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Open accessJournal ArticleDOI: 10.1038/S41598-021-84597-9

Dissociable electrophysiological measures of natural language processing reveal differences in speech comprehension strategy in healthy ageing

02 Mar 2021-Scientific Reports (Springer Science and Business Media LLC)-Vol. 11, Iss: 1, pp 4963-4963
Abstract: Healthy ageing leads to changes in the brain that impact upon sensory and cognitive processing. It is not fully clear how these changes affect the processing of everyday spoken language. Prediction is thought to play an important role in language comprehension, where information about upcoming words is pre-activated across multiple representational levels. However, evidence from electrophysiology suggests differences in how older and younger adults use context-based predictions, particularly at the level of semantic representation. We investigate these differences during natural speech comprehension by presenting older and younger subjects with continuous, narrative speech while recording their electroencephalogram. We use time-lagged linear regression to test how distinct computational measures of (1) semantic dissimilarity and (2) lexical surprisal are processed in the brains of both groups. Our results reveal dissociable neural correlates of these two measures that suggest differences in how younger and older adults successfully comprehend speech. Specifically, our results suggest that, while younger and older subjects both employ context-based lexical predictions, older subjects are significantly less likely to pre-activate the semantic features relating to upcoming words. Furthermore, across our group of older adults, we show that the weaker the neural signature of this semantic pre-activation mechanism, the lower a subject’s semantic verbal fluency score. We interpret these findings as prediction playing a generally reduced role at a semantic level in the brains of older listeners during speech comprehension and that these changes may be part of an overall strategy to successfully comprehend speech with reduced cognitive resources.

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Topics: Verbal fluency test (57%), Cognition (55%), Comprehension (52%) ... show more

7 results found

Open accessJournal ArticleDOI: 10.3389/FNINS.2021.640502
Lars Meyer1, Lars Meyer2, Peter Lakatos3, Yifei He4Institutions (4)
Abstract: Deficits in language production and comprehension are characteristic of schizophrenia. To date, it remains unclear whether these deficits arise from dysfunctional linguistic knowledge, or dysfunctional predictions derived from the linguistic context. Alternatively, the deficits could be a result of dysfunctional neural tracking of auditory information resulting in decreased auditory information fidelity and even distorted information. Here, we discuss possible ways for clinical neuroscientists to employ neural tracking methodology to independently characterize deficiencies on the auditory-sensory and abstract linguistic levels. This might lead to a mechanistic understanding of the deficits underlying language related disorder(s) in schizophrenia. We propose to combine naturalistic stimulation, measures of speech-brain synchronization, and computational modeling of abstract linguistic knowledge and predictions. These independent but likely interacting assessments may be exploited for an objective and differential diagnosis of schizophrenia, as well as a better understanding of the disorder on the functional level-illustrating the potential of neural tracking methodology as translational tool in a range of psychotic populations.

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6 Citations

Open accessPosted ContentDOI: 10.31234/OSF.IO/JBZ2W
11 May 2021-
Abstract: Cognitive neuroscience has seen an increase in the use of linear modelling techniques for studying the processing of natural, environmental stimuli. The availability of such computational tools has prompted similar investigations in many clinical domains, facilitating the study of cognitive and sensory deficits within an ecologically relevant context. However, studying clinical (and often highly-heterogeneous) cohorts introduces an added layer of complexity to such modelling procedures, leading to an increased risk of improper usage of such techniques and, as a result, inconsistent conclusions. Here, we outline some key methodological considerations for applied research and include worked examples of both simulated and empirical electrophysiological (EEG) data. In particular, we focus on experimental design, data preprocessing and stimulus feature extraction, model design, training and evaluation, and interpretation of model weights. Throughout the paper, we demonstrate how to implement each stage in MATLAB using the mTRF-Toolbox and discuss how to address issues that could arise in applied cognitive neuroscience research. In doing so, we highlight the importance of understanding these more technical points for experimental design and data analysis, and provide a resource for applied and clinical researchers investigating sensory and cognitive processing using ecologically-rich stimuli.

