PROCESSING AND REPRESENTATION OF TONES IN SWEDISH
4.5 Experiment 3: EEG Experiment .1 Introduction
Having established in the forced choice study that the accent information of the first syllable is sufficient to identify accent correctly, we used the same stimulus material in an EEG study. The forced choice study strengthened the hypothesis that ACC1 in monomorphemic trochees is lexically specified in the mental lexicon, while monosyllabic words and words with ACC2 are assigned tone by default rules and this encouraged us to investigate similar questions using a different design:
In an electroencephalographic cross-modal fragment priming study we addressed the following issues:
(a) What is the relative contribution of Swedish tone as compared to segments?
(b) Is the P350 effect sensitive to tonal information? This would strengthen the claim that it reflects lexical activation.
(c) What are the effects of lexical specification on brain responses.
In the experiment, a disyllabic trochaic noun was presented visually on a screen (the same stimuli as used before in the forced choice task). It was preceded by an auditory prime fragment of the length of one syllable. This was either the first syllable of the target, a syllable that was identical to the target on the segmental level, but differed in tone, a syllable with the same tone as the target but with different segments, or a syllable differing in segments as well as in tone (see Table 4.5).
We predicted different patterns of priming for the various conditions, based on the representational status of accent in the mental lexicon. Segments are known to prime the words they correspond to (see Chapter 3) and thus we expected a segmental overlap between prime and target (e.g. bul-BULLER) in an EEG experiment to lead to priming – reflected in more negative P350 amplitudes in left anterior sites and a less negative N400 amplitude in centro-posterior regions than in case of a segmental mismatch (e.g. ham-BULLER). Effects of tonal information depend on the impact that tonal information has on word processing and, probably, on the status of lexical specification of tone in Swedish.
Table 4.5: Example of one word (buller1) in all experimental conditions, indicating whether segmental and tonal information is same or different between prime fragment and target word.
Prime Target Segments Tone
bul1 buller1 same same
bul2 buller1 same different
ham1 buller1 different same ham2 buller1 different different
The prime fragments are taken from the following words: bul1 < buller1, bul2 < bulle2, ham1
< hambo1, ham2 < hampa2.
The experiments on Asian tone languages reviewed above show that suprasegmental information plays an important role in word recognition.
Depending on the method used, tone is reported to be either as strong as segments (Schirmer, Tang, Penney, Gunter & Chen, 2005) or less important but still not ignored (cf. Ye and Connine, 1999; Cutler and Chen, 1997). We assumed that this is also the case for Swedish, although tone has a weaker position here because minimal pairs differing only in tone but not in segments (e.g. buren1, ‘the cage’ vs.
buren2, ‘carry-PERFECT PARTICIPLE’) are very rare.
Hence, concerning the relative contribution of Swedish tone as compared to segments, we did not expect priming for words with identical tonal information only (e.g. ham1-BULLER1), as this would imply activation of approximately half the lexicon. For prime-target pairs with identical segmental information, a difference in accent between prime and target should weaken or totally prevent priming effects, with the strength of this effect depending on the impact of tonal information in word access. That is, we do expect full priming for targets that are preceded by primes with identical segmental and tonal information, no priming for targets preceded by primes with different segmental information, irrespective of tone, and reduced or no priming for targets preceded by primes with same segmental but different tonal information (see Table 4.6).
If our assumptions concerning the lexical specification of Swedish accents are correct, and if a similar logic holds for tonal representations as was shown in previous experiments for effects of lexical specification of segments (see Chapter 3), then we do not assume that a difference in accent information will lead to a loss or reduction of priming in any event. Consequently, we would expect a more complex picture than outlined in Table 4.6.
Table 4.6: Predicted priming effects depending on the segmental and tonal information of prime fragment and target word.
