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Benjamin L, Sablé-Meyer M, Fló A, Dehaene-Lambertz G, Al Roumi F. Long-Horizon Associative Learning Explains Human Sensitivity to Statistical and Network Structures in Auditory Sequences. J Neurosci 2024; 44:e1369232024. [PMID: 38408873 PMCID: PMC10993028 DOI: 10.1523/jneurosci.1369-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 01/16/2024] [Accepted: 02/07/2024] [Indexed: 02/28/2024] Open
Abstract
Networks are a useful mathematical tool for capturing the complexity of the world. In a previous behavioral study, we showed that human adults were sensitive to the high-level network structure underlying auditory sequences, even when presented with incomplete information. Their performance was best explained by a mathematical model compatible with associative learning principles, based on the integration of the transition probabilities between adjacent and nonadjacent elements with a memory decay. In the present study, we explored the neural correlates of this hypothesis via magnetoencephalography (MEG). Participants (N = 23, 16 females) passively listened to sequences of tones organized in a sparse community network structure comprising two communities. An early difference (∼150 ms) was observed in the brain responses to tone transitions with similar transition probability but occurring either within or between communities. This result implies a rapid and automatic encoding of the sequence structure. Using time-resolved decoding, we estimated the duration and overlap of the representation of each tone. The decoding performance exhibited exponential decay, resulting in a significant overlap between the representations of successive tones. Based on this extended decay profile, we estimated a long-horizon associative learning novelty index for each transition and found a correlation of this measure with the MEG signal. Overall, our study sheds light on the neural mechanisms underlying human sensitivity to network structures and highlights the potential role of Hebbian-like mechanisms in supporting learning at various temporal scales.
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Affiliation(s)
- Lucas Benjamin
- Cognitive Neuroimaging Unit, CNRS ERL 9003, INSERM U992, CEA, Université Paris-Saclay, NeuroSpin Center, 91190 Gif/Yvette, France
| | - Mathias Sablé-Meyer
- Cognitive Neuroimaging Unit, CNRS ERL 9003, INSERM U992, CEA, Université Paris-Saclay, NeuroSpin Center, 91190 Gif/Yvette, France
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, London W1T 4JG, United Kingdom
| | - Ana Fló
- Cognitive Neuroimaging Unit, CNRS ERL 9003, INSERM U992, CEA, Université Paris-Saclay, NeuroSpin Center, 91190 Gif/Yvette, France
- Department of Developmental Psychology and Socialization, University of Padova, Padova 35131, Italy
| | - Ghislaine Dehaene-Lambertz
- Cognitive Neuroimaging Unit, CNRS ERL 9003, INSERM U992, CEA, Université Paris-Saclay, NeuroSpin Center, 91190 Gif/Yvette, France
| | - Fosca Al Roumi
- Cognitive Neuroimaging Unit, CNRS ERL 9003, INSERM U992, CEA, Université Paris-Saclay, NeuroSpin Center, 91190 Gif/Yvette, France
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Al Roumi F, Planton S, Wang L, Dehaene S. Brain-imaging evidence for compression of binary sound sequences in human memory. eLife 2023; 12:e84376. [PMID: 37910588 PMCID: PMC10619979 DOI: 10.7554/elife.84376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 10/14/2023] [Indexed: 11/03/2023] Open
Abstract
According to the language-of-thought hypothesis, regular sequences are compressed in human memory using recursive loops akin to a mental program that predicts future items. We tested this theory by probing memory for 16-item sequences made of two sounds. We recorded brain activity with functional MRI and magneto-encephalography (MEG) while participