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Senkowski D, Engel AK. Multi-timescale neural dynamics for multisensory integration. Nat Rev Neurosci 2024; 25:625-642. [PMID: 39090214 DOI: 10.1038/s41583-024-00845-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/02/2024] [Indexed: 08/04/2024]
Abstract
Carrying out any everyday task, be it driving in traffic, conversing with friends or playing basketball, requires rapid selection, integration and segregation of stimuli from different sensory modalities. At present, even the most advanced artificial intelligence-based systems are unable to replicate the multisensory processes that the human brain routinely performs, but how neural circuits in the brain carry out these processes is still not well understood. In this Perspective, we discuss recent findings that shed fresh light on the oscillatory neural mechanisms that mediate multisensory integration (MI), including power modulations, phase resetting, phase-amplitude coupling and dynamic functional connectivity. We then consider studies that also suggest multi-timescale dynamics in intrinsic ongoing neural activity and during stimulus-driven bottom-up and cognitive top-down neural network processing in the context of MI. We propose a new concept of MI that emphasizes the critical role of neural dynamics at multiple timescales within and across brain networks, enabling the simultaneous integration, segregation, hierarchical structuring and selection of information in different time windows. To highlight predictions from our multi-timescale concept of MI, real-world scenarios in which multi-timescale processes may coordinate MI in a flexible and adaptive manner are considered.
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Affiliation(s)
- Daniel Senkowski
- Department of Psychiatry and Neurosciences, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Andreas K Engel
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
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2
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Antonello R, Huth A. Predictive Coding or Just Feature Discovery? An Alternative Account of Why Language Models Fit Brain Data. NEUROBIOLOGY OF LANGUAGE (CAMBRIDGE, MASS.) 2024; 5:64-79. [PMID: 38645616 PMCID: PMC11025645 DOI: 10.1162/nol_a_00087] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 10/26/2022] [Indexed: 04/23/2024]
Abstract
Many recent studies have shown that representations drawn from neural network language models are extremely effective at predicting brain responses to natural language. But why do these models work so well? One proposed explanation is that language models and brains are similar because they have the same objective: to predict upcoming words before they are perceived. This explanation is attractive because it lends support to the popular theory of predictive coding. We provide several analyses that cast doubt on this claim. First, we show that the ability to predict future words does not uniquely (or even best) explain why some representations are a better match to the brain than others. Second, we show that within a language model, representations that are best at predicting future words are strictly worse brain models than other representations. Finally, we argue in favor of an alternative explanation for the success of language models in neuroscience: These models are effective at predicting brain responses because they generally capture a wide variety of linguistic phenomena.
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Affiliation(s)
- Richard Antonello
- Department of Computer Science, University of Texas at Austin, Austin, TX, USA
| | - Alexander Huth
- Department of Computer Science, University of Texas at Austin, Austin, TX, USA
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3
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Nourski KV, Steinschneider M, Rhone AE, Dappen ER, Kawasaki H, Howard MA. Processing of auditory novelty in human cortex during a semantic categorization task. Hear Res 2024; 444:108972. [PMID: 38359485 PMCID: PMC10984345 DOI: 10.1016/j.heares.2024.108972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 02/05/2024] [Accepted: 02/10/2024] [Indexed: 02/17/2024]
Abstract
Auditory semantic novelty - a new meaningful sound in the context of a predictable acoustical environment - can probe neural circuits involved in language processing. Aberrant novelty detection is a feature of many neuropsychiatric disorders. This large-scale human intracranial electrophysiology study examined the spatial distribution of gamma and alpha power and auditory evoked potentials (AEP) associated with responses to unexpected words during performance of semantic categorization tasks. Participants were neurosurgical patients undergoing monitoring for medically intractable epilepsy. Each task included repeatedly presented monosyllabic words from different talkers ("common") and ten words presented only once ("novel"). Targets were words belonging to a specific semantic category. Novelty effects were defined as differences between neural responses to novel and common words. Novelty increased task difficulty and was associated with augmented gamma, suppressed alpha power, and AEP differences broadly distributed across the cortex. Gamma novelty effect had the highest prevalence in planum temporale, posterior superior temporal gyrus (STG) and pars triangularis of the inferior frontal gyrus; alpha in anterolateral Heschl's gyrus (HG), anterior STG and middle anterior cingulate cortex; AEP in posteromedial HG, lower bank of the superior temporal sulcus, and planum polare. Gamma novelty effect had a higher prevalence in dorsal than ventral auditory-related areas. Novelty effects were more pronounced in the left hemisphere. Better novel target detection was associated with reduced gamma novelty effect within auditory cortex and enhanced gamma effect within prefrontal and sensorimotor cortex. Alpha and AEP novelty effects were generally more prevalent in better performing participants. Multiple areas, including auditory cortex on the superior temporal plane, featured AEP novelty effect within the time frame of P3a and N400 scalp-recorded novelty-related potentials. This work provides a detailed account of auditory novelty in a paradigm that directly examined brain regions associated with semantic processing. Future studies may aid in the development of objective measures to assess the integrity of semantic novelty processing in clinical populations.
