Speaking waves: neuronal oscillations in language production
Vitória Piai1,2, Xiaochen Zheng1
1. Radboud University, Donders Centre for Cognition, Nijmegen, the Netherlands
2. Radboudumc, Donders Centre for Medical Neuroscience, Department of Medical
Psychology, Nijmegen, the Netherlands
Vitória Piai, PhD
To Ardi Roelofs, who planted the seed for our fascination with language production.
Language production involves the retrieval of information from memory, the planning of an
articulatory programme, and executive control and self-monitoring. These processes can be
related to the domains of long-term memory, motor control, and executive control. Here, we
argue that studying neuronal oscillations provides an important opportunity to understand how
general neuronal computational principles support language production, also helping elucidate
relationships between language and other domains of cognition. For each relevant domain, we
provide a brief review of the findings in the literature with respect to neuronal oscillations.
Then, we show how similar patterns are found in the domain of language production, both
through review of previous literature and novel findings. We conclude that
neurophysiological mechanisms, as reflected in modulations of neuronal oscillations, may act
as a fundamental basis for bringing together and enriching the fields of language and
Psycholinguistic models of language production, despite differing from one another in many
ways, generally agree that producing words involves the retrieval and selection of a concept to
be expressed, retrieval and selection of syntactic and morphophonological properties of an
associated word, and post-lexical articulatory planning and self-monitoring processes (Bock,
1982; Dell, 1986; Hickok, 2012; Levelt, Roelofs, & Meyer, 1999; Rapp & Goldrick, 2000).
Roughly speaking, these processes can be related to three other (cognitive) domains, namely
long-term memory (i.e., access of conceptual, lexical, and phonological information in long-
term memory), motor control (i.e., motor preparation and execution of an articulatory
programme), and executive control (i.e., regulatory processes involved in selection and
Understanding language production in relation to these other domains is important for various
reasons. Firstly, partly thanks to studies in animals, much is known about memory functioning
(Buzsáki, 2005; Düzel, Penny, & Burgess, 2010; Hasselmo & Stern, 2013; Jacobs, 2014),
executive control (Knight, Staines, Swick, & Chao, 1999; Lundqvist et al., 2016; E. K. Miller
& Cohen, 2001), and motor control (Cheyne, 2013; K. J. Miller et al., 2012; Murthy & Fetz,
1996; van Wijk, Beek, & Daffertshofer, 2012). If the neurophysiological underpinnings of
these processes are shared with language, the existing knowledge can help us achieve a better
understanding of language functioning. Conversely, language research can have an impact on
knowledge in other cognitive domains, not least thanks to providing more naturalistic means
of probing the underlying physiological mechanisms. For example, long-term memory is
often studied with episodic memory paradigms that create artificial pairings between stimuli.
By contrast, language provides the means for assessing the binding between concepts and
words, acquired in a naturalistic manner, and carried in our memories for much longer periods
than the short setting of an experiment. Finally, the ultimate aim of the cognitive
(neuro)sciences is integrating knowledge to arrive at a unified theory of brain and cognition,
and the type of cross-domain fertilisation we just described helps move us in the right
1.1. Electrophysiology and neuronal oscillations
Language production relies on dynamic and rapid cognitive processes, best demonstrated by
the fact that an average speaker produces 2 to 5 words every second. Investigating such
processes requires techniques that can track brain activity at a high temporal resolution. This
is what makes the brain’s electrophysiological signal instrumental in our undertaking (see for
similar arguments e.g., Cohen, 2011b; Hauk, 2016; Lopes da Silva, 2013).
Most electrophysiological studies done in the language domain have measured the
electroencephalogram (EEG) or magnetoencephalogram (MEG) over the scalp. The post-
synaptic activity of a large group of synchronised neurones generates an electric field, often
called the local field potential. This electric field also generates a magnetic field around it.
Both electric and magnetic fields can be measured over a distance from their sources, for
example, over the scalp. When recorded over the scalp, an attenuated and distorted version of
these fields is measured with the EEG or MEG.
As already mentioned, neurones work in a synchronised fashion. Individual neurones have
intrinsic oscillatory properties and, under many circumstances, they will oscillate collectively
in different frequencies. These collective oscillations are the most efficient way for a
population to achieve synchrony. At the level of neuronal populations, oscillations enable
controlling the timing of neuronal firing. They also allow neuronal assemblies (even in more
distant regions) to become temporally linked (Buzsáki, 2002; Buzsáki & Draguhn, 2004).
Morevoer, oscillations are preserved across species, suggesting that they are relevant for brain
function (Buzsáki, Logothetis, & Singer, 2013). Large-scale oscillations manifest in the
brain’s electrophysiological signal, measured over the scalp with EEG or MEG, or
intracranially with depth electrodes or electrocorticography (Buzsáki, Anastassiou, & Koch,
2012; Lopes da Silva, 2013).
In sum, oscillations are thought to enable the dynamic coordination of neuronal networks and
we can measure this activity in humans at the scalp level with EEG or MEG, or with
1.2. Neuronal oscillations and language production: A thesis
Neuronal oscillations have been argued to provide the link between cognitive and
neurophysiological computations (e.g., Friederici & Singer, 2015; Siegel, Donner, & Engel,
2012). Here, we argue that neuronal oscillations provide an important and exciting avenue to
understand how general neuronal computational principles support language functioning,
enabling us to link language to other domains of cognition. In order to connect the domains,
we will draw parallels in the multidimensional space afforded by oscillations, i.e., whether a
presupposed process (shared across domains) is reflected in oscillatory activity modulated in
the same direction, in the same frequency band, with the same time course, in respectively
analogous brain areas.
In humans, neuronal oscillations are typically studied with MEG, and scalp or intracranial
EEG. Therefore, most work on oscillations in humans is not at the level of individual
neurones, but rather at the scale of large neuronal populations. Thus, neuronal synchronisation
and desynchronisation cannot be directly observed and must be inferred from increases and
decreases, respectively, in power in a particular frequency band (e.g., Cohen & Gulbinaite,
2014). In keeping with suggestions in the literature, we will use the terms changes in power,
or increases/decreases in power to indicate modulations of neuronal oscillations in the
context of cognitive tasks. Moreover, following the literature, we will discuss the classic
frequency-bands of theta (typically 4-8 Hz), alpha (typically 8-12 or 8-15 Hz), and beta
(typically 15-30 Hz), which are more relevant for language production. Most of the figures in
this chapter show time-resolved power spectra (see e.g., Figure 1), which provide a
visualisation of how power (the colour scale) changes over time (represented in the x axis)
and frequency (represented in the y axis).
For each relevant domain mentioned above (i.e., motor, memory, and executive control), we
provide a brief review of the findings in the literature with respect to neuronal oscillations.
