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Speaking waves: Neuronal oscillations in language production


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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 cognition. 4
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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
Corresponding author:
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
1. Introduction
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
intracranial EEG.
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
from Elsevier.
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
other hand.
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
of speaking.
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.
Interim summary
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 &
Zhou, 2009).
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
Meyer (2018).
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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... Neural oscillations have been argued to provide an avenue to understand how general neuronal computational principles support language (Friederici & Singer, 2015;Piai & Zheng, 2019). In other cognitive domains, different frequency bands have been associated with specific functions, such as power increases in the theta (4-7 Hz) and decreases in the alpha and beta bands with successful memory encoding and retrieval (Hanslmayr et al., 2012;Nyhus & Curran, 2010), and alpha and beta power decreases with motor preparation and execution (Cheyne, 2013). ...
... If power at the peak alpha and beta frequencies correlates across trials, this could be attributed to either arrhythmic or rhythmic activity, especially because correlated patterns of change across frequency bands may be more parsimoniously explained as a change in broadband arrhythmic activity (Donoghue et al., 2021). The alpha-beta power decreases in the pre-picture interval were considered to reflect conceptual preparation and word-planning processes (Piai et al., 2015Piai & Zheng, 2019), which can take place before picture onset only in the constraining condition, but not in the non-constraining condition. Thus, we T A B L E 1 Individual frequency peaks in Hz for each task and each frequency band defined on the 1/f free power spectra (osci) and on the original power spectra (orig) obtained from the procedures described for sensor-level analysis focused on the constraining condition for this analysis (see Roos & Piai, 2020). ...
... According to this view, information is encoded by neuronal desynchronization in the neocortex (Hanslmayr et al., 2012). This information-based mechanistic account of alpha/beta power decreases may also hold for conceptually driven lexical-semantic retrieval (see also Fellner et al., 2013;Piai et al., 2020;Piai & Zheng, 2019). Compared with the Judgment task, the Naming task elicited more left frontal power decreases. ...
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Decreases in oscillatory alpha- and beta- band power have been consistently found in spoken- word production. These have been linked to both motor prepa-ration and conceptual- lexical retrieval processes. However, the observed power decreases have a broad frequency range that spans two “classic” (sensorimotor) bands: alpha and beta. It remains unclear whether alpha- and beta- band power decreases contribute independently when a spoken word is planned. Using a re- analysis of existing magnetoencephalography data, we probed whether the ef-fects in alpha and beta bands are spatially distinct. Participants read a sentence that was either constraining or non- constraining toward the final word, which was presented as a picture. In separate blocks participants had to name the pic-ture or score its predictability via button press. Irregular- resampling auto- spectral analysis (IRASA) was used to isolate the oscillatory activity in the alpha and beta bands from the background 1- over- f spectrum. The sources of alpha- and beta- band oscillations were localized based on the participants’ individualized peak frequencies. For both tasks, alpha- and beta- power decreases overlapped in left posterior temporal and inferior parietal cortex, regions that have previously been associated with conceptual and lexical processes. The spatial distributions of the alpha and beta power effects were spatially similar in these regions to the extent we could assess it. By contrast, for left frontal regions, the spatial distributions differed between alpha and beta effects. Our results suggest that for conceptual- lexical retrieval, alpha and beta oscillations do not dissociate spatially and, thus, are distinct from the classical sensorimotor alpha and beta oscillations.
... Specifically, it has been suggested that alpha and beta power decreases provide an optimal brain state for information processing (Hanslmayr et al., 2012), which can account for their correlations with successful memory encoding in a variety of tasks (Klimesch, 1997;Khader and Rösler, 2011;Hanslmayr et al., 2011). Also, alpha and beta power decreases have been associated with lexico-semantic retrieval before naming a picture that is preceded by an HC context in both visual and auditory modalities (for a review, see Piai and Zheng, 2019). ...