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Affiliation(s)
- Kirill V Nourski
- Department of Neurosurgery, The University of Iowa, Iowa City, IA 52242, United States; Iowa Neuroscience Institute, The University of Iowa, Iowa City, IA 52242, United States.
| | - Mitchell Steinschneider
- Department of Neurosurgery, The University of Iowa, Iowa City, IA 52242, United States; Departments of Neurology, Neuroscience, and Pediatrics, Albert Einstein College of Medicine, Bronx, NY 10461, United States
| | - Ariane E Rhone
- Department of Neurosurgery, The University of Iowa, Iowa City, IA 52242, United States
| | - Emily R Dappen
- Department of Neurosurgery, The University of Iowa, Iowa City, IA 52242, United States; Iowa Neuroscience Institute, The University of Iowa, Iowa City, IA 52242, United States
| | - Hiroto Kawasaki
- Department of Neurosurgery, The University of Iowa, Iowa City, IA 52242, United States
| | - Matthew A Howard
- Department of Neurosurgery, The University of Iowa, Iowa City, IA 52242, United States; Iowa Neuroscience Institute, The University of Iowa, Iowa City, IA 52242, United States; Pappajohn Biomedical Institute, The University of Iowa, Iowa City, IA 52242, United States
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4
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Ryskin R, Nieuwland MS. Prediction during language comprehension: what is next? Trends Cogn Sci 2023; 27:1032-1052. [PMID: 37704456 DOI: 10.1016/j.tics.2023.08.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 08/03/2023] [Accepted: 08/04/2023] [Indexed: 09/15/2023]
Abstract
Prediction is often regarded as an integral aspect of incremental language comprehension, but little is known about the cognitive architectures and mechanisms that support it. We review studies showing that listeners and readers use all manner of contextual information to generate multifaceted predictions about upcoming input. The nature of these predictions may vary between individuals owing to differences in language experience, among other factors. We then turn to unresolved questions which may guide the search for the underlying mechanisms. (i) Is prediction essential to language processing or an optional strategy? (ii) Are predictions generated from within the language system or by domain-general processes? (iii) What is the relationship between prediction and memory? (iv) Does prediction in comprehension require simulation via the production system? We discuss promising directions for making progress in answering these questions and for developing a mechanistic understanding of prediction in language.
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Affiliation(s)
- Rachel Ryskin
- Department of Cognitive and Information Sciences, University of California Merced, 5200 Lake Road, Merced, CA 95343, USA.