Then, we show how similar patterns are found in the domain of language production, both
through review of previous literature and novel findings. Although writing (and typing) are
also forms of language production, in this chapter, we will focus on speaking. When
reviewing the literature in relation to the motor domain, we include studies employing a range
of production or articulation tasks, ranging from picture naming to the articulation of simple
syllables and execution of mouth movements. By contrast, for the memory and executive
domains, we focus on so-called conceptually driven production tasks, e.g., picture naming and
verb or noun generation, that is, tasks that require the initial access to concepts, followed by
subsequent stages of production (Indefrey & Levelt, 2004). Repeating words or reading aloud
does not require access to lexical concepts and lemmas, as evidenced by the fact that healthy
speakers can repeat or read aloud words that they have never encountered before.
2. Motor domain
The motor domain perhaps forms the most straightforward case for our comparison for two
reasons. Firstly, the articulation of speech is a motor activity. Secondly, movement is
associated with a well-characterised oscillatory signaure in associated motor regions, namely
power decreases (also termed desynchronisation) in the beta band, typically defined as 15-30
Hz (see for reviews Cheyne, 2013; Pfurtscheller & Lopes da Silva, 1999). Beta-power
decreases over sensorimotor areas start prior to movement onset and continue throughout
execution, increasing again after movement execution, often termed “beta rebound”. Put
together, one would expect to find beta-band power decreases in motor regions associated
with speaking. For language production, cortical areas associated with motor-related
processes are the ventral precentral gyrus (Penfield & Roberts, 1959), and inferior frontal
cortex and insula of the language-dominant hemisphere (e.g., Baldo, Wilkins, Ogar, Willock,
& Dronkers, 2011; Flinker et al., 2015; Henseler, Regenbrecht, & Obrig, 2014; Indefrey &
Levelt, 2004; Krieg et al., 2016).
One of the earliest investigations of brain rhythms related to movements executed with speech
organs has been performed by participants being monitored for epilepsy with intracranial
EEG before undergoing surgery to remove the epileptic focus (Crone et al., 1998; see for an
overview of the procedure and review of early language studies, Flinker, Piai, & Knight,
2018; Llorens, Trébuchon, Liégeois-Chauvel, & Alario, 2011). In the study of Crone et al.,
participants were asked to execute tongue protrusion. Tongue movements, just like fist-
clenching, elicited beta-power decreases and subsequent beta rebound in premotor cortical
Evidence of power decreases in lower frequencies over motor-related areas was also found in
other intracranial EEG studies (e.g., Conner, Chen, Pieters, & Tandon, 2014; Flinker et al.,
2015; Grappe et al., 2019; Kojima et al., 2013). However, these studies focused on gamma
and broadband high gamma signals above 50 Hz, hence the exact frequency range of these
power decreases cannot be determined. One study that used the repetition of monosyllabic
words found low-frequency power decreases over left ventral premotor cortex and inferior
frontal gyrus roughly prior to speech onset and during articulation (Flinker et al., 2015). Two
other studies examining picture naming also reported alpha and beta power decreases over the
left inferior frontal gyrus starting after picture presentation relative to a prestimulus baseline
(Conner et al., 2014; Grappe et al., 2019). Another study employed picture naming and
auditory naming tasks, where participants are asked to provide an answer to questions such as
“What do you hear with?” (Kojima et al., 2013). This study reported time-resolved spectra
time locked to response onset (i.e., time aligned to when people start speaking), enabling a
more precise inspection of the time course of power decreases over the ventral precentral
gyrus. These results are showin in Figure 1, where the timepoint of 0 ms indicates response
onset. Despite the lack of precise information regarding the lower frequency range, power
decreases are observed already prior to response onset and during articulation, especially in
electrode 6 over ventral precentral/postcentral gyrus.
Figure 1. Time-resolved spectra of power changes relative to a rest baseline period for picture
naming (left) and auditory naming (right) time locked to response onset (0 ms). The spectra are
shown for each of the contacts in the brain model to the left. Reprinted from Clinical
Neurophysiology, 124/9, Kojima, K., Brown, E. C., Matsuzaki, N., Rothermel, R., Fuerst, D.,
Shah, A., Mittal, S., Sood, S., and Asano, E. “Gamma activity modulated by picture and
auditory naming tasks: intracranial recording in patients with focal epilepsy”, 1737–1744,
Copyright (2013), with permission from Elsevier. This figure has been modified relative to its
original in that only panel B is presented here.
Figure 2. Spectral power changes for naming the months of the year and counting in
subthalamic nucleus contacts. (A, E) Power spectral density over the entire recording (“All”),
over speech production intervals (“Speech”), and over a pre-speech baseline (“Baseline”). (B,
F) Time-resolved spectra locked to speech onset (left panel, 0-ms time point) and speech offset
(right panel, 0-ms time point). (C, G) Averaged power in the 13-30 Hz range over speech
production intervals (black and red lines), locked to speech onset (left panel, 0-ms time point)
and speech offset (right panel, 0-ms time point). Red lines indicate time points differing
significantly from baseline. The averaged audio track is shown in grey. (D, H) Atlas illustration
and electrode placement. Reprinted from Neuroscience, 202, Hebb, A. O., Darvas, F., and
Miller, K. J., “Transient and state modulation of beta power in human subthalamic nucleus
during speech production and finger movement”, 218-233, Copyright (2012), with permission
Another study employing intracranial EEG provided further evidence of the similarity in
terms of neuronal oscillations between motor aspects of speaking and finger movement
(Hebb, Darvas, & Miller, 2012). Participants undergoing surgery for implantation of a deep-
brain stimulator in the subthalamic nucleus (STN) named the months of the year and counted
from one up. Given that both types of utterances are fairly stereotypical, these tasks are more
likely to probe the motor aspects of speaking, rather than the access to conceptual and lexical
information. Motor-related beta-power modulations had previously been found in the STN,
for example for hand movements (Cassidy et al., 2002). Hebb et al. (2012) showed that beta-
power decreased in the STN just before speech onset and remained decreased during speech
production, as shown in Figure 2.
MEG recordings in healthy adults have provided further evidence for beta-power decreases,
localised to the mouth area along the central sulcus, during speech-related movements and
during speech (Salmelin, Hámáaláinen, Kajola, & Hari, 1995; Salmelin, Schnitzler, Schmitz,
& Freund, 2000; Salmelin & Sams, 2002). For example, in one study participants performed
tongue movements, lip protrusion, articulation of one vowel, utterance of the same word
repeatedly, and free generation of words (Salmelin & Sams, 2002). For all tasks, beta-power
decreases were observed bilaterally over the face motor area, whereas beta rebound was
stronger over the left face area.
