
|
3.65
|
3.88
|
60
|
-6
|
6
|
|
R-MFG
|
10
|
51
|
3.62
|
3.84
|
36
|
42
|
24
|
|
L-STG
|
41
|
19
|
3.26
|
3.43
|
-42
|
-34
|
12
|
|
|
|
|
|
|
|
|
|
L-FRb
|
Corpus callosum
|
-
|
475
|
2.14
|
2.19
|
6
|
-18
|
16
|
|
R-STG
|
-
|
356
|
3.05
|
3.19
|
68
|
-6
|
-8
|
|
|
22
|
|
2.72
|
2.82
|
58
|
-8
|
8
|
|
Midbrain
|
-
|
103
|
2.10
|
2.15
|
4
|
-16
|
-16
|
|
L-Brainstem
|
-
|
65
|
2.41
|
2.48
|
-16
|
-22
|
-14
|
|
R-MFG
|
6
|
55
|
2.71
|
2.80
|
60
|
4
|
52
|
|
L-STG
|
41
|
53
|
2.58
|
2.66
|
-40
|
-24
|
10
|
|
|
42
|
41
|
2.32
|
2.38
|
-58
|
-16
|
10
|
|
|
|
|
|
|
|
|
|
NL-NRa
|
L-STG
|
-
|
1364
|
4.83
|
5.35
|
-70
|
-30
|
18
|
|
|
42
|
|
4.82
|
5.34
|
-60
|
20
|
12
|
|
|
40
|
|
4.82
|
5.34
|
-60
|
-20
|
12
|
|
R-STG
|
-
|
796
|
4.92
|
5.48
|
58
|
6
|
54
|
|
|
22
|
|
4.22
|
4.57
|
66
|
-6
|
8
|
|
|
43
|
|
4.53
|
4.96
|
54
|
-10
|
10
|
|
L-Precentral gyrus
|
6
|
81
|
3.69
|
3.92
|
-52
|
-4
|
56
|
|
L-Pons
|
-
|
69
|
3.48
|
3.68
|
-10
|
-34
|
-38
|
|
L-Cerebellum anterior lobe
|
-
|
56
|
3.33
|
3.50
|
-8
|
-50
|
-24
|
|
R-PoCG
|
2
|
47
|
4.17
|
4.50
|
62
|
-30
|
54
|
|
R-Pons
|
-
|
18
|
3.29
|
3.45
|
14
|
-32
|
-36
|
|
Midbrain
|
-
|
15
|
3.30
|
3.47
|
8
|
-18
|
-18
|
Notes: BA, Brodmann area; k, cluster size (number of voxels); L, left hemisphere; R, right hemisphere; MFG, middle frontal gyrus; IFG, inferior frontal gyrus; STG, superior temporal gyrus; SFG, superior frontal gyrus; Cun, cuneus; MTG, middle temporal gyrus; PoCG, postcentral gyrus.
a Clusters are thresholded at p < .001 (uncorrected).
b Clusters are thresholded at p < .03 (uncorrected).
The FL-FR signals induced involvement of both hemispheres, including the left middle frontal gyrus (MFG), superior temporal gyrus (STG), and putamen. In the right hemisphere, the inferior frontal gyrus (IFG), superior frontal gyrus (SFG); STG, Cuneus (Cun); and middle temporal gyrus (MTG) were activated as well.
For FL-R, stronger activity was found in the left hemisphere, in which the inferior parietal lobule (BA 40 and 42), MFG, and STG (Heschl’s gyrus, BA 41) were activated. Increased activation was also observed in the right hemisphere, including the areas of the inferior parietal lobule (BA 40), precuneus, STG, and MFG.
L-FR led to relatively lower activation levels compared to the other three stimuli, which did not reveal significant activation at the cluster threshold of p < .001. When the threshold of the p-cluster was set at < .03, increased activation was detected in the right STG and MFG, and the left brainstem and STG. However, the corpus callosum was more activated than the above-mentioned areas.