| | - Mante S Nieuwland
- Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands; Donders Institute for Brain, Cognition, and Behaviour, Nijmegen, The Netherlands
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5
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Deniz F, Tseng C, Wehbe L, Dupré la Tour T, Gallant JL. Semantic Representations during Language Comprehension Are Affected by Context. J Neurosci 2023; 43:3144-3158. [PMID: 36973013 PMCID: PMC10146529 DOI: 10.1523/jneurosci.2459-21.2023] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 02/17/2023] [Accepted: 02/26/2023] [Indexed: 03/29/2023] Open
Abstract
The meaning of words in natural language depends crucially on context. However, most neuroimaging studies of word meaning use isolated words and isolated sentences with little context. Because the brain may process natural language differently from how it processes simplified stimuli, there is a pressing need to determine whether prior results on word meaning generalize to natural language. fMRI was used to record human brain activity while four subjects (two female) read words in four conditions that vary in context: narratives, isolated sentences, blocks of semantically similar words, and isolated words. We then compared the signal-to-noise ratio (SNR) of evoked brain responses, and we used a voxelwise encoding modeling approach to compare the representation of semantic information across the four conditions. We find four consistent effects of varying context. First, stimuli with more context evoke brain responses with higher SNR across bilateral visual, temporal, parietal, and prefrontal cortices compared with stimuli with little context. Second, increasing context increases the representation of semantic information across bilateral temporal, parietal, and prefrontal cortices at the group level. In individual subjects, only natural language stimuli consistently evoke widespread representation of semantic information. Third, context affects voxel semantic tuning. Finally, models estimated using stimuli with little context do not generalize well to natural language. These results show that context has large effects on the quality of neuroimaging data and on the representation of meaning in the brain. Thus, neuroimaging studies that use stimuli with little context may not generalize well to the natural regime.SIGNIFICANCE STATEMENT Context is an important part of understanding the meaning of natural language, but most neuroimaging studies of meaning use isolated words and isolated sentences with little context. Here, we examined whether the results of neuroimaging studies that use out-of-context stimuli generalize to natural language. We find that increasing context improves the quality of neuro-imaging data and changes where and how semantic information is represented in the brain. These results suggest that findings from studies using out-of-context stimuli may not generalize to natural language used in daily life.
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Affiliation(s)
- Fatma Deniz
- Helen Wills Neuroscience Institute, University of California, Berkeley, California 94720
- Institute of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin 10623, Germany
| | - Christine Tseng
- Helen Wills Neuroscience Institute, University of California, Berkeley, California 94720
| | - Leila Wehbe
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
| | - Tom Dupré la Tour
- Helen Wills Neuroscience Institute, University of California, Berkeley, California 94720
| | - Jack L Gallant
- Helen Wills Neuroscience Institute, University of California, Berkeley, California 94720
- Department of Psychology, University of California, Berkeley, California 94720
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6
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Nakai T, Nishimoto S. Artificial neural network modelling of the neural population code underlying mathematical operations. Neuroimage 2023; 270:119980. [PMID: 36848969 DOI: 10.1016/j.neuroimage.2023.119980] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 02/10/2023] [Accepted: 02/23/2023] [Indexed: 02/28/2023] Open
Abstract
Mathematical operations have long been regarded as a sparse, symbolic process in neuroimaging studies. In contrast, advances in artificial neural networks (ANN) have enabled extracting distributed representations of mathematical operations. Recent neuroimaging studies have compared distributed representations of the visual, auditory and language domains in ANNs and biological neural networks (BNNs). However, such a relationship has not yet been examined in mathematics. Here we hypothesise that ANN-based distributed representations can explain brain activity patterns of symbolic mathematical operations. We used the fMRI data of a series of mathematical problems with nine different combinations of operators to construct voxel-wise encoding/decoding models using both sparse operator and latent ANN features. Representational similarity analysis demonstrated shared representations between ANN and BNN, an effect particularly evident in the intraparietal sulcus. Feature-brain similarity (FBS) analysis served to reconstruct a sparse representation of mathematical operations based on distributed ANN features in each cortical voxel. Such reconstruction was more efficient when using features from deeper ANN layers. Moreover, latent ANN features allowed the decoding of novel operators not used during model training from brain activity. The current study provides novel insights into the neural code underlying mathematical thought.
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Affiliation(s)
- Tomoya Nakai
- Center for Information and Neural Networks, National Institute of Information and Communications Technology, Suita, Japan; Lyon Neuroscience Research Center (CRNL), INSERM U1028 - CNRS UMR5292, University of Lyon, Bron, France.
