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Verhoef T, Marghetis T, Walker E, Coulson S. Brain responses to a lab-evolved artificial language with space-time metaphors. Cognition 2024; 246:105763. [PMID: 38442586 DOI: 10.1016/j.cognition.2024.105763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 01/05/2024] [Accepted: 02/26/2024] [Indexed: 03/07/2024]
Abstract
What is the connection between the cultural evolution of a language and the rapid processing response to that language in the brains of individual learners? In an iterated communication study that was conducted previously, participants were asked to communicate temporal concepts such as "tomorrow," "day after," "year," and "past" using vertical movements recorded on a touch screen. Over time, participants developed simple artificial 'languages' that used space metaphorically to communicate in nuanced ways about time. Some conventions appeared rapidly and universally (e.g., using larger vertical movements to convey greater temporal durations). Other conventions required extensive social interaction and exhibited idiosyncratic variation (e.g., using vertical location to convey past or future). Here we investigate whether the brain's response during acquisition of such a language reflects the process by which the language's conventions originally evolved. We recorded participants' EEG as they learned one of these artificial space-time languages. Overall, the brain response to this artificial communication system was language-like, with, for instance, violations to the system's conventions eliciting an N400-like component. Over the course of learning, participants' brain responses developed in ways that paralleled the process by which the language had originally evolved, with early neural sensitivity to violations of a rapidly-evolving universal convention, and slowly developing neural sensitivity to an idiosyncratic convention that required slow social negotiation to emerge. This study opens up exciting avenues of future work to disentangle how neural biases influence learning and transmission in the emergence of structure in language.
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Affiliation(s)
- Tessa Verhoef
- Leiden Institute of Advanced Computer Science, Leiden University, Gorlaeus Building, Einsteinweg 55, 2333 CC Leiden, the Netherlands; Department of Cognitive Science, University of California, San Diego, Mail Code 0515; 9500, Gilman Drive, La Jolla, CA 92093-0515, USA.
| | - Tyler Marghetis
- Department of Cognitive and Information Sciences, University of California, Merced, 5200 North Lake Rd., Merced, CA 95343, USA
| | - Esther Walker
- Department of Cognitive Science, University of California, San Diego, Mail Code 0515; 9500, Gilman Drive, La Jolla, CA 92093-0515, USA
| | - Seana Coulson
- Department of Cognitive Science, University of California, San Diego, Mail Code 0515; 9500, Gilman Drive, La Jolla, CA 92093-0515, USA
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2
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Bianco R, Zuk NJ, Bigand F, Quarta E, Grasso S, Arnese F, Ravignani A, Battaglia-Mayer A, Novembre G. Neural encoding of musical expectations in a non-human primate. Curr Biol 2024; 34:444-450.e5. [PMID: 38176416 DOI: 10.1016/j.cub.2023.12.019] [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: 07/28/2023] [Revised: 10/26/2023] [Accepted: 12/07/2023] [Indexed: 01/06/2024]
Abstract
The appreciation of music is a universal trait of humankind.1,2,3 Evidence supporting this notion includes the ubiquity of music across cultures4,5,6,7 and the natural predisposition toward music that humans display early in development.8,9,10 Are we musical animals because of species-specific predispositions? This question cannot be answered by relying on cross-cultural or developmental studies alone, as these cannot rule out enculturation.11 Instead, it calls for cross-species experiments testing whether homologous neural mechanisms underlying music perception are present in non-human primates. We present music to two rhesus monkeys, reared without musical exposure, while recording electroencephalography (EEG) and pupillometry. Monkeys exhibit higher engagement and neural encoding of expectations based on the previously seeded musical context when passively listening to real music as opposed to shuffled controls. We then compare human and monkey neural responses to the same stimuli and find a species-dependent contribution of two fundamental musical features-pitch and timing12-in generating expectations: while timing- and pitch-based expectations13 are similarly weighted in humans, monkeys rely on timing rather than pitch. Together, these results shed light on the phylogeny of music perception. They highlight monkeys' capacity for processing temporal structures beyond plain acoustic processing, and they identify a species-dependent contribution of time- and pitch-related features to the neural encoding of musical expectations.
