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W4285406906 abstract "Article Figures and data Abstract Editor's evaluation Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Linguistic phrases are tracked in sentences even though there is no one-to-one acoustic phrase marker in the physical signal. This phenomenon suggests an automatic tracking of abstract linguistic structure that is endogenously generated by the brain. However, all studies investigating linguistic tracking compare conditions where either relevant information at linguistic timescales is available, or where this information is absent altogether (e.g., sentences versus word lists during passive listening). It is therefore unclear whether tracking at phrasal timescales is related to the content of language, or rather, results as a consequence of attending to the timescales that happen to match behaviourally relevant information. To investigate this question, we presented participants with sentences and word lists while recording their brain activity with magnetoencephalography (MEG). Participants performed passive, syllable, word, and word-combination tasks corresponding to attending to four different rates: one they would naturally attend to, syllable-rates, word-rates, and phrasal-rates, respectively. We replicated overall findings of stronger phrasal-rate tracking measured with mutual information for sentences compared to word lists across the classical language network. However, in the inferior frontal gyrus (IFG) we found a task effect suggesting stronger phrasal-rate tracking during the word-combination task independent of the presence of linguistic structure, as well as stronger delta-band connectivity during this task. These results suggest that extracting linguistic information at phrasal rates occurs automatically with or without the presence of an additional task, but also that IFG might be important for temporal integration across various perceptual domains. Editor's evaluation This MEG study elegantly assesses human brain responses to spoken language at the syllable, word, and sentence level. Although prior studies have shown significant cortical tracking of the speech signal, the current work uses clever task manipulation to direct attention to different timescales of speech, thus demonstrating tracking mechanisms that are both automatic and task-dependent operate in tandem during spoken language comprehension. https://doi.org/10.7554/eLife.77468.sa0 Decision letter Reviews on Sciety eLife's review process Introduction Understanding spoken language likely requires a multitude of processes (Friederici, 2011; Martin, 2020; Halle and Stevens, 1962). Although not always an exclusively bottom-up affair, acoustic patterns must be segmented and mapped onto internally-stored phonetic and syllabic representations (Halle and Stevens, 1962; Marslen-Wilson and Welsh, 1978; Martin, 2016). Phonemes must be combined and mapped onto words, which in turn form abstract linguistic structures such as phrases (e.g., Martin, 2020; Pinker and Jackendoff, 2005).In proficient speakers of a language, this process seems to happen so naturally that one might almost forget the complex parallel and hierarchical processing which occurs during natural speech and language comprehension. It has been shown that it is essential to track the temporal dynamics of the speech signal in order to understand its meaning (e.g., Giraud and Poeppel, 2012; Peelle and Davis, 2012). In natural speech, syllables follow each other in the theta range (3–8 Hz; Rosen, 1992; Ding et al., 2017; Pellegrino et al., 2011), while higher-level linguistic features such as words and phrases tend to occur at lower rates (0.5–3 Hz; Rosen, 1992; Kaufeld et al., 2020; Keitel et al., 2018). Tracking of syllabic features is stronger when one understands a language (Luo and Poeppel, 2007; Zoefel et al., 2018a; Doelling et al., 2014) and tracking of phrasal rates is more prominent when the signal contains phrasal information (Kaufeld et al., 2020; Keitel et al., 2018; Ding et al., 2016; e.g., word lists versus sentences). Importantly, phrasal tracking even occurs when there are no distinct acoustic modulations at the phrasal rate (Kaufeld et al., 2020; Keitel et al., 2018; Ding et al., 2016). These results seem to suggest that tracking of relevant temporal timescales is critical for speech understanding. An observation one could make regarding these findings is that tracking occurs only at the rates that are meaningful and thereby behaviourally relevant (Kaufeld et al., 2020; Ding et al., 2016). For example, in word lists, the word rate is the slowest rate that is meaningful during natural listening. Modulations at slower phrasal rates might not be tracked as they do not contain behaviourally relevant information. In contrast, in sentences, phrasal rates contain linguistic information and therefore these slower rates are also tracked. Thus, when listening to speech one automatically tries to extract the meaning, which requires extracting information at the highest linguistic level (Halle and Stevens, 1962; Martin, 2016). However, it remains unclear if tracking at these slower rates is a unique feature of language processing, or rather is dependent on attention to relevant temporal timescales. As understanding language requires a multitude of processes, it is difficult to figure out what participants actually are doing when listening to natural speech. Moreover, designing a task in an experimental setting that does justice to this multitude of processing is difficult. This is probably why tasks in language studies vary vastly. Tasks include passively listening (e.g., Kaufeld et al., 2020), asking comprehension questions (e.g., Keitel et al., 2018), rating intelligibility (e.g., Luo and Poeppel, 2007; Doelling et al., 2014), working memory tasks (e.g., Kayser et al., 2015), or even syllable counting (e.g., Ding et al., 2016). It is unclear whether outcomes are dependent on the specifics of the task. There has so far not been a study that investigates if task instructions focusing on extracting information at different temporal rates or timescales have an influence on the tracking that occurs on these timescales. It is therefore not clear whether tracking phrasal timescales is unique for language stimuli which contain phrasal structures, or could also occur for other acoustic materials where participants are instructed to pay attention to information happening at these temporal rates or timescales. To answer this question, we designed an experiment in which participants were instructed to pay attention to different temporal modulation rates while listening to the same stimuli. We presented participants with naturally spoken sentences and word lists and asked them to either passively listen, or perform a task on the temporal scales corresponding to syllables, words, or phrases. We recorded brain activity using magnetoencephalography (MEG) while participants performed these tasks and investigated tracking as well as power and connectivity at three nodes that are part of the language network: the superior temporal gyrus (STG), the middle temporal gyrus (MTG), and the inferior frontal gyrus (IFG). We hypothesized that if tracking is purely based on behavioural relevance, it should mostly depend on the task instructions, rather than the nature of the stimuli. In contrast, if there is something automatic and specific about language information, tracking should depend on the level of linguistic information available to the brain. Results Behaviour Overall task performance was above chance and participants complied with task instructions (Figure 1; see Figure 1—figure supplement 1 for individual data). We found a significant interaction between condition and task (F(2,72.0) = 11.51, p < 0.001) as well as a main effect of task (F(2,19.7) = 44.19, p < 0.001) and condition (F(2,72.0) = 29.0, p < 0.001). We found that only for the word-combination (phrasal-level) task, the sentence condition had a significantly higher accuracy than the word list condition (t(54.0) = 6.97, p < 0.001). For the other two tasks, no significant condition effect was found (syllable: t(54.0) = 0.62, p = 1.000; word list: t(54.0) = 1.74, p = 0.176). Investigating the main effect of task indicated a difference between all tasks (phrase–syllable: t(18.0) = 3.71, p = 0.003; phrase–word: t(22.4) = −6.34, p < 0.001; syllable–word: t(19.2) = −8.67, p < 0.001). Figure 1 with 1 supplement see all Download asset Open asset Behavioural results. Accuracy for the three different tasks. Double asterisks indicate significance at the 0.01 level using a paired samples t-test (n=19). Box edges indicate the standard error of the mean. Mutual information The overall time–frequency response in the three different regions of interest (ROI) using the top-20 PCA components was as expected, with an initial evoked response followed by a more sustained response to the ongoing speech (Figure 2). From these regions-of-interest, we extracted mutual information (MI) in three different frequency bands (phrasal, word, and syllable). Here, we focus on the phrasal band as this is the band that differentiates word lists from sentences and showed the strongest modulation for this contrast in our previous study (Kaufeld et al., 2020). MI results for all other bands are reported in the supplementary materials. Figure 2 Download asset Open asset Anatomical regions of interests (ROIs). (A) ROIs displayed on one exemplar participant surface. (B) Time–frequency response at each ROI. STG = superior temporal gyrus, MTG = medial temporal gyrus, IFG = inferior frontal gyrus. For the phrasal timescale in STG, we found significantly higher MI in the sentence compared to the word list condition (F(3,126) = 67.39, p < 0.001; Figure 3; see Figure 3—figure supplement 1 for individual data). No other effects were significant (p > 0.1). This finding paralleled the effect