Behavioural Neuroscience assignment sample examines how language is represented in the brain, highlighting EEG evidence for collocation processing, integrative fMRI/ECoG–ANN models of predictive coding, and biopsychological methods such as EEG and rTMS applied to sleep and cognitive function.
Defining Language and Its Importance
One of the most complex and vital human cognitive functions is language. It helps communicate ideas, means of expression, and social interactions. A good language supports education, culture, relationships and even thought processes (Altarriba & Basnight-Brown, 2022). For the sake of cognitive neuroscience, it is essential to know how the brain processes language, and for improving diagnosis and treatment of disorders of language.
Neural Correlates of Language
The ‘neural correlates of language’ refers to the unique regions and networks of the brain involved in language comprehension, production, and representation. Traditionally, language has been linked to specific areas in the left hemisphere. However, recent research shows that the network of brain regions involved in a variety of linguistic functions is broader than previously thought (Altarriba & Basnight-Brown, 2022). Among them are the temporal, frontal, and parietal lobes and subcortical and cerebellar structures.
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Role of Biopsychological Methods
Using biopsychological methods has been central to determining how the human brain represents and processes language. These bridge the gap between biology and psychology by directly linking neural activity and cognitive functions. Functional magnetic resonance imaging (fMRI), electroencephalography (EEG), electrocorticography (ECoG) and transcranial magnetic stimulation (TMS) are among the most commonly employed methods, which measure changes in blood flow linked to neural activity, electrophysiological signals from the brain in high temporal precision, signals from underlying cortical surface or temporarily and non invasively stimulate or inhibit the targeted brain area respectively (Oathes et al., 2021).
Recent Advances in Language Neuroscience
In the past few years, language neuroscience has shifted its focus from word and sentence level language studies to ones examining how brains process language in continuous naturalistic settings (Garibyan et al., 2022). It comprises investigation of linguistic collocations, idiomatic phrases, and real-time discourse processing that are closer to real-world communication. One significant development is that language processing is not purely reactive but also predictive. In the brain, the upcoming linguistic input is anticipated based on prior context, lexical frequency of use, and semantic relatedness of the input. Such support has come through the use of event-related potentials (ERPs) and time-resolved fMRI in which the brain is shown to make predictions before the word is even spoken or heard (Oathes et al., 2021). In this regard, computational modelling needs to be further developed by being coupled with neuroimaging.
A big step forward is coupling computational models to neuroimaging. Collectively, researchers now use artificial neural network (ANN) trained on language data and assess how well these outputs match to how the brain responds to language in real life to evaluate how near the ANNs are bringing hardware to the machine representation of some real cognitive process (Lin et al., 2023). The work has extended the idea of predictive processing as a computational basis for language comprehension with its work on a dynamic interaction between language input and the brain.
8 Years | MSc
Narrowing the Focus
Before focusing on two related but distinct subfields of biopsychological research ideally suited to reveal neural correlates of language, this review will narrow down to two of them. The first one looks at how the brain processes linguistic collocations — phrases such as ‘strong coffee’ and ‘make a decision’ that are commonly ‘co-occurring’ — word pair or phrases. They are supposed to be stored in the pre-assembled chunks in the mental lexicon for their faster recovery than novel word combinations (Perrier et al., 2015). In this review, the neural signatures of collocation processing unique to the ERP components like the N400 that is known to be sensitive to semantic processing will be discussed along with the studies using EEG which determined these signatures. The second area is predictive language processing, which considers the brain as a prediction machine, predicting, in advance, the upcoming linguistic input.
This section will study if it has been possible to understand how coordination between different brain regions is performed in identifying anticipatory mechanisms through fMRI and ECoG studies and computational modeling (Schrimpf et al., 2021). These two strands of research are good at giving us insights into both the static and dynamic aspects of language processing in the brain. The review will then highlight how biopsychological methods performed today are not only mapping out what the brain regions are involved in language, but also revealing that comprehension of language involves 'temporal dynamics and this is predictive.'
Aim of the Review
This review aims to critically examine recent empirical biopsychological research on the neural substrate of language using state-of-the-art brain-based techniques. It compares and contrasts key findings in each of these studies, first showing how they have contributed to enhanced understanding of language-related brain functions and second pointing out their implications for future research and clinical practice.
Neural Processing of Linguistic Collocations
Study 1: Garibyan et al. (2022)
This study examined how the human brain manages linguistic collocations—word pairs that frequently co-occur and are semantically or syntactically attached—during continuous speech perception. The purpose was to examine whether predictable and familiar collocations should be given different treatment based on being collocational word combinations than non collocational word combinations. For instance, the researchers assumed that collocations might be processed more effectively or ‘facilitated’, and therefore this elicits different neural responses.