... For example, Gastaldon et al. (2020) recently adopted a within-subject design to uncover similar and correlated alpha-beta constraint-related power decreases in comprehension and production. In production tasks, alpha-beta (8-25 Hz) power decreases have been consistently found at left inferior parietal and temporal (and frontal) areas before naming a picture in HC contexts (e.g., Piai et al., 2015;Piai et al., 2018;Hustá et al., 2021;Roos and Piai, 2020), which is taken as a fingerprint of lexical and semantic retrieval (see for a review Piai and Zheng, 2019). However, in Gastaldon et al. (2020), the effects were shorter-lived and weaker in the comprehension than in the production task. ...
Alpha and beta power decreases have been associated with prediction in a variety of cognitive domains. Recent studies in sentence comprehension have also reported alpha and/or beta power decreases preceding contextually predictable words, albeit with remarkable spatiotemporal variability across reports. To contribute to the understanding of the mechanisms underlying this phenomenon, and the sources of variability, the present study explored to what extent these prediction-related alpha and beta power decreases might be common across different modalities of comprehension. To address this, we re-analysed the data of two EEG experiments that employed the same materials in written and in spoken comprehension. Sentence contexts were weakly or strongly constraining about a sentence-final word, which was presented after a 1 s delay, either matching or mismatching the expectation. In written comprehension, alpha power (8–12 Hz) decreased before final words appearing in strongly (relative to weakly) constraining contexts, in line with previous reports. Furthermore, and for the first time, a similar oscillatory phenomenon was evidenced in spoken comprehension, although with relevant spatiotemporal differences. Altogether, the findings agree with the involvement of both modality-specific and general-domain mechanisms in the elicitation of prediction-related alpha power decreases in sentence comprehension. Specifically, we propose that this phenomenon might partly reflect richer and more precise information representation when linguistic contexts afford prediction.
... We focus on ERS/ERD in the theta (approximately 4-7 Hz), alpha (approximately 8-13 Hz), and beta (approximately 13-30 Hz) frequency bands, which support a wide range of functions (Buzsáki & Draguhn, 2004;Siegel et al., 2012) and play crucial roles in the comprehension of syntactic and semantic dependencies (Bastiaansen, Magyari, & Hagoort, 2010; Roehm, Schlesewsky, Bornkessel, Frisch, & Haider, 2004;Vassileiou, Meyer, Beese, & Friederici, 2018). These three frequency bands also relate to memory processes, such as different aspects of working memory engagement (theta : Cavanagh, Zambrano-Vazquez, & Allen, 2012;Jensen & Tesche, 2002;Karrasch, Laine, Rapinoja, & Krause, 2004, alpha: Riddle, Scimeca, Cellier, Dhanani, & D'Esposito, 2020, beta: Zammit, Falzon, Camilleri, & Muscat, 2018 and the retrieval of information from memory (alpha and beta: Hanslmayr, Staudigl, & Fellner, 2012;Klimesch, Schack, & Sauseng, 2005), also for language (Cross, Kohler, Schlesewsky, Gaskell, & Bornkessel-Schlesewsky, 2018;Piai et al., 2016;Piai, Roelofs, Rommers, & Maris, 2015;Piai & Zheng, 2019). ...
... Bögels, Casillas, and Levinson (2018) show that alpha-beta ERD is related to response planning in conversation. Piai and Zheng (2019) review the role of theta, alpha, and beta power changes in the planning of single words. ...
Languages differ in how they mark the dependencies between verbs and arguments, e.g., by case. An eye tracking and EEG picture description study examined the influence of case marking on the time course of sentence planning in Basque and Swiss German. While German assigns an unmarked (nominative) case to subjects, Basque specifically marks agent arguments through ergative case. Fixations to agents and event-related synchronization (ERS) in the theta and alpha frequency bands, as well as desynchronization (ERD) in the alpha and beta bands revealed multiple effects of case marking on the time course of early sentence planning. Speakers decided on case marking under planning early when preparing sentences with ergative-marked agents in Basque, whereas sentences with unmarked agents allowed delaying structural commitment across languages. These findings support hierarchically incremental accounts of sentence planning and highlight how cross-linguistic differences shape the neural dynamics underpinning language use.