Prime Target Segments Tone Predicted Effect
ham1 hambo1 same same priming
ham2 hambo1 same different reduced priming
pad1 hambo1 different same no priming
pad2 hambo1 different different no priming
The prime fragments are taken from the following words: ham1 < hambo1, ham2 < hampa2, pad1 < paddel1, pad2 < padda2
Table 4.7: Predicted priming effects depending on the segmental and tonal information of prime fragment and target word, taking the status of lexical specification of the target-accent into consideration.
Target-Type Prime Target Segments Tone Predicted Effect
ACC1-S ham1 hambo1 same same priming
ACC1-S ham2 hambo1 same different no priming ACC1-S pad1 hambo1 different same no priming ACC1-S pad2 hambo1 different different no priming
ACC1-U hum1 hummer1 same same priming
ACC1-U hum2 hummer1 same different priming
ACC1-U kon1 hummer1 different same no priming
ACC1-U kon2 hummer1 different different no priming
ACC2 hum2 humla2 same same priming
ACC2 hum1 humla2 same different priming
ACC2 kon2 humla2 different same no priming
ACC2 kon1 humla2 different different no priming The prime fragments are taken from the following words: ham1 < hambo1, ham2 < hampa2, pad1 < paddel1, pad2 < padda2, kon1 < konjak1, kon2 < konto2, hum1 < hummer1, hum2 <
humla2; ACC2 includes both, the ACC2-CS and ACC2-CU words as defined above.
More specifically, if segments differ, no priming is expected. If segments are identical, but tones differ, the amount of priming depends on the lexical specification of the target-accent. Given the target word is lexically specified for accent (ACC1-S) and the prime carries different accent information (ACC2), the target should not be preactivated by the prime and consequently no priming should result. This follows the logic of the FUL-model outlined in Chapter 1 and has been
shown to apply for segments before (see Chapter 3). If no lexical entry for tone exists for a target (ACC1-U, ACC2), then it is not sensitive to the tonal information of the prime. This is said to result in a nomismatch. It follows that an ACC1-U word is activated by both, ACC1 and ACC2 surface contours, because it is unspecified for tone and thus has no means for detecting a mismatch between the tone extracted from the signal and its own lexical specification. Similarly, an ACC2 word is not specified for accent and thus can be preactivated by both an ACC1 and an ACC2 surface contour. Table 4.7 shows the predictions based on these assumptions.
If the processing of tone in the brain is comparable to that of segments, and if the EEG components known to be sensitive to segmental information are also affected by the processing of tonal information, then we expect that any condition that is effectively primed will elicit more negative P350 and less negative N400 amplitudes than unprimed conditions.
4.5.2 Methods
4.5.2.1 Stimulus Material
The same auditory and visual stimuli as in Experiment 1 were used.
4.5.2.2 Experimental Design
A cross-modal fragment priming design with a lexical decision task was employed.
Auditory word fragments were used as primes, followed by visual target words.
The subjects’ task was to decide if the visual target was a word or a pseudoword.
Segmental and tonal overlap between prime fragments and target words was systematically varied.
In detail, each experimental trial (using Presentation 10.0, Neurobehavioral Systems Inc., Albany, CA) started with a visual fixation cross that remained on the screen for 300 ms. After the cross disappeared, the auditory prime fragment (i.e.
the first syllable of a word) was played via headphones (Sennheiser HD215) (duration of the fragment: mean = 305ms, sd = 76ms, range = 314ms) followed by a 500 ms inter-stimulus interval of silence. Then the target word appeared on the screen (Samsung SyncMaster 17GLsi) for 500 ms. Participants were asked to indicate their lexical decisions as quickly and as correctly as possible by pressing the mouse buttons with their left and right thumbs. The correspondence between side of the button and the lexical word status was balanced across subjects. The next trial started after a response was made, or, if no response followed, 3500 ms after target onset.