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Affiliation(s)
- Roberta Bianco
- Neuroscience of Perception & Action Lab, Italian Institute of Technology, Viale Regina Elena 291, 00161 Rome, Italy.
| | - Nathaniel J Zuk
- Department of Psychology, Nottingham Trent University, 50 Shakespeare Street, Nottingham NG1 4FQ, UK
| | - Félix Bigand
- Neuroscience of Perception & Action Lab, Italian Institute of Technology, Viale Regina Elena 291, 00161 Rome, Italy
| | - Eros Quarta
- Department of Physiology and Pharmacology, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
| | - Stefano Grasso
- Department of Physiology and Pharmacology, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
| | - Flavia Arnese
- Neuroscience of Perception & Action Lab, Italian Institute of Technology, Viale Regina Elena 291, 00161 Rome, Italy
| | - Andrea Ravignani
- Comparative Bioacoustics Group, Max Planck Institute for Psycholinguistics, Wundtlaan 1, 6525 XD Nijmegen, the Netherlands; Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music, Universitetsbyen 3, 8000 Aarhus, Denmark; Department of Human Neurosciences, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
| | - Alexandra Battaglia-Mayer
- Department of Physiology and Pharmacology, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
| | - Giacomo Novembre
- Neuroscience of Perception & Action Lab, Italian Institute of Technology, Viale Regina Elena 291, 00161 Rome, Italy.
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3
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Lestang JH, Cai H, Averbeck BB, Cohen YE. Functional network properties of the auditory cortex. Hear Res 2023; 433:108768. [PMID: 37075536 PMCID: PMC10205700 DOI: 10.1016/j.heares.2023.108768] [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: 11/28/2022] [Revised: 03/27/2023] [Accepted: 04/11/2023] [Indexed: 04/21/2023]
Abstract
The auditory system transforms auditory stimuli from the external environment into perceptual auditory objects. Recent studies have focused on the contribution of the auditory cortex to this transformation. Other studies have yielded important insights into the contributions of neural activity in the auditory cortex to cognition and decision-making. However, despite this important work, the relationship between auditory-cortex activity and behavior/perception has not been fully elucidated. Two of the more important gaps in our understanding are (1) the specific and differential contributions of different fields of the auditory cortex to auditory perception and behavior and (2) the way networks of auditory neurons impact and facilitate auditory information processing. Here, we focus on recent work from non-human-primate models of hearing and review work related to these gaps and put forth challenges to further our understanding of how single-unit activity and network activity in different cortical fields contribution to behavior and perception.
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Affiliation(s)
- Jean-Hugues Lestang
- Departments of Otorhinolaryngology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Huaizhen Cai
- Departments of Otorhinolaryngology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Bruno B Averbeck
- Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, USA.
| | - Yale E Cohen
- Departments of Otorhinolaryngology, University of Pennsylvania, Philadelphia, PA 19104, USA; Neuroscience, University of Pennsylvania, Philadelphia, PA 19104, USA; Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
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Kulik V, Reyes LD, Sherwood CC. Coevolution of language and tools in the human brain: An ALE meta-analysis of neural activation during syntactic processing and tool use. PROGRESS IN BRAIN RESEARCH 2023; 275:93-115. [PMID: 36841572 DOI: 10.1016/bs.pbr.2022.10.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Language and complex tool use are often cited as behaviors unique to humans and may be evolutionarily linked owing to the underlying cognitive processes they have in common. We executed a quantitative activation likelihood estimation (ALE) meta-analysis (GingerALE 2.3) on published, whole-brain neuroimaging studies to identify areas associated with syntactic processing and/or tool use in humans. Significant clusters related to syntactic processing were identified in areas known to be related to language production and comprehension, including bilateral Broca's area in the inferior frontal gyrus. Tool use activation clusters were all in the left hemisphere and included the primary motor cortex and premotor cortex, in addition to other areas involved with sensorimotor transformation. Activation shared by syntactic processing and tool use was only significant at one cluster, located in the pars opercularis of the left inferior frontal gyrus. This minimal overlap between syntactic processing and tool use activation from our meta-analysis of neuroimaging studies indicates that there is not a widespread common neural network between the two. Broca's area may serve as an important hub that was initially recruited in early human evolution in the context of simple tool use, but was eventually co-opted for linguistic purposes, including the sequential and hierarchical ordering processes that characterize syntax. In the future, meta-analyses of additional components of language may allow for a more comprehensive examination of the functional networks that underlie the coevolution of human language and complex tool use.