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3 Citations

Open accessPosted ContentDOI: 10.17605/OSF.IO/TV7KG
23 Nov 2020-bioRxiv
Abstract: Older people with hearing problems often experience difficulties understanding speech in the presence of background sound. As a result, they may disengage in social situations, which has been associated with negative psychosocial health outcomes. Measuring listening (dis-)engagement during challenging listening has received little attention thus far. We recruit normal-hearing human adults (both sexes) and investigate how speech intelligibility and engagement during naturalistic story listening is affected by the level of acoustic masking (12-talker babble). In Experiment 1, we observed that word-report scores were above 80% for all but the lowest SNR (-3 dB SNR) we tested, at which performance dropped to 54%. In Experiment 2, we calculated inter-subject correlation (ISC) in electroencephalography (EEG) data to identify dynamic spatial patterns of shared neural activity evoked by the stories. ISC was stronger during story listening compared to rest. The magnitude of ISC was high and stable across all SNRs except for the lowest one, at which it dropped substantially. Comparing ISC and intelligibility directly demonstrated that word-report performance declined more strongly with decreasing SNR compared to ISC. Observing significant ISC despite the presence of background noise suggests that participants were able to remain engaged despite missing segments of the story during especially difficult SNRs. Our work provides a novel approach to observe speech intelligibility and listener engagement using ecologically valid spoken materials, which can be used to investigate (dis)engagement in older adults with hearing impairment.

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2 Citations

Open accessJournal ArticleDOI: 10.3389/FNINS.2021.635126
Abstract: Speech-in-noise comprehension difficulties are common among the elderly population, yet traditional objective measures of speech perception are largely insensitive to this deficit, particularly in the absence of clinical hearing loss. In recent years, a growing body of research in young normal-hearing adults has demonstrated that high-level features related to speech semantics and lexical predictability elicit strong centro-parietal negativity in the EEG signal around 400 ms following the word onset. Here we investigate effects of age on cortical tracking of these word-level features within a two-talker speech mixture, and their relationship with self-reported difficulties with speech-in-noise understanding. While undergoing EEG recordings, younger and older adult participants listened to a continuous narrative story in the presence of a distractor story. We then utilized forward encoding models to estimate cortical tracking of four speech features: (1) word onsets, (2) "semantic" dissimilarity of each word relative to the preceding context, (3) lexical surprisal for each word, and (4) overall word audibility. Our results revealed robust tracking of all features for attended speech, with surprisal and word audibility showing significantly stronger contributions to neural activity than dissimilarity. Additionally, older adults exhibited significantly stronger tracking of word-level features than younger adults, especially over frontal electrode sites, potentially reflecting increased listening effort. Finally, neuro-behavioral analyses revealed trends of a negative relationship between subjective speech-in-noise perception difficulties and the model goodness-of-fit for attended speech, as well as a positive relationship between task performance and the goodness-of-fit, indicating behavioral relevance of these measures. Together, our results demonstrate the utility of modeling cortical responses to multi-talker speech using complex, word-level features and the potential for their use to study changes in speech processing due to aging and hearing loss.

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Topics: Speech processing (63%), Speech perception (60%)

1 Citations

Open accessPosted ContentDOI: 10.1101/2020.11.20.391227
20 Nov 2020-bioRxiv
Abstract: Prior knowledge facilitates perception and allows us to interpret our sensory environment. However, the neural mechanisms underlying this process remain unclear. Theories of predictive coding propose that feedback connections between cortical levels carry predictions about upcoming sensory events whereas feedforward connections carry the error between the prediction and the sensory input. Although predictive coding has gained much ground as a viable mechanism for perception, in the context spoken language comprehension it lacks empirical support using more naturalistic stimuli. In this study, we investigated theories of predictive coding using continuous, everyday speech. EEG recordings from human participants listening to an audiobook were analysed using a 2-stage regression framework. This tested the effect of top-down linguistic information, estimated using computational language models, on the bottom-up encoding of acoustic and phonetic speech features. Our results show enhanced encoding of both semantic predictions and surprising words, based on preceding context. This suggests that signals pertaining to prediction and error units can be observed in the same electrophysiological responses to natural speech. In addition, temporal analysis of these signals reveals support for theories of predictive coding that propose that perception is first biased towards what is expected followed by what is informative.

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Topics: Perception (53%), Language model (52%), Context (language use) (50%)

1 Citations


98 results found

Proceedings ArticleDOI: 10.3115/V1/D14-1162
01 Oct 2014-
Abstract: Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arithmetic, but the origin of these regularities has remained opaque. We analyze and make explicit the model properties needed for such regularities to emerge in word vectors. The result is a new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods. Our model efficiently leverages statistical information by training only on the nonzero elements in a word-word cooccurrence matrix, rather than on the entire sparse matrix or on individual context windows in a large corpus. The model produces a vector space with meaningful substructure, as evidenced by its performance of 75% on a recent word analogy task. It also outperforms related models on similarity tasks and named entity recognition.