The experiment consisted of four blocks with 240 trials each. Each target word appeared once in each block. In addition, the same number of pseudowords was included. Pseudowords were created by changing the second syllable of the target words. A target could be preceded by four different kinds of primes, varying in segmental and tonal overlap. Two of the primes had the same segmental structure as the visual target. One of them was the original first syllable of the target word, being identical in segmental and tonal structure. The other fragment was taken from the corresponding member of the pair, sharing the segmental information but differing in tone. Another two primes violated the segmental structure of the target word, once with the same and once with different tonal information. See Table 4.7 for an example of one pair of words. These prime - target relations hold for words as well as pseudowords and were balanced across the four blocks. The order of presentation of the blocks was balanced across subjects. The duration of the pause between two blocks could be determined by the subjects.
4.5.2.3 Data Acquisition and Analysis
Electrical brain activity was measured with 64 Ag/AgCl electrodes attached to an Easy Cap (Falk Minow Services, Herrsching-Breitbrunn, Germany; type: Berg).
Signals were amplified (TMS International, Type porti-S 64) and registered with COGNITRACE (Advanced Neuro Technology, Enschede, the Netherlands) using average reference derivation and a sampling rate of 256 Hz. Scalp locations included 62 standard International 10-10 system locations. Two additional electrodes were placed below both eyes to control for eye movements. Electrode impedances were kept below 5 kΩ. The EEG raw data were processed with BESA (Berg & Scherg, 1994). They were filtered offline with a 30Hz zero phase high cutoff filter and eye movement artefacts were subtracted from the raw data, using averages from pre-recorded EOG-data. Epochs that still contained artefacts were rejected by visual inspection and thresholds for data point rejection were set to an amplitude of 65µV and a gradient of 75µV. Due to drifts and noise, a total of 15 channels had to be interpolated distributed over seven subjects. EEG responses were averaged for all targets that were classified correctly as words (mean = 89.3%, min= 66.6%, max = 100% per subject and condition), with a pre-stimulus baseline of 200 ms and a window of 1000 ms starting at target onset.
4.5.2.4 Behavioural Post-test
In order to correctly interpret the EEG results, we had to make sure that all subjects were able to identify the word accent with only the tonal information of the first syllable. In Experiment 1 we already showed that subjects could do this with high accuracy. Nevertheless we wanted to show similar proficiency in performance for the EEG participants. Therefore the same forced choice task was run after the EEG recording: subjects heard the same word fragments as in the priming experiment and had to chose between the ACC1 and ACC2 word after each. Mean correct responses ranged from 95% for ACC2-CS words to 98% for ACC1-U responses. The correctness ranged from 80 to 100% per subject and condition.
4.5.2.5 Participants
Subjects (19 students, aged 19-42 years, mean = 25, sd = 5.5) were recruited at the University of Stockholm by advertising in class and on notice boards. Importantly, the subjects had all grown up in Stockholm and had native Swedish parents, however, not necessarily from Stockholm area. They gave informed consent and were paid 300 SEK for their participation.
Three of them had to be excluded from the analyses due to congenital deafness on one ear, a foreign parent and exceeding artefacts, respectively. The remaining 16 participants, 7 men and 9 women, were all right handed with a minimum lateralisation quotient of 90 (mean = 98.75, sd = 3.4) (Oldfield, 1971). None of them suffered from a neurological disorder, or had uncorrected auditory or visual impairments.
4.5.3 Results
Nine Regions of Interest (ROI) were selected, each including data of four electrodes, given in Table 4.8.
Table 4.8: Electrodes averaged into the respective Regions of Interest.
Left Central Right
Front F5, F7, FC5, FT7 FC1, Fz, FCz, FC2 F6, F8, FC6, FT8 Mid TP7, CP3, CP5, P7 C1, Cz, CPz, C2 TP8, CP4, CP6, P8 Back P3, PO1, PO5, O1 P1, Pz, POz, P2 P4, PO2, PO6, O2
Two time windows were chosen for statistical analyses, one ranging from 300-500 ms and one from 400 to 600 ms. Repeated measurement ANOVAs (analysis of variance) for these time windows were conducted in SPSS including the factors Accent (ACC1 vs. ACC2 target word), Specification Set (specified set = ACC1-S
and corresponding ACC2-CS targets; unspecified set= ACC1-U and corresponding ACC2-CU targets), Segment (same vs. different segments in prime and target), Tone (same vs. different tonal information in prime and target), Horizontal Dimension (front, mid or back position of electrodes in the anterior-posterior dimension of the head), Saggital Dimension (left, central or right position of electrodes in the left-right dimension of the head) and the random factor Subject. The factor Specification Set might be a bit misleading at first, because the specified set consists of specified ACC1 words as well as unspecified ACC2 targets. The reason for unifying them in one set is the segmental correspondence in the first syllable of ACC1 and ACC2 words within each set. Significant results of these analyses are reported in Table 4.9 for the 300-500ms time window and in Table 4.10 for the 500-600ms time window.