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Affiliation(s)
- Veronika Kulik
- Department of Anthropology and Center for the Advanced Study of Human Paleobiology, The George Washington University, Washington, DC, United States
| | - Laura D Reyes
- Department of Anthropology and Center for the Advanced Study of Human Paleobiology, The George Washington University, Washington, DC, United States
| | - Chet C Sherwood
- Department of Anthropology and Center for the Advanced Study of Human Paleobiology, The George Washington University, Washington, DC, United States.
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Cross ZR, Corcoran AW, Schlesewsky M, Kohler MJ, Bornkessel-Schlesewsky I. Oscillatory and Aperiodic Neural Activity Jointly Predict Language Learning. J Cogn Neurosci 2022; 34:1630-1649. [PMID: 35640095 DOI: 10.1162/jocn_a_01878] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Memory formation involves the synchronous firing of neurons in task-relevant networks, with recent models postulating that a decrease in low-frequency oscillatory activity underlies successful memory encoding and retrieval. However, to date, this relationship has been investigated primarily with face and image stimuli; considerably less is known about the oscillatory correlates of complex rule learning, as in language. Furthermore, recent work has shown that nonoscillatory (1/ƒ) activity is functionally relevant to cognition, yet its interaction with oscillatory activity during complex rule learning remains unknown. Using spectral decomposition and power-law exponent estimation of human EEG data (17 females, 18 males), we show for the first time that 1/ƒ and oscillatory activity jointly influence the learning of word order rules of a miniature artificial language system. Flexible word-order rules were associated with a steeper 1/ƒ slope, whereas fixed word-order rules were associated with a shallower slope. We also show that increased theta and alpha power predicts fixed relative to flexible word-order rule learning and behavioral performance. Together, these results suggest that 1/ƒ activity plays an important role in higher-order cognition, including language processing, and that grammar learning is modulated by different word-order permutations, which manifest in distinct oscillatory profiles.