... When considering previous M/EEG studies measuring oscillatory dynamics during speech production in monolinguals, theta (4-8 Hz) power increases 27 and alpha-beta (8-25 Hz) power decreases [28][29][30] have been reported in association to the retrieval of lexical-semantic information from long-term memory. In addition, frontal theta power increases during speech production have been related to executive control in the face of increased cognitive demands 31 . ...
... In line with the view that the retrieval of lexico-semantic information is enabled via power decreases of alpha-beta (8-25 Hz) oscillations 31 , we observed reduced alpha-beta power for nouns as compared to verbs in both Spanish and Basque, suggesting that similar mechanisms as those used by monolinguals might be called to play in bilingual speakers when both languages are mastered in a native-like fashion. This is also in keeping with previous evidence from our lab 32 , showing that L1 Spanish speakers recruit different networks in the alpha ...
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Words representing objects (nouns) and words representing actions (verbs) are essential components of speech across languages. While there is evidence regarding the organizational principles governing neural representation of nouns and verbs in monolingual speakers, little is known about how this knowledge is represented in the bilingual brain. To address this gap, we recorded neuromagnetic signals while highly proficient Spanish–Basque bilinguals performed a picture-naming task and tracked the brain oscillatory dynamics underlying this process. We found theta (4–8 Hz) power increases and alpha–beta (8–25 Hz) power decreases irrespectively of the category and language at use in a time window classically associated to the controlled retrieval of lexico-semantic information. When comparing nouns and verbs within each language, we found theta power increases for verbs as compared to nouns in bilateral visual cortices and cognitive control areas including the left SMA and right middle temporal gyrus. In addition, stronger alpha–beta power decreases were observed for nouns as compared to verbs in visual cortices and semantic-related regions such as the left anterior temporal lobe and right premotor cortex. No differences were observed between categories across languages. Overall, our results suggest that noun and verb processing recruit partially different networks during speech production but that these category-based representations are similarly processed in the bilingual brain.
... This process is both hierarchical, each higher level providing the context for the next [69,70], and integrative, lower level elements binding to build the higher level across time [71]). The neural sources of linguistic representations are spatially distributed and temporally integrated [69], with synchronised oscillatory activity offering the physiological basis for many emergent aspects of language [72,73]. In this context, the speech production system can be construed as an oscillator whose spatiotemporal dynamics determine the speech outputs. ...
... The spatial distribution of beta-oscillations relevant to speech acts differs from those of simple motor tasks. Beta desynchronisation precedes the initiation of the motor speech act (as shown by [91]), especially in the motor-related speech areas of left IFG and anterior insula (frontoinsular cortex) [72]. When maintaining verbal material for further processing (n-back paradigm), bursts of beta oscillations relate to Blood Oxygenation Level Dependent fMRI activation only in taskrelevant sensorimotor speech areas (perisylvian regions of superior temporal and postcentral gyri) and not the midline non-motor regions; after the motor response, beta bursts show a rebound increase in the same task-relevant regions [92], indicating a likely reactivation of taskrelevant latent representations [88]. ...
Schizophrenia provides a quintessential disease model of how disturbances in the molecular mechanisms of neurodevelopment lead to disruptions in the emergence of cognition. The central and often persistent feature of this illness is the disorganisation and impoverishment of language and related expressive behaviours. Though clinically more prominent, the periodic perceptual distortions characterised as psychosis are non-specific and often episodic. While several insights into psychosis have been gained based on study of the dopaminergic system, the mechanistic basis of linguistic disorganisation and impoverishment is still elusive. Key findings from cellular to systems-level studies highlight the role of ubiquitous, inhibitory processes in language production. Dysregulation of these processes at critical time periods, in key brain areas, provides a surprisingly parsimonious account of linguistic disorganisation and impoverishment in schizophrenia. This review links the notion of excitatory/inhibitory (E/I) imbalance at cortical microcircuits to the expression of language behaviour characteristic of schizophrenia, through the building blocks of neurochemistry, neurophysiology, and neurocognition.