Table 4.9: Statistically significant results of the ANOVA with the factors Accent, Specification Set, Segment, Tone, Horizontal Dimension, Saggital Dimension and Subjects (random) for the 300-500ms time window. All p-values were Greenhouse-Geisser corrected.
Factors F-value p-value
Horizontal 14.9 < .01
Saggital 16.3 < .001
Segment x Horizontal 31.1 < .001
Specification x Saggital 4.0 < .05
Segment x Saggital 12.2 < .001
Horizontal x Saggital 4.2 < .05
Accent x Tone x Saggital 5.2 < .02
Specification x Horizontal x Saggital 4.0 < .02
Segment x Horizontal x Saggital 13.7 < .001
Accent x Specification x Segment x Tone 4.8 < .05 Accent x Segment x Tone x Horizontal 7.8 < .02
Accent x Segment x Tone x Saggital 4.7 < .05
Accent x Tone x Horizontal x Saggital 3.0 < .05 Accent x Segment x Tone x Horizontal x Saggital 3.8 < .02
Table 4.10: Statistically significant results of the ANOVA with the factors Accent, Specification Set, Segment, Tone, Horizontal Dimension, Saggital Dimension and Subjects (random) for the 400-600ms time window. All p-values were Greenhouse-Geisser corrected.
Factors F-value p-value
Horizontal 11.7 < .01
Saggital 39.6 < .001
Segment x Horizontal 39.0 < .001
Specification x Saggital 3.6 < .05
Horizontal x Saggital 5.2 < .02
Segment x Tone x Horizontal 5.3 < .05
Accent x Tone x Saggital 5.5 < .02
Specification x Horizontal x Saggital 5.2 < .01
Segment x Horizontal x Saggital 10.1 < .001
Accent x Segment x Tone x Horizontal 7.7 < .02 Accent x Segment x Tone x Horizontal x Saggital 2.8 < .05 Accent x Specification x Tone x Horizontal x Saggital 4.0 < .02
Due to the numerous interactions with the factors Horizontal and Saggital Dimension, we analysed the ROIs separately. All analyses described in the following were repeated measurement ANOVAs with mean amplitude as the dependent variable and Accent (ACC1 vs. ACC2 target word), Specification Set (specified set = ACC1-S and corresponding ACC2-CS targets; unspecified set = ACC1-U and corresponding ACC2-CU targets), Segment (same vs. different segments in prime and target), and Tone (same vs. different tonal information in prime and target) as independent variables as well as Subject as random factor.
Results for both time windows are reported per ROI. Due to the vast number of factors and interactions, graphs and summaries of results are presented in thematic order in the discussion section.
Front Left ROI
In the 300-500ms time window, analyses revealed significant main effects for Accent (F = 4.57, p < .05), and Segment (F = 68.47, p < .0001), as well as significant Accent x Tone (F = 5.04, p < .05) and Segment x Tone (F = 5.09, p <
.04) interactions.
The main effects were caused by generally more negative amplitudes for ACC2 than for ACC1 targets and also more negative amplitudes for same as
compared to different segmental information in prime and target. Post-tests on the Accent x Tone interaction showed that responses to ACC1 targets did not differ depending on whether they were preceded by an ACC1 or ACC2 prime fragment (p
= .87). On the contrary, ACC2 targets revealed more negative amplitudes when preceded by an ACC2 prime fragment than when preceded by an ACC1 prime (t = 3.01, p < .01). There was no difference in amplitude between the ACC1 targets on the one hand and the ACC2 targets preceded by ACC1 primes on the other hand (p
= .91).