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Polyanskaya L, Manrique HM, Samuel AG, Marín A, García‐Palacios A, Ordin M. Intermodality differences in statistical learning: phylogenetic and ontogenetic influences. Ann N Y Acad Sci 2022; 1511:191-209. [DOI: 10.1111/nyas.14749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 12/07/2021] [Accepted: 12/19/2021] [Indexed: 12/01/2022]
Affiliation(s)
- Leona Polyanskaya
- Departamento de Psicología y Sociología Universidad de Zaragoza Teruel Spain
| | - Héctor M. Manrique
- Departamento de Psicología y Sociología Universidad de Zaragoza Teruel Spain
| | - Arthur G. Samuel
- Department of Psychology Stony Brook University New York City New York
- Basque Centre on Cognition Brain and Language San Sebastian Spain
| | | | - Azucena García‐Palacios
- Department of Basic Psychology, Clinical and Psychobiology Jaume I University Castellon Spain
- CIBER Fisiopatología Obesidad y Nutrición (CIBERObn) Instituto Carlos III Madrid Spain
| | - Mikhail Ordin
- Universität Konstanz Allgemeine Sprachwissenschaft Konstanz Germany
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Ordin M, Polyanskaya L, Samuel AG. An evolutionary account of intermodality differences in statistical learning. Ann N Y Acad Sci 2020; 1486:76-89. [PMID: 33020959 DOI: 10.1111/nyas.14502] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 08/22/2020] [Accepted: 09/01/2020] [Indexed: 11/27/2022]
Abstract
The cognitive mechanisms underlying statistical learning are engaged for the purposes of speech processing and language acquisition. However, these mechanisms are shared by a wide variety of species that do not possess the language faculty. Moreover, statistical learning operates across domains, including nonlinguistic material. Ancient mechanisms for segmenting continuous sensory input into discrete constituents have evolved for general-purpose segmentation of the environment and been readopted for processing linguistic input. Linguistic input provides a rich set of cues for the boundaries between sequential constituents. Such input engages a wider variety of more specialized mechanisms operating on these language-specific cues, thus potentially reducing the role of conditional statistics in tokenizing a continuous linguistic stream. We provide an explicit within-subject comparison of the utility of statistical learning in language versus nonlanguage domains across the visual and auditory modalities. The results showed that in the auditory modality statistical learning is more efficient with speech-like input, while in the visual modality efficiency is higher with nonlanguage input. We suggest that the speech faculty has been important for individual fitness for an extended period, leading to the adaptation of statistical learning mechanisms for speech processing. This is not the case in the visual modality, in which linguistic material presents a less ecological type of sensory input.
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Affiliation(s)
- Mikhail Ordin
- BCBL - Basque Centre on Cognition, Brain and Language, San Sebastian, Spain.,Ikerbasque - Basque Foundation for Science, Bilbao, Spain
| | - Leona Polyanskaya
- BCBL - Basque Centre on Cognition, Brain and Language, San Sebastian, Spain
| | - Arthur G Samuel
- BCBL - Basque Centre on Cognition, Brain and Language, San Sebastian, Spain.,Ikerbasque - Basque Foundation for Science, Bilbao, Spain.,Psychology Department, Stony Brook University, New York, New York
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Conway CM. How does the brain learn environmental structure? Ten core principles for understanding the neurocognitive mechanisms of statistical learning. Neurosci Biobehav Rev 2020; 112:279-299. [PMID: 32018038 PMCID: PMC7211144 DOI: 10.1016/j.neubiorev.2020.01.032] [Citation(s) in RCA: 96] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Revised: 01/22/2020] [Accepted: 01/25/2020] [Indexed: 10/25/2022]
Abstract
Despite a growing body of research devoted to the study of how humans encode environmental patterns, there is still no clear consensus about the nature of the neurocognitive mechanisms underpinning statistical learning nor what factors constrain or promote its emergence across individuals, species, and learning situations. Based on a review of research examining the roles of input modality and domain, input structure and complexity, attention, neuroanatomical bases, ontogeny, and phylogeny, ten core principles are proposed. Specifically, there exist two sets of neurocognitive mechanisms underlying statistical learning. First, a "suite" of associative-based, automatic, modality-specific learning mechanisms are mediated by the general principle of cortical plasticity, which results in improved processing and perceptual facilitation of encountered stimuli. Second, an attention-dependent system, mediated by the prefrontal cortex and related attentional and working memory networks, can modulate or gate learning and is necessary in order to learn nonadjacent dependencies and to integrate global patterns across time. This theoretical framework helps clarify conflicting research findings and provides the basis for future empirical and theoretical endeavors.
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Affiliation(s)
- Christopher M Conway
- Center for Childhood Deafness, Language, and Learning, Boys Town National Research Hospital, Omaha, NE, United States.