| | - Shinji Nishimoto
- Center for Information and Neural Networks, National Institute of Information and Communications Technology, Suita, Japan; Graduate School of Frontier Biosciences, Osaka University, Suita, Japan; Graduate School of Medicine, Osaka University, Suita, Japan
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7
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Benetti S, Ferrari A, Pavani F. Multimodal processing in face-to-face interactions: A bridging link between psycholinguistics and sensory neuroscience. Front Hum Neurosci 2023; 17:1108354. [PMID: 36816496 PMCID: PMC9932987 DOI: 10.3389/fnhum.2023.1108354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 01/11/2023] [Indexed: 02/05/2023] Open
Abstract
In face-to-face communication, humans are faced with multiple layers of discontinuous multimodal signals, such as head, face, hand gestures, speech and non-speech sounds, which need to be interpreted as coherent and unified communicative actions. This implies a fundamental computational challenge: optimally binding only signals belonging to the same communicative action while segregating signals that are not connected by the communicative content. How do we achieve such an extraordinary feat, reliably, and efficiently? To address this question, we need to further move the study of human communication beyond speech-centred perspectives and promote a multimodal approach combined with interdisciplinary cooperation. Accordingly, we seek to reconcile two explanatory frameworks recently proposed in psycholinguistics and sensory neuroscience into a neurocognitive model of multimodal face-to-face communication. First, we introduce a psycholinguistic framework that characterises face-to-face communication at three parallel processing levels: multiplex signals, multimodal gestalts and multilevel predictions. Second, we consider the recent proposal of a lateral neural visual pathway specifically dedicated to the dynamic aspects of social perception and reconceive it from a multimodal perspective ("lateral processing pathway"). Third, we reconcile the two frameworks into a neurocognitive model that proposes how multiplex signals, multimodal gestalts, and multilevel predictions may be implemented along the lateral processing pathway. Finally, we advocate a multimodal and multidisciplinary research approach, combining state-of-the-art imaging techniques, computational modelling and artificial intelligence for future empirical testing of our model.
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Affiliation(s)
- Stefania Benetti
- Centre for Mind/Brain Sciences, University of Trento, Trento, Italy,Interuniversity Research Centre “Cognition, Language, and Deafness”, CIRCLeS, Catania, Italy,*Correspondence: Stefania Benetti,
| | - Ambra Ferrari
- Max Planck Institute for Psycholinguistics, Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Francesco Pavani
- Centre for Mind/Brain Sciences, University of Trento, Trento, Italy,Interuniversity Research Centre “Cognition, Language, and Deafness”, CIRCLeS, Catania, Italy
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8
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Long Y, Zhong M, Aili R, Zhang H, Fang X, Lu C. Transcranial direct current stimulation of the right anterior temporal lobe changes interpersonal neural synchronization and shared mental processes. Brain Stimul 2023; 16:28-39. [PMID: 36572209 DOI: 10.1016/j.brs.2022.12.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 12/08/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Previous studies have shown that interpersonal neural synchronization (INS) is a ubiquitous phenomenon between individuals, and recent studies have further demonstrated close associations between INS and shared external sensorimotor input and/or internal mental processes within a dyad. However, most previous studies have employed an observational approach to describe the behavior-INS correlation, leading to difficulties in causally disentangling the relationship among INS, external sensorimotor input and the internal mental process. OBJECTIVE/HYPOTHESIS The present study aimed to directly change the level of INS through anodal transcranial direct current stimulation (tDCS) to test whether the change in INS would directly impact the internal mental process (Hypothesis 1) or indirectly through external sensorimotor input; the interaction behaviors were also changed (Hypothesis 2) or not (Hypothesis 3). METHODS Thirty pairs of romantically involved heterosexual couples were recruited for a within-subjects design. Three conditions were assessed: a true stimulation condition with 20-min anodal high-definition tDCS to the right anterior temporal lobe (rATL) of women before they communicated with their partners, a sham stimulation condition and a control brain region stimulation condition. The comparison between the true and sham or control brain region conditions allows us to detect the true effect of brain stimulation on INS. Functional near-infrared spectroscopy (fNIRS) hyperscanning was used to simultaneously collect dyadic participants' hemodynamic signals during communication. INS, empathy, and interaction behaviors were examined and compared among different stimulation conditions. RESULTS True brain stimulation significantly decreased INS between the rATL of the women and sensorimotor cortex (SMC) of the men compared to the sham stimulation condition (t(27.8) = -2.821, P = 0.009, d = 0.714) and control brain region stimulation condition (t(27.2) = -2.606, P = 0.015, d = 0.664) during communication. It also significantly decreased the level of emotional empathy (F(2,145) = 6.893, P = 0.001) but did not change sensorimotor processes, such as verbal or nonverbal interaction behaviors. However, nonverbal behaviors mediated the relationship between the changes in INS and emotional empathy (lower limit confidence interval = 0.01, upper limit confidence interval = 2.66). CONCLUSION(S) These findings support the third hypothesis, suggesting that INS is associated with the shared internal mental process indirectly via the sensorimotor process, but the sensorimotor process itself does not covary with the INS and the associated internal mental process. These results provide new insight into the hierarchical architecture of dual-brain function from a bottom-up perspective.