As to the NL-NR signals, activation of bilateral STG was the strongest relative to the activation levels induced by the other three stimuli. In the left hemisphere, increased activation was found in STG (BA 42 and 40), precentral gyrus, pons, and cerebellum anterior lobe. The right STG (BA 22 and 43), postcentral gyrus (PoCG), and pons were detected with increased activation. Activation of the midbrain was detected as well.
fMRI results indicate that the neural processing patterns of low-pass filtered and unfiltered language signals do differ in the four dichotic configurations of stimuli (i.e., FL-FR, FL-R, L-FR, and NL-NR stimuli). Even though the English sentences as listening materials were the same in four runs of the experiment, the only difference of dichotic listening to filtered and unfiltered sentences induced different activation patterns of processing. Generally, 320 Hz low-pass filtered signals led to lower activation levels in the brain. The both-ears-filtered stimuli FL-FR and the unfiltered sentence signals (NL-NR) induced increased activation in the regions of bilateral STG, but NL-NR induced more activated regions extended to bilateral inferior parietal lobule and the right precentral gyrus, further, the midbrain, cerebellum, pons, posterior cingulate, and corpus callosum were increasingly activated by NL-NR. In general, higher levels of activation were induced by the NL-NR signals relative to FL-FR. It confirms the previous assumption by the authors that low-pass filtered language signals could lighten the listener’s mental workload for processing the meanings of words (Asp et al., 2012; Guberina, 1972; He et al., 2015; Lian, 1980; Yang, 2016).
FL-FR, as a signal of speech intonation and rhythm, presents a neural processing pattern of linguistic prosody. It revealed that left MFG, bilateral STG, right IFG, and SFG were involved in prosodic processing, in addition, more frontal areas of the right hemisphere got involved. This result is consistent with the findings of the recent studies conducted by Chien et al. (2020, 2021) that Chinese (a tonal language) speakers recruit bilateral fronto-temporal regions for intonation processing. By using the unfiltered monosyllabic Mandarin words as auditory stimuli, Chien et al. (2020, 2021) proposed that the connection between left IFG and bilateral temporal areas may reflect the phonological processing network for auditory intonation perception and prosodic categorization. However, the current study revealed stronger activation in right IFG induced by FL-FR. This may result from the stimuli used in the current study. Unlike the phonological processing of the unfiltered words, the low-pass filtered sentence signals contain only prosodic information without noticeable segmental features of the speech sounds. Thus, the FL-FR signals induced activation in the bilateral fronto-temporal areas for intonation and rhythm processing, in addition, a pathway linking posterior temporal to IFG in the right hemisphere was prominently involved in the prosodic processing (Meyer et al., 2002; Sammler et al., 2015).
The FL-R stimuli that are assumed to be consistent with ear advantage sent prosodic signals to the right hemisphere and directed linguistic signals to the left hemisphere. It revealed a left-dominant processing pattern that the left hemisphere was actively involved in the processing of the unfiltered signals, in the meantime, the activated areas were smaller and the activation levels were lowered by the filtered signals in the right hemisphere. Similar to the results in FL-FR, this result may suggest that the low-pass filtered stimuli, as discussed above, do induce lower activation levels and smaller activated areas in the right hemisphere. Whether the filtered prosodic signals were sent to the brain through both-ears-filtered configuration (FL-FR) or dichotically (FL-R), low-pass filtering induced activation of the right-hemispheric STG and MFG, indicating an auditory prosodic processing pattern with a lower activation level relative to the unfiltered signal. As to the linguistic signal dichotically sent to the left hemisphere, stronger involvement of left inferior parietal lobule (supramarginal gyrus, BA 40, and posterior transverse temporal area, BA 42), Heschl’s gyrus (BA 41), and MFG were detected. This result is basically consistent with the “semantic hubs” for manipulating a concept or meaning of the spoken or written language symbols (Pulvermüller, 2013; Pulvermüller & Fadiga, 2016), including left-hemispheric inferior frontal areas (Bookheimer, 2002), inferior parietal regions (Binder & Desai, 2011), anterior or posterior-middle temporal areas (Hickok & Poeppel, 2007; Patterson et al., 2007). Although left inferior frontal regions in the “semantic hubs” were not significantly activated by the FL-R signals, increased activation was observed in the middle frontal gyrus. This result may be led by the L2 auditory stimuli that MFG is related to higher-level cognitive control and essential for effective communication together with inferior frontal regions especially in a foreign language (Mårtensson et al., 2012; Sierpowska et al., 2018). Thus, FL-R induces prosodic and semantic processing patterns with lower activation levels and small activated areas in the right hemisphere. Further, the left-hemispheric semantic processing pattern and higher-level cognitive processing of foreign language signals are significantly induced by the FL-R signals.