For high temporal resolution, the researchers used electroencephalography (EEG), which is a non-invasive technique that measures scalp’s electric activity. Specifically, they examined the N400 event related potential (ERP) component, which, although not entirely correlated to semantic processing, is relatively linked with the ease or the difficulty of integrating a word into a given context (Garibyan et al., 2022). The participants were presented with naturally spoken German sentences with collocational and non collocational word pairs. By using continuous, ecological and naturalistic speech rather than isolated stimuli, the ecological validity of the study was increased and more realistic mimicry of real world language comprehension.
It was found that N400 amplitudes to collocations were much lower than N400 amplitudes to non-collocational word pairs. The fact that collocations are less cognitively demanding may be because they are more frequent and predictable. Theories supporting the idea that collocations are stored as lexical units in the mental lexicon materialise when the brain processes these pre-fabricated language chunks more efficiently (Garibyan et al., 2022). Therefore, the ERP response is further evidence of the early emergence of the ERP response in real-time speech processing and supports the idea that collocations are predicted or anticipated.
The strength of this study is that EEG is used to time the neural events that underlie language processing precisely. The semantic integration processes are directly measurable thanks to the N400 component peaking roughly 400 milliseconds after word onset (Garibyan et al., 2022). The naturalistic design of the experiment is another strength that promotes the generalisability of the effect to everyday language use. Nevertheless, the study also has limitations. EEG is strong for temporal precision but poor spatial resolution and cannot pinpoint the exact brain regions where the effect occurs. Furthermore, since the study only included German-speaking participants, it may not apply to other languages and linguistic structures.
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A second limitation is the lack of additional behavioural measures, e.g., reaction times or comprehension accuracy, which could serve as a further means to validate the neural data. Including these would have permitted adding neural efficiency to the equation and testing its correlation with task performance (Garibyan et al., 2022). Additionally, it did not measure individual differences such as vocabulary size or familiarity with specific collocations, which could also have affected the observed neural responses.
While these may seem like limitations, the study shows that the brain treats frequent word pairings as cognitive entities that feel more natural to process than new combinations. It helps us learn how semantic predictability and linguistic familiarity shape brain processing (Garibyan et al., 2022). This aligns with the idea that real-time communication is a product of word-by-word decoding and activation of larger preassembled linguistic units, which supports the notion.1
Study 2: Schrimpf et al. (2021)
This study's focus was to see if the predictive mechanisms that advanced artificial language models employ are the same as the mechanisms used by the human brain when processing language. In particular, it proved in testing that the brain holds the mistaken belief that active anticipation of upcoming input in the language is occurring, akin to how artificial neural networks (ANNs) predict Further words. The study examined how those models compare with human neural responses during language comprehension.
For this analysis, the researchers use a multimodal dataset containing functional magnetic resonance imaging (fMRI) and electrocorticography (ECoG) recordings from human participants listening to natural language stimuli. Neural responses to text passages are recorded while participants read or listen to text passages (Schrimpf et al., 2021). Moreover, the recorded brain data was compared with the internal representations produced by a set of artificial language models that spanned from the most straightforward word embedding models to more state-of-the-art predictive models trained on vast amounts of linguistic data.
The results found a strong correspondence between how well a model predicted and how well it matched a human's brain activity (Schrimpf et al., 2021). In particular, the higher the accuracy that the model predicted the Further word in a sequence of words, the more neural responses in language-sensitive brain regions, such as the inferior frontal gyrus and the posterior superior temporal cortex correlated with the model. These are the areas implicated in syntactic and semantic processing. One notable aspect was that the alignment was robust in this situation, especially with data derived from ECoG, which provides high temporal and spatial resolution (Schaworonkow & Voytek, 2021).
The major strength of this study is the integration of computational modelling with biological data, resulting in a novel comparison between artificial and human prediction mechanisms. The researchers showed that brain-like predictions are not universal to all language models but instead appear in those most capable of capturing statistical regularities in natural language by analysing multiple models of increasing complexity (Schrimpf et al., 2021). This confirms the theory of predictive coding, which states that the brain actively produces predictions of incoming information to decrease cognitive load and enhance processing speed.
Nevertheless, the study has some limitations. However, ECoG data is typically collected on clinical populations undergoing neurosurgery, for example, epileptic patients (Moon et al., 2024). Thus, there is a cause for concern about generalisability to the general population. Also, the observed correlation between model predictions and brain activity looks convincing, but it does not necessarily mean that the brain is enacting the same design strategies as artificial networks. It may be functional rather than structural. Furthermore, this work did not mention the behavioral metrics, namely accuracy or reading time, that better explain predictive processing to real-world language performance.