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Affiliation(s)
- Yuhang Long
- Institute of Developmental Psychology, Faculty of Psychology, Beijing Normal University, Beijing, 100875, China; State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Miao Zhong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Ruhuiya Aili
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Huan Zhang
- Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin, 300387, China
| | - Xiaoyi Fang
- Institute of Developmental Psychology, Faculty of Psychology, Beijing Normal University, Beijing, 100875, China
| | - Chunming Lu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.
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9
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de Lange FP, Schmitt LM, Heilbron M. Reconstructing the predictive architecture of the mind and brain. Trends Cogn Sci 2022; 26:1018-1019. [PMID: 36150970 DOI: 10.1016/j.tics.2022.08.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 06/18/2022] [Accepted: 08/15/2022] [Indexed: 01/12/2023]
Abstract
Predictive processing has become an influential framework in cognitive neuroscience. However, it often lacks specificity and direct empirical support. How can we probe the nature and limits of the predictive brain? We highlight the potential of recent advances in artificial intelligence (AI) for providing a richer and more computationally explicit test of this theory of cortical function.
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Affiliation(s)
- Floris P de Lange
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Kapittelweg 29, 6525, EN, Nijmegen, The Netherlands.
| | - Lea-Maria Schmitt
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Kapittelweg 29, 6525, EN, Nijmegen, The Netherlands
| | - Micha Heilbron
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Kapittelweg 29, 6525, EN, Nijmegen, The Netherlands
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10
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Heilbron M, Armeni K, Schoffelen JM, Hagoort P, de Lange FP. A hierarchy of linguistic predictions during natural language comprehension. Proc Natl Acad Sci U S A 2022; 119:e2201968119. [PMID: 35921434 PMCID: PMC9371745 DOI: 10.1073/pnas.2201968119] [Citation(s) in RCA: 75] [Impact Index Per Article: 37.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 06/28/2022] [Indexed: 02/05/2023] Open
Abstract
Understanding spoken language requires transforming ambiguous acoustic streams into a hierarchy of representations, from phonemes to meaning. It has been suggested that the brain uses prediction to guide the interpretation of incoming input. However, the role of prediction in language processing remains disputed, with disagreement about both the ubiquity and representational nature of predictions. Here, we address both issues by analyzing brain recordings of participants listening to audiobooks, and using a deep neural network (GPT-2) to precisely quantify contextual predictions. First, we establish that brain responses to words are modulated by ubiquitous predictions. Next, we disentangle model-based predictions into distinct dimensions, revealing dissociable neural signatures of predictions about syntactic category (parts of speech), phonemes, and semantics. Finally, we show that high-level (word) predictions inform low-level (phoneme) predictions, supporting hierarchical predictive processing. Together, these results underscore the ubiquity of prediction in language processing, showing that the brain spontaneously predicts upcoming language at multiple levels of abstraction.