In the 400-600ms time window, analyses revealed a main effect for Segment (F = 39.32, p < .0001). Same segments in prime and target elicited more negative amplitudes than different segments.
Front Central ROI
In the early time window, analyses revealed a significant main effect for Specification Set (F = 6.46, p < .05) and significant Accent x Specification Set (F = 4.81, p < .05), Accent x Tone (F = 6.66, p < .05) and Accent x Segment x Tone (F
= 12.13, p < .01) interactions.
The main effect for Specification Set indicated overall more negative amplitudes for targets in the unspecified set (ACC1-U, ACC2-CU) than for those in the specified set (ACC1-S, ACC2-CS). This effect was relativized by the Accent x Specification Set interaction, indicating that ACC1-S targets were less negative than all others (ACC1-S vs.ACC1-U: t = 3.06, p < .01; ACC1-S vs.ACC2-CS: t = 2.23, p <
.05; ACC1-S vs. ACC2-CU: t = 2.19, p < .05) while there was no statistical difference between the latter three. The Accent x Segment x Tone interaction showed that if the segmental information was different between prime and target, it did not matter whether tonal information was same or different (p > .1). However, if the segmental information between prime and target was identical, then tonal information had an effect on target processing, and the direction of this effect depended on the accent of the target. We found more negative amplitudes for an ACC1 target preceded by an ACC2 prime than when preceded by an ACC1 prime (t
= -3.23, p < .01). For ACC2 targets the pattern was reversed. Amplitudes for an ACC2 target preceded by an ACC2 prime were more negative than when preceded by an ACC1 prime (t = 2.79, p < .02).
In the late time window, analyses revealed a main effect for Segment (F = 12.01, p < .01), and, as in the early time window, Accent x Tone (F = 6.35, p < .05) and Accent x Segment x Tone (F = 7.09, p < .02) interactions.
Same segments in prime and target elicited more negative amplitudes than different segments. As in the early time window, the Accent x Segment x Tone interaction revealed no effects of tonal information in case of different segmental information between prime and target (p > .12). Also, when an ACC2 target was preceded by identical segmental information, correct tonal information elicited less positive amplitudes than different tone did (t = 3.31, p < .01). In contrast to the early time window, there was no effect of tonal information on ACC1 target words, irrespective of segmental information (p > .19).
Front Right ROI
In the early time window, analyses revealed a significant main effect for Segment (F = 7.71, p < .02) and a significant Specification Set x Segment x Tone interaction (F = 8.20, p < .02).
The main effect for Segment was caused by more negative amplitudes for targets preceded by primes with same segments than by primes with different segments. Post-tests on the Specification Set x Segment x Tone interaction suggested that the tonal information of the prime only affected targets in the specified set (ACC1-S, ACC2-CS) (t = 3.12, p < .01) while the unspecified set (ACC1-U, ACC2-CU) was unaffected by tonal information in the prime (p > .09).
Both, ACC1-S and ACC2-CS targets elicited more negative amplitudes when preceded by correct rather than incorrect tonal information.
Similar to the early time window, analyses in the late time window revealed a main effect for Segment (F = 27.26, p < .0001) and a Specification Set x Segment x Tone interaction (F = 8.49, p < .02).
Tonal information only exerted an effect on targets in the specified set with same segments in prime and target (t = 2.99, p < .01; else p > .14). In the latter case correct tonal information in the prime elicited more negative amplitudes than incorrect tone.
Mid Left ROI
In the early time window, analyses revealed a significant main effect for Segment only (F = 12.22, p < .01). Same segments in primes and targets elicited more
In the early time window, analyses revealed a significant main effect for Segment only (F = 12.22, p < .01). Same segments in primes and targets elicited more