Study 3: Perrier et al. (2015)
The study of Perrier et al. was to find whether the brains of patients with primary insomnia (PI) exhibited specific alterations of neural activity during sleep in the prefrontal cortex. The researchers were interested in looking for patterns in the sleep EEG (electroencephalography) power spectra for these differences between PI subjects and good sleepers and the differentiation between NREM and REM sleep stages.
To achieve this, the researchers recruited 14 patients diagnosed with primary insomnia and 10 matched good sleepers. This was done to minimise the effects of confounding from the medication since participants were drug-free for at least two years. All participants documented one night of standard polysomnographic (PSG) recording (8 EEG channels, 2 EOG channels, one submental electromyography [EMG] channel) (Perrier et al., 2015). EEG power spectral analysis was performed across the prefrontal, central, temporal, and occipital regions in five EEG frequency bands (delta, theta, alpha, sigma, beta) in NREM and REM sleep.
It was found that individuals with insomnia have significantly higher beta and sigma power and lower delta power in NREM sleep compared to good sleepers, especially in the nonprefrontal cortical sites. Unlike other regions, there was a less pronounced difference in beta activity in PI patients in the prefrontal cortex, which may indicate a localised dysfunction (Perrier et al., 2015). There was no statistical difference between control and PI participants in the beta activity in the prefrontal region during REM sleep. The findings indicate that while PI is associated with cortical hyperarousal with an overactive waking-type neuronal pattern across all cortex regions except the prefrontal area.
The first is that the use of PSG and spectral EEG analysis offers robust, objective physiological data regarding sleep, and adding in participants with long-term drug-free status improves internal validity (Perrier et al., 2015). Second, the study of sleep-related brain activity is carried out in several cortical regions, leading to a more fine-grained analysis of regional differences. However, the major limitation of the data collected during only one night of sleep could be that participants slept poorly because of the 'first-night effect,' a well-known phenomenon in which people sleep poorly due to an unfamiliar sleep lab environment (Wick et al., 2024). It may have affected the accuracy of sleep architecture and EEG recordings.
A second limitation is the small sample size, so the results are not generalizable. Moreover, no behavioural or subjective sleep assessment could be correlated with these EEG differences. The EEG offered great neurophysiological insights into the data (Perrier et al., 2015). However, combining the EEG with other imaging techniques (e.g., fMRI) would have provided higher spatial resolution and helped to confirm regional differences.
However, this study is meaningful to the neural mechanism underlying insomnia. As hyperarousal is postulated for insomnia, the elevated beta and sigma activity that has been observed in PI patients suggests hyperarousal during sleep (Scott et al., 2023). The study refines the idea that insomnia is not only behavioural. Still, it has a unique neurophysiological signature, to the extent that EEG is an excellent diagnostic and research tool in sleep neuroscience, the proof of which is finding the patterns.
Study 4: Lin et al. (2023)
Lin et al.. evaluated the efficacy of low-frequency repetitive transcranial magnetic stimulation (rTMS) of the left dorsal medial prefrontal cortex (DMPFC) as an adjunctive treatment for primary insomnia. The objective was to see if combining this form of noninvasive brain stimulation with pharmacotherapy would improve objective and subjective sleep quality measures.
A total of 49 participants with primary insomnia, as diagnosed by DSM-5 criteria, were studied. All continued on their pre-trial hypnotic medication to mimic real-world treatment practice (Lin et al., 2023). The participants were randomly assigned to either active or sham (placebo) stimulation groups. The intervention was based on 10 sessions of 2 weeks, involving low frequency (1 Hz) rTMS over the left DMPFC. Two approaches to measure sleep were done before and after the treatment: overnight polysomnography (PSG) to obtain objective sleep parameters (total sleep time, sleep efficiency, sleep onset latency, wake after sleep onset) and the Pittsburgh Sleep Quality Index (PSQI) to assess subjective sleep quality.
The effects in the rTMS group were a significant reduction of wake after sleep onset (WASO) and improved sleep efficiency. Overall, the sham group also improved sleep latency and daytime dysfunction on a subjective scale, suggesting a placebo effect (Lin et al., 2023). Intriguingly, the PSG findings revealed that both groups had a longer total sleep time and shorter sleep onset latency in the placebo group compared to the rTMS group, with more consistent favorable improvements in sleep quality and sleep disturbances in the rTMS group on subjective measures.
Analysis of this study's double-blind, sham-controlled design increases the robustness of findings and decreases experimenter bias. Using subjective and objective sleep measures improves the validity of results, as these different dimensions of sleep experience are captured (Lin et al., 2023). Additionally, the DMPFC is theoretically justified as a region of arousal regulation and emotional control, as such mechanisms are known to be doped controlled in insomnia.