It is important to point out that the alpha band has also been implicated in movement, in
which case it is also commonly termed the mu rhythm in the literature. Studies have identified
differences (but also commonalities) between the beta power decreases and mu power
decreases (Cheyne, 2013; Crone et al., 1998; Salmelin et al., 1995), but this distinction falls
outside the scope of our review.
2.1. Interim summary
To summarise, beta-band power decreases are found prior to movement onset and during
movement execution over the cortical and subcortical motor areas responsible for that
movement. This pattern holds true for speaking: Beta-band power decreases are observed
prior to and during speech and speech-related mouth movements. These power decreases are
localised to motor-related areas not only over the cortex (along the central sulcus), but also in
subcortical motor-related areas such as the subthalamic nucleus.
3. Memory domain
Episodic memory is perhaps the most relevant subfield of memory to discuss in relation to
language production. Language is tightly related to semantic memory and both episodic and
semantic memory form the declarative memory system (Squire, 1992). Episodic memory is
mainly subserved by medial temporal lobe structures, including the hippocampus (Squire &
Wixted, 2011). With respect to language, however, it is debated to what extent these medial
structures are critical for producing words and sentences (Hamamé, Alario, Llorens, Liégeois-
Chauvel, & Trébuchon-Da Fonseca, 2014; Kurczek & Duff, 2011; MacKay, Burke, &
Stewart, 1998; Skotko, Andrews, & Einstein, 2005). The evidence for the involvement of
lateral, as opposed to medial, cortical regions in the memory aspects of production (i.e.,
retrieval of conceptual, lexical, and phonological information), is clearer. These processes
have been mainly associated with the temporal and inferior parietal lobes of the language-
dominant hemisphere (e.g., Baldo, Arévalo, Patterson, & Dronkers, 2013; Henseler et al.,
2014; Indefrey & Levelt, 2004; Krieg et al., 2016; Roelofs, 2008; Schwartz, Faseyitan, Kim,
& Coslett, 2012; Walker et al., 2011).
Episodic memory processes are often studied with tasks that require participants to encode
information (e.g., pictures, words, or pairs of stimuli) and later retrieve the encoded
information via recall or recognition tasks. A well-studied effect in the field of episodic
memory is the subsequent memory effect: Trials are categorised depending on whether the
respective items were successfully recalled or recognised during the retrieval phase. Then a
comparison is made for the brain activity originating from the encoding phase between the
later forgotten versus later remembered trials (Sanquist, Rohrbaugh, Syndulko, & Lindsley,
1980). Thus, the subsequent memory effect has been used to examine what makes the
encoding of information successful so that it can later be retrieved.
The subsequent memory effect has been extensively characterised in terms of neuronal
oscillations and two patterns of oscillatory activity have clearly emerged: successful encoding
is associated with increases in theta power and decreases in alpha and beta power (see for
reviews Hanslmayr, Staudigl, & Fellner, 2012; Nyhus & Curran, 2010).
3.1. Memory-related theta oscillations
Theta oscillations are prominent in the mammalian hippocampus and have been well studied
in relation to memory processes (Buzsáki & Moser, 2013; Jacobs, 2014; Kahana, Seelig, &
Madsen, 2001). Current views largely converge on theta oscillations functioning as a
mechanism enabling binding in memory via the coordination of spike timing of groups of
neurones (Buzsáki, 2002; Hasselmo, Bodelón, & Wyble, 2002; Jacobs, Kahana, Ekstrom, &
Fried, 2007; Rutishauser, Ross, Mamelak, & Schuman, 2010). Intracranial recordings from
electrodes placed in the hippocampus, as well as scalp recordings, have shown assocations
between increased theta power and successfull memory encoding (e.g., Klimesch,
Doppelmayr, Russegger, & Pachinger, 1996; Lega, Jacobs, & Kahana, 2012; Osipova et al.,
3.1.2. Memory-related theta oscillations in language
To the best of our knowledge, the only evidence for the role of hippocampal theta oscillations
in language use comes from a recent study that used depth recordings from medial temporal
lobe structures of patients with intractable epilepsy (Piai et al., 2016). Even though in this
study the hippocampal theta oscillations were not associated with language production, the
results are worth discussing as an illustration of how hippocampal theta oscillations are also
found in the language domain (for a review of scalp theta oscillations and language
comprehension, we refer the reader to Meyer, 2018). To examine binding in memory during
sentence comprehension, the authors utilised a context-driven word production paradigm. In
this task, participants complete a sentence by naming a picture that appears at the end of the
sentence, as shown in Figure 3. The sentences are either semantically constrained (e.g., “She
locked the door with the”) or neutral (e.g., “She walked in here with the”) towards one final
ending (e.g., “key”). Theta power increased for contextually constraining sentences relative to
neutral sentences in medial temporal lobe structures during sentence comprehension,
preceding picture presentation, as shown in Figure 4. These results demonstrated how
hippocampal theta oscillations also play a role in language processing. Importantly, the
semantic associations given by the sentence are naturalistic, as they also occur in everyday
life. Moreover, the theta oscillatory effect was observed without the requirement that the
associations be encoded first for retrieval at a later time point. The authors interpreted these
findings as suggesting that the neuronal computations used by the hippocampus to support
memory functioning are also utilised by language processes.
Figure 3. An example of the context-driven picture naming task for a constraining trial (upper)
and a neutral trial (lower). Particiapnts name the picture after hearing or reading the incomplete
3.2. Memory-related alpha-beta oscillations
In episodic memory tasks, it has been noted that memory effects are not only reflected in theta
power increases, but also in alpha and beta power decreases (e.g., Hanslmayr, Spitzer, &
Bäuml, 2009; Khader & Rösler, 2011; Klimesch, Doppelmayr, Schimke, & Ripper, 1997;
Lega et al., 2012; see for reviews Fellner & Hanslmayr, 2017; Hanslmayr et al., 2012;
Klimesch, 1997). The exact sources of the alpha-beta power decreases are still unclear,
however. Whereas some studies suggest cortical sources, especially the left inferior frontal
gyrus (Hanslmayr et al., 2011; Hanslmayr, Matuschek, & Fellner, 2014), power decreases in
this range have also been found in the hippocampus (e.g., Lega et al., 2012).
A theoretical view has been advanced on the functional meaning of the alpha-beta power
decreases in episodic memory and their relation to the hippocampal theta power increases
(Hanslmayr, Staresina, & Bowman, 2016; Hanslmayr et al., 2012). According to this view,
information is represented by neuronal desynchronisation. To demonstrate this, Hanslmayr
and collagues (2012) simulated neuronal populations that varied in their degree of synchrony,
while keeping the sum of neuronal spikes constant. These simulations are shown in Figure
5A, with the no synchrony condition shown in green, the low synchrony condition in blue,
and the high synchrony condition in red. The resulting local field potentials are shown under
each panel. Figure 5B shows how power in the local field potential increases with increasing
synchrony. Information was then operationalised with Shannon’s Entropy over the firing rates
of the three synchronisation conditions. The resulting entropy values are shown in Figure 5C.
A state of high neuronal synchrony (in red) is associated with less (specific) information
being encoded in the pattern of neuronal spiking. By contrast, a state of low synchrony (in
blue) is associated with more (specific) information being encoded. In Figure 5D, results of
more simulations with varying degrees of synchrony, reflected in the power of the local field
potential, are shown in relation to entropy values. Synchrony of the firing patterns is inversely
related to the richness of information encoded in the firing rate (Figure 5D).
Figure 4. Time-resolved power spectra of the context effect (constrained vs. neutral) time
locked to picture presentation for ten individuals with electrode contacts in medial temporal
lobe. Significant effects are shown in stronger colors (multiple comparisons corrected). Trial
events are shown at the bottom. The timing of each word position is indicated by the continuous
lines. The left end of each line indicates the earliest possible word onset. The right end indicates
the latest possible word offset (and next word onset). Median word onset (and previous word
offset) is indicated by the orange vertical bars. Adapted from Vitória Piai, Kristopher L.
Anderson, Jack J. Lin, Callum Dewar, Josef Parvizi, Nina F. Dronkers, and Robert T. Knight,
Direct brain recordings reveal hippocampal rhythm underpinnings of language processing,
Proceedings of the National Academy of Sciences of the United States of America, 113 (40),
pp. 11366–71, Figure 3, doi: 10.1073/ pnas.1603312113 ©2016 Vitória Piai, Kristopher L.
Anderson, Jack J. Lin, Callum Dewar, Josef Parvizi, Nina F. Dronkers, and Robert T. Knight.
This work is licensed under the Creative Commons.
Figure 5. A. Simulated firing rates of neuronal populations vayring in degree of synchrony with
a constant number of spikes. A state of no synchrony is shown in green, a state of low synchrony
is shown in blue, and a high synchrony state is shown in red. The corresponding local field
potentials are shown under each panel. B. Power spectra of each state of synchrony shown in
panel A. C. Information, measured with Shannon’s Entropy over the firing rates shown in A
for each state of synchrony. D. Association between power of the local field potentials for
various simulations with varying degrees of synchrony and Shannon’s Entropy values.
Reprinted with permission from Hanslmayr, S., Staudigl, T., & Fellner, M.-C. (2012).
Oscillatory power decreases and long-term memory: the information via desynchronization
hypothesis. Frontiers in Human Neuroscience, 6, 74.
3.2.2. Memory-related alpha-beta oscillations in language production
Although the hypothesis that information is represented by patterns of alpha-beta
desynchronisation was proposed for the domain of episodic memory, it is possible that these
same principles apply to the domains of semantic memory and language (e.g., Jafarpour, Piai,
Lin, & Knight, 2017; Piai et al., 2016). In the current memory and language literatures, a
distinction between alpha and beta frequency bands is not always drawn nor are there clear
bases for how and why that distinction should be drawn. Therefore, for the remainder of this
section, we will refer to alpha-beta oscillations without drawing a clear distinction between
them, except for the cases where the reviewed articles do make a distinction. See also section
6, “Concluding remarks and open questions” for further comments on this issue.
Early evidence for the involvement of alpha-beta power decreases in the memory aspects of
language production comes from picture naming studies using electrode grids placed on the
cortical surface. For example, in a single-case study (Hart et al., 1998), direct cortical
stimulation was used to identify critical sites for language. One site was identified in the left
lateral occipitotemporal gyrus that, when stimulated, affected naming, spontaneous speech,
and comprehension, while leaving repetition and object recognition intact. Therefore, this site
can be considered critical for lexical-level processes. The alpha and beta bands in this site
were examined in an overt picture naming task. Power in the alpha-beta band decreased
relative to a pre-stimulus baseline starting around 250 ms. Meta-analysis estimations indicate
that the reported time window is roughly aligned with the timing when lexical-level processes
start (Indefrey & Levelt, 2004). In another study including seven patients with intractable
epilepsy, a silent picture naming task was administered (Ojemann, Fried, & Lettich, 1989).
Critical sites for language in temporoparietal areas, that is, middle and superior temporal gyri
and inferior parietal lobe, in the language-dominant hemisphere were identified by means of
direct cortical stimulation. These sites showed power decreases in the alpha range between
200-700 ms and 700-1200 ms after stimulus onset. Moreover, the power decreases were
always greater in the critical language sites than in surrounding sites. Power decreases were
also greater for naming than for visuo-spatial processes, with the latter measured with a
matching task in which participants indicated whether two lines presented in succession on
the screen had the same angle. A more recent study using depth electrodes also reported beta
power decreases in the fusiform gyrus during picture naming, but before speech onset. This
pattern was consistently found across individuals (Grappe et al., 2019).
The context-driven word production paradigm described above (see Figure 3) has been used
in various EEG and MEG studies to study conceptual and lexical retrieval in a manner that is
not triggered by a picture, but rather more naturalistically (Piai et al., 2016; Piai, Meyer,
Dronkers, & Knight, 2017; Piai, Roelofs, Rommers, & Maris, 2015; Piai, Rommers, &
Knight, 2018; Piai, Roelofs, & Maris, 2014). In a real conversation, words are typically
produced embedded in the context of a speaker’s own sentence or that of the interlocutor.
Although the context-driven word production paradigm is not perfect in simulating such a
naturalistic situation, it is a fair yet controlled approximation thereof (Griffin & Bock, 1998).
Attesting to the contextual influence on the ease of word production processes, picture
naming times are about 200-300 ms faster following constraining relative to neutral sentences.
This strongly suggests that certain, presumably early, processes necessary for picture naming
are already initiated prior to picture onset, enabled by the semantic information in the
sentence. Besides faster word production latencies, studies have also shown that power in the
alpha-beta band decreases consistently for constraining relative to neutral sentences, an effect
particularly prominent prior to picture presentation (Piai, Meyer, Dronkers, & Knight, 2017;
Piai, Roelofs, & Maris, 2014; Piai, Roelofs, Rommers, & Maris, 2015; Piai, Rommers, &
Knight, 2018, see also Piai et al., 2016 and Figure 4 above). An example is given in Figure 6,
which shows the relative power differences for constrained relative to neutral sentences, with
significant patterns shown in stronger colour (family-wise error corrected for multiple
Figure 6. Time-resolved spectra showing the time course of the context effect. The trial events
are shown at the bottom. The spectra are shown for the channel marked in black (big dot) in the
topographical maps. Rel.=Relative. Reprinted from Neuropsychologia, 53, Piai, V., Roelofs,
A., and Maris, E., “Oscillatory brain responses in spoken word production reflect lexical
frequency and sentential constraint”, 146-156, Copyright (2014), with permission from
The results shown in Figure 6 were the first to demonstrate the alpha-beta power decreases in
a context-driven word production task (Piai, Roelofs, & Maris, 2014). As such, it was
somewhat difficult to interpret this effect. Attention is known to modulate power in a similar
frequency range (e.g., van Ede, de Lange, Jensen, & Maris, 2011). Moreover, as mentioned in
Section 2 above, motor preparation also modulates power in a comparable frequency range.
