It is the nature of the temporal distortion implemented here, with applies an alternation of compression and expansion within the same sentence, that may be most harmful for breaking timing expectations. Uniform transformations to speech timing are less detrimental to recognition as the perceptual system is thought to adjust future predictions based on the ongoing speaking rate. As Dilley and Pitt (2010) showed, the identification of a speech target depends on the speaking rate of the preceding context, and a mismatch between the speaking rate of the target and preceding context leads to the reinterpretation of the target to match the preceding context.

It should be noted here that the timing statistics which together make up the speech rhythm of a particular language do not usually result in periodicity. Similarly, the literature does not posit a fixed frequency for oscillation but a frequency range. If periodicity were a necessary dimension of successful speech communication then talkers would use this form of speech. On an informational theoretic angle, microvariation in timing also allow the encoding of information—a perfectly regular "carrier" like speech rhythm would be impoverished in information, and would limit suprasegmental encoding. We instead propose that periodicity could have a facilitatory effect that would be exploited in situation where the input is corrupted by noise, and the minute temporal adjustements to track the ongoing speech stream is impaired.

The idea that in noise, cortical sampling falls back to a default sampling period has interesting implications for the design of a follow-up study. In the current one, the period of isochrony was derived from the average occurrence of anchor points, which, for stressed syllables, ranged from 2.38 to 2.48 Hz (see **Figure 2**). Sentences were presented in increasing order of inter-anchor point frequency but no explicit indication of this frequency was given, nor was any explicit indication of the beginning of the first period provided. That is, no attempt was made to manipulate initial phase resetting or subsequent phase tracking and indeed, none of the participants reported periodicity in the stimuli. Given this, one could hypothesize that providing explicit cues for onset and periodicity may result in a greater intelligibility increase for isochronous timing, potentially due to increased alignment between brain oscillations and stimulus characteristics.

# 4.2. Relative Contribution of Syllabic Information vs. Amplitude Envelope to Cortical Tracking

In addition to finding support for isochrony anchored to the stressed syllable (Experiment I), an important finding was that the isochronous modification anchored to the peaks in the amplitude envelope did not improve intelligibility compared to anisochronous retiming (Experiment II). At a methodological level, this null result confirms the validity of the anisochronous modification (i.e., reversing the polarity of any retiming does not in itself degrade intelligibility) and provides additional support for the positive result obtained for the linguistically based modifications. Note that we do not directly compare the listeners' performance across Experiment I and II as they used different cohorts of participants and had different intelligibility baselines for unmodified speech.

The null result in Experiment II is interesting in the light of the many studies that have proposed that the fluctuations of the amplitude envelope of speech play a crucial role in driving cortical entrainment (Ahissar et al., 2001; Luo and Poeppel, 2007; Aiken and Picton, 2008; Giraud and Poeppel, 2012a). In this regard, we point out that our results may be specific to the timing of important information in speech recognition in noise and as such do not call into question the importance of the amplitude envelope for speech recognition in general.

Both syllable and amplitude envelope based transformations led to modifications to the timing of the amplitude envelope. But while the time instants of amplitude envelope peaks were derived from the controlled retiming of syllable onsets in the syllable based retiming conditions, they were directly manipulated in the amplitude envelope retiming conditions. Interestingly, this direct control of the timing of amplitude envelope peaks did not reveal an isochronous advantage, suggesting that if periodicity may facilitate processing through alleviating the need of phase resetting, then amplitude envelope peaks were not the appropriate cues for phase resetting. In fact, it would be more correct to conclude that regularly occurring amplitude peaks misinform upcoming predictions, at least as much as irregular ones.

More specifically, it seems more plausible that amplitude envelope peaks act as second-order temporal cues in signaling neighboring linguistically meaningful units, and that it is the latter that constitute the primary temporal cues for speech recognition, and the support for phase resetting. This result is all the more relevant considering that amplitude envelope peaks are more more audible than syllable onsets in the type of speech shape noise employed here, and therefore the periodicity should be more readily accessed by the oscillatory system if it would be purely acoustically driven. In all, the results of this study support an increasingly shared view that if oscillations track incoming speech, top-down linguistic cues may play a stronger role than bottom-up acoustic cues (Obleser et al., 2012; Gross et al., 2013; Zoefel and VanRullen, 2015b; Ding et al., 2016).

Amplitude envelope peaks are usually assumed to constitute the critical time instants for amplitude envelope tracking, for example, in Ghitza (2012), the location of audible pulses indicating rhythm are placed at amplitude peaks. Our results nuance that account, indicating that P-centers, which are perceptually defined and linguistically informed temporal cues, could constitute a more appropriate level of description of the cues that drive cortical entrainment, at least when speech is presented in noise. While we contrasted two types of cues in the

#### REFERENCES

current study, other cues such as consonantal onsets or amplitude acceleration could provide further insight in the nature of the cues that support tracking.

English (including the Australian variety studied here) is traditionally considered as a stress-timed language as opposed to syllable- or mora-timed languages, although this distinction, which classically makes a hypothesis of constant duration of the corresponding units, has not been robustly verified empirically (Lehiste, 1977; Dauer, 1983; White and Mattys, 2007; Nolan and Jeon, 2014; Cummins, 2015). Nevertheless, in our study, isochronous retiming based on stressed syllable was the transformation resulting in the minimum temporal distortion, and also the condition that led to greater intelligibility compared to an all-syllable isochronous retiming. Apart from providing a weak support for stressed-syllable based isochrony in English, this result promotes the hypothesis that for syllabletimed languages, an opposite pattern of isochronous retiming benefit may be observed, with a greater benefit for isochronous retiming at the all-syllable level as opposed to higher metrical units.

A final point is that the current study employed behavioral measures of intelligibility to assess the effect of imposed periodicity in noise. A future line of research will be concerned with the evaluation of electrophysiological measures associated with this type of isochronous stimuli.

#### AUTHOR CONTRIBUTIONS

VA, CD, and JK designed research; VA performed research; VA analyzed data; VA, CD, and JK wrote the paper.

#### ACKNOWLEDGMENTS

The authors thank Lauren Smith for her help in organizing recruitment and testing and acknowledge support of the Australian Research Council under grant agreement DP130104447.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fnhum. 2016.00430

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2016 Aubanel, Davis and Kim. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# The Role of High-Level Processes for Oscillatory Phase Entrainment to Speech Sound

Benedikt Zoefel 1,2\* and Rufin VanRullen1,2

1 Université Paul Sabatier, Toulouse, France, 2 Centre de Recherche Cerveau et Cognition (CerCo), CNRS, UMR5549, Pavillon Baudot CHU Purpan, Toulouse, France

Constantly bombarded with input, the brain has the need to filter out relevant information while ignoring the irrelevant rest. A powerful tool may be represented by neural oscillations which entrain their high-excitability phase to important input while their low-excitability phase attenuates irrelevant information. Indeed, the alignment between brain oscillations and speech improves intelligibility and helps dissociating speakers during a "cocktail party". Although well-investigated, the contribution of low- and highlevel processes to phase entrainment to speech sound has only recently begun to be understood. Here, we review those findings, and concentrate on three main results: (1) Phase entrainment to speech sound is modulated by attention or predictions, likely supported by top-down signals and indicating higher-level processes involved in the brain's adjustment to speech. (2) As phase entrainment to speech can be observed without systematic fluctuations in sound amplitude or spectral content, it does not only reflect a passive steady-state "ringing" of the cochlea, but entails a higher-level process. (3) The role of intelligibility for phase entrainment is debated. Recent results suggest that intelligibility modulates the behavioral consequences of entrainment, rather than directly affecting the strength of entrainment in auditory regions. We conclude that phase entrainment to speech reflects a sophisticated mechanism: several highlevel processes interact to optimally align neural oscillations with predicted events of high relevance, even when they are hidden in a continuous stream of background noise.

#### Edited by:

Johanna Maria Rimmele, University Medical Center Hamburg-Eppendorf, Germany

#### Reviewed by:

Christian Keitel, University of Glasgow, UK Elana Zion Golumbic, Bar Ilan University, Israel

\*Correspondence: Benedikt Zoefel<EMAIL_ADDRESS>
Received: 30 July 2015 Accepted: 16 November 2015 Published: 02 December 2015

#### Citation:

Zoefel B and VanRullen R (2015) The Role of High-Level Processes for Oscillatory Phase Entrainment to Speech Sound. Front. Hum. Neurosci. 9:651. doi: 10.3389/fnhum.2015.00651 Keywords: EEG, oscillation, phase, entrainment, high-level, speech, auditory, intelligibility

## PHASE ENTRAINMENT AS A TOOL FOR INPUT GATING

In virtually every situation of our life, the brain has to cope with an enormous amount of incoming information, only a fraction of which is essential for the scene's interpretation or resulting behavior. Clearly, the brain must have evolved strategies to deal with this vast influx, and both amplification of relevant input and suppression of irrelevant information will be critical for survival. Based on recent research, one prominent tool for the described purpose are neural oscillations, assumed to reflect cyclic changes in the excitability of groups of neurons (Buzsáki and Draguhn, 2004; Rajkai et al., 2008; Mazzoni et al., 2010). These endogenous fluctuations in neural excitability per se might seem without function at first glance, as long as they are passive and unrelated to the environment (**Figure 1A**). However, as previous studies showed, both on a theoretical (Schroeder et al., 2008, 2010;

entrainment. (B) Phase entrainment results in an alignment of the oscillation's high and low excitability phases (blue) with the input's high and low informational

Schroeder and Lakatos, 2009; Ghitza, 2011; Morillon et al., 2015) and experimental level (Lakatos et al., 2005, 2008, 2013; Stefanics et al., 2010; Besle et al., 2011; Henry and Obleser, 2012; Henry et al., 2014; Morillon et al., 2014; Nozaradan, 2014; O'Connell et al., 2014; Arnal et al., 2015; Park et al., 2015), these oscillations might become an interesting tool when introducing the possibility that they can be controlled by the brain. By using the low and high excitability phases of those oscillations, the brain might actively ''decide'' what part of the incoming information should be amplified (the information coinciding with the oscillation's high excitability phase) and what part should be suppressed (the information coinciding with the oscillation's low excitability phase; **Figure 1B**). This phenomenon, the synchronization of an oscillatory system (here: brain oscillations) with external input has been termed phase entrainment (Schroeder and Lakatos, 2009). Of course, this kind of ''input gating'' can only be exploited functionally if the input is (1) rhythmic (i.e., predictable), (2) has a relatively stable frequency that the brain can entrain to, and (3) alternates between low and high informational content. Interestingly, one of the most salient stimuli in everyday life fulfills these criteria: speech sound. Although only considered ''pseudo-rhythmic'' (Cummins, 2012; but see Ghitza, 2013), the frequency of the speech envelope (roughly defined as the sum of energy across sound frequencies at a given point in time; shown as gray line in **Figure 1**) is relatively stable between 2 and 8 Hz and phases of low phonetic information (e.g., the silence between syllables) rhythmically alternate with phases of high phonetic information.

content. It can thus be used as a tool for input gating.

Indeed, the number of studies reporting an adaptation of neural oscillations to the envelope of speech sound is increasing continuously (Ding and Simon, 2012a,b, 2013, 2014; Peelle and Davis, 2012; Zion Golumbic et al., 2012, 2013b; Ding et al., 2013; Gross et al., 2013; Horton et al., 2013; Peelle et al., 2013; Power et al., 2013; Steinschneider et al., 2013; Doelling et al., 2014; Millman et al., 2015; Park et al., 2015). But not only speech sound is able to evoke an entrainment of neural oscillations, even simple stimuli, such as pure tones, have been found to produce phase entrainment (Stefanics et al., 2010; Besle et al., 2011; Gomez-Ramirez et al., 2011; Zoefel and Heil, 2013). Furthermore, rhythmic fluctuations in stimulus amplitude (which are present in both trains of pure tones and speech sound) introduce fluctuations at a level of auditory processing as low as the cochlea, a notion that is obviously not compatible with phase entrainment as an active or ''high-level'' process.  Based on these concerns, it might be argued that a mere ''following'' of stimulus amplitude (leading to a series of evoked potentials) and the entrainment of endogenous neural oscillations might be completely different processes with different types of underlying mechanisms. Most studies investigating phase entrainment did not differentiate these components and might have measured a mix of evoked and entrained responses. For the sake of simplicity, and because it is not straightforward to disentangle the two, we will call both processes ''phase entrainment'' throughout this manuscript, to describe an experimentally observable metric without assuming one or the other underlying process. However, we dedicated the last paragraph of Section ''Phase Entrainment to High-Level Features of Speech Sound'' to this issue, in which the controversy ''evoked vs. entrained'' is discussed in more detail.

The issues outlined in the previous paragraph lead to the fact that the role of high-level processes for phase entrainment to speech sound is far from clear. Nevertheless, significant progress has been made within the last decade, and the aim of this review is to summarize the obtained results in a systematic way. The scope of this review is not a summary of existing literature showing an alignment between brain oscillations and speech sound, as comprehensive reviews have been published recently (Peelle and Davis, 2012; Zion Golumbic et al., 2012; Ding and Simon, 2014). Rather, we will focus on high-level processes that can modulate or even underlie this alignment. Critically, it is necessary to differentiate between (i) highlevel modulations of phase entrainment and (ii) high-level entrainment: In (i), phase entrainment can be produced as a ''following'' response to a low-level rhythmic stimulus sequence (potentially in early brain areas, as early as the cochlea); however, the entrainment is modulated by high-level processes that include attention or predictions. In this review, lowlevel features of speech are defined as stimulus amplitude and spectral content, as those two properties can passively entrain the lowest level of auditory processing and evoke steady-statepotential-like (ASSR; Galambos et al., 1981) fluctuations in the cochlea. In contrast to (i), high-level entrainment (ii) represents phase entrainment that can be observed even in the absence of systematic fluctuations of low-level properties. In this case, a simple ''following'' of stimulus amplitude is not possible anymore. Thus, it is the process of phase entrainment itself that operates on a higher level, as a certain level of processing is required in order to adjust to the rhythm of high-level features. Convincing results have been obtained in the last years for both types of high-level processes, and we will address them in separate sections. We conclude this review with a section dedicated to the role of intelligibility for phase entrainment to speech sound, as the influence of semantic information on the brain's adjustment to speech is currently a strongly debated topic.

#### HIGH-LEVEL MODULATIONS OF PHASE ENTRAINMENT TO SPEECH SOUND

Certain cognitive processes, such as attention, expectation or interpretation, are often considered ''high-level'' functions of the human brain, as they require, for instance, evaluation, selection, and the comparison of the actual stimulation with experience (Lamme and Spekreijse, 2000; Gilbert and Li, 2013; Peelen and Kastner, 2014). A modulation of phase entrainment to speech sound by those cognitive processes would argue for phase entrainment being a process that is not restricted to a purely sensory mechanism, but rather the active gating mechanism (or ''active sensing''; Schroeder et al., 2010) that was explained above. Indeed, there is accumulating evidence for phase entrainment critically relying on attentional processes: one example is based on the so-called ''cocktail party effect'' (Cherry, 1953), describing a situation of several competing speakers, one of which has to be selected within the ''noise'' of the other, potentially distracting, speakers.

Several recent studies have shown a relation between the ''cocktail party effect'' and phase entrainment (the theoretical background is shown in **Figure 2A** and underlined by experimental results in **Figure 2B**). In Kerlin et al. (2010), two different speech streams were presented to the participants, one to each ear, and they were asked to selectively attend one of those two competing streams. They found that the representation of the attended speech stream in the delta/theta range (∼2–8 Hz; the dominant frequency range of the speech envelope) of the electroencephalogram (EEG) signal was enhanced compared to that of the unattended stream. In other words, phase-locking between the EEG signal and the speech envelope of the attended stream was stronger than that between the EEG signal and the unattended stream. A similar paradigm was used in the studies by Ding and Simon (2012a), Horton et al. (2013) and Zion Golumbic et al. (2013b) in magnetoencephalographic (MEG), EEG and intracranial recordings in human subjects, respectively. All studies confirmed the finding that the phase of delta/theta brain oscillations ''tracks'' the envelope of speech sound, and that this ''tracking'' is enhanced when the speech is attended in a multi-speaker scenario. Interestingly, all studies reported that even the unattended speech signal is still represented (albeit weakly) in lower-level auditory cortices (i.e., regions closely related to sensory processing). However, as shown in the work by Zion Golumbic et al. (2013b), this unattended signal is ''lost'' in higher-level (e.g., frontal) regions. Ding and Simon (2012a) demonstrated that only the representation of the attended (and not the unattended) speech envelope varies as a function of stimulus intensity. This finding is important, because it suggests that attended and unattended inputs are processed separately in the brain, and that the alignment between neural phase and speech rhythm is used to form individual ''auditory objects'' (for a review on this notion, see Simon, 2015). In line with the notion of phase entrainment as an ''amplifier-attenuator mechanism'' (see ''Phase Entrainment as a Tool for Input Gating''), Horton et al. (2013) reported cross-correlations between speech envelope and EEG signal for both attended and unattended streams, but with opposite signs, suggesting that phase entrainment is indeed used to amplify one stream while the other is attenuated. Finally, it has been shown in several studies that the speech envelope can be reconstructed (i.e., it can be identified which stimulus the listener is attending) in multi-speaker (Ding and Simon, 2012a; Zion Golumbic et al., 2013b; O'Sullivan et al., 2015) or noisy environments (Ding and Simon, 2013) by using the delta/theta phase of neural oscillations (but also their gamma power; Mesgarani and Chang, 2012; Zion Golumbic et al., 2013b). It is possible that in those kind of situations, where one speech stream has to be actively extracted from a noisy environment, attention is of particular importance for phase entrainment to speech sound, whereas clear speech can

be processed largely independently of attention (Wild et al., 2012).

Not only attention can be considered a high-level process: predictions reflect a comparison between present and previous experiences and its projection to the future and must therefore involve high-level functions of the brain (Friston, 2005; Arnal and Giraud, 2012). Indeed, it has been shown that predictions do influence phase entrainment to speech sound. For instance, in the ''cocktail party'' scenario described above, Zion Golumbic et al. (2013a) paired the auditory speech input with the speaker's face and found that phase entrainment to the speech envelope was significantly enhanced by this visual input. Similar results were obtained by Arnal et al. (2011) using congruent and incongruent audiovisual stimuli (syllables) and by Luo et al. (2010) when subjects were watching audiovisual movies. A common interpretation of these findings is that, due to the slight delay between visual and auditory components of a conversation (the visual input preceding the auditory one), the former can be used to predict the timing of speech sound, thus enabling a better alignment between the oscillatory phase and speech envelope (Arnal et al., 2009, 2011; Zion Golumbic et al., 2013a; Perrodin et al., 2015; for a review, summarizing several existing theories, see Peelle and Sommers, 2015). A phase-reset of neural oscillations in primary auditory cortex by visual input seems to be an important underlying mechanism (Thorne and Debener, 2014; Mercier et al., 2015; Perrodin et al., 2015). Although this would indicate an involvement of low or intermediate hierarchical levels, we emphasize here that a purely low-level mechanism is insufficient to explain many findings reported in the literature. For instance, introducing an additional delay between visual and auditory input disrupts the benefits of additional visual information for speech processing and incongruent visual information (which would result in a similar phase-reset as congruent information, assuming a purely low-level process) does not result in enhanced phase entrainment (e.g., Crosse et al., 2015; for a review, see Peelle and Sommers, 2015) but instead generates an increased neural response associated with error processing (Arnal et al., 2011). Finally, using a McGurk paradigm (McGurk and MacDonald, 1976; van Wassenhove et al., 2005) were able to show a correlation between the amount of prediction conveyed by the preceding visual input for the upcoming speech and the latency of speech processing. Together, these results speak for a mechanism that is tailored to speech-specific processing (Crosse et al., 2015) and against a purely low-level mechanism. The timing of the cross-modal phase-reset seems to have evolved in such a way that oscillations in the auditory system arrive at their high excitability phase exactly when the relevant auditory input is expected to be processed (Lakatos et al., 2009; Thorne and Debener, 2014). Finally, recent research suggests that not only the visual, but also the motor system plays a critical role for an efficient adjustment of excitability fluctuations in auditory cortex to expected upcoming events (Fujioka et al., 2012; Doelling et al., 2014; Morillon and Schroeder, 2015; Morillon et al., 2015). For instance, it has been suggested that the motor system possesses its own representation of expected auditory events and can therefore prepare oscillations in auditory cortex for relevant upcoming stimuli (Arnal and Giraud, 2012; Arnal, 2012). This mechanism might underlie recent findings describing an enhanced segregation of relevant and irrelevant auditory events in the presence of rhythmic tapping (Morillon et al., 2014).

Not an experimental, but rather an analytical proof of highlevel processes involved in phase entrainment was provided by two recent studies (Fontolan et al., 2014; Park et al., 2015). Fontolan et al. (2014) used Granger causality (Granger, 1969), applied on data recorded intracranially in human subjects, to demonstrate that information reflected in the phase of lowfrequency oscillations in response to speech sound travels in top-down direction from higher-order auditory to primary auditory regions, where it modulates the power of (gamma) oscillations at higher frequencies. Park et al. (2015) analyzed their data, recorded with MEG, using transfer entropy measures (Schreiber, 2000). They were able to show that frontal and motor areas can modulate the phase of delta/theta oscillations in auditory cortex (note that the spatial resolution in this study was lower than for intracranial recordings. It is thus unclear whether these delta/theta oscillations correspond to those in higher-order auditory or primary auditory cortices described in Fontolan et al., 2014). Importantly, these top-down signals were correlated with an enhanced phase entrainment to speech sound when tracking of forward vs. backward speech was compared, indicating that higher-level processes can directly control the alignment between neural oscillations and speech sound.

The results described in this section strongly support the view that phase entrainment is a tool for attentional selection (Schroeder and Lakatos, 2009), filtering out irrelevant input and enhancing the representation of the attended stimulus in the brain. Predictions, potentially reflected by top-down mechanisms, help ''designing'' this filter by providing the timing for the alignment of ''good'' and ''bad'' phases of the oscillation to predicted relevant and irrelevant stimuli, respectively. This mechanism would not only help selecting relevant input in a noisy background, but also parse the speech signal at the same time: here, one cycle of the aligned oscillation would represent one segment of information (or ''chunk''; Ghitza, 2011, 2013, 2014; Doelling et al., 2014) that is analyzed by means of faster oscillations (Giraud and Poeppel, 2012; Luo and Poeppel, 2012; for reviews, see Peelle and Davis, 2012; Ding and Simon, 2014). Thus, phase entrainment could function as a means of discretization (equivalent ideas are mentioned by Peelle and Davis, 2012; Zion Golumbic et al., 2012), similar to ''perceptual cycles'' commonly observed in vision (VanRullen et al., 2014).

## PHASE ENTRAINMENT TO HIGH-LEVEL FEATURES OF SPEECH SOUND

In the previous section, we have seen that high-level mechanisms of the brain, related to attention or prediction, clearly contribute to phase entrainment to speech sound. However, it should be noted that this contribution may just be modulatory: highlevel mechanisms could merely influence a process, namely phase entrainment, that itself might rely on purely low-level processes. Indeed, speech sound consists of large fluctuations in low-level properties (i.e., stimulus amplitude and spectral content) that might evoke systematic fluctuations in neural activity already at the earliest level of auditory processing: the cochlea. These fluctuations in neural activity accompanying changes in the speech envelope would be indistinguishable from an active entrainment response. It is therefore necessary to construct stimuli without systematic fluctuations in those low-level properties in order to prove genuine high-level entrainment. In a recent publication (Zoefel and VanRullen, 2015b), we were able to construct such stimuli and we review the most important findings in this section, together with supporting results from other studies. **Figure 3** shows the idea underlying stimulus construction in Zoefel and VanRullen (2015b). In everyday speech sound (**Figure 3A**), spectral energy (color-coded) clearly differs between different phases of the speech envelope. In the view of a single cochlear cell, this sound would periodically alternate between weak (e.g., at phase ± pi, which is the trough of the speech envelope) and strong excitation (e.g., at phase 0, which is the peak of the speech envelope). Consequently, at a larger scale, we would measure an oscillatory pattern of neural activity that strongly depends on envelope phase. This pattern, however, would only reflect the periodicity of the stimulation. Therefore, we constructed noise sound whose spectral energy was tailored to counterbalance spectral differences as a function of envelope phase of the original speech sound (for details of stimulus construction, see Zoefel and VanRullen, 2015b). This noise was mixed with the original speech and resulted in speech/noise sound that did, on average, not show those systematic differences in spectral content anymore (**Figure 3B**). Critically, as those stimuli remain intelligible, high-level features of speech (such as, but not restricted to, phonetic information) are still present and enable the listener to entrain to the speech sound that is now ''hidden'' inside the noise (note that the degree to which the speech is ''hidden'' in noise depends on the original envelope phase, with speech perceptually dominant at the original envelope peak, and noise perceptually dominant at the original envelope trough). We applied those stimuli in two studies: in the first (Zoefel and VanRullen, 2015b), a psychophysical study, we found that the detection of a short tone pip was significantly modulated (p-values shown in **Figure 4A**) by the remaining high-level features. Performance (**Figure 4B**) depended on the original envelope phase and thus differed between periods of dominant speech and noise. Note that speech and noise were spectrally matched; differences in performance could thus not be due to spectral differences between speech and noise, but rather due to the remaining high-level features that enable the listener to differentiate speech and noise. In the second study (Zoefel and VanRullen, 2015a), those stimuli were presented to listeners while their EEG was recorded. We found that EEG oscillations phase-lock to those high-level features of speech sound (**Figure 4C**), and the degree of entrainment (but not the phase relation between speech and EEG signal; see insets in **Figure 4C**) was similar to when the original everyday

speech was presented. These results suggest an entrainment of neural oscillations as the mechanism underlying our perceptual findings.

It is not only interesting to investigate phase entrainment to speech stimuli without potentially entraining low-level features, but also to speech stimuli only containing the latter. This was done in a study by Ding et al. (2013) that might be seen as complementary to the other two described in this section. In their study, noise-vocoding (Green et al., 2002) was used in order to design stimuli where spectro-temporal fine structure (which can be considered as high-level features) was strongly reduced, but the speech envelope was essentially unchanged. Those stimuli were presented either in noise or in quiet, and MEG was recorded in parallel. Ding et al. (2013) showed that, indeed, the reduction of spectro-temporal fine structure in noise-vocoded speech results in a decline in phase entrainment as compared to that in response to natural speech sound. This result suggests that oscillations do not merely (and passively) follow the slow fluctuations in lowlevel features of speech (e.g., the speech envelope), as they are present in both natural and noise-vocoded speech. Instead, phase entrainment to speech sound involves an additional adjustment to rhythmic changes in spectro-temporal fine structure. It is important to mention that the effect was only observed in noise (and not in quiet), stressing the idea that separating speech and noise might be one of the main functions of phase entrainment to speech sound (see ''Phase Entrainment as a Tool for Input Gating'' and ''High-Level Modulations of Phase Entrainment to Speech Sound''). Using similar stimuli as in Ding et al. (2013), Rimmele et al. (2015) both extended their findings and built a bridge to our section ''High-Level Modulations of Phase Entrainment to Speech Sound''. In contrast to Ding et al. (2013), they presented natural and noise-vocoded speech concurrently and asked their subjects to attend one of them while ignoring the other. Interestingly, they were able to show that the enhanced ''envelope tracking'' for natural compared to noise-vocoded speech (as in Ding et al., 2013) is only present when the speech is attended. They interpret their results as evidence for a high-level mechanism (''linguistic processing'') that is only possible when speech is in the focus of the listener's attention, and only when speech contains spectro-temporal fine structure (i.e., highlevel features). Finally, no attentional modulation of phase entrainment was found for noise-vocoded speech which might be taken as evidence for a tracking of low-level features that does not depend on top-down processes (e.g., attention; see ''High-Level Modulations of Phase Entrainment to Speech Sound'').

Taken together, the results reported in this section suggest that phase entrainment to speech sound is not only a reflection of fluctuations in low-level features of speech sound, but entails an adaption to phonetic information—and thus a genuine highlevel process.

As briefly mentioned before, there is an ongoing debate which is directly related to the results presented in this section: as shown by Capilla et al. (2011), seemingly entrained

shown as insets above the bars (channels without significant entrainment are shaded out). P-values of phase entrainment, obtained by permutation tests, are shown as dashed lines. Note that, in contrast to the degree of entrainment which is comparable in all three conditions, the entrained phase does differ between everyday speech sound (original condition) and speech/noise sound in which systematic fluctuations in low-level features have been removed (constructed and constructed reversed conditions). Modified with permission from Zoefel and VanRullen (2015b) (A,B) and Zoefel and VanRullen (2015a), copyright Elsevier (C).

oscillations can be explained by a superposition of evoked responses (see also Keitel et al., 2014). Transferring this result to speech sound, it has been argued specifically that a phase-reset of neural oscillations by (e.g.) ''acoustic edges'' of speech might be an important mechanism underlying phase entrainment (Doelling et al., 2014; Howard and Poeppel, 2010)—assuming that these ''edges'' occur regularly in speech, a periodic sequence of phase-resets might thus be sufficient to explain the observed ''phase entrainment''. This paragraph provides arguments against phase entrainment reflecting a purely passive mechanism, reflecting merely sequences of neural phase-resets or evoked potentials; however, we do emphasize here that most studies likely measure a mixture of evoked and entrained neural responses. As already outlined above, the two studies described in the first paragraph of this section (Zoefel and VanRullen, 2015a,b) support the notion that phase entrainment is more than a steady-state response to rhythmic stimulation: it can be observed even when the presented speech sound does not contain systematic fluctuations in amplitude or spectral content. Indeed, there are more studies, using simpler, non-speech stimuli, that also support this conclusion. For instance, it has been found, for both vision (Spaak et al., 2014) and audition (Hickok et al., 2015), that behavioral performance fluctuates for several cycles after the offset of an entraining stimulus. A mere ''following'' of the stimulation would not produce these after-effects. Moreover, using entraining stimuli at threshold level, it has been shown that neural oscillations (as measured with EEG) entrain to the stimulation rate even when the stimulus is not perceived (e.g., in the case of several subsequent ''misses'') and would therefore not evoke a strong neural response (Zoefel and Heil, 2013). Finally, in a clever experimental design, Herring et al. (2015) measured visual alpha oscillations (∼8–12 Hz) after a single pulse of transcranial magnetic stimulation (TMS) that has previously been hypothesized to re-set (or entrain, in the case of multiple, rhythmic TMS pulses) endogenous oscillations (Thut et al., 2011). They then asked the question: how is the measured ''alpha'' modulated by attention? In the case of a simple evoked response (or ''alpha-ringing''), the observed ''alpha'' would exhibit an increased amplitude when attention is allocated to the visual domain; however, in the case of endogenous alpha, visual attention would decrease the alpha amplitude, as described already by Adrian (1944). Indeed, the latter is what Herring et al. (2015) observed. To conclude, although the issue remains open, there are promising first results suggesting that phase entrainment—to speech or other stimuli, including brain stimulation—is more than steadystate responses evoked by the rhythmic stimulation—it entails high-level processes and an adjustment of endogenous neural oscillations.

# THE ROLE OF INTELLIGIBILITY FOR PHASE ENTRAINMENT TO SPEECH SOUND

Of course, the ultimate goal of every conversation is to transmit information, and without intelligibility, this goal cannot be achieved. Thus, it is all the more surprising that the role of intelligibility for phase entrainment to speech is currently strongly debated. This controversy is due to seemingly contradictory results that have been published. On the one hand, both Ahissar et al. (2001) and Luo and Poeppel (2007) found a correlation between phase entrainment (i.e., alignment of delta/theta oscillations and speech envelope) and speech intelligibility, a finding that has been confirmed by recent studies (Ding et al., 2013; Doelling et al., 2014; Park et al., 2015). On the other hand, phase entrainment is not a phenomenon that is unique to speech sound and can also be found in response to much simpler stimuli, such as pure tones (Lakatos et al., 2005, 2008; Stefanics et al., 2010; Besle et al., 2011; Gomez-Ramirez et al., 2011; Zoefel and Heil, 2013). Also, the manipulation of speech intelligibility might destroy acoustic (i.e., non-semantic) properties of the sound that the brain actually entrains to (such as acoustic ''edges''; Doelling et al., 2014), leading to a decline in phase entrainment and speech intelligibility at the same time, but without any relation between the two (Peelle and Davis, 2012; Millman et al., 2015). Moreover, several studies showed phase entrainment of neural oscillations to unintelligible speech sound (Howard and Poeppel, 2010; Peelle et al., 2013; Millman et al., 2015) suggesting that phase entrainment does not necessarily depend on intelligibility. The whole picture gets even more complicated, as, although phase entrainment to speech sound is possible even when the speech is unintelligible, is seems to be enhanced by intelligible speech in some (but not all) studies (Gross et al., 2013; Peelle et al., 2013; Park et al., 2015) and attention seems to be important for this enhancement (Rimmele et al., 2015). Further evidence that the role of intelligibility for phase entrainment is not trivial was reported in two of the studies described in the previous section. In Zoefel and VanRullen (2015b), it was found that perceptual entrainment to high-level features of speech sound is disrupted when the speech/noise sound is reversed (**Figures 4A,B**; red line) and this result was interpreted as a critical role of intelligibility for perceptual phase entrainment. On the other hand, in Zoefel and VanRullen (2015a), using the same reversed speech/noise stimuli, the observed EEG phase entrainment was similar to that obtained in response to everyday speech and to (forward) speech/noise sound (**Figure 4C**), seemingly in contradiction to the behavioral results obtained in Zoefel and VanRullen (2015b).

How can we reconcile these studies, some of them clearly arguing against, and some for an important role of intelligibility for phase entrainment? Based on the current state of research, it is important to avoid overhasty conclusions and our interpretations have to remain speculative. Overall, phase entrainment seems to be a necessary, but not sufficient condition for speech comprehension. Speech intelligibility might not be possible without phase-locking, as we are not aware of any study reporting intelligible stimuli without oscillations (or perception) aligned to critical (low- and high-level) features of the speech sound. On the other hand, neural oscillations entrain to rhythmic structures (including reversed speech) even in the absence of intelligibility. It is clear that phase entrainment is a much more general phenomenon, and the brain might continuously scan its input for rhythmic patterns (indeed, popularity for auditory rhythms can be found in all cultures across the world and synchronization with rhythms—e.g., by clapping or dancing—is a general reaction to them). Once a rhythmic pattern has been detected, neural oscillations will align their phase to it (operating in the ''rhythmic mode'' described in Schroeder and Lakatos, 2009; see also Zoefel and Heil, 2013). Based on this notion, neural oscillations might always align to sound, as long as a rhythmic pattern can be detected (note that even the reversed speech/noise sound used in Zoefel and VanRullen, 2015a,b, contains a rhythmic pattern, as speech and noise can perceptually be differentiated). But what is the role of intelligibility? It is important to find a model that is at the same time parsimonious and can explain most results described in the literature. These findings are shortly summarized in the following:

One model that can potentially reconcile these findings is presented in **Figure 5**, and the different parts and implications of this model are discussed in the following. However, we

acknowledge that it is only one out of possibly several candidate models to explain the data available in the literature. Nevertheless, in our view, this model is currently the most parsimonious explanation for existing findings and we therefore focus our review on it. The first implication of our model is that different regions in the brain are ''responsible'' for different processes: Phase entrainment might be found throughout the whole auditory system, but most studies emphasize primary auditory cortex (A1; Lakatos et al., 2005, 2013; O'Connell et al., 2014) or early temporal regions (Gomez-Ramirez et al., 2011; Ding and Simon, 2012b; Zion Golumbic et al., 2013b). An influence of intelligibility is commonly related to regions specifically processing speech sound (Binder et al., 2000; Scott et al., 2000; Hickok and Poeppel, 2007; DeWitt and Rauschecker, 2012; Poeppel et al., 2012; Mesgarani et al., 2014). Finally, frontal regions are a likely candidate for behavioral outcome (Krawczyk, 2002; Coutlee and Huettel, 2012; Rushworth et al., 2012; Romo and de Lafuente, 2013). In order to satisfy point (1), we assume that the entrainment in temporal regions can directly influence behavior as determined in frontal regions, as long as the entrainment is introduced by non-speech stimuli (**Figure 5A**). This results in a periodic modulation of performance as often described (Fiebelkorn et al., 2011; Vanrullen and Dubois, 2011; Landau and Fries, 2012; Thut et al., 2012; Song et al., 2014; Spaak et al., 2014; Zoefel and Sokoliuk, 2014; Hickok et al., 2015; note, however, that most studies report effects for the visual and not for the auditory system—it needs to be clarified whether this fact is biased by the number of studies investigating the visual system or whether there are genuine differences between the two systems). But not only non-speech stimuli can entrain temporal regions, the same is true for speech sound, irrespective of its intelligibility (point 2). However, speech intelligibility affects high-order auditory regions and they might directly influence the impact of temporal on frontal regions (**Figure 5B**). This notion is based on the increasing number of studies supporting the idea that the state of connectivity (or synchronization) between two (potentially distant) brain regions is crucial for perceptual outcome (Fries, 2005; Ruhnau et al., 2014; Weisz et al., 2014). Thus, speech intelligibility might modulate the state of connectivity between temporal and frontal regions. We hypothesize that speech-specific regions only become responsive if the input contains acoustic highlevel (i.e., speech-specific) features of speech; otherwise these regions remain irrelevant and do not exhibit any modulatory effect on other regions or their connectivity. However, once the input is identified as speech (based on these acoustic features), linguistic features determine whether the modulatory effect is negative (desynchronizing temporal and frontal regions, resulting in no behavioral effect of the entrainment; in case of unintelligible speech) or positive (synchronizing temporal and frontal regions, resulting in a behavioral effect of the entrainment; in case of intelligible speech). This assumption satisfies point (3). In contrast to unintelligible speech, intelligible speech might result in an entrainment that also includes highorder (speech-specific) auditory regions. They might have to entrain to the speech sound in order to be able to synchronize temporal and frontal regions. That might be the reason that some studies show an increased entrainment for intelligible as compared to unintelligible speech whereas others do not (point 4). They might have captured the entrainment in those higher-level auditory regions—something which, due to the low spatial resolution in most EEG/MEG studies, is difficult to determine but could be resolved in future studies. More research is clearly needed: what are those behavioral variables that are differentially affected by intelligible and unintelligible speech? Where exactly are those brain regions hypothesized to be responsible for (or affected by) phase entrainment, for behavioral decisions and for the modulation of their relation by speech intelligibility? What are the mechanisms connecting these functional networks? Answering these questions has critical implications for our understanding of the brain's processing of human speech and rhythmic input in general.

# CONCLUSION

Recently, phase entrainment has attracted researchers' attention as a potential reflection of the brain's mechanism to efficiently allocate attentional resources in time (for a recent review, see, e.g., Frey et al., 2015). Nevertheless, the periodicity of the stimulation itself complicates this interpretation, as the brain might simply follow the rhythm of its input. In this review, we presented an increasing amount of evidence that speaks against a merely passive role of neural oscillations for phase entrainment to speech sound. Instead, the brain might constantly predict the timing of relevant and irrelevant events of speech sound, including acoustic high-level features, and actively align neural oscillations so that they efficiently

#### REFERENCES

boost the current locus of attention in a noisy background. Linguistic high-level features, reflecting intelligibility, might play a modulatory, and speech-specific, role by determining the behavioral consequences of phase entrainment to speech sound.

#### ACKNOWLEDGMENTS

The authors thank Mitchell Steinschneider, Peter Lakatos, Daniel Pressnitzer, Alain de Cheveigné and Jesko Verhey for helpful comments and dicussions. This study was supported by a Studienstiftung des deutschen Volkes (German National Academic Foundation) scholarship to BZ, and an ERC grant (''P-Cycles'', number 614244) to RV.

**Conflict of Interest Statement**: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2015 Zoefel and VanRullen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution and reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# A Predictive Coding Perspective on Beta Oscillations during Sentence-Level Language Comprehension

Ashley G. Lewis1,2,3, Jan-Mathijs Schoffelen2,3, Herbert Schriefers4 and Marcel Bastiaansen2,5 \*

1 Haskins Laboratories, New Haven, CT, USA, 2 Neurobiology of Language Department, Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands, 3 Center for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands, 4 Donders Center for Cognition, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands, 5 Academy for Leisure, NHTV Breda University of Applied Sciences, Breda, Netherlands

Oscillatory neural dynamics have been steadily receiving more attention as a robust and temporally precise signature of network activity related to language processing. We have recently proposed that oscillatory dynamics in the beta and gamma frequency ranges measured during sentence-level comprehension might be best explained from a predictive coding perspective. Under our proposal we related beta oscillations to both the maintenance/change of the neural network configuration responsible for the construction and representation of sentence-level meaning, and to top–down predictions about upcoming linguistic input based on that sentence-level meaning. Here we zoom in on these particular aspects of our proposal, and discuss both old and new supporting evidence. Finally, we present some preliminary magnetoencephalography data from an experiment comparing Dutch subject- and object-relative clauses that was specifically designed to test our predictive coding framework. Initial results support the first of the two suggested roles for beta oscillations in sentence-level language comprehension.

#### Edited by:

Anne Keitel, University of Glasgow, UK

#### Reviewed by:

Lars Meyer, Max-Planck-Gesellschaft, Germany Aneta Kielar, Baycrest Hospital, Canada

> \*Correspondence: Marcel Bastiaansen<EMAIL_ADDRESS>
Received: 05 November 2015 Accepted: 18 February 2016 Published: 03 March 2016

#### Citation:

Lewis AG, Schoffelen J-M, Schriefers H and Bastiaansen M (2016) A Predictive Coding Perspective on Beta Oscillations during Sentence-Level Language Comprehension. Front. Hum. Neurosci. 10:85. doi: 10.3389/fnhum.2016.00085 Keywords: language comprehension, neural oscillations, beta, predictive coding, EEG, MEG

# INTRODUCTION

Language comprehension requires the fast and efficient integration of information represented at a multitude of different levels and timescales (Jackendoff, 2007). This means that numerous different and often spatially distant brain regions have to interact quickly and dynamically in order to achieve even the most basic linguistic processing. It is therefore not surprising that oscillatory neural dynamics have been steadily receiving more attention as a robust and temporally precise signature of network activity related to language processing (e.g., Weiss and Mueller, 2012; Friederici and Singer, 2015; Lewis et al., 2015). We have recently suggested a role for beta and gamma oscillations in supporting sentence-level language comprehension (Lewis and Bastiaansen, 2015). In this article we zoom in on the role that beta oscillations play in our proposal, reviewing the available evidence old and new, and presenting some preliminary findings from an experiment designed to directly test one of our hypotheses.

# THE PROPOSAL IN A NUTSHELL

fnhum-10-00085 March 2, 2016 Time: 19:31 # 2

Our proposal (for a detailed outline see Lewis and Bastiaansen, 2015) suggests that oscillatory neural activity in the beta frequency range (13–30 Hz) during sentence-level language comprehension reflects both the active maintenance/change of the underlying neurocognitive network (NCN; Bressler and Richter, 2014) responsible for the representation and construction of the current sentence-level meaning, and the top– down propagation of predictions from higher to lower levels of the cortical hierarchy based on that sentence-level meaning. When the language comprehension system actively maintains the current mode of processing, beta activity within the associated NCN should increase, while a change in the current mode of processing should result in a decrease in beta activity within that NCN (Engel and Fries, 2010; Lewis and Bastiaansen, 2015; Lewis et al., 2015). Similarly, for predictions about upcoming linguistic information with high levels of certainty, beta activity in the NCN should increase in a direction-specific manner (from higher to lower levels of the cortical processing hierarchy; Bastos et al., 2012; Friston et al., 2014). If there are cues in the linguistic input indicating that the current mode of processing is expected to change, the language comprehension system should place less emphasis on top–down predictions, which in turn should result in a decrease in top–down beta activity (Bastos et al., 2012; Friston et al., 2014; Lewis and Bastiaansen, 2015). Such a role for beta in top–down signaling of predictions based on a generative model within a predictive coding framework has been proposed outside of the domain of language comprehension (Bastos et al., 2012; Friston et al., 2014), and we simply apply these ideas to sentence-level comprehension. It may turn out that certain aspects of these two suggested roles for beta activity are complementary, while others are incompatible. The evidence reported here (see If the Evidence Fits . . . and (If the Evidence Fits. . .) Test the Hypothesis) does not allow us to differentiate between them. We would like to make it explicit that we are not arguing for a relationship between beta activity and measure of word surprisal (cf. Levy, 2008), although it is entirely possible that such a relationship may exist.

Before moving on to examine evidence from previous literature, we think it is important to specify exactly what we mean by top-down predictions. In our opinion a clearer distinction has to be made between predictions at the cognitive level and predictions at the neural level. At the cognitive level, and for sentence-level language comprehension in particular, we consider prediction to refer to the activation of specific lexical information stored in long-term memory prior to the appearance of that information in the linguistic input stream (e.g., DeLong et al., 2005; Van Berkum et al., 2005; Szewczyk and Schriefers, 2013; see also Huettig, 2015 for discussion). On the other hand, within the domain of predictive coding implementations of hierarchical Bayesian inference in the brain, predictions are nothing more (but nothing less) than the neural activity at representational units at a 'higher' hierarchical level, that is propagated down to the error units at a hierarchically 'lower' level (Friston, 2005). This neural activity may sometimes directly correspond to prediction at the cognitive level, but most often it will not, because cognitive predictions, and neural predictions generally operate on different timescales. Neural predictions are updated in an ongoing fashion based on numerous factors, including prediction errors sent up the cortical processing hierarchy. Predictions at the cognitive level likely involve evidence accumulation over time until some critical threshold is reached, after which lexical (or more generally longterm memory) pre-activation occurs. This lexical pre-activation may in turn serve as a neural prediction signal that influences activity at lower levels of the cortical hierarchy. Conflating prediction at the cognitive and at the neural level can often lead to confusion in discussions of predictive processing. Our proposal relating beta to top-down prediction refers to predictions at the neural level, but allows for the possibility that predictive processing at the cognitive level may drive these neural prediction signals.

# IF THE EVIDENCE FITS . . .

Next we turn our attention to the evidence supporting our proposed role for beta oscillations during sentence-level language comprehension. We start by briefly summarizing the evidence we have already reviewed elsewhere (Lewis and Bastiaansen, 2015; Lewis et al., 2015), and then move on to discuss one new piece of evidence.

There are by now a number of studies reporting that beta power is sensitive to both syntactic violations (Davidson and Indefrey, 2007; Bastiaansen et al., 2010; Pérez et al., 2012; Kielar et al., 2014) and semantic incongruities (Luo et al., 2010; Wang et al., 2012; Kielar et al., 2014). In all of these studies, beta power was higher following some target word for syntactically and semantically acceptable sentences compared to target words that resulted in a syntactic violation or a semantic incongruity. Similarly, Luo et al. (2010) showed that beta power was higher for rhythmically normal compared to abnormal target nouns in Chinese verb-noun pairs. In addition to grammatical violations, Pérez et al. (2012) showed that beta power following a target word was lower for the case of Spanish 'Unagreement' (where the sentence remains grammatical despite a mismatch between the grammatical person feature marking on the subject and that on the verb of a sentence) compared to grammatically legal target words. These studies all have in common that there is some cue in the linguistic input (e.g., syntactic violation, semantic incongruity, etc.) that indicates to the language comprehension system that the current representation of the sentence-level meaning is not correct and needs to be changed. We suggest that the result is a change in the NCN responsible for that representation, and that this leads to a decrease in beta power in that NCN (or in one or multiple nodes of that network). It may also result in the system assigning less value to top–down predictions as that information has proven unreliable, which would also result in a decrease in beta activity.

Another group of studies has shown that beta activity is higher when sentences are more syntactically demanding, but still grammatical (Weiss et al., 2005; Bastiaansen and Hagoort, 2006; Meyer et al., 2013). Bastiaansen and Hagoort (2006) reported

that beta power was higher for syntactically more demanding center-embedded compared to right-branching relative clauses. Meyer et al. (2013) showed that beta power was higher for long- compared to short-distance subject-verb agreement dependencies at the point in the sentences where the dependency could be resolved. Weiss et al. (2005) found higher beta coherence between frontal and posterior electrode sites for syntactically more complex object-relative (OR) compared to subject-relative (SR) clauses. We suggest that in all these cases the increased beta activity reflects the active maintenance of the current NCN configuration responsible for the construction and representation of the current sentence-level meaning. It may also indicate a greater reliance on top-down predictions based on that sentencelevel meaning (i.e., the increased activity may be related to greater weighting of the top-down signal based on the current generative model), in order to actively try to integrate the new linguistic input into the current sentence-level meaning representation.

Bastiaansen et al. (2010) showed that beta power increased linearly over the course of syntactically legal sentences, but returned to baseline levels at the point of a syntactic violation within sentences. They also showed that lists of the same words contained in the sentences in random order (no syntactic structure) did not exhibit any increase in beta power over the course of presentation of the lists (see also Bastiaansen and Hagoort, 2015). We suggest that the gradual buildup of beta power over the course of sentences might be related to the gradually increasing activation of a NCN responsible for the construction and representation of the sentence-level meaning, and that this network becomes disengaged upon reaching a syntactic violation resulting in the decrease in beta power at that point. For random word lists no sentence-level meaning can be constructed, and thus beta power does not increase over the course of their presentation.

Finally, Magyari et al. (2014) presented participants with natural speech, where the ends of speaking turns were either highly predictable or unpredictable, and asked them to press a button when they thought a speaker's turn was about to end. They showed a decrease in beta power just before a button press in the highly predictable condition and an increase in beta power in the unpredictable condition. We suggest that the decrease in beta power in the predictable condition occurs because the language comprehension system anticipates that the current processing mode will have to change (from comprehending the sentence to giving a meta-linguistic judgment by making a button press). In the unpredictable condition the language comprehension system does not predict that the processing mode will change, and instead the current sentence-level meaning representation is actively maintained, resulting in the increased beta power in that condition.

There is one new beta finding that was not included in our previous reviews. Kielar et al. (2015) have followed up on their EEG study investigating syntactic violations and semantic anomalies compared to control sentences (Kielar et al., 2014) by adding conditions with auditory stimulus presentation (the original used only visual presentation), and by using a beamforming approach [in this case applied to magnetoencephalography (MEG) data] to obtain more precise information about the spatial extent of their effects. They replicate the finding of higher beta (and alpha; see Kielar et al., 2015 for details) power for control sentences compared to both syntactic violations and semantic anomalies, this time for both the visual, and auditory input modalities. Furthermore, their source localization results (albeit computed for the broadband data in the alpha and beta frequency ranges combined; 8–30 Hz) implicated what are arguably the main nodes of the core language network (e.g., Hagoort, 2005, 2013; Hickok and Poeppel, 2007), namely left inferior frontal regions, left superior temporal cortex, and left angular and supramarginal gyri. Our suggestion that when a syntactic violation or semantic incongruity is encountered, decreased beta power reflects a change in the NCN responsible for the representation and construction of a sentence-level meaning holds here as well. However, this study makes an important next step by more precisely mapping out the cortical regions involved. In our opinion, the use of source reconstruction techniques with electrophysiological data is important in future language comprehension studies in order to gain more fine-grained insights into the spatial distribution of the cortical networks whose temporal dynamics are being investigated. At this stage we can only speculate that the critical cortical nodes comprising the NCN that supports sentencelevel language comprehension include the core language regions mentioned above. Depending on the context in which language comprehension takes place, this network may interact with other cortical networks like the attention network (e.g., in case the listener/reader finds themselves in a particularly distracting environment) or the theory of mind network (e.g., when interacting with a conversation partner). Working out these details is one important avenue for future investigation.

#### (IF THE EVIDENCE FITS. . .) TEST THE HYPOTHESIS

So far, all evidence presented in favor of our hypothesis is based on a re-interpretation of the results of studies that were not specifically designed to test the hypothesis that beta power is related to the maintenance/change of the NCN responsible for representing a sentence-level meaning. Now we present some preliminary data from a MEG experiment that was designed to test this hypothesis. Participants read Dutch SR and OR clause sentences, where the input was identical up to an auxiliary verb presented at the end of the relative clause, disambiguating between the two relative clause types (see **Table 1** for example stimuli). The auxiliary could agree in grammatical number with either the referent in the matrix clause (SR) or with the referent in the relative clause (OR). There was no information in the linguistic input prior to the auxiliary that provided any indication about whether the sentence should be read as a subject- or an object-relative clause (the past participle did not bias the reader to have a preference for either of the possible referents). In all cases both referents were animate and the verb in the relative clause was not semantically biased toward having either of the two referents as a grammatical subject. Dutch readers show a clear preference for the SR reading of these sentences, which appears

#### TABLE 1 | Example materials used in preliminary experimental findings reported and their direct English translation (in italics).

SR, subject-relative clause condition; OR, object-relative clause condition; auxiliary verb and referent that agrees with it in grammatical number are underlined.

range. Position of selected representative sensors indicated by black circles. Data presented were high-pass filtered above 0.1 Hz and artifacts related to power-line interference, superconducting quantum interference device (SQUID) jumps, muscle activity, eye-movements, eye-blinks, and cardiac activity were removed. The planar gradient representation of the data for each participant (25 in total – written ethical approval was obtained) was computed and a time-frequency decomposition was carried out using a series of Slepian tapers (Mitra and Pesaran, 1999), and a sliding-window approach in time steps of 20 ms and frequency steps of 2 Hz. Time windows of 500 ms and frequency smoothing of 4 Hz were employed. Data were then averaged over the time and frequency ranges of interest (see above) separately for each condition, and grand-averages across all participants were computed for comparison.

more frequently in Dutch corpora (Mak et al., 2002). This means that Dutch readers typically parse the sentence as a SR clause, and when encountering linguistic input indicating that it should instead be read as an OR clause they experience a disruption in processing, resulting in longer reading times (Mak et al., 2002, 2006, 2008). We hypothesized that this 'unexpectedness' of the OR reading should result in a decrease in beta power at the disambiguating auxiliary at the end of the relative clause compared to the SR condition (as an aside, one may wonder why this is not also predicted in the case of the center-embedded compared to right-branching relative clause sentences reported in Section "If the Evidence Fits . . .," but in that case the center-embedded relative clause sentences were not unexpected). This is hypothesized because although the sentence is grammatical, there is a cue in the linguistic input (a mismatch between the grammatical number feature on the verb and the grammatical number feature on the expected referent), which indicates that the current representation of the sentencelevel meaning (and therefore the underlying NCN) needs to change.

**Figure 1** shows bar plots of the average power at selected MEG sensors in the beta frequency range (12–16 Hz), between 750 and 1050 ms relative to the onset of the disambiguating auxiliary, for the two conditions. There is a small but clear difference in lower-beta power over left temporal and right frontal regions (also present but much less clear over centroparietal sensors; top, left middle, and bottom panels, respectively, in **Figure 1**), with higher beta power for SR compared to OR clauses, exactly as predicted. This provides further support for the idea that a decrease in beta power is related to the 'unexpectedness' of the incoming linguistic input, regardless of whether or not the sentence becomes ungrammatical or semantically anomalous. Added to the findings reviewed in Section "If the Evidence Fits . . .," the available evidence suggests that upon encountering unexpected linguistic input the language comprehension system prepares for a change in the current mode of processing, and a change in the NCN responsible for representing the current sentence-level meaning. This change is reflected in a decrease in beta power in the underlying NCN (or in certain nodes of that NCN). We would like to emphasize that since this is only preliminary data it should only be considered tentative support for our hypothesis.

The beta power decrease may also reflect diminished 'confidence' in top-down predictions by the language comprehension system after encountering unexpected linguistic input. Our experiment does not directly address hypotheses about beta carrying top–down predictions, but it is possible that the local modulations of beta power do reflect such predictions. In order to directly test the hypothesis about top–down information in a predictive coding framework one

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Bastiaansen, M. C. M., and Hagoort, P. (2006). Oscillatory neuronal dynamics during language comprehension. Prog. Brain Res. 159, 179–196. doi: 10.1016/S0079-6123(06)59012-0

first needs to define the different hierarchical levels involved at the cognitive level (e.g., a unification component sending predictions down the hierarchy to a memory component; cf. Hagoort, 2005, 2013) and the cortical regions responsible for instantiating those cognitive components (e.g., left inferior frontal cortex, and left temporal cortex). Then a directional measure of oscillatory activity (e.g., Granger causality, dynamic causal modeling, or transfer entropy; Friston et al., 2013; Park et al., 2015) can be used to directly test whether or not beta activity is predominant from higher to lower levels of the cortical hierarchy (e.g., from left inferior frontal cortex to left temporal cortex).

#### CONCLUSION

In this article we have zoomed in on our proposed role for oscillatory activity in the beta frequency range in both the maintenance/change of the NCN underlying the construction and representation of a sentence-level meaning, and the propagation of top-down predictions to lower levels of the cortical processing hierarchy based on that sentence-level meaning. We reviewed old and new evidence supporting our proposed roles for beta, and presented some preliminary findings from an experiment designed to directly test one of our hypotheses. The results make a compelling case for beta as an index of maintenance/change of the current NCN underlying sentence-level meaning representation and construction. It will be important for future research to directly test the proposed role of beta in top–down predictions, to further specify which cortical nodes are incorporated into the NCN in different linguistic contexts, and to investigate the extent of overlap between the two potential roles for beta (maintenance and top–down predictions). Performing analyses at the level of cortical sources rather than at the sensor/electrode level will be an important part of this endeavor.

#### AUTHOR CONTRIBUTIONS

AL and MB conceived the structure of the article. AL, JS, HS, and MB wrote the manuscript. For the preliminary data presented, AL, JS, HS, and MB designed the experiment, AL collected the data, AL and JS analyzed the data.

## ACKNOWLEDGMENTS

This work is partly supported by an NWO VIDI grant to JS (grant number 864.14.011), and an IMPRS PhD fellowship from the Max Planck Society to AL.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2016 Lewis, Schoffelen, Schriefers and Bastiaansen. This is an openaccess article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Auditory cortical deactivation during speech production and following speech perception: an EEG investigation of the temporal dynamics of the auditory alpha rhythm

David Jenson1 , Ashley W. Harkrider 1 , David Thornton1 , Andrew L. Bowers 2 and Tim Saltuklaroglu1 \*

1 Department of Audiology and Speech Pathology, University of Tennessee Health Science Center, Knoxville, TN, USA, 2 Department of Communication Disorders, University of Arkansas, Fayetteville, AR, USA

#### Edited by:

Anne Keitel, University of Glasgow, UK

#### Reviewed by:

Naomi S. Kort, University of California, San Francisco, USA Daniel Callan, Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology (NICT), Japan

#### \*Correspondence:

Tim Saltuklaroglu, Department of Audiology and Speech Pathology, University of Tennessee Health Science Center, 578 South Stadium Hall, Knoxville, TN 37996, USA<EMAIL_ADDRESS>
> Received: 12 May 2015 Accepted: 14 September 2015 Published: 08 October 2015

#### Citation:

Jenson D, Harkrider AW, Thornton D, Bowers AL and Saltuklaroglu T (2015) Auditory cortical deactivation during speech production and following speech perception: an EEG investigation of the temporal dynamics of the auditory alpha rhythm. Front. Hum. Neurosci. 9:534. doi: 10.3389/fnhum.2015.00534 Sensorimotor integration (SMI) across the dorsal stream enables online monitoring of speech. Jenson et al. (2014) used independent component analysis (ICA) and event related spectral perturbation (ERSP) analysis of electroencephalography (EEG) data to describe anterior sensorimotor (e.g., premotor cortex, PMC) activity during speech perception and production. The purpose of the current study was to identify and temporally map neural activity from posterior (i.e., auditory) regions of the dorsal stream in the same tasks. Perception tasks required "active" discrimination of syllable pairs (/ba/ and /da/) in quiet and noisy conditions. Production conditions required overt production of syllable pairs and nouns. ICA performed on concatenated raw 68 channel EEG data from all tasks identified bilateral "auditory" alpha (α) components in 15 of 29 participants localized to pSTG (left) and pMTG (right). ERSP analyses were performed to reveal fluctuations in the spectral power of the α rhythm clusters across time. Production conditions were characterized by significant α event related synchronization (ERS; pFDR < 0.05) concurrent with EMG activity from speech production, consistent with speechinduced auditory inhibition. Discrimination conditions were also characterized by α ERS following stimulus offset. Auditory α ERS in all conditions temporally aligned with PMC activity reported in Jenson et al. (2014). These findings are indicative of speechinduced suppression of auditory regions, possibly via efference copy. The presence of the same pattern following stimulus offset in discrimination conditions suggests that sensorimotor contributions following speech perception reflect covert replay, and that covert replay provides one source of the motor activity previously observed in some speech perception tasks. To our knowledge, this is the first time that inhibition of auditory regions by speech has been observed in real-time with the ICA/ERSP technique.

Keywords: sensorimotor integration, auditory alpha, EEG, dorsal stream, speech-induced suppression

# Introduction

Human communication relies heavily on the functional integrity of the auditory system. Auditory cortical regions reside bilaterally in the temporal lobes, extending posteriorly along the superior temporal gyri (STG) to include primary and association regions. These regions allow humans to sense sounds and are particularly tuned to speech, providing spectro-temporal analysis of complex acoustic speech signals (Specht, 2014). Auditory association regions, such as the posterior superior temporal gyri (pSTG), comprise the posterior aspects of the dorsal stream, which function in tandem with anterior regions (e.g., the premotor cortex, PMC) of this network to facilitate sensorimotor integration (SMI) for speech (Hickok and Poeppel, 2000, 2004, 2007; Rauschecker, 2012). While there is clear evidence of dorsal stream activity in both speech perception and production (Numminen and Curio, 1999; Callan et al., 2000; Curio et al., 2000; Houde et al., 2002; Herman et al., 2013; Jenson et al., 2014), its temporal dynamics are still not well understood.

According to contemporary models of speech such as the Directions into Velocities of Articulators (DIVA; Tourville and Guenther, 2011; Guenther and Vladusich, 2012) and State Feedback Control (SFC; Hickok et al., 2011; Houde and Nagarajan, 2011), SMI for speech production is dependent upon the integrity of the dorsal stream network. When motor commands for speech are initiated, the PMC produces a corollary discharge (i.e., efference copy) containing an internal model of the expected sensory consequences of the movement (von Holst, 1954; Blakemore et al., 2000; Wolpert and Flanagan, 2001). The efference copy is sent from the PMC to higher order association regions for comparison with available acoustic reafferent feedback, delivered with the execution of the motor commands (Guenther, 2006; Tourville and Guenther, 2011; Guenther and Hickok, 2015). Any mismatch between prediction and reafference (i.e., an error signal) quickly results in corrective feedback sent to motor planning regions (e.g., PMC) for online updating of subsequent commands (Guenther et al., 2006; Houde and Chang, 2015). However, during continuous error-free unperturbed speech production, internally based predictions match the reafferent feedback, minimizing the need for corrective feedback. This accurate matching is thought to have a subtractive (i.e., canceling) effect, producing a net attenuation of activity in auditory regions, which is paramount to distinguishing our own speech from that of others (Blakemore et al., 2000; Wolpert and Flanagan, 2001). This suppression of predicted feedback is thought to enhance sensitivity to deviations from the intended production, facilitating online monitoring of speech (Niziolek et al., 2013; Sitek et al., 2013). This proposal is supported by evidence of lowered auditory thresholds to self-produced vs. externally produced sound (Reznik et al., 2014).

Evidence of speech-induced suppression (SIS; Curio et al., 2000; Sitek et al., 2013) has been demonstrated using various neuroimaging techniques. Positron electron tomography (PET) studies have shown reduced STG activation during speech production compared to listening to playback of one's own speech (Frith et al., 1991; Hirano et al., 1997; Wise et al., 1999). Similarly, in ERP studies, the amplitude of the N100/M100 response has been found to be reduced in normal overt speech compared to replay tasks (Curio et al., 2000; Houde et al., 2002; Greenlee et al., 2011; Chang et al., 2013), and when compared to speech under altered auditory feedback conditions (Heinks-Maldonado et al., 2005; Behroozmand and Larson, 2011; Kort et al., 2014). Using electrocorticography (ECoG), Chang et al. (2013) found suppression of the pSTG in speaking vs. listening conditions. Taken together, these PET, ERP, and ECoG studies support the deactivation of posterior dorsal stream auditory regions via efference copy during normal speech production in accord with DIVA (Tourville and Guenther, 2011; Guenther and Vladusich, 2012) and SFC (Hickok et al., 2011; Houde and Nagarajan, 2011) models. While the pSTG appears to be the primary site of posterior dorsal stream activity in speech, some studies have reported similar activity in the posterior MTG (Christoffels et al., 2007; Herman et al., 2013; Bowers et al., 2014). Functional magnetic resonance imaging (fMRI) has also produced results that are consistent with these models, identifying suppression in the pSTG during overt speech production (Christoffels et al., 2011). However, some fMRI studies have produced conflicting results. For example, Reznik et al. (2014) reported enhanced responses in the auditory cortex (i.e., pSTG) to self-generated sounds contrasted with externally-generated sounds, interpreting this enhancement as evidence of efference copy improving perceptual sensitivity. Other studies have reported auditory suppression to selfgenerated stimuli in anterior (i.e., medial) locations of the STG while observing enhancement in posterior regions (Agnew et al., 2013). These mixed findings have been interpreted as representing two functionally distinct and spatially differentiated processes (Agnew et al., 2013; Chang et al., 2013; Houde and Chang, 2015), resolvable with the superior spatial resolution of fMRI.

Though there is ample evidence for SIS around posterior dorsal regions in speech production, a better understanding of its functional role is likely to be achieved by temporally mapping activity within the dorsal stream regions in reference to speech events. Increased temporal precision also may enhance understanding of the functional role of dorsal stream activity observed during speech perception. While dorsal stream activity is not typically observed during ''passive'' listening tasks (Scott et al., 2009; Szenkovits et al., 2012; Bowers et al., 2013), it has been reported in a variety of more challenging ''active'' perception tasks, such as discrimination of foreign phonemes (Callan et al., 2006), segmentation (Burton et al., 2000; LoCasto et al., 2004), and discrimination of speech in noise (Bowers et al., 2013, 2014). These mixed findings leave unanswered questions regarding the extent to which auditoryto-motor mapping functionally supports accurate perception vs. being merely a by-product of increased processing. One way to address these questions is by examining the timing of dorsal stream activity relative to stimulus presentation. Early activity may be indicative of predictive coding (Sohoglu et al., 2012), in which early motor representations are used to constrain the analysis of incoming sensory information to aid accurate discrimination; a form of experience-based Constructivist hypothesis testing (Stevens and Halle, 1967; Callan et al., 2010; Skipper, 2014). In contrast, late activity following stimulus offset might reflect covert replay (Burton et al., 2000; Rogalsky et al., 2008; Jenson et al., 2014), in which stimuli are covertly rehearsed in working memory to facilitate discrimination (Baddeley, 2003). Thus, fine-grained temporal data are critical to addressing the functional role of dorsal stream activity in speech perception.

The excellent temporal and spectral detail inherent to neural oscillations makes them a prime candidate for evaluating the dynamics of neural activity in anterior and posterior regions of the dorsal stream (e.g., PMC, pSTG). These oscillations originate from local synchrony between the action potentials of neurons, giving rise to neuronal assemblies with periodic variations in their activity levels (Schnitzler and Gross, 2005; Buszaki, 2006). Fluctuations in spectral power relative to baseline within frequency bands can be measured as relative increases (event related synchronization, ERS) and decreases (event related desynchronization, ERD) in activity, respectively. The ''gating by inhibition'' hypothesis (Jensen and Mazaheri, 2010) proposes that spectral power within the alpha (α) band (8–13 Hz) can be interpreted as a measure of cortical activation. In support of this hypothesis, α spectral power has been shown to be inversely correlated with the fMRI BOLD signal (Laufs et al., 2003; Brookes et al., 2005; Scheeringa et al., 2009; Mayhew et al., 2013), leading to the interpretation of α ERS and ERD as indices of cortical inhibition and disinhibition, respectively (Klimesch et al., 2007; Weisz et al., 2011).

α oscillations are ubiquitous across the brain, having been implicated in the modulation of cortical activity found in both attention (Muller and Weisz, 2012; Frey et al., 2014) and working memory tasks (Jokisch and Jensen, 2007; van Dijk et al., 2010). A growing body of evidence also points to the existence of an independent auditory α rhythm distinct from other known α generators (Tiihonen et al., 1991; Lehtela et al., 1997; Weisz et al., 2011). Tiihonen et al. (1991) identified a magnetoencephalographic (MEG) α rhythm that demonstrated ERD during auditory stimulation that was not modulated by opening the eyes or clenching the fist, concluding that this was a distinct auditory α rhythm (Tiihonen et al., 1991; Lehtela et al., 1997). Subsequent investigation has implicated this auditory α rhythm in top-down attentional control during dichotic listening (Muller and Weisz, 2012), neural excitability and stimulus detection (Weisz et al., 2014), and auditory hallucinations (Weisz et al., 2011). These studies demonstrate the utility of auditory α oscillations to the investigation of cognitive processes underlying speech.

The temporal precision, economy, and non-invasive nature of electroencephalography (EEG) makes it well suited for capturing oscillatory activity from SMI in speech perception and production (Cuellar et al., 2012; Bowers et al., 2013; Jenson et al., 2014). However, historically EEG analysis has been limited by poor spatial resolution due to volume conduction (the fact that each channel contains information from multiple neural sources) and its susceptibility to contamination by movement artifact. Recently, independent component analysis (ICA) has offered an effective means of overcoming these limitations. ICA is a method of blind source separation that decomposes complex mixtures of non-neural (i.e., artifact) and neural EEG signals into temporally independent and spatially fixed components (Stone, 2004; Onton et al., 2006). In effect, ICA provides a means of both separating muscle movement from neural activity and reliably identifying cortical sources of activity. Independent EEG components can be further decomposed across time and frequency via event related spectral perturbations (ERSP) to reveal patterns of ERS/ERD that characterize regional neural activity in cognitive and motor tasks. Identification of auditory α rhythm components can be followed by ERSP analysis to better understand auditory activity across the time course of speech production and perception tasks.

This ICA/ERSP analysis is well established in perception tasks (Lin et al., 2007, 2011; McGarry et al., 2012) and has more recently been applied to speech perception, examining changes in spectral activity in the sensorimotor µ rhythm components (Bowers et al., 2013, 2014; Jenson et al., 2014). Consistent with Callan et al. (2010) and constructivist interpretations of predictive coding and analysis-by-synthesis (Stevens and Halle, 1967), Bowers et al. (2013) found active syllable discrimination produced more robust mu (µ) ERD (indicating sensorimotor processing) than passive listening to syllables or discriminating between tones. In addition to identifying sensorimotor µ components, Bowers et al. (2014) reported bilateral components from posterior superior temporal lobes (pSTG) with characteristic α spectra, similar to those described by Muller and Weisz (2012). Though ICA clearly has demonstrated the capacity for identifying sources of neural activity in perceptual tasks, its application to motor tasks has been limited due to questions pertaining to its ability to accurately localize and estimate cortical activity within neural sources in the presence of competing myogenic activity (Oken, 1986; Shackman et al., 2009; McMenamin et al., 2010, 2011).

Recently, Jenson et al. (2014) used an ICA/ERSP technique to measure anterior dorsal stream activity in speech perception and production. Specifically, they identified µ components with characteristic α and beta (β; ∼20 Hz) peaks and related the changes in spectral power within these peaks to sensory (α) and motor (β) contributions to anterior dorsal stream activity in various tasks. Participants listened to passive noise, discriminated pairs of syllables with and without background noise, and performed overt productions of syllable pairs and tri-syllable nouns. ICA of concatenated raw EEG data from all (perception and production) tasks yielded independent left and right µ components localized to the PMC common to all conditions, supporting the use of ICA in speech production. The ERSP analysis revealed concurrent α and β ERD (reflecting sensory and motor processing) time-locked to muscle movement during overt production. The authors interpreted these findings as evidence of a normal continuous sensorimotor loop for speech production. Interestingly, this same pattern of concurrent α and β µ ERD was observed in the discrimination conditions in the time period following acoustic offset. The authors cautiously interpreted this µ ERD in discrimination conditions as evidence of late covert rehearsal while the stimuli was being held in memory prior to a response (Burton et al., 2000; Baddeley, 2003). This interpretation supports the suggestion that similar anterior dorsal stream sensorimotor processes can be involved in covert and overt speech production (Gehrig et al., 2012; Ylinen et al., 2014), However, to achieve a better understanding of dorsal stream activity in speech perception and production, it is necessary to also examine the temporal dynamics of sensorimotor activity in the posterior aspects of the network.

The purpose of the current study is twofold. The first is to use ICA of raw EEG data to identify temporal lobe auditory components common to speech perception and production tasks. The second is to use ERSP analysis to provide highresolution temporal and spectral information regarding the dynamics of this auditory α rhythm during speech perception and production. It is hypothesized that ICA will identify bilateral components with α spectra (∼10 Hz) localized to auditory association regions, representing activity within posterior regions of the dorsal stream. The second hypothesis is that ERSP analysis of these components will reveal α ERS, representing reduced activity in posterior aspects of the dorsal stream (i.e., pSTG) by an efference copy while speech is being produced. Though the current study employs no connectivity measures, ERSPs from auditory components can be examined alongside those from anterior sensorimotor µ components reported in Jenson et al. (2014) to better understand dorsal stream activity in speech perception and production. Thus, the third hypothesis is that µ ERD and α ERS will be observed simultaneously reflecting synchronous complementary activity across anterior and posterior aspects of the dorsal stream. Observing this pattern of activity following speech perception will support the theory that dorsal stream activity in speech discrimination is characterized at least in part by covert replay.

#### Materials and Methods

#### Participants

Twenty-nine right-handed native English speakers were recruited from the audiology and speech pathology program at the University of Tennessee Health Science Center. Subjects (24 females, 5 males) had a mean age of 25.16 years (range 21–46) and no history of cognitive, communicative, or attentional disorders. The Edinburgh Handedness Inventory (Oldfield, 1971) was administered to establish handedness dominance for each subject. The Institutional Review Board for the University of Tennessee approved this study, and all subjects provided informed consent prior to participation.

#### Stimuli

#### Perception

Syllable stimuli (/ba/ and /da/) for the active perception conditions were generated with AT&T naturally speaking textto-speech software, which utilizes synthetic analogs of a male speaker. Syllable stimuli were combined to create syllable pairs such that half of the stimuli consisted of identical pairs (e.g., /ba ba/) and half of the stimuli contained different pairs (e.g., /da ba/). Syllable pairs were then low pass filtered at 5 kHz and normalized for root-mean-square (RMS) amplitude. Each syllable was 200 ms in duration and paired syllables were separated by 200 ms, yielding stimuli that were 600 ms from onset of the first syllable to offset of the second syllable.

One of the active perception conditions (discrimination in noise—Ndis) required subjects to discriminate syllable pairs embedded in white noise with a signal-to-noise ratio (SNR) of +4 dB. This condition was included as previous studies have reported that this SNR produces increased dorsal stream activity while allowing participants to accurately discriminate between the syllables (Binder et al., 2004; Osnes et al., 2011; Bowers et al., 2013, 2014). Another discrimination condition (quiet discrimination—Qdis) required participants to discriminate syllable pairs in the absence of background noise. In order to control for a discrimination response bias (Venezia et al., 2012), an equal number of different and identical syllable pairs were used in each discrimination condition. Discrimination responses were made using a button press. The stimulus used for the control (passive listening) condition was continuous white noise.

#### Production

conditions.

Targets for speech production consisted of the same syllable pairings used in the discrimination conditions (e.g., /ba da/), as well as tri-syllable nouns initiated with either /b/ or /d/ and followed by a vowel (e.g., buffalo, daffodil). Visual stimuli for production were presented at the center of the visual field on Microsoft PowerPoint slides consisting of white text on a black background (Arial font) subtending a visual angle of 1.14◦ . The timelines for perception and production tasks are illustrated in **Figure 1**.

#### Design

The experiment consisted of a five condition, within subjects design. The conditions were designed to require gradually increased motoric demands, progressing from the perception of white noise to the production of tri-syllable nouns. The five conditions were:

Condition 1 was a passive perception task, conditions 2–3 were active perception tasks, and conditions 4–5 were production tasks. The PasN condition required no discrimination, but was used as a control task for the Qdis and Ndis conditions. To control for the neural activity related to the button press response required in the Qdis and Ndis conditions, a button press response was used in the PasN condition. Conditions 2–4 used paired /ba/ and /da/ syllables. Qdis and Ndis required active discrimination of syllable pairs, while SylP required overt productions of /ba/ /da/ syllable pairs, respectively. The WorP condition required overt production of tri-syllable nouns initiated by a /b/ or /d/ and followed by a vowel. Stimuli for the WorP condition were selected from Blockcolsky et al. (2008).

#### Procedure

The experiment was conducted in an electrically and magnetically shielded, double-walled, soundproof booth. Participants were seated in a comfortable chair with their head and neck supported. Stimuli were presented and button press responses were recorded by a PC computer running Compumedics NeuroScan Stim 2, version 4.3.3. A button press response was embedded in the PasN condition for two reasons: (1) to control for anticipatory β suppression which has been previously reported in tasks requiring a button press response (Makeig et al., 2004; Graimann and Pfurtscheller, 2006; Hari, 2006) and (2) requiring a button press response in a condition with no active discrimination ensured that the subjects were attending to and engaged in the task. The response cue for all perception conditions was a 100 ms, 1000 Hz tone presented at the end of the trial epoch (2000 ms post stimulus). In the PasN condition, subjects were instructed to sit quietly, listen to the stimulus (i.e., white noise), and press the button when they heard the response cue. In the Qdis and Ndis conditions, subjects were instructed to press one of two buttons after hearing the response cue depending on whether the syllables were judged to be the same or different. Handedness of button press response was counterbalanced across all subjects and conditions. Discrimination accuracy was determined as percentage of trials correctly discriminated, and subjects who did not discriminate at a level significantly above chance were excluded from the analysis.

In the production conditions, visual stimuli were presented on a monitor (69.5 × 39 cm) placed 132 cm in front of the participant's chair. Visual stimuli (syllable pairs and words) remained on the screen for 1 s, and participants were instructed to begin their production when the visual stimuli disappeared. Thus, stimulus offset was the response cue in the production conditions. In the SylP and WorP conditions, subjects were instructed to produce the syllable pairs in their normal speaking voice. All productions were complete within the 2000 ms window between the response cue and the end of the trial epoch. Each of the five conditions was comprised of 2 blocks of 40 trials each, yielding a total of 10 blocks (5 conditions × 2 blocks). Order of block presentation was randomized for each subject.

#### EEG Acquisition

Whole head EEG data were acquired from 68 channels. These channels included two electromyography (EMG) and two electrocardiogram (EKG) electrodes. Data were recorded with an unlinked, sintered NeuroScan Quik Cap, based on the extended international standard 10–20 system (Jasper, 1958; Towle et al., 1993). All recording channels were referenced to the linked mastoid channels (M1, M2). The electro-oculogram was recorded by means of two electrode pairs placed above and below the orbit of the left eye (VEOL, VEOU) and on the medial and lateral canthi of the left eye (HEOL, HEOR) to monitor vertical and horizontal eye movement. The two EMG electrodes were placed above and below the lips to capture labial lip movement related to overt speech.

EEG data were recorded using Compumedics NeuroScan Scan 4.3.3 software in tandem with the Synamps 2 system. EEG data were band pass filtered (0.15–100 Hz) and digitized with a 24-bit analog to digital converter with a sampling rate of 500 Hz. Data collection was time locked to stimulus onset in the perception conditions, and to the response cue in the production conditions. The visual stimuli to be produced were displayed on the screen for 1 s prior to disappearing, which served as the response cue for production. Thus, time zero was defined as stimulus onset for the perception conditions, and stimulus offset served as time zero for the production conditions.

#### EEG Data Processing

Data processing and analysis were performed with EEGLAB 12 (Brunner et al., 2013), an open source MATLAB toolbox. Data were processed at the individual level and analyzed at both the individual and group level. The following steps were performed at each stage:

	- (a) Preprocessing of 10 raw EEG files for each participant (5 conditions × 2 blocks);
	- (b) ICA of preprocessed files across conditions for each participant; and
	- (c) Localization of all neural and non-neural dipoles for each independent component.
	- (a) Two separate analyses using the STUDY module of EEGLAB 12; one study targeting neural components only (''in-head'') and the other targeting neural and myogenic components (''all'');

#### Analysis for Hypothesis 1

#### Data Preprocessing

Raw EEG data files from both blocks of each condition were appended to create one dataset per condition per participant, and then downsampled to 256 Hz to reduce the computational requirements of further processing steps. Trial epochs of 5000 ms (ranging from −3000 to +2000 ms around time zero) were extracted from the continuous EEG data. The data were then filtered from 3–34 Hz, which allowed for clear visualization of α and β bands, while filtering muscle artifact from surrounding frequency bands. All EEG channels were referenced to the mastoids (M1, M2) to remove common mode noise. Trials were visually inspected, and all epochs containing gross artifact (in excess of 200 µV) were removed. Additionally, trials were rejected if the participant performed the discrimination incorrectly, or if the response latency exceeded 2000 ms. A minimum of 40 useable trials per subject per participant was required in order to ensure a successful ICA decomposition.

#### ICA

Following data preprocessing and prior to ICA analysis, data files for each participant were concatenated to yield a single set of ICA weights common to all conditions. This allowed for comparison of activity across conditions within spatially fixed components. The data matrix was decorrelated through the use of an extended Infomax algorithm (Lee et al., 1999). Subsequent ICA training was accomplished with the ''extended runica'' algorithm in EEGLAB 12 with an initial learning rate of 0.001 and the stopping weight set to 10–7. ICA decomposition yielded 66 ICs for each participant, corresponding to the number of recording electrodes (68 data channels–2 reference channels; M1, M2). Scalp maps for each component were generated by projecting the inverse weight matrix (W-1) back onto the original spatial channel configuration.

After ICA decomposition, equivalent current dipole models (ECD) were generated for each component by using the boundary element model (BEM) in the DIPFIT toolbox, an open source MATLAB plugin available at sccn.ucsd.edu/eeglab/dipfit.html (Oostenveld and Oostendorp, 2002). Electrode coordinates conforming to the standard 10–20 configuration were warped to the head model. Automated coarse-fitting to the BEM yielded a single dipole model for each of the 1914 ICs (29 participants × 66 ICs). Dipole localization entails a back projection of the signal to a potential source that could have generated the signal, followed by computing the best forward model from that hypothesized source that accounts for the highest proportion of the scalp recorded signal (Delorme et al., 2012). The residual variance (RV) is the mismatch between the original scalp recorded signal and this forward projection of the ECD model. The RV can be interpreted as a goodness of fit measure for the ECD model.

#### STUDY (Group Level Analyses)

Group level analyses were performed in the EEGLAB STUDY module. The STUDY module allows for the comparison of ICA data across participants and conditions. The STUDY module also allows for the inclusion or exclusion of ICs based on RV and location (in head vs. outside head). Two different STUDY analyses were performed on participants' ICA files containing dipole information. The ''in head'' analysis (neural) was limited to dipoles originating within the head, and the RV threshold was set to <20%.

In order to capture peri-labial EMG activity, a second STUDY (''all'') was performed, which included dipoles originating both within the head and outside the head. Additionally, the RV threshold was lifted to <50% to account for the fact that EMG activity inherently contains higher levels of RV. Peri-labial EMG activity was extracted from the ''all'' STUDY, while all neural data were analyzed within the ''in head'' study only.

#### PCA Clustering

In both of the STUDY analyses (''in head'' and ''all''), IC preclustering was performed based on commonalities of spectra, dipoles, and scalp maps. The K-means statistical toolbox was used to group similar components across participants based on the specified criteria via PCA. ICs from the ''in head'' analysis were assigned to 25 neural clusters, from which left and right auditory clusters were identified. ICs from the ''all'' analysis were assigned to 66 possible clusters (both neural and non-neural), one of which contained peri-labial EMG activity.

Designation to auditory (STG) clusters for the ''in head'' STUDY was based primarily on the initial results of PCA, followed by inspection of all ICs in the auditory cluster and surrounding clusters based on spectra, dipoles, and scalp maps. Inclusion criteria for the auditory clusters were based on previously observed posterior dorsal stream activity in speech and, therefore, included components that were localized to the pSTG or pMTG regions, showed a characteristic α spectrum, and could be localized with RV <20%.

The majority of the 66 clusters generated in the ''all'' STUDY contained non-neural (myogenic) activity. The cluster containing peri-labial EMG activity was identified based on dipole location and verified by ERSP analysis, demonstrating activity during the overt speech conditions only.

#### Source Localization

Source localization for ECD clusters identified in the STUDY module is the mean of the Talairach coordinates (x, y, z) for each of the contributing dipole models (identified by the DIPFIT module). A further method of source localization is standardized low-resolution brain electromagnetic tomography (sLORETA), which addresses the inverse problem by using CSD from scalp recorded electrical signals to estimate source location (Pascual-Marqui, 2002). Solutions are based on the Talairach cortical probability brain atlas, digitized at the Montreal Neurological Institute (MNI). Electrode locations are co-registered between both spherical models (BESA) and realistic head geometry (Towle et al., 1993). The 3-D brain space was divided into 6,239 voxels, yielding a spatial resolution of 5 mm. The inverse weight projections from the original EEG channels for each component contributing to the temporal α clusters were exported to sLORETA. Cross-spectra were computed and mapped to the Talairach atlas and cross-registered with MNI coordinates, resulting in CSD estimates for each contributing component. The analysis of statistical significance of CSD estimates across participants was performed in the sLORETA software package. The analysis was non-parametric, based on the estimation (via randomization) of the probability distribution of the t-statistic expected under the null hypothesis (Pascual-Marqui, 2002). This method corrects for multiple comparisons across all voxels and frequencies (3–34 Hz). Voxels that were significant at p < 0.001 were considered to be active across participants. Group level source localizations are based on the CSD source estimates computed via sLORETA, though the ECD localizations are also reported as they serve to demonstrate the inter-subject variability present in the data.

#### Analysis for Hypotheses 2 and 3 ERSP

ERSP analyses were used to measure fluctuations in spectral power (in normalized decibel units) across time in the frequency bands of interest (3–34 Hz). Time-frequency transformations were computed using a Morlet wavelet rising linearly from three cycles at 3 Hz to 34 cycles at 34 Hz. Trials were referenced to a 1000 ms pre-stimulus baseline selected from the intertrial interval. A surrogate distribution was generated from 200 randomly sampled latency windows from this silent baseline (Makeig et al., 2004). Individual ERSP changes across time were calculated with a bootstrap resampling method (p < 0.05 uncorrected). Single trial data for all experimental conditions for frequencies between 4 and 30 Hz and ranging from −500 to 1500 ms were entered into the time-frequency analysis.

In the ''in head'' study, permutation statistics (2000 permutations) were used to assess inter-condition differences. The significance threshold was set at p < 0.05, and Type 1 error was controlled by false discovery rate (FDR) correction (Benjamini and Hochberg, 2000). Statistical analyses used a 1 × 5 repeated measures ANOVA design (PasN, Qdis, Ndis, SylP, WorP). Further post hoc analyses of differences in perception and production conditions used 1 × 3 and 1 × 2 repeated measures ANOVA designs, respectively.

#### Results

#### Discrimination Accuracy

All subjects that contributed to the temporal α clusters performed the discrimination tasks with a high degree of accuracy. As it has been shown that activity in sensorimotor regions is susceptible to the effects of response bias (Venezia et al., 2012), d' values also are reported to tease out the differential effects of sensitivity and response bias on perceptual accuracy. The average number of useable trials (out of 80) for each condition was: PasN = 74.4 (SD 6.9), Qdis = 74 (SD 4.77), Ndis = 69.1 (SD 12.52), SylP = 73.73 (SD 5.19), and WorP = 73.14 (SD 6.39). Subjects performed the discrimination with similar high accuracy in both the Qdis [96.5%, SD 2.55; d' 3.38, SE 0.09] and Ndis [94.5, SD 8.69; d' 3.63, SE 0.19] conditions. The greater variability in the Ndis condition was due primarily to one participant, who performed the task with 65% accuracy. A paired t-test on d' values for each condition indicated that subject accuracy for these two conditions was not significantly different (p > 0.05). The mean reaction time for discrimination conditions was 506.3 ms in the Qdis conditions (SD 133.3) and 568.1 ms in the Ndis condition (SD 298.3). A paired t-test indicated that the mean response latency between conditions was not significantly different (p > 0.05). Taken together, these findings indicate that subjects performed both discrimination tasks with similar levels of accuracy and efficiency. A response contingent analysis was performed in which incorrectly discriminated trials were excluded from subsequent analysis, and thus the analysis of neural data was restricted only to those associated with correctly discriminated trials.

#### Results Pertaining to Hypothesis 1 Temporal α Cluster Characteristics

In line with the hypothesis that ICA would identify bilateral α components localized to pSTG, 15/29 participants generated components with less than 20% RV contributing to both the left and right temporal α clusters. The clusters had peaks at 10 Hz on both the left and right. For the ECD dipole models, the average dipole localization was at Talairach [−48, −45, 15] for the left temporal cluster and Talairach [57, −42, 10] on the right. The percentage of unexplained variance for these two clusters was 11.7% and 11.9%, respectively. The CSD model computed with sLORETA showed active voxels (p < 0.001) localized to the pSTG on the left and the pMTG on the right. In both hemispheres, activation spread across the pSTG and pMTG. CSD source maxima were localized to MNI [−50, −55, 10] on the left and MNI [55, −45, 0] on the right. The Euclidean distance between ECD and CSD sources were 11.4 mm on the left and 10.6 mm on the right. The peri-labial EMG cluster, identified on the basis of dipole location and the time course of activity, consisted of nonneural components with an average of 20.07% RV. **Figures 2**, **3** demonstrate: (A) the average scalp map; (B) the spectra; (C) the distribution of ECD dipoles; and (D) CSD source localization for the left and right temporal clusters, respectively. As the component activations were generated from data concatenated across conditions, the source localizations reported pertained to temporal lobe clusters from all experimental conditions.

#### Results Pertaining to Hypothesis 2 ERSP Analysis in Production (SylP, WorP)

The second hypothesis was that ERSP analysis of auditory α clusters would reveal α ERS in time periods coinciding with overt production. **Figure 4** shows van Essen maps (computed with sLORETA) demonstrating activated voxels at (p < 0.001)

FIGURE 3 | Results for right temporal cluster. (A) Scalp distribution, scaled in RMS microvolts, (B) Mean spectra of cluster components, (C) ECD localization for cluster components demonstrating inter-subject variability, (D) CSD cluster localization projected onto a van Essen cortical model. Active voxels are significant at

in the (A) left and (C) right hemisphere temporal clusters. ERSP analyses show differential patterns of ERS/ERD measured against baseline across the two production conditions (SylP, WorP), within the 4–30 Hz bandwidth. The final column shows significant differences (pFDR < 0.05) compared to PasN. **Figure 4B** shows the average ECD localization for the EMG cluster corresponding to peri-labial muscle activity, as well as the ERSP analysis of that component cluster.

p < 0.001 (corrected for multiple comparisons).

In the left temporal cluster, α ERD in production conditions (SylP, WorP) began prior to stimulus onset, peaking after the cue to produce speech. Approximately 500 ms after the production cue, α ERD began to decrease accompanied by the emergence of α ERS, which extended into low β frequencies. As in perception conditions, the right temporal cluster showed identical patterns of activation, though with weaker spectral power.

#### Temporal Alignment Between Temporal α, Sensorimotor µ, and Peri-labial EMG Activity

EMG activity was found in the SylP and WorP conditions only. EMG ERS (corresponding to speech production) began at approximately 300 ms and peaked at about 500 ms post response cue. These response latencies are within the expected range for speech production tasks (Heinks-Maldonado et al., 2005). α ERS in left and right temporal clusters was aligned temporally with EMG ERS in the SylP and WorP conditions.

Jenson et al. (2014) analyzed data from the same subject pool in identical conditions and interpreted concurrent α and β ERD over the PMC as evidence of covert replay during perception and overt production of speech. In the current study, the emergence of α ERS in the temporal cluster also was aligned temporally with the peak α and β ERD in the sensorimotor µ rhythm reported by Jenson et al. (2014) in both perception and production. **Figure 5** demonstrates the temporal synchrony between α ERS in the temporal lobe, sensorimotor α/β ERD (representing SMI during production), and EMG ERS, as well as the alignment of temporal α ERS and sensorimotor α/β ERD (consistent with covert rehearsal) during discrimination tasks.

#### Results Pertaining to Hypothesis 3 ERSP Analysis in Perception (PasN, Qdis, Ndis)

The third hypothesis was that ERSP analysis of auditory α clusters in discrimination conditions would demonstrate α ERS during time periods of µ ERD, consistent with the interpretation of PMC activity during speech discrimination as evidence of covert replay. **Figure 6** shows van Essen maps (computed with sLORETA) showing activated voxels (p < 0.001) in the left (A) and right (B) hemisphere temporal clusters. ERSP analyses show differential patterns of ERS/ERD measured against baseline across the three perception conditions (PasN, Qdis, Ndis), within the 4–30 Hz bandwidth. The final column shows significant differences (pFDR < 0.05) among the three conditions.

For the left temporal cluster, α ERD began subsequent to acoustic stimulation and persisted until approximately 500 ms post stimulus offset. At approximately 500 ms post stimulus offset, α ERD began to decrease, giving way to

α ERS in both discrimination conditions (Qdis and Ndis) that extended into low β frequencies. A post hoc comparison of the two discrimination conditions (Qdis and Ndis) revealed no significant differences between conditions. The right hemisphere temporal cluster showed the same patterns of α ERD fading to ERS as the left temporal cluster, though with weaker spectral power. Post hoc comparisons of Qdis and Ndis to the PasN condition produced identical results to those found in the left temporal cluster.

## Discussion

The first hypothesis (that ICA would identify bilateral α clusters localized to auditory association regions) was well supported by the data obtained from the left hemisphere. This finding is consistent with the MEG/EEG findings of Weisz et al. (2011) who also found evidence of an independent auditory α rhythm. Clusters of neural activity emanating from these regions with <20% unexplained RV were localized to the pSTG via both ECD and CSD localization methods (although their exact source averages varied by ∼1 cm). This localization is in agreement with previous findings of auditory α oscillatory activity identified via EEG/ICA (Bowers et al., 2014) and MEG (Muller and Weisz, 2012) and is consistent with a left-hemisphere dominance for dorsal stream activity in speech-based tasks (Hickok and Poeppel, 2004). In the right hemisphere, the two localization techniques also produced source averages that were separated by ∼1 cm. However, the average ECD source was in the pSTG, while the average CSD source was located slightly inferiorly in the pMTG, possibly highlighting the uncertainty of EEG source localization and a reduced role of the right hemisphere in speech processing.

The finding that only 15/29 participants contributed to the clusters requires examination. Reasons for this include (1) the application of a standard head model reducing localization accuracy; (2) the location of auditory regions along the Sylvian fissure. As anatomic variability increases at greater distances from midline, the potential impact of a standard head model may have been maximal along the lateral and dorsal surfaces of the STG; (3) EEG's superior sensitivity to signals arising from cortical gyri rather than sulci; and (4) the fact that only components from pSTG and pMTG regions were included though all participants produced temporal lobe components. It should be noted that α activity has been observed across more anterior portions of the STG in addition to the pSTG (Weisz et al., 2011). However, due to the possibility that anterior and posterior regions of the STG perform functionally distinct tasks (Agnew et al., 2013; Chang et al., 2013; Houde and Chang, 2015) and that the current goal was to examine dorsal stream activity, only components localized to posterior regions were included in this study. Therefore, it is likely that these inclusion criteria limited the number of contributors to the clusters. Even with the inherent limitations in source localization, the ERSP analyses produced significant changes in α spectral power across time in both speech perception and production conditions, providing evidence of auditory cortical deactivation that can be interpreted in light of current models.

#### Auditory α ERS/ERD in Speech Production

Auditory α activity in both production conditions (SylP, WorP) was characterized by ERD prior to production with no significant differences in activity between the two conditions. Before initiating speech, participants read the target and prepared to speak while attending to the visual cue to do so. Early activation of the auditory cortex (following stimulus presentation and prior to production) has previously been demonstrated during

silent and overt reading (Kell et al., 2011). Reduced levels of α activity in sensory regions prior to stimulus presentation have been shown to facilitate detection of near-threshold stimuli in the somatosensory domain (Weisz et al., 2014), and a similar mechanism in the auditory domain may facilitate monitoring of speech by an SMI loop. Additionally, α ERD also is known to result from simple increases in attention (Jensen and Mazaheri, 2010). Therefore, considering that the current speech production tasks required the coordination of multiple cognitive processes prior to speaking, all of which rely on attention to some extent, it is not possible to parse out the individual contributions of all cognitive processes to ERD prior to speech. Rather, it is likely that pre-speech α ERD resulted from contributions of attention, reading, and integration of auditory regions into an error-free SMI loop for speech.

The hypothesis that speech production would be characterized by increases in oscillatory α power was supported. A positive shift in auditory α power emerged concurrently with robust peri-labial EMG activity (i.e., muscle movement) that marked the initiation of speech. These patterns of neural and muscular activity were observed after ∼300 ms (i.e., reaction time) from the cue to speak (time 0, **Figure 4**). It should be noted that these ERS/ERD changes were measured in reference to a ''silent baseline'' prior to each trial and that they were statistically significant when compared to a control passive listening condition in which little pSTG α activity was observed. As α ERS is associated with reduced activity (Laufs et al., 2003; Brookes et al., 2005; Scheeringa et al., 2009; Mayhew et al., 2013), the current findings are consistent with speech-induced suppression (i.e., modulation of auditory cortical activity during speech production; Frith et al., 1991; Hirano et al., 1997; Curio et al., 2000; Houde et al., 2002; Heinks-Maldonado et al., 2005; Christoffels et al., 2007, 2011). However, it is also interesting to note that though auditory oscillatory activity was characterized by α spectra, the observed ERS spread into higher frequencies and may be somewhat consistent with ECoG and fMRI studies that have implicated modulations in auditory gamma frequencies during speech production (Greenlee et al., 2011; Agnew et al., 2013; Reznik et al., 2014).

It is also important to note that both myogenic and auditory activity during speech production is characterized by ERS, which may raise questions pertaining to the possibility of muscle activity contaminating neural activity. There are multiple reasons to refute this notion. First, if ICA was not able to adequately separate neural signals from myogenic artifact, muscle activity would have overwhelmed the α and β ERD recorded in the sensorimotor µ cluster (Jenson et al., 2014). Second, α ERS was noted in the perception conditions (coinciding with periods of covert rehearsal—see below), during which no overt response was required. Together, this evidence suggests that α ERS resulted from neural activity as opposed to myogenic artifact.

The larger picture of dorsal stream activity in speech production becomes apparent when the current data are viewed alongside those of Jenson et al. (2014). Data from the same participants in the same conditions showed sensorimotor µ ERD (i.e., disinhibition) beginning with muscle movement in speech production. Thus, when viewed together, sensorimotor disinhibition and auditory inhibition coincided with speech production (as indicated by EMG activity; see **Figure 5**). Current models of SMI for speech indicate that the sensorimotor loop is initiated by the generation of a motor plan in PMC (Tian and Poeppel, 2010; Houde and Nagarajan, 2011; Jenson et al., 2014). Concurrent with the delivery of this motor plan to primary motor cortex (M1), an efference copy of the expected sensory consequences is sent to auditory regions for comparison with the goals and outcomes of the movements. Any deviations from expectations are detected and corrective feedback is sent to the PMC. As true auditory and somatosensory reafferent feedback is received, this information is also integrated into feedback to the PMC (Tian and Poeppel, 2010). During normal error-free production, articulatory predictions are matched to the available sensory (i.e., auditory) information, resulting in the observed net deactivation in auditory association regions. Based on this model, it was not surprising to see near perfect temporal concordance between α/β µ ERD, auditory α ERS, and peri-labial EMG activity in normal, unperturbed speech production. Thus, the results of the current study demonstrate inhibition of auditory regions during overt speech, consistent with the suppression that would be expected based on the delivery of an efference copy from the PMC.

#### Auditory α ERS/ERD in Accurate Speech Discrimination