... This process is both hierarchical, each higher level providing the context for the next [69,70], and integrative, lower level elements binding to build the higher level across time [71]). The neural sources of linguistic representations are spatially distributed and temporally integrated [69], with synchronised oscillatory activity offering the physiological basis for many emergent aspects of language [72,73]. In this context, the speech production system can be construed as an oscillator whose spatiotemporal dynamics determine the speech outputs. ...
... The spatial distribution of beta-oscillations relevant to speech acts differs from those of simple motor tasks. Beta desynchronisation precedes the initiation of the motor speech act (as shown by [91]), especially in the motor-related speech areas of left IFG and anterior insula (frontoinsular cortex) [72]. When maintaining verbal material for further processing (n-back paradigm), bursts of beta oscillations relate to Blood Oxygenation Level Dependent fMRI activation only in task-relevant sensorimotor speech areas (perisylvian regions of superior temporal and postcentral gyri) and not the midline non-motor regions; after the motor response, beta bursts show a rebound increase in the same task-relevant regions [92], indicating a likely reactivation of task-relevant latent representations [88]. ...
Schizophrenia provides a quintessential disease model of how disturbances in the molecular mechanisms of neurodevelopment lead to disruptions in the emergence of cognition. The central and often persistent feature of this illness is the disorganisation and impoverishment of language and related expressive behaviours. Though clinically more prominent, the periodic perceptual distortions characterised as psychosis are non-specific and often episodic. While several insights into psychosis have been gained based on study of the dopaminergic system, the mechanistic basis of linguistic disorganisation and impoverishment is still elusive. Key findings from cellular to systems-level studies highlight the role of ubiquitous, inhibitory processes in language production. Dysregulation of these processes at critical time periods, in key brain areas, provides a surprisingly parsimonious account of linguistic disorganisation and impoverishment in schizophrenia. This review links the notion of excitatory/inhibitory (E/I) imbalance at cortical microcircuits to the expression of language behaviour characteristic of schizophrenia, through the building blocks of neurochemistry, neurophysiology, and neurocognition.
... Future research should examine these potential differences in underlying processing. As of yet, a mechanistic theory linking alpha-beta power decreases to lexical-semantic retrieval is lacking (for developments in this direction, refer to Piai & Zheng, 2019;Meyer, 2018). We note that the current study used strongly biasing contexts to bias the interpretation of a sentence that has a potential idiomatic interpretation as either a literal or figurative sentence. ...
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Idioms can have both a literal interpretation and a figurative interpretation (e.g., to “kick the bucket”). Which interpretation should be activated can be disambiguated by a preceding context (e.g., “The old man was sick. He kicked the bucket.”). We investigated whether the idiomatic and literal uses of idioms have different predictive properties when the idiom has been biased toward a literal or figurative sentence interpretation. EEG was recorded as participants performed a lexical decision task on idiom-final words in biased idioms and literal (compositional) sentences. Targets in idioms were identified faster in both figuratively and literally used idioms than in compositional sentences. Time–frequency analysis of a prestimulus interval revealed relatively more alpha–beta power decreases in literally than figuratively used idiomatic sequences and compositional sentences. We argue that lexico-semantic retrieval plays a larger role in literally than figuratively biased idioms, as retrieval of the word meaning is less relevant in the latter and the word form has to be matched to a template. The results are interpreted in terms of context integration and word retrieval and have implications for models of language processing and predictive processing in general.
... In people without speech production deficits, a power decrease in these frequency bands is found during speech planning and execution. This power decrease is thought to reflect the engagement of regions associated with memory and motor processes for language and speech production (Piai & Zheng, 2019;Saltuklaroglu et al., 2018). In the case of DS, these frequencies have been shown to be abnormally modulated -especially in motor and premotor associative regions -in syllable and word production tasks, indexing inefficient motor-to-sensory transformation and reduced coordination in engaging cortical regions devoted to speech production Joos et al., 2014;Mersov et al., 2016;Mock et al., 2016). ...