found in Kaufeld et al., 2020. For the MTG, we saw a different picture: Besides the main effect of condition (F(3,126) = 50.24, p < 0.001), an interaction between task and condition was found (F(3,126) = 2.948, p = 0.035). We next investigated the effect of condition per task and found for all tasks except the passive task a significant effect of condition, with stronger MI for the sentence condition (passive: t(126) = 1.07, p = 0.865; syllable: t(126) = 4.06, p = 0.003; word: t(126) = 5.033, p < 0.001; phrase: t(126) = 4.015, p = 0.003). For the IFG, we found a main effect of condition (F(3,108) = 21.89, p < 0.001) as well as a main effect of task (F(3,108) = 2.74, p = 0.047). The interaction was not significant (F(3,108) = 1.49, p = 0.220). Comparing the phrasal task with the other tasks indicated higher MI for the phrasal compared to the word task (t(111) = 2.50, p = 0.028). We also found a trend for the comparison between the phrasal and syllable tasks (t(111) = 2.17, p = 0.064), as well as the phrasal and passive tasks (t(111) = 2.25, p = 0.052). Figure 3 with 2 supplements see all Download asset Open asset Mutual information (MI) analysis at the phrasal band (0.8–1.1 Hz) for the three different regions of interests (ROIs). Single and double asterisks indicate significance at the 0.05 and 0.01 level using a paired samples t-test (n=19). T indicates trend level significance (p < 0.1). Inset at the top left of the graph indicates whether a main effect of condition was present (with higher MI for sentences versus word lists; this inset does not reflect real data). Averages of conditions are only shown if there was a main task effect without an interaction. Box edges indicate the standard error of the mean. For the word and syllable frequency bands no interactions were found (all p > 0.1; Figure 3—figure supplement 2). For all six models, there was a significant effect of condition, with stronger MI for word lists compared to sentences (all p < 0.001). The main effect of task was not significant in any of the models (p > 0.1; for the MTG syllable level there was a trend: F(3,126) = 2.40, p = 0.071). When running the power control analysis, we did not find that significant effects in power differences (also see next section for power in generic bands; mostly due to main effects of condition) influenced our tracking results for any of the bands investigated. Power We repeated the linear mixed modelling using power instead of MI to investigate if power changes paralleled the MI effects. For the delta band, we found for the STG a main effect of condition (F(1,18) = 6.11, p = 0.024; Figure 4. See Figure 4—figure supplement 1 for individual data) and task (F(3,108) = 3.069, p = 0.031). For the interaction we found a trend (F(3,108) = 2.620, p = 0.054). Overall sentences had stronger delta power than word lists. We found lower power for the phrase compared to the passive task (t(111) = 2.31, p = 0.045) and lower power for the phrase compared to the syllable task (t(111) = 2.43, p = 0.034). There was no significant difference between the phrase and word task (t(111) = 0.642, p = 1.00). Figure 4 with 2 supplements see all Download asset Open asset Power effects for the different regions of interests (ROIs). Single and double asterisks indicate significance at the 0.05 and 0.01 level using a paired samples t-test (n=19). T indicates trend significance (p < 0.1) Inset at the right top of the graph indicates whether a main effect of condition was present (with higher activity for sentences versus word lists; this inset does not reflect real data). Averages of conditions are only shown if there was a main task effect. Box edges indicate the standard error of the mean. The MTG delta power effect overall paralleled the STG effects with a significant condition (F(1,124.94) = 12.339, p < 0.001) and task effect (F(3,124.94) = 4.326, p = 0.006). The interaction was trend significant (F(3,124.94) = 2.58, p = 0.056). Pairwise comparisons of the task effect showed significantly stronger power for the phrase compared to the passive task (t(128) = 2.98, p = 0.007) and lower power for the phrase compared to the syllable task (t(128) = 3.10, p = 0.024). The passive–word comparison was not significant (t(128) = 2.577, p = 0.109). Finally, for the IFG we only found a trend effect for condition (F(1,123.27)=4.15, p = 0.057), with stronger delta power in the sentence condition. The results for all other bands can be found in the supplementary materials (Figure 4—figure supplement 2). In summary, no interaction effects were found for any of the models (all p > 0.1). In all bands, power was generally higher for sentences than for word lists. Any task effect generally showed stronger power for the lower hierarchical level (e.g. generally higher power for passive versus word-combination tasks). Connectivity Overall connectivity patterns showed the strongest connectivity in the delta and alpha frequency band (Figure 5). In the