However, the study also has notable limitations. Further, participants continued with their existing pharmacological treatments, which created a confounding variable, and it remains unclear whether the observed effects were due to rTMS alone or in combination with medication (Scott et al., 2023). Besides that, for the duration of treatment (two weeks), observation of longer-term or cumulative impact may not be possible. Furthermore, the sample size is small, especially the sham group, which may have reduced statistical power. Further, PSG is the gold standard for sleep architecture, but this study would have been strengthened with a follow-up to see whether rTMS effects are durable (Lin et al., 2023).
Although these limitations exist, these results suggest positive evidence that treating primary insomnia with low-frequency rTMS to the left DMPFC may be viable (Lin et al., 2023). These findings indicate that this neuromodulation positively influences sleep maintenance and sleep quality perception by reducing wakefulness and rumination cortical region hyperactivity (Scott et al., 2023). It also adds to the research that argues for the incorporation of biopsychological strategies as an additional approach to the treatment of sleep disorders, playing an essential role in the use of brain-based therapies combined with conventional pharmacological methods.
Summary
The purpose of this literature review was to explore biopsychological research in current times that can help us understand the neural correlates of language. Its experimental focus was on two key domains of predictive processing of naturalistic linguistic utterances: neural processing of linguistic collocations and language processing using EEG, fMRI, ECoG and rTMS methods (Shain et al., 2019). Thus, it has revealed the complexity of language representation in the brain and the power of interdisciplinary methodology to inform understanding of how the brain encodes, predicts, and responds to linguistically relevant input.
In the first of these reviewed, Garibyan et al. (2022) evaluated, at the level of empirical investigations, how the brain processes collocations, previously co-occurring word pairs, while processing continuous speech. The EEG study discovered collocations produced less maximal N400 amplitude than non-collocational phrases. An interesting finding is that if these linguistic units are grouped lexically like a word, the brain is more efficient at processing them (Berberyan et al., 2021). Such higher top speed is consistent with the notion that frequent, predictable word combinations are more straightforward to integrate semantically, and the reduced N400 component is usually linked to facilitated semantic processing. Nevertheless, this first study proposes that the brain does not process language in isolation but is sensitive to higher order linguistic structures, a point that resists the view that 'collocations' are only psychologically and neurally unreal. The findings are one of a growing panoply of evidence that semantic predictability and familiarity guide initial neural processing of language.
The second study, by Schrimpf et al., (2021) employed fMRI and ECoG to question the human participants to compare neural activity and predictions coming from advanced artificial language models. The purpose was to see if computational models were trained to predict what words would come next and to describe how the human brain processes language. As proven by their study, neural responses to language models have been more strongly aligned with language models of greater predictive accuracy, including the inferior frontal gyri and superior temporal sulci (Berberyan et al., 2021). This is in line with the predictive processing framework, which is that the brain guesses what will come next from the statistics of language. Integrative modelling, in combining measurements from different scales, can offer strong evidence of a correspondence between model simulations and neural activity in human brain, and provide evidence to the utility of integrative modelling in forging a deeper understanding of brain and language relationships. The surprising thing about these findings is that prediction seems central, indeed not optional, to on-line language comprehension.
Together, these two studies point to a dynamic, predictive, and context-sensitive model of language processing in the brain. They show that the brain is not a passive decoder for incoming language but actively engages linguistic knowledge and anticipatory mechanisms in producing interpretation (Khurana et al., 2022). It deviates from classical static notions of neuronal language areas towards distributed and flexible representations.
The remaining two studies by Perrier et al. (2015) and Lin et al., (2023), although dealing with sleep and not language, provide useful parallels to understand how the biopsychological methods can uncover the neural basis of cognitive functions. Perrier et al. have utilised EEG to identify EEG spectral patterns distinguishing patients with primary insomnia in the prefrontal cortex and thus established a neurophysiological marker of disrupted sleep. Like Lin et al, (2023), they rocked their socks with rTMS to the same region and observed observable objective changes to sleep parameters with the behavioural outcome of sleep efficiency. While these findings do not directly address language as a topic of study, the broader theme of this review finds support in these findings, which point to the ability to study, understand, and alter the brain’s structure and function by using biopsychological methods, in turn suggesting the possibility of impact in the practice of clinical work (Khurana et al., 2022).
These studies make the case for the importance of biopsychological methods in describing language. Both techniques (EEG and fMRI & ECoG) are different but complementary techniques to understand when linguistic processes occur (EEG) or where (fMRI & ECoG) they are occurring. Additionally, non-invasive stimulation such as rTMS allows for possible intervention in the future, of course not only for sleep disorders but potentially in connection with language disorders, such as aphasia or dyslexia(Finisguerra et al., 2019). The second theme concerns a methodological shift in neuroscience toward more holistic and theoretically grounded approaches integrating computational models with brain data.
References
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