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It is well attested that people predict forthcoming information during language comprehension. The literature presents different proposals on how this ability could be implemented. Here, we tested the hypothesis according to which language production mechanisms have a role in such predictive processing. To this aim, we studied two electroencephalographic correlates of predictability during speech comprehension ‒ pretarget alpha‒beta (8-30 Hz) power decrease and the post-target N400 event-related potential (ERP) effect, ‒ in a population with impaired speech-motor control, i.e., adults who stutter (AWS), compared to typically fluent adults (TFA). Participants listened to sentences that could either constrain towards a target word or not, allowing or not to make predictions. We analyzed time-frequency modulations in a silent interval preceding the target and ERPs at the presentation of the target. Results showed that, compared to TFA, AWS display: i) a widespread and bilateral reduced power decrease in posterior temporal and parietal regions, and a power increase in anterior regions, especially in the left hemisphere (high vs . low constraining) and ii) a reduced N400 effect (non-predictable vs . predictable). The results suggest a reduced efficiency in generating predictions in AWS with respect to TFA. Additionally, the magnitude of the N400 effect in AWS is correlated with alpha power change in the right pre-motor and supplementary motor cortex, a key node in the dysfunctional network in stuttering. Overall, the results support the idea that processes and neural structures prominently devoted to speech planning and execution support prediction during language comprehension. Significance Statement The study contributes to the developing enterprise of investigating language production and comprehension not as separate systems, but as sets of processes which may be partly shared. We showed that a population with impaired speech-motor control, i.e., adults who stutter, are characterized by atypical electrophysiological patterns associated with prediction in speech comprehension. The results highlight that an underlying atypical function of neural structures supporting speech production also affects processes deployed during auditory comprehension. The implications are twofold: on the theoretical side, the study supports the need for a more integrated view of language comprehension and production as human capabilities, while on the applied and clinical side, these results might open new venues for efficient treatments of developmental stuttering.
... Power decreases slightly before and during the production of pseudowords were also observed at 7 individual recording sites (mostly in the precentral gyrus) in the low beta and alpha frequency bands (Fig. 7). Desynchronization of activity in the beta (and sometimes also alpha) band over sensory and motor areas is a well-known neural signature of preparation and execution of a voluntary movement (for review, see Engel & Fries, 2010;Piai & Zheng, 2019;Weiss & Mueller, 2012). In language studies, a desynchronization of beta activity has been repeatedly demonstrated during overt movement (e. g., word generation) (Singh, Barnes, Hillebrand, Forde, & Williams, 2002) and even covert, or imaginary, movement (e.g., processing of action verbs) (van Elk, van Schie, Zwaan, & Bekkering, 2010). ...
Many language functions are traditionally assigned to cortical brain areas, leaving the contributions of subcortical structures to language processing largely unspecified. The present study examines a potential role of the subthalamic nucleus (STN) in lexical processing, specifically, reading aloud of words (e.g., 'fate') and pseudowords (e.g., 'fape'). We recorded local field potentials simultaneously from the STN and the cortex (precentral, postcentral, and superior temporal gyri) of 13 people with Parkinson's disease undergoing awake deep brain stimulation and compared STN's lexicality-related neural activity with that of the cortex. Both STN and cortical activity demonstrated significant task-related modulations, but the lexicality effects were different in the two brain structures. In the STN, an increase in gamma band activity (31-70 Hz) was present in pseudoword trials compared to word trials during subjects' spoken response. In the cortex, a greater decrease in beta band activity (12-30 Hz) was observed for pseudowords in the precentral gyrus. Additionally, 11 individual cortical sites showed lexicality effects with varying temporal and topographic characteristics in the alpha and beta frequency bands. These findings suggest that the STN and the sampled cortical regions are involved differently in the processing of lexical distinctions.
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Post-stroke aphasia is a consequence of localized stroke-related damage as well as global disturbances in a highly interactive and bilaterally-distributed language network. Aphasia is increasingly accepted as a network disorder and it should be treated as such when examining the reorganization and recovery mechanisms after stroke. In the current study, we sought to investigate reorganized patterns of electrophysiological connectivity, derived from resting-state magnetoencephalography (rsMEG), in post-stroke chronic (>6 months after onset) aphasia. We implemented amplitude envelope correlations (AEC), a metric of connectivity commonly used to describe slower aspects of interregional communication in resting-state electrophysiological data. The main focus was on identifying the oscillatory frequency bands and frequency-specific spatial topology of connections associated with preserved language abilities after stroke. RsMEG was recorded for 5 minutes in 21 chronic stroke survivors with aphasia and in 20 matched healthy controls. Source-level MEG activity was reconstructed and summarized within 72 atlas-defined brain regions (or nodes). A 72×72 leakage-corrected connectivity (of AEC) matrix was obtained for frequencies from theta to low-gamma (4–50 Hz). Connectivity was compared between groups, and, the correlations between connectivity and subscale scores from the Western Aphasia Battery (WAB) were evaluated in the stroke group, using partial least squares analyses. Posthoc multiple regression analyses were also conducted on a graph theory measure of node strengths, derived from significant connectivity results, to control for node-wise properties (local spectral power and lesion sizes) and demographic and stroke-related variables. Connectivity among the left hemisphere regions, i.e. those ipsilateral to the stroke lesion, was greatly reduced in stroke survivors with aphasia compared to matched healthy controls in the alpha (8-13 Hz; p=0.011) and beta (15-30 Hz; p=0.001) bands. The spatial topology of hypoconnectivity in the alpha vs. beta bands was distinct, revealing a greater involvement of ventral frontal, temporal and parietal areas in alpha, and dorsal frontal and parietal areas in beta. The node strengths from alpha and beta group differences remained significant after controlling for nodal spectral power. AEC correlations with WAB subscales of object naming and fluency were significant. Greater alpha connectivity was associated with better naming performance (p=0.045), and greater connectivity in both the alpha (p=0.033) and beta (p=0.007) bands was associated with better speech fluency performance. The spatial topology was distinct between these frequency bands. The node strengths remained significant after controlling for age, time post stroke onset, nodal spectral power and nodal lesion sizes. Our findings provide important insights into the electrophysiological connectivity profiles (frequency and spatial topology) potentially underpinning preserved language abilities in stroke survivors with aphasia.
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This study investigated the nature of the interference effect of semantically related distractors in the picture-word interference paradigm, which has been claimed to be caused by either competition between lexical representations of target and distractor or by a late response exclusion mechanism that removes the distractor from a response buffer. EEG was recorded while participants overtly named pictures accompanied by categorically related versus unrelated written distractor words. In contrast to previous studies, stimuli were presented for only 250 ms to avoid any re-processing. ERP effects of relatedness were found around 290, 470, 540, and 660 ms post stimulus onset. In addition, related distractors led to an increase in midfrontal theta power, especially from about 440 to 540 ms, as well as to decreased high beta power between 40 and 110 ms and increased high beta power between 275 and 340 ms post stimulus onset. Response-locked analyses showed no differences in ERPs, however increased low and high beta power for related distractors in various time windows, most importantly a high beta power increase between −175 and −155 ms before speech onset. These results suggest that the semantic distractor effect is a combination of various effects and that the lexical competition account and the response exclusion account each capture a part, but not all aspects of the effect.
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Although bilingual speakers are very good at selectively using one language rather than another, sometimes language selection errors occur. To investigate how bilinguals monitor their speech errors and control their languages in use, we recorded event-related potentials (ERPs) in unbalanced Dutch-English bilingual speakers in a cued language-switching task. We tested the conflict-based monitoring model of Nozari and colleagues by investigating the error-related negativity (ERN) and comparing the effects of the two switching directions (i.e., to the first language, L1 vs. to the second language, L2). Results show that the speakers made more language selection errors when switching from their L2 to the L1 than vice versa. In the EEG, we observed a robust ERN effect following language selection errors compared to correct responses, reflecting monitoring of speech errors. Most interestingly, the ERN effect was enlarged when the speakers were switching to their L2 (less conflict) compared to switching to the L1 (more conflict). Our findings do not support the conflict-based monitoring model. We discuss an alternative account in terms of error prediction and reinforcement learning.
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Language is mediated by pathways connecting distant brain regions that have diverse functional roles. For word production, the network includes a ventral pathway, connecting temporal and inferior frontal regions, and a dorsal pathway, connecting parietal and frontal regions. Despite the importance of word production for scientific and clinical purposes, the functional connectivity underlying this task has received relatively limited attention, and mostly from techniques limited in either spatial or temporal resolution. Here, we exploited data obtained from depth intra-cerebral electrodes stereotactically implanted in eight epileptic patients. The signal was recorded directly from various structures of the neocortex with high spatial and temporal resolution. The neurophysiological activity elicited by a picture naming task was analyzed in the time-frequency domain (10–150 Hz), and functional connectivity between brain areas among ten regions of interest was examined. Task related-activities detected within a network of the regions of interest were consistent with findings in the literature, showing task-evoked desynchronization in the beta band and synchronization in the gamma band. Surprisingly, long-range functional connectivity was not particularly stronger in the beta than in the high-gamma band. The latter revealed meaningful sub-networks involving, notably, the temporal pole and the inferior frontal gyrus (ventral pathway), and parietal regions and inferior frontal gyrus (dorsal pathway). These findings are consistent with the hypothesized network, but were not detected in every patient. Further research will have to explore their robustness with larger samples.
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Intracranial electrophysiological recording in humans has been a long standing technique in neurosurgical treatment for epilepsy and have served as an important window in to how the human brain processes language. This chapter is aimed to introduce the reader to the technique, how it historically contributed to language mapping, its advantages and disadvantages as a research tool, and analysis techniques that have provided novel findings and approaches in the area of language processing.
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In addition to the role of left frontotemporal areas in language processing, there is increasing evidence that language comprehension and production require cognitive control and working memory resources involving the left dorsolateral prefrontal cortex (DLPFC). The aim of this study was to investigate the role of the left DLPFC in both language comprehension and production. In a double-blind, sham-controlled crossover experiment, thirty-two participants received cathodal or sham transcranial direct current stimulation (tDCS) to the left DLPFC while performing a language comprehension and a language production task. Results showed that cathodal tDCS increases reaction times in the language comprehension task, but decreases naming latencies in the language production task. However, additional analyses revealed that the polarity of tDCS effects was highly correlated across tasks, implying differential individual susceptibility to the effect of tDCS within participants. Overall, our findings demonstrate that left DLPFC is part of the complex cortical network associated with language processing.
The contribution of the motor cortex to the semantic retrieval of verbs remains a subject of debate in neuroscience. Here, we examined whether additional engagement of the cortical motor system was required when access to verbs semantics was hindered during a verb generation task. We asked participants to produce verbs related to presented noun cues that were either strongly associated with a single verb to prompt fast and effortless verb retrieval, or were weakly associated with multiple verbs and more difficult to respond to. Using power suppression of magnetoencephalography beta oscillations (15–30 Hz) as an index of cortical activation, we performed a whole‐brain analysis in order to identify the cortical regions sensitive to the difficulty of verb semantic retrieval. Highly reliable suppression of beta oscillations occurred 250 ms after the noun cue presentation and was sustained until the onset of verbal response. This was localized to multiple cortical regions, mainly in the temporal and frontal lobes of the left hemisphere. Crucially, the only cortical regions where beta suppression was sensitive to the task difficulty, were the higher order motor areas on the medial and lateral surfaces of the frontal lobe. Stronger activation of the premotor cortex and supplementary motor area accompanied the effortful verb retrieval and preceded the preparation of verbal responses for more than 500 ms, thus, overlapping with the time window of verb retrieval from semantic memory. Our results suggest that reactivation of verb‐related motor plans in higher order motor circuitry promotes the semantic retrieval of target verbs.
Investigations into the neurophysiological underpinnings of control suggest that frontal theta activity is increased with the need for control. However, these studies typically show this link by reporting associations between increased theta and RT slowing – a process that is contemporaneous with cognitive control but does not strictly reflect the specific use of control. In this study, we assessed frontal theta responses that underpinned the switch cost in task switching – a specific index of cognitive control that does not rely exclusively on RT slowing. Here, we utilised a single-trial regression approach to assess 1) how cognitive control demands beyond simple RT slowing were linked to midfrontal theta and 2) whether midfrontal theta effects remained stable over time. In a large cohort that included a longitudinal subsample, we found that midfrontal theta was modulated by switch costs, with enhanced theta power when preparing to switch vs. repeating a task. These effects were reliable after a two-year interval (Cronbach's α.39-0.74). In contrast, we found that trial-by-trial modulations of midfrontal theta power predicted the size of the switch cost – so that switch trials with increased theta produced smaller switch costs. Interestingly, these relationships between theta and behaviour were less stable over time (Cronbach's α 0-0.61), with participants first using both delta and theta bands to influence behaviour whereas after two years only theta associations with behaviour remained. Together, these findings suggest midfrontal theta supports the need for control beyond simple RT slowing and reveal that midfrontal theta effects remain relatively stable over time.
In patients with gliomas, changes in hemispheric specialization for language determined by magnetoencephalography (MEG) were analyzed to elucidate the impact of treatment and tumor recurrence on language networks. Demonstration of reorganization of language networks in these patients has significant implications on the prevention of postoperative functional loss and recovery. Whole‐brain activity during an auditory verb generation task was estimated from MEG recordings in a group of 73 patients with recurrent gliomas. Hemisphere of language dominance was estimated using the language laterality index (LI), a measure derived from the task. The initial scan was performed prior to resection; patients subsequently underwent surgery and adjuvant treatment. A second scan was performed upon recurrence prior to repeat resection. The relationship between the shift in LI between scans and demographics, anatomic location, pathology, and adjuvant treatment was analyzed. Laterality shifts were observed between scans; the median percent change was 29.1% across all patients. Laterality shift magnitude and relative direction were associated with the initial position of language dominance; patients with increased lateralization experienced greater shifts than those presenting more bilateral representation. A change in LI from left or right to bilateral (or vice versa) occurred in 23.3% of patients; complete switch occurred in 5.5% of patients. Patients with tumors within the language‐dominant hemisphere experienced significantly greater shifts than those with contralateral tumors. The majority of patients with glioma experience shifts in language network organization over time which correlate with the relative position of language lateralization and tumor location.
This article presents a new account of the color-word Stroop phenomenon (J. R. Stroop, 1935) based on an implemented model of word production, WEAVER++ (W. J. M. Levelt, A. Roelofs, & A. S. Meyer, 1999b; A, Roelofs, 1992, 1997c). Stroop effects are claimed to arise from processing interactions within the language-production architecture and explicit goal-referenced control. WEAVER++ successfully simulates 16 classic data sets, mostly taken from the review by C. M. MacLeod (1991), including incongruency, congruency, reverse-Stroop, response-set, semantic-gradient, time-course, stimulus, spatial, multiple-task, manual, bilingual, training, age, and pathological effects. Three new experiments tested the account against alternative explanations. It is shown that WEAVER++ offers a more satisfactory account of the data than other models.
The chapter links empirical neurophysiological findings and concepts from two different disciplines: semantic processing, a sub-discipline of linguistics that refers to any sort of cognitive processing which focuses on the meaning of a sensory stimulus (word, picture, or sound), and episodic memory, a sub-discipline of psychology that refers to memories with a unique temporal and spatial context. The combination of these two disciplines have led to several key findings and strongly influenced memory models and frameworks. The authors focus on a very special marker of neural activity, namely brain oscillations, which provide the glue by which they link the two different disciplines. The studies utilize brain oscillations to address the question of how local and global neural assemblies interact by means of synchronization and desynchronization during semantic processing and memory encoding. As yet, there is no conclusive answer on how the brain carries out these tasks, but brain oscillations might contribute an important piece to the solution of this puzzle, by guiding and linking these processes.