delta band, we found a main effect of task for the STG–IFG connectivity (F(3,122.06) = 4.1078, p = 0.008; Figure 6; see Figure 6—figure supplement 1 for individual data). Follow-up analysis showed a significant difference between the phrasal and passive tasks with higher connectivity in the phrasal compared to the passive task (t(125) = 3.254, p = 0.003). The other comparisons with the phrasal task were not significant. The effect of task remained significant even when correcting for power differences between the passive and phrasal tasks (F(1,53.02) = 12.39, p < 0.001; note the change in degrees of freedom as only the passive and phrasal tasks were included in this mixed model as any power correction is done on pairs). Initially, we also found main effects of condition for the delta and beta bands for the MTG–IFG connectivity (stronger connectivity for the sentence compared to the word list condition), however after controlling for power, these effects did not remain significant (Figure 6—figure supplement 2). Figure 5 Download asset Open asset Connectivity pattern between anatomical regions of interests (ROIs). (A) ROI connections displayed on one exemplar participant surface. (B) Time–frequency weighted phase-lagged index (WPLI) response at each ROI. Figure 6 with 2 supplements see all Download asset Open asset Weighted phase lag index (WPLI) effects for the different regions of interests (ROIs). Double asterisks indicate significance at the 0.01 level using a paired samples t-test (n=19) after correcting for power differences between the two conditions (we plot the original data, not corrected for power, as we can only perform pairwise power and consequently data will be different for each control). Averages of conditions are only shown if there was a main task effect. Box edges indicate the standard error of the mean. MEG–behavioural performance relation We found for the MI analysis a significant effect of accuracy only in the MTG. Here, we found a three-way interaction between accuracy × task × condition (F(2,91.9) = 3.459, p = 0.036). Splitting up for the three different tasks we found only an uncorrected significant effect for the condition × accuracy interaction for the phrasal task (F(1,24.8) = 5.296, p = 0.03) and not for the other two tasks (p > 0.1). In the phrasal task, we found that when accuracy was high, there was a stronger difference between the sentence and the word list condition compared to when accuracy was low, with stronger accuracy for the sentence condition (Figure 7A). Figure 7 with 1 supplement see all Download asset Open asset MEG–behavioural performance relation. (A) Predicted values for the phrasal band MI in the middle temporal gyrus (MTG) for the word-combination task separately for the two conditions. (B) Predicted values for the delta-band weighted phase lag index (WPLI) in the superior temporal gyrus (STG)–MTG connection separately for the two conditions. Error bars indicate the 95% confidence interval of the fit. Coloured lines at the bottom indicate individual datapoints. No relation between accuracy and power was found. For the connectivity analysis, we found a significant condition × accuracy interaction for the STG–MTG connection (F(1,80.23) = 5.19, p = 0.025; Figure 7B). Independent of task, when accuracy was low the difference between sentence and word lists was stronger with higher weighted phase lag index (WPLI) fits for the sentence condition. After correcting for accuracy there was also a significant task × condition interaction (F(2,80.01) = 3.348, p = 0.040) and a main effect of condition (F(1,80.361) = 5.809, p = 0.018). While overall there was a stronger WPLI for the sentence compared to the word list condition, the interaction seemed to indicate that this was especially the case during the word task (p = 0.005), but not for the other tasks (p > 0.1). Age control Adding age to the analysis did not change any of the original findings (all original effects were still significant). We did however find for the power analysis age-specific interactions with condition and task. Specifically, for both the STG and the MTG we found an interaction between age and condition (F(1,28.87) = 6.156, p = 0.0192 and F(1,31) = 10.31, p = 0.003). In both ROIs, there was a stronger difference between sentences and word lists (higher delta power for sentences) for the younger compared to the older participants (Figure 7—figure supplement 1). In the MTG, there was also an interaction between task and age (F(1,31) = 5.020, p = 0.006). Here, in a follow-up we found that only in the word task there was a correlation between age and power (p = 0.023 uncorrected), but not for the other tasks (p > 0.1). Number of component control Overall, the amount of PCA components did not influence any of the qualitative differences in the condition. It did seem however that 10 PCA components were not sufficient to show all original effects with the same power. Specifically, the IFG task and MTG task × condition effect were only trend significant for 10 components (p = 0.06 and p = 0.1, respectively). The other effects did remain significant with 10 components. Using 30 components made some of our effects stronger than with 20 components. Here, the IFG task and MTG task × condition effects had p values of 0.034 and 0.006, respectively. We conclude that the amount of PCAs components did not qualitative change any of our reported effects. Discussion In the current study, we investigated the effects of ‘additional’ tasks on the neural tracking of sentences and word lists at temporal modulations that matched phrasal rates. Different nodes of the language network showed different tracking patterns. In STG, we found stronger tracking of phrase-timed dynamics in sentences compared to word lists, independent of task. However, in MTG we found this sentence-improved tracking only for active tasks. In IFG, we also found an overall increase of tracking for sentences compared to word lists. Additionally, stronger phrasal tracking was found for the phrasal-level word-combination task compared to the other tasks (independent of stimulus type; note that for the syllable and passive comparison we found a trend), which was paralleled with increased IFG–STG connectivity in the delta band for the word-combination task. Behavioural performance seemed to relate to MI tracking in the MTG and STG–MTG connections. This suggests that tracking at phrasal timescales depends both on the linguistic information present in the signal, and on the specific task that is performed. The findings reported in this study are in line with previous results, with overall stronger tracking of low-frequency information in the sentences compared to the word list condition (Kaufeld et al., 2020). Crucially, for the stimuli used in our study it has been shown that the condition effects are not due to acoustic differences in the stimuli and also do not occur for reversed speech (Kaufeld et al., 2020). It is therefore most likely that our results reflect an automatic inference-based extraction of relevant phrase-level information in sentences, indicating automatic processing in participants as they understand the meaning of the speech they hear using stored, structural linguistic knowledge (Martin, 2020; Ding et al., 2016; Har-Shai Yahav and Zion Golumbic, 2021). Overall, it did not seem that making participants pay attention to the temporal dynamics at the same hierarchical level through an additional task – instructing them to remember word combinations at the phrasal rate during word list presentation – could counter this main effect of condition. Even though there was an overall main effect of condition, task did influence neural responses. Interestingly, the task effects differed for the three ROIs. In the STG, we found no task effects, while in the MTG we found an interaction between task and condition. In the MTG increased phrasal-level tracking for sentences only occurred when participants were specifically instructed to perform an active task on the materials. It therefore seems that in MTG all levels of linguistic information are used to do an active language operation on the stimuli. Importantly, the tracking at the phrasal rate in MTG seemed relevant for behavioural performance when attending to phrasal timescales (Figure 7A). This is in line with previous theoretical and empirical research suggesting a strong top-down modulatory response of speech processing in which predictions flow from the highest hierarchical levels (e.g., syntax) down to lower levels (e.g., phonemes) to aid language understanding (Martin, 2016; Hagoort, 2017; Federmeier, 2007). As in the word list condition no linguistic information is present at the phrasal rate, this information cannot be used to provide useful feedback for processing lower-level linguistic information. Instead, it could have been expected that the same type of increased tracking should have happened at the word rate rather than the phrasal rate for word lists (i.e., stronger word-rate tracking for word lists for the active tasks versus passive task). This effect was not found; this could either be attributed to different computational operations occurring at different hierarchical levels or to signal-to-noise/signal detection issues. We found that across participants both the MI and the connectivity in temporal cortex influenced behavioural performance. Specifically, MTG–STG connections were, independent of task, related to accuracy. There was higher connectivity between MTG and STG for sentences compared to word lists at low accuracies. At high accuracies, we found that stronger MTG tracking at phrasal rates (measured with MI) for sentences compared to word lists during the word-combination task. These results suggest that indeed tracking of phrasal structure in MTG is relevant to understand sentences compared to word lists. This was reflected in a general increase in delta