Thus, instead of reflecting conceptual and lexical retrieval, the alpha-beta power decreases
prior to picture onset could be the index of attentional effects or motor preparation. To
elucidate this issue, a follow-up MEG study examined context-driven picture naming,
requiring conceptual and lexical retrieval, versus picture judgement via a button press (with
the left hand) and localised the sources of the power decreases during the interval prior to
picture presentation (Piai et al., 2015). The neuronal sources where pre-picture beta power
decreases are observed for constraining relative to neutral sentences are shown in Figure 7 for
picture naming (upper) and picture judgement (lower). For picture judgement, the power
decreases were localised to the left inferior parietal lobe and posterior temporal cortex, in
addition to right motor cortex, in agreement with the left hand button-press responses. By
contrast, for picture naming, the power decreases were localised to the left inferior parietal
lobe, the entire temporal lobe, and left inferior frontal gyrus, all areas associated with
conceptual processing (Binder, Desai, Graves, & Conant, 2009) and word production
processes (Indefrey & Levelt, 2004). An additional study examined the across-session
consistency of the alpha-beta power decreases in healthy young adults (Roos & Piai, in
preparation). Participants were tested twice in the context-driven word production task, with
an interval of 2-4 weeks in between. The alpha-beta power decreases for constraining relative
to neutral sentences were replicated and showed consistency across the two sessions in the left
temporal and inferior parietal lobes. By contrast, the alpha-beta power decreases in the left
frontal lobe was more variable across the two sessions, with no consistent across-session
patterns being observed anywhere in the frontal cortex.
To further clarify the role of the previously identified brain areas in generating the power
decreases, on the one hand, and the link between the power decreases and the behavioural
context-facilitation effect, on the other hand, a follow-up EEG study examined individuals
with stroke-induced lesions to the areas previously identified (Piai et al., 2018). A group of
individuals had lesions overlapping in left frontal areas and another group had lesions in the
left temporal lobe, also involving the inferior parietal lobe in some cases. These areas were
previously identified in an MEG study, as shown in Figure 7 (Piai et al., 2015). The
facilitation effect in picture naming times was absent for individuals with lesions involving
the left temporal and inferior parietal cortex. These were also the areas found to show
consistent alpha-beta power decreases over the course of weeks (Roos & Piai, in preparation).
Importantly, for these same individuals, the context alpha-beta power decreases were also
absent. These findings demonstrated a causal link between the alpha-beta power decreases
and the left temporoparietal cortex, on the one hand, and also between the alpha-beta power
decreases in the left posterior cortex and the context facilitation in word production, on the
Figure 7. Source localisation of the beta power differences (15-25 Hz) for constraining relative
to neutral contexts during the blank pre-picture interval for picture naming (upper) and picture
judgement (lower). The color bars show relative power changes, masked by the statistically
significant effect corrected for multiple comparisons. Rel = relative.
Another way to study conceptually driven production is through verb generation, which has
been extensively used to study brain organisation for language functioning, both in healthy
and neurological populations (e.g., Edwards et al., 2010; Pang, Wang, Malone, Kadis, &
Donner, 2011; Petersen, Fox, Posner, Mintun, & Raichle, 1988; Thompson-Schill et al.,
1998). In a verb generation task, participants are given a noun (e.g., “apple”) and are asked to
generate a verb associated with it (e.g., “eat”). Haemodynamic measures, acquired with
functional magnetic resonance imaging or positron emission tomography, have shown a
relatively good consistency in picking up signal changes in the left prefrontal cortex
associated with verb generation (Fiez, Raichle, Balota, Tallal, & Petersen, 2007; McCarthy,
Blamire, Rothman, Gruetter, & Shulman, 1993; Petersen et al., 1988; Rutten, Ramsey, Van
Rijen, Alpherts, & Van Veelen, 2002).
For MEG in particular, the verb generation task elicits power decreases in the beta band
(Findlay et al., 2012; Fisher et al., 2008; Pang et al., 2011; Pavlova et al., 2019; Traut et al.,
2019). The hemisphere in which these beta power decreases are found largely agrees with the
hemispheric dominance for langauge as determined by the Wada test (Findlay et al., 2012;
Pang et al., 2011). The Wada test (Wada, 1949) is (or was) the gold-standard procedure for
determining language lateralisation: It consists of injecting sodium amobarbital into the
vasculature nourishing one cerebral hemisphere, shutting it down. If language functioning is
disrupted – e.g., the patient can no longer name pictures – one can conclude that that
particular hemisphere is critical for language. However, this procedure has the disadvantage
of being highly invasive, potentially risky in terms of complications, and also unsuitable for
certain populations (Meador & Loring, 1999), motivating the use of neuroimaging alternatives
(e.g., Benke et al., 2006; Fisher et al., 2008; Watanabe et al., 1998).
The beta power decreases found during verb generation are typically localised to the inferior
and middle frontal gyri and regions in the temporal and inferior parietal lobes of the language
dominant hemisphere (Findlay et al., 2012; Fisher et al., 2008; Pang et al., 2011; Pavlova et
al., 2019; Traut et al., 2019). An example is shown in Figure 8, for verb generation following
a picture cue (upper) and a word cue (lower). Additionally, it has recently been found that
differences between strongly associated noun-verb pairs (e.g., noun: nightingale, response:
“sing”) and weakly associated pairs (e.g., noun: paper, responses differ widely across
participants) are also reflected in beta-power decreases (Pavlova et al., 2019). These
differences were localised to areas in the frontal lobe bilaterally, i.e., anterior and middle
portions of the cingulate cortex and superior frontal gyrus, comprising the supplementary and
pre-supplementary motor areas, and to the left lateral precentral gyrus and sulcus.