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Topics: Word2vec (64%), Word embedding (56%), Sparse matrix (54%) ... show more

23,307 Citations

Open accessJournal ArticleDOI: 10.1162/153244303322533223
Abstract: A goal of statistical language modeling is to learn the joint probability function of sequences of words in a language. This is intrinsically difficult because of the curse of dimensionality: a word sequence on which the model will be tested is likely to be different from all the word sequences seen during training. Traditional but very successful approaches based on n-grams obtain generalization by concatenating very short overlapping sequences seen in the training set. We propose to fight the curse of dimensionality by learning a distributed representation for words which allows each training sentence to inform the model about an exponential number of semantically neighboring sentences. The model learns simultaneously (1) a distributed representation for each word along with (2) the probability function for word sequences, expressed in terms of these representations. Generalization is obtained because a sequence of words that has never been seen before gets high probability if it is made of words that are similar (in the sense of having a nearby representation) to words forming an already seen sentence. Training such large models (with millions of parameters) within a reasonable time is itself a significant challenge. We report on experiments using neural networks for the probability function, showing on two text corpora that the proposed approach significantly improves on state-of-the-art n-gram models, and that the proposed approach allows to take advantage of longer contexts.

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Topics: Language model (63%), Cache language model (61%), Word embedding (59%) ... show more

6,194 Citations

Journal ArticleDOI: 10.1037/0033-295X.103.3.403
Timothy A. Salthouse1Institutions (1)
Abstract: A theory is proposed to account for some of the age-related differences reported in measures of Type A or fluid cognition. The central hypothesis in the theory is that increased age in adulthood is associated with a decrease in the speed with which many processing operations can be executed and that this reduction in speed leads to impairments in cognitive functioning because of what are termed the limited time mechanism and the simultaneity mechanism. That is, cognitive performance is degraded when processing is slow because relevant operations cannot be successfully executed (limited time) and because the products of early processing may no longer be available when later processing is complete (simultaneity). Several types of evidence, such as the discovery of considerable shared age-related variance across various measures of speed and large attenuation of the age-related influences on cognitive measures after statistical control of measures of speed, are consistent with this theory.

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Topics: Simultaneity (54%), Cognition (53%)

4,773 Citations

Journal ArticleDOI: 10.1126/SCIENCE.7350657
Marta Kutas, Steven A. Hillyard1Institutions (1)
11 Jan 1980-Science
Abstract: In a sentence reading task, words that occurred out of context were associated with specific types of event-related brain potentials. Words that were physically aberrant (larger than normal) elecited a late positive series of potentials, whereas semantically inappropriate words elicited a late negative wave (N400). The N400 wave may be an electrophysiological sign of the "reprocessing" of semantically anomalous information.

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Topics: N400 (58%), Prediction in language comprehension (55%), Late positive component (54%) ... show more

4,014 Citations

Open accessJournal ArticleDOI: 10.1093/CERCOR/BHP055
01 Dec 2009-Cerebral Cortex
Abstract: Semantic memory refers to knowledge about people, objects, actions, relations, self, and culture acquired through experience. The neural systems that store and retrieve this information have been studied for many years, but a consensus regarding their identity has not been reached. Using strict inclusion criteria, we analyzed 120 functional neuroimaging studies focusing on semantic processing. Reliable areas of activation in these studies were identified using the activation likelihood estimate (ALE) technique. These activations formed a distinct, left-lateralized network comprised of 7 regions: posterior inferior parietal lobe, middle temporal gyrus, fusiform and parahippocampal gyri, dorsomedial prefrontal cortex, inferior frontal gyrus, ventromedial prefrontal cortex, and posterior cingulate gyrus. Secondary analyses showed specific subregions of this network associated with knowledge of actions, manipulable artifacts, abstract concepts, and concrete concepts. The cortical regions involved in semantic processing can be grouped into 3 broad categories: posterior multimodal and heteromodal association cortex, heteromodal prefrontal cortex, and medial limbic regions. The expansion of these regions in the human relative to the nonhuman primate brain may explain uniquely human capacities to use language productively, plan, solve problems, and create cultural and technological artifacts, all of which depend on the fluid and efficient retrieval and manipulation of semantic knowledge.

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Topics: Semantic memory (63%), Angular gyrus (60%), Inferior frontal gyrus (60%) ... show more

2,841 Citations

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