connectivity differences when the task was difficult (Figure 7B). Participants might compensate for the difficulty using phrasal structure present in the sentence condition. When phrasal structure in sentences are accurately tracked (as measured with MI) performance is better when these rates are relevant (Figure 7A). These results point to a role for phrasal tracking for accurately understanding the higher-order linguistic structure in sentences, though more research is needed to verify this. It is evident that the connectivity and tracking correlations to behaviour do not explain all variation in the behavioural performance (compare Figure 1 with Figure 3). Plainly, temporal tracking does not explain everything in language processing. Besides tracking there are many other components important for our designated tasks, such as memory load and semantic context which are not captured by our current analyses. It is interesting that MTG, but not STG, showed an interaction effect. Both MTG and STG are strong hubs for language processing and have been involved in many studies which contrasted pseudo-words and words (Hickok and Poeppel, 2007; Turken and Dronkers, 2011; Vouloumanos et al., 2001). It is likely that STG does the more lower-level processing of the two regions, as it is earlier in the cortical hierarchy, thereby being more involved in initial segmentation and initial phonetic abstraction rather than a lexical interface (Hickok and Poeppel, 2007). This could also explain why STG does not show task-specific tracking effects; STG could be earlier in a workload bottleneck, receiving feedback independent of task, while MTG feedback is recruited only when active linguistic operations are required. Alternatively, it is possible that either small differences in the acoustics are detected by STG (even though this effect was not previously found with the same stimuli, Kaufeld et al., 2020), or that our blocked designed put participants in a sentence or word list ‘mode’ which could have influenced the state of these early hierarchical regions. The IFG was the only region that showed an increase in phrasal-rate tracking specifically for the word-combination task. Note, however, that this was a weak effect, as the comparison between the phrase task and the syllable and passive tasks only reached a trend towards significance. Nonetheless, this effect is interesting for understanding the role of IFG in language. Traditionally, IFG has been viewed as a hub for articulatory processing (Hickok and Poeppel, 2007), but its role during speech comprehension, specifically in syntactic processing, has also been acknowledged (Friederici, 2011; Hagoort, 2017; Nelson et al., 2017; Dehaene et al., 2015; Zaccarella et al., 2017). Integrating information across time and relative timing is essential for syntactic processing (Martin, 2020; Dehaene et al., 2015; Martin and Doumas, 2019), and IFG feedback has been shown to occur in temporal dynamics at lower (delta) rates during sentence processing (Park et al., 2015; Keitel and Gross, 2016). However, it has also been shown that syntactic-independent verbal working memory chunking tasks recruit the IFG (Dehaene et al., 2015; Osaka et al., 2004; Fegen et al., 2015; Koelsch et al., 2009). This is in line with our findings that show that IFG is involved when we need to integrate across temporal domains either in a language-specific domain (sentences versus word lists) or for language-unspecific tasks (word-combination versus other tasks). We also show increased delta connectivity with STG for the only temporal-integration tasks in our study (i.e., the word-combination task), independent of the linguistic features in the signal. Our results therefore support a role of the IFG as a combinatorial hub integrating information across time (Gelfand and Bookheimer, 2003; Schapiro et al., 2013; Skipper, 2015). In the current study, we investigated power as a neural readout during language comprehension from speech. This was both to ensure that any tracking effects we found were not due to overall signal-to-noise (SNR) differences, as well as to investigate task-and-condition dependent computations. SNR is better for conditions with higher power, which therefore leads to more reliable phase estimations, critical for computing MI as well as connectivity (Zar, 1998). We will therefore discuss the power differences as well as their consequences for the interpretation of the MI and connectivity results. Generally, it seemed that there was stronger power in the sentence condition compared to the word list condition in the delta band. However, the pattern was very different than the MI pattern. For the power, the word list-sentence difference was t" @default.
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W4285406906 title "Author response: Neural tracking of phrases in spoken language comprehension is automatic and task-dependent" @default.
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