The findings of beta-power decreases during verb generation in temporal and inferior parietal
areas agree with the presupposed role of beta oscillations in retrieval from memory for word
production. Although in the sections above, we considered the inferior frontal gyrus to be
mainly implicated in the motor aspects of speaking, this same region is also involved in top-
down control aspects of retrieval in word production (Badre & Wagner, 2002; Riès,
Greenhouse, Dronkers, Haaland, & Knight, 2014; Schnur et al., 2009; Thompson-Schill et al.,
1998). The beta-power decreases found in the inferior frontal cortex of the language-dominant
hemisphere, even when particiapnts generate verbs covertly, without articulation, are
presumably more related to the controlled aspects of retrieval, rather than to the motor aspects
Figure 8. Source localisation of the beta power decreases (in blue) between 600-800 ms post-
picture onset for picture-induced verb generation (upper) and between 400-600 ms post-word
onset for word-induced verb generation (lower). The areas in dark grey indicate where the beta
power decreases from MEG overlap with clusters from the fMRI counterpart (orange) of the
experiment. Reprinted from Neuroscience Letters, 490, Pang, E. W., Wang, F., Malone, M.,
Kadis, D. S., and Donner, E. J., “Localization of Broca’s area using verb generation tasks in the
MEG: Validation against fMRI”, 215-219, Copyright (2011), with permission from Elsevier.
In sum, alpha-beta power decreases are found in temporal and inferior parietal brain areas,
and under certain circumstances also in frontal areas, in tasks that require conceptually driven
word production, such as context-driven word production, picture naming, and verb
generation. Importantly, these brain areas are not implicated in the motor aspects of speaking,
but rather in the memory aspects of language production -- in particular, the retrieval of
conceptual and lexical information from memory. Despite the lack of abudant evidence, the
results in the current literature indicate that, similarly to the episodic-memory domain,
retrieving conceptual and lexical information from memory is associated with power
decreases in the alpha-beta band.
4. Language production and executive control
Broadly speaking, executive control is an umbrella term to refer to regulatory and monitoring
processes that ensure that our actions are in accordance with our goals. Several components
are implicated in executive control, including monitoring and updating of working memory
representations (e.g., Diamond, 2013; Miyake et al., 2000).
When planning a word or a multi-word utterance, speakers need to engage executive control
processes. At a more general level, speakers need to maintain the conversation goals and
update the contents of working memory during the planning process, especially in the case of
multi-word utterances and sentences (e.g., Levelt et al., 1999; Martin & Slevc, 2014; Piai &
Roelofs, 2013; Roelofs, 2003). They also need to prevent interference from semantically
related words that get co-activated in their lexicon, or they need to choose between alternative
words that refer to a concept they want to express (e.g., Piai et al., 2013; Shao, Roelofs,
Martin, & Meyer, 2015). As part of the control process, speakers also constantly monitor what
they have just said and what they are about to say, inspecting (potential) speech errors and
further recruiting top-down control when necessary (Hartsuiker, 2014). In the case of
individuals who speak more than one language, executive control is also engaged to inhibit
the nontarget language, and to overcome previous inhibition when switching from one
language to another (Green, 1998).
Executive control in language production is commonly investigated using the picture-word
interference task (Hermans, Bongaerts, De Bot, & Schreuder, 1998; Lupker, 1979; Piai et al.,
2013; Shitova, Roelofs, Schriefers, Bastiaansen, & Schoffelen, 2017) or the switching task
(Meuter & Allport, 1999; Sikora, Roelofs, & Hermans, 2016; Zheng, Roelofs, Farquhar, &
Lemhöfer, 2018). In a picture-word interference task, participants name pictures while trying
to ignore distractor words presented either visually superimposed on the word or auditorily.
The distractor words can be, for example, semantically related (e.g., pictured dog, distractor
cat) or unrelated (e.g., pictured dog, distractor pin) to the target picture name, or congruent
with the target picture name (e.g., pictured dog, distractor dog). In a switching task, speakers
are instructed to switch, according to a given cue, between different types of phrases, for
example a bare noun (e.g., “dog”) versus a complex noun phrase (e.g., “the small dog”), or
between languages. In repeat trials, the response type is the same as in the previous trial,
whereas in switch trials, the type of response changes. Compared to repeat trials, switching to
the alternative language or to the alternative type of noun phrase requires more executive
With respect to the anatomy, previous research on control processes in language production
has shown the engagement of brain regions involved in (domain-general) executive control,
including the anterior cingulate cortex, lateral prefrontal cortex, and (pre-)supplementary
motor area (e.g., Alario, Chainay, Lehericy, & Cohen, 2006; de Bruin, Roelofs, Dijkstra, &
Fitzpatrick, 2014; Gauvin, De Baene, Brass, & Hartsuiker, 2016; Klaus & Schutter, 2018; Piai
et al., 2013; Piai, Roelofs, Jensen, Schoffelen, & Bonnefond, 2014), suggesting a domain-
general control mechanism underlying speech production (Nozari & Novick, 2017; Ye &
4.1. Theta-band oscillations and executive control
A hallmark EEG signature of executive control and working-memory manipulation is midline
frontal theta oscillations (e.g., Cavanagh, Zambrano-Vazquez, & Allen, 2012; Cohen, 2014;
Cohen & Donner, 2013; Cooper et al., 2019; Itthipuripat, Wessel, & Aron, 2013; Sauseng,
Griesmayr, Freunberger, & Klimesch, 2010; Sauseng, Hoppe, Klimesch, Gerloff, & Hummel,
2007). Increases in theta-band power have been found for tasks manipulating working-
memory load (e.g., Jensen & Tesche, 2002), when items in working memory are successfully
manipulated (Itthipuripat et al., 2013), during the monitoring of errors (Cavanagh et al., 2012;
Cohen, 2011a; Luu, Tucker, & Makeig, 2004), and when the amount of top-down control is
increased due to interfering information. In this latter case, tasks have been used with a
conflicting stimulus dimension. For example, in the Stroop task (Stroop, 1935), an ink colour
has to be named that is either congruent (red written in red) or incongruent (red written in
blue) with the written word. Theta-band power increases were observed for the incongruent
relative to the congruent condition (Hanslmayr et al., 2008). In the Simon task, stimuli are
presented in relative locations that are congruent or incongruent to the response, despite
stimulus location being irrelevant to the task (Simon, 1969). Again, theta-band power
increases were observed for the incongruent relative to the congruent condition (Cohen &
Donner, 2013; Nigbur, Ivanova, & Stürmer, 2011). Other tasks manipulating various aspects
of congruency, such as a flanker task (Eriksen & Eriksen, 1974) or a go/no-go task, also elicit
the same pattern (Cohen, Ridderinkhof, Haupt, Elger, & Fell, 2008; Nigbur et al., 2011).
Based on intracranial recordings and source localisation of scalp effects, it is known that
midline frontal theta effects reflecting executive control are generated by the anterior
cingulate cortex and superior frontal gyrus (Asada, Fukuda, Tsunoda, Yamaguchi, & Tonoike,
1999; Cohen et al., 2008; Hanslmayr et al., 2008; Sauseng et al., 2007).
In conclusion, increases in midline frontal theta power, generated by the anterior cingulate
cortex and superior frontal gyrus, provide a neuronal signature of executive-control
mechanisms (Cavanagh & Frank, 2014; Cohen, 2014).
4.2. Theta-band oscillations and control in language production
In the language domain, a few electrophysiological studies have utilised interference
paradigms to investigate control demands during language production. Piai and colleagues
(Piai, Roelofs, Jensen, et al., 2014) employed the picture-word interference task in an MEG
study with congruent picture-distractor pairs and two types of incongruent picture-distractor
pairs: semantically related and unrelated pairs. Semantically related pairs were constrasted to
congruent pairs (i.e., an interference effect due to congruency) and, in addition, to
semantically unrelated pairs, a contrast well-known as “semantic interference” in the language
production literature (Glaser & Düngelhoff, 1984; Lupker, 1979). Theta-power increases were
observed for both types of interference roughly around 350-650 ms post-stimulus onset, as
showin in Figure 9 for the congruency interference (upper panel) and semantic interference
(lower panel). In line with the literature outside of the language domain, the theta-power
increases were localised to the superior frontal gyrus, possibly also including the anterior
cingular cortex, as shown in Figure 9. A more recent EEG study has replicated the theta-
power increases for semantically related relative to unrelated picture-word pairs in a roughly
similar time window (Krott, Medaglia, & Porcaro, 2019).
Figure 9. Time-resolved spectra of the contrast semantically related versus congruent (upper)
and semantically related versus unrelated (lower) for the source in the superior frontal gyrus.
Colour scale indicates the amount of relative power differences between the conditions.
Modified from Piai, V., Roelofs, A., Jensen, O., Schoffelen, J.-M., & Bonnefond, M. (2014).
Distinct patterns of brain activity characterise lexical activation and competition in spoken word
production. PloS One, 9(2), e88674.
In another EEG study, semantically related pairs were also constrasted to congruent pairs
(Shitova et al., 2017). Theta-power increases were again observed for the semantically-related
pairs relative to congruent pairs. Moreover, in this same study, trial-by-trial adaptations in
top-down control given the interference from a previous trial, known as the Gratton effect
(Gratton, Coles, & Donchin, 1992) also modulated theta power.
4.3. Theta-band oscillations and control: New evidence from bilingual word production
Here we report new evidence on the similarity in terms of neurophysiological signatures
between general executive control and language control from a bilingual word production
study. To properly speak one language rather than another, bilingual speakers need to
constantly control their language in use and monitor for errors, such as selecting the nontarget
language for use, generating so-called language selection errors. Previous research in the
domain of action monitoring has consistently shown theta power increases in the anterior
cingulate cortex and superior frontal gyrus immediately following an error commission, such
as pressing the wrong button in a flanker or a Simon task (Cavanagh, Cohen, & Allen, 2009;
Cohen, 2011a; Trujillo & Allen, 2007). The theta power increases have been interpreted as
reflecting the signal that increased executive control is needed.
To test whether speech monitoring shares the same neural mechanism with other domains of
action monitoring, we reanalysed the EEG data from a recent bilingual picture naming study
(Zheng et al., 2018). In that study, 24 unbalanced Dutch-English bilinguals were asked to
name pictures in either English or Dutch and switch languages according to a color cue.
Figure 10. Response-locked time-resolved spectrum of the contrast between language selection
errors versus correct responses on switch trials, averaged over a cluster of frontocentral
channels highlighted in red on the right upper corner. Dashed lines indicate the cluster for
plotting the topographical map shown in the right bottom corner. The target pictures were
presented in a coloured frame, indicating the response language (i.e., yellow or blue for English
and red or green for Dutch, or vice versa). The bottom left scheme depicts a trial where
participants had to switch to English. Language selection errors are defined as the use of the
translation equivalent of the target word (e.g., saying the Dutch translation word “boom” instead
of the target English word “tree”). Response-locked time-resolved spectra were computed
between 750 ms pre-response to 1 s post-response, at frequencies between 2 and 20 Hz. A
variable length Hanning-tapered window was applied to estimate the power at each frequency
using three oscillation cycles (e.g., the window was 300 ms long at 10 Hz), advanced in steps
of 50 ms and of 1 Hz.
Under severe time pressure, the speakers made languge selection errors (e.g., saying the
Dutch translation equivalent “boom” instead of the target English word “tree”) on 37.3% of
the trials where they were supposed to switch languages. For more details on the methods of
that study, we refer the reader to the original article. Here, we contrasted the time-resolved
spectra of the trials with language selection errors versus those with correct responses. Time-
resolved power was estimated with the same method described previously in other studies
(Piai, Roelofs, Jensen, et al., 2014; Shitova et al., 2017) and a cluster-based permutation test
(Maris & Oostenveld, 2007) was applied to the spectrotemporal data points of interest (i.e., 4-
8Hz, 0-400 ms relative to response onset). A cluster of power increases for trials with
language selection errors relative to trials with correct responses was identified by the
statistical testing (Monte-Carlo p = .002, family-wise error corrected for multiple
comparisons). As can be seen in Figure 10, power increases were prominent in the theta band
(4-8 Hz) following language selection errors compared to correct responses, starting after
(incorrect) speech onset (the 0-ms time point) and sustained until around 400 ms after
response onset. The theta power increases had a frontocentral distribution. Thus, language
selection errors in bilingual word production show a neurophysiological response that
resembles the one reported in action monitoring in all its respects, i.e., in the temporal, spatial,
and spectral dimensions, and in the same direction of relative power increases. We interpret
the observed midline frontal theta power increases to reflect domain-general monitoring of
speech errors, supporting an account of (partially) shared neural mechanisms between speech
monitoring and action monitoring.
4.4. Interim summary
Midline frontal theta oscillations, originating from the anterior cingulate cortex and superior
frontal gyrus, are a hallmark electrophysiololgical signature of executive control processes.
We reviewed three recent language production studies that reported midline frontal theta
power increases for the condition requiring more control due to stimuli interfering with
production processes. We also reported novel evidence from bilingual word production
showing that, similarly to the domain of action monitoring, midline frontal theta power
increases when participants select the wrong language for speaking relative to when the
correct language is selected. Thus, the same hallmark signature of executive control is found
in language production tasks once the need for control is increased due to task circumstances.
5. Beyond speaking
Humans spend a substantial part of their days speaking, which implies that they also spend a
substantial amount of time listening to another speaker. It is widely accepted that conceptual
reprensetations are shared between comprehenion and production (Levelt et al., 1999).