In terms of L-FR, significant activation was shown at the p-cluster threshold < .03 rather than p-cluster < .001 in the other three stimuli. It indicated that a relatively lower activation level was induced by L-FR in the whole brain. In other words, L-FR could not induce as significant activation or active involvement as the other three signals. According to ear advantage, L-FR is presumably a non-optimal language input that violates the left and right ear advantages, which may be the reason that both hemispheres are reluctant to process the signal. Activation of the primary auditory cortex in bilateral STG especially Heschl’s gyrus (Da Costa et al., 2011) was revealed, but not a specific language processing pattern. In addition, greater activation was found in the corpus callosum induced by the L-FR signals, even greater than other cerebral regions. The corpus callosum, connecting both hemispheres, was identified as assisting language processing and language lateralization (Hinkley et al., 2016). As L-FR is not favored by both hemispheres, stronger activation of the corpus callosum may indicate that the left and right hemispheres redirect the signal to the other hemisphere after they receive the signal. As a result, L-FR leads to activation of the primary auditory cortex and lower activation levels in the brain. However, both hemispheres seem to send the signals away instead of processing them.
3.3 Relationship between ERP and fMRI results
ERP and fMRI results revealed distinct neural processing patterns of the FL-FR, FL-R, L-FR, and NL-NR stimuli, which supported our assumption that dichotic listening to the low-pass filtered prosodic and unfiltered linguistic signals induces different processing mechanisms due to the left and right ear advantages. As the English (L2) sentences as auditory stimuli in each run of the combined ERP and fMRI experiment were the same, the differences in processing patterns were led by the dichotic configurations of the filtered and unfiltered stimuli. Thus, there should be an optimal or non-optimal language signal for foreign language learners to process, which are consistent with or violate the left and right ear advantages.
Compared with NL-NR, FL-FR in ERP and fMRI experiments showed lower mental load for processing. Amplitudes of the ERP components regarding semantic and syntactic processing elicited by FL-FR suggested that FL-FR released the processing load compared with the unfiltered NL-NR stimuli, although response time for semantic and syntactic manipulations was longer. The reason for the longer response time may be that the filtered stimuli are limited in semantic and syntactic information and unexpected for the participant to process. fMRI results revealed lower levels of activation compared to NL-NR, in addition, a neural processing pattern for prosody was detected in the bilateral fronto-temporal areas, especially in the right hemisphere. Topographic maps obtained from the ERP experiment showed that FL-FR induced a slightly left-lateralized distribution in the central areas regarding syntactic processing, which was basically consistent with the fMRI results that stronger activation was found in the left middle frontal gyrus.
The brain was actively involved in semantic manipulation of the FL-R signals with higher mental load and longer response time in the ERP results relative to NL-NR. But it was syntactically easier to process FL-R with a lower mental workload. A left-lateralized distribution of the components was identified in the ERP experiment, which was confirmed in the fMRI experiment. fMRI results showed stronger involvement of the left hemisphere with prosodic and semantic processing patterns. The “semantic hubs” in the left hemisphere (Pulvermüller, 2013; Pulvermüller & Fadiga, 2016) were activated by the FL-R signals with a higher level of cognitive control of L2 signals. Meanwhile, the right hemisphere showed lower activation levels with smaller areas. FL-R, consistent with the left and right ear advantages, induced lower mental workload in the right hemisphere by sending prosodic information, at the same time, linguistic signals sent to the left hemisphere led to stronger activation for semantic processing. The higher mental load and longer response time detected in the ERP experiment may result from the foreign language signals as stimuli, which required a higher level of cognitive control due to significant activation of left MFG as observed in the fMRI experiment (Mårtensson et al., 2012; Sierpowska et al., 2018). FL-R can be identified as an optimal language input because prosodic and semantic processing patterns can be significantly activated in the left hemisphere, and the activation level or processing load in the right hemisphere was lowered. FL-R optimized the auditory language input by actively involving left-hemispheric dominance for language and lowering cognitive load in the right hemisphere for other higher-order/complex cognitive processes (Galotti, 2017; Levine, 2009).
L-FR induced a higher processing load for semantic and syntactic manipulations in the ERP experiment. Shorter response time for semantic processing and longer response time for syntactic manipulation were observed compared to the NL-NR signals. The N400 and P600 effects elicited by L-FR indicated that the brain struggled to process this signal. Different from the higher processing load detected in the ERP experiment, fMRI results suggested a quite smaller activation level relative to the other three stimuli. In addition, both hemispheres were struggling with the signals that they received and were reluctant to process, apparently redirecting the signals to the other hemisphere via the corpus callosum. As a result, stronger activation induced by L-FR was found in the corpus callosum rather than other cerebral areas. Thus, L-FR can be identified as a non-optimal auditory language input for language learners.
The ERP study also revealed that the neural mechanisms for processing language signals and environmental sounds (non-verbal signals) were different, so environmental sounds did not trigger any language-related ERP components.