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Affiliation(s)
- Micha Heilbron
- Donders Institute, Radboud University, 6525 EN Nijmegen, The Netherlands
- Max Planck Institute for Psycholinguistics, 6525 XD Nijmegen, The Netherlands
| | - Kristijan Armeni
- Donders Institute, Radboud University, 6525 EN Nijmegen, The Netherlands
| | | | - Peter Hagoort
- Donders Institute, Radboud University, 6525 EN Nijmegen, The Netherlands
- Max Planck Institute for Psycholinguistics, 6525 XD Nijmegen, The Netherlands
| | - Floris P. de Lange
- Donders Institute, Radboud University, 6525 EN Nijmegen, The Netherlands
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11
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Heilbron M, Armeni K, Schoffelen JM, Hagoort P, de Lange FP. A hierarchy of linguistic predictions during natural language comprehension. Proc Natl Acad Sci U S A 2022; 119:e2201968119. [PMID: 35921434 DOI: 10.1101/2020.12.03.410399] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/21/2023] Open
Abstract
Understanding spoken language requires transforming ambiguous acoustic streams into a hierarchy of representations, from phonemes to meaning. It has been suggested that the brain uses prediction to guide the interpretation of incoming input. However, the role of prediction in language processing remains disputed, with disagreement about both the ubiquity and representational nature of predictions. Here, we address both issues by analyzing brain recordings of participants listening to audiobooks, and using a deep neural network (GPT-2) to precisely quantify contextual predictions. First, we establish that brain responses to words are modulated by ubiquitous predictions. Next, we disentangle model-based predictions into distinct dimensions, revealing dissociable neural signatures of predictions about syntactic category (parts of speech), phonemes, and semantics. Finally, we show that high-level (word) predictions inform low-level (phoneme) predictions, supporting hierarchical predictive processing. Together, these results underscore the ubiquity of prediction in language processing, showing that the brain spontaneously predicts upcoming language at multiple levels of abstraction.
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Affiliation(s)
- Micha Heilbron
- Donders Institute, Radboud University, 6525 EN Nijmegen, The Netherlands
- Max Planck Institute for Psycholinguistics, 6525 XD Nijmegen, The Netherlands
| | - Kristijan Armeni
- Donders Institute, Radboud University, 6525 EN Nijmegen, The Netherlands
| | | | - Peter Hagoort
- Donders Institute, Radboud University, 6525 EN Nijmegen, The Netherlands
- Max Planck Institute for Psycholinguistics, 6525 XD Nijmegen, The Netherlands
| | - Floris P de Lange
- Donders Institute, Radboud University, 6525 EN Nijmegen, The Netherlands
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12
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Age-related differences in the neural network interactions underlying the predictability gain. Cortex 2022; 154:269-286. [DOI: 10.1016/j.cortex.2022.05.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 03/30/2022] [Accepted: 05/03/2022] [Indexed: 11/20/2022]
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13
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Kern P, Heilbron M, de Lange FP, Spaak E. Cortical activity during naturalistic music listening reflects short-range predictions based on long-term experience. eLife 2022; 11:80935. [PMID: 36562532 PMCID: PMC9836393 DOI: 10.7554/elife.80935] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 12/22/2022] [Indexed: 12/24/2022] Open
Abstract
Expectations shape our experience of music. However, the internal model upon which listeners form melodic expectations is still debated. Do expectations stem from Gestalt-like principles or statistical learning? If the latter, does long-term experience play an important role, or are short-term regularities sufficient? And finally, what length of context informs contextual expectations? To answer these questions, we presented human listeners with diverse naturalistic compositions from Western classical music, while recording neural activity using MEG. We quantified note-level melodic surprise and uncertainty using various computational models of music, including a state-of-the-art transformer neural network. A time-resolved regression analysis revealed that neural activity over fronto-temporal sensors tracked melodic surprise particularly around 200ms and 300-500ms after note onset. This neural surprise response was dissociated from sensory-acoustic and adaptation effects. Neural surprise was best predicted by computational models that incorporated long-term statistical learning-rather than by simple, Gestalt-like principles. Yet, intriguingly, the surprise reflected primarily short-range musical contexts of less than ten notes. We present a full replication of our novel MEG results in an openly available EEG dataset. Together, these results elucidate the internal model that shapes melodic predictions during naturalistic music listening.
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Affiliation(s)
- Pius Kern
- Radboud University Nijmegen, Donders Institute for Brain, Cognition and BehaviourNijmegenNetherlands
| | - Micha Heilbron
- Radboud University Nijmegen, Donders Institute for Brain, Cognition and BehaviourNijmegenNetherlands
| | - Floris P de Lange
- Radboud University Nijmegen, Donders Institute for Brain, Cognition and BehaviourNijmegenNetherlands
| | - Eelke Spaak
- Radboud University Nijmegen, Donders Institute for Brain, Cognition and BehaviourNijmegenNetherlands
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