However, the extent to which other levels of representation are also shared is not fully
The electrophysiological signal, and in particular neuronal oscillations, could potentially
provide clues for answering this latter question. For the relationship between language
comprehension and production, memory-related processes are relevant, so below we focus on
a few, relevant studies examining lexical selection for single words. An excellent review of
other processes involved in comprehension and their oscillatory underpinnings is provided by
Previous word comprehension studies have observed alpha and beta power decreases as a
function of manipulations affecting lexical-semantic processes. Bastiaansen and colleagues
(Bastiaansen, van der Linden, Ter Keurs, Dijkstra, & Hagoort, 2005; Mellem, Bastiaansen,
Pilgrim, Medvedev, & Friedman, 2012) compared the oscillatory signal time locked to open
class words (i.e., nouns, verbs, and adjectives) versus closed class words (i.e., determiners,
prepositions, and conjunctions). Open class words contain more semantic information than
closed class words. Stronger power decreases were observed for open relative to closed class
words between 8-12 Hz (Mellem et al., 2012) and 8-21 Hz (Bastiaansen et al., 2005) roughly
between 200-600 ms after word presentation. In a different study, participants were asked to
perform a semantic identification or a voice identification task on spoken words (Shahin,
Picton, & Miller, 2009). Alpha-beta power decreases were found for the semantic
identification task relative to the voice identification task for which no access to lexical
concepts is needed. Another study manipulated the intelligibility of spoken words
parametrically and participants had to indicate how comprehensible the words were (Obleser
& Weisz, 2012). Alpha power decreases were correlated with comprehension ratings as well
as with speech degradation such that stronger alpha-power decreases were associated with
better comprehension on the one hand, and with less degraded speech on the other hand. In a
lexical decision study, “lexicality” was manipulated such that not only real words and
pseudowords were presented to participants, but also ambiguous words, for which only one
vowel of an existing word was changed, forming a lexicality continuum (Strauß et al., 2014).
Alpha power decreases were strongest for real words, for which lexical-semantic
representations exist, followed by ambiguous words.
The earlier studies followed the state of the field at that point and interpreted the alpha power
decreases as reflecting sensory processes or selective attention and inhibition (e.g., Jensen &
Mazaheri, 2010; Klimesch, Doppelmayr, Russegger, Pachinger, & Schwaiger, 1998).
However, some authors also conjectured the possibility that the alpha-power decreases
reflected retrieval of lexical-semantic representations (e.g., Mellem et al., 2012; Strauß et al.,
2014). The latter interpretation is in line with the hypothesis (and evidence) reviewed above
that alpha-beta power decreases are related to the richness of the information being retrieved.
It is important to note that the hypothesis that information is represented in alpha-beta power
decreases was formulated for episodic memory. As such, most of the evidence in its support
comes from studies investigating the encoding stage in episodic-memory tasks (see for
discussion Hanslmayr et al., 2012). However, for the language domain, retrieval from
memory is more relevant: It underlies both word production and comprehension. The brief
review above illustrates how alpha-beta power decreases are also found in comprehension
tasks tapping lexical-level processes (e.g., Bastiaansen et al., 2005; Brennan, Lignos, Embick,
& Roberts, 2014; Mellem et al., 2012; Rommers, Dickson, Norton, Wlotko, & Federmeier,
2017; Strauß et al., 2014). It is conceivable that alpha-beta power decreases support the more
fundamental computation of retrieving information from memory, regardless of whether that
is episodic information, or lexical-semantic information necessary for word production or
comprehension. Notably, retrieval, on the one hand, and sensory processes or selective
attention, on the other, are not necessarily mutually exclusive. In many cases, retrieval is
associated with attentional demands (e.g., Craik, Naveh-Benjamin, Govoni, & Anderson,
1996). Moreover, conceptual (and lexical) retrieval are argued to also include retrieving
sensorimotor information stored in sensorimotor areas (e.g., Fernandino, Humphries, Conant,
Seidenberg, & Binder, 2016). In future research, it could be fruitful to consider retrieval
processes as the explanation of alpha-power effects observed in language tasks, which (due to
historical reasons) have been explained in terms of sensory or attentional processes1 (see also
the “Concluding remarks and open questions” section 6 below for further discussion on this
1 We are grateful to Kara Federmeier for this suggestion.
6. Concluding remarks and open questions
In this article, we have argued that studying neuronal oscillations provides an important
opportunity to understand how general neuronal computational principles support language
functioning, also helping elucidate relationships between language and other domains of
cognition. We have reviewed the literature on beta oscillations in relation to the motor aspects
of speaking and how it resembles the neurophysiological signature in motor tasks not
involving speech or mouth movements. We have also reviewed the literature on the memory
aspects of speaking and described the parallels with the domain of episodic memory, both for
the theta and alpha-beta bands. Finally, we discussed the literature on executive control and
midline frontal theta, and how it parallels the findings on executive aspects of speaking.
Note that we have argued for shared neuronal computations across cognitive domains on the
basis of similarity in terms of oscillatory patterns across domains. However, it is known that
the “same macroscopic extracellular signal can be generated by diverse cellular events. Thus,
a seemingly similar theta oscillation in the hippocampus and neocortex may be brought about
by different elementary mechanisms” (Buzsáki et al., 2012, p. 414). The parallels we have
drawn between language production and other cognitive domains, however, were between
oscillations generated within the same area across two different domains of cognition.
Therefore, we believe that the approach we suggest here is less problematic than comparing,
for example, theta oscillations between two different areas. It is worth noting that the
argumentation we have presented focuses on language production rather than on
comprehension. Therefore, our argument may not be directly extendable to comprehension
If the approach we adopted is valid, it opens many exciting avenues for future research. For
example, with respect to the memory domain, we have refered to alpha-beta oscillations
throughout, without drawing a clear distinction between alpha and beta oscillations. This
choice is driven by the fact that currently, evidence is lacking on what basis that distinction
should be drawn. It may turn out that memory-related processes in language production (and
possibly comprehension) are reflected in a frequency band that is neither the classic alpha (8-
12 Hz) or beta (15-30 Hz) bands, as these are more often conceived of as sensorimotor
rhythms. Using the labels alpha and beta has been important for advancing our understanding
of oscillations, but it does not necessarily mean that neuronal operations always respect the
alpha versus beta boundaries researchers have created. We may find that memory-related
processes operate in a frequency band that is intermediate to the classic alpha and beta bands2,
explaining why the language and memory literatures often have difficulty in making findings
fit in either one or the other, therefore adopting the term alpha-beta band. We hope that future
studies will elucidate these questions. Moreover, oscillations aside, it is easier to draw a
parallel between motor and executive processes necessary for language production and the
motor and executive-control domains as such. For memory-related mechanisms, however, the
fields being compared (i.e, episodic memory versus language) are more distinct and have less
often been discussed in relation to each other. As such, strong evidence is still lacking in
favour of the hypothesis that alpha-beta power decreases represent conceptual and lexical
2 We would like to thank Marina Laganaro for discussing this idea with us.
information that is retrieved by speakers. We hope that future studies will expand this
In conclusion, neurophysiological mechanisms, as reflected in modulations of neuronal
oscillations, may act as a fundamental basis for bringing together and enriching the fields of
language and cognition.
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