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9
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Calmus R, Wilson B, Kikuchi Y, Petkov CI. Structured sequence processing and combinatorial binding: neurobiologically and computationally informed hypotheses. Philos Trans R Soc Lond B Biol Sci 2020; 375:20190304. [PMID: 31840585 PMCID: PMC6939361 DOI: 10.1098/rstb.2019.0304] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/04/2019] [Indexed: 12/13/2022] Open
Abstract
Understanding how the brain forms representations of structured information distributed in time is a challenging endeavour for the neuroscientific community, requiring computationally and neurobiologically informed approaches. The neural mechanisms for segmenting continuous streams of sensory input and establishing representations of dependencies remain largely unknown, as do the transformations and computations occurring between the brain regions involved in these aspects of sequence processing. We propose a blueprint for a neurobiologically informed and informing computational model of sequence processing (entitled: Vector-symbolic Sequencing of Binding INstantiating Dependencies, or VS-BIND). This model is designed to support the transformation of serially ordered elements in sensory sequences into structured representations of bound dependencies, readily operates on multiple timescales, and encodes or decodes sequences with respect to chunked items wherever dependencies occur in time. The model integrates established vector symbolic additive and conjunctive binding operators with neurobiologically plausible oscillatory dynamics, and is compatible with modern spiking neural network simulation methods. We show that the model is capable of simulating previous findings from structured sequence processing tasks that engage fronto-temporal regions, specifying mechanistic roles for regions such as prefrontal areas 44/45 and the frontal operculum during interactions with sensory representations in temporal cortex. Finally, we are able to make predictions based on the configuration of the model alone that underscore the importance of serial position information, which requires input from time-sensitive cells, known to reside in the hippocampus and dorsolateral prefrontal cortex. This article is part of the theme issue 'Towards mechanistic models of meaning composition'.
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Affiliation(s)
- Ryan Calmus
- Newcastle University Medical School, Framlington Place, Newcastle upon Tyne, UK
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Ordin M, Polyanskaya L, Soto D. Neural bases of learning and recognition of statistical regularities. Ann N Y Acad Sci 2020; 1467:60-76. [PMID: 31919870 DOI: 10.1111/nyas.14299] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Revised: 12/18/2019] [Accepted: 12/18/2019] [Indexed: 02/06/2023]
Abstract
Statistical learning is a set of cognitive mechanisms allowing for extracting regularities from the environment and segmenting continuous sensory input into discrete units. The current study used functional magnetic resonance imaging (fMRI) (N = 25) in conjunction with an artificial language learning paradigm to provide new insight into the neural mechanisms of statistical learning, considering both the online process of extracting statistical regularities and the subsequent offline recognition of learned patterns. Notably, prior fMRI studies on statistical learning have not contrasted neural activation during the learning and recognition experimental phases. Here, we found that learning is supported by the superior temporal gyrus and the anterior cingulate gyrus, while subsequent recognition relied on the left inferior frontal gyrus. Besides, prior studies only assessed the brain response during the recognition of trained words relative to novel nonwords. Hence, a further key goal of this study was to understand how the brain supports recognition of discrete constituents from the continuous input versus recognition of mere statistical structure that is used to build new constituents that are statistically congruent with the ones from the input. Behaviorally, recognition performance indicated that statistically congruent novel tokens were less likely to be endorsed as parts of the familiar environment than discrete constituents. fMRI data showed that the left intraparietal sulcus and angular gyrus support the recognition of old discrete constituents relative to novel statistically congruent items, likely reflecting an additional contribution from memory representations for trained items.
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Affiliation(s)
- Mikhail Ordin
- BCBL - Basque Centre on Cognition, Brain and Language, San Sebastián, Spain.,Ikerbasque - Basque Foundation for Science, San Sebastián, Spain
| | - Leona Polyanskaya
- BCBL - Basque Centre on Cognition, Brain and Language, San Sebastián, Spain
| | - David Soto
- BCBL - Basque Centre on Cognition, Brain and Language, San Sebastián, Spain.,Ikerbasque - Basque Foundation for Science, San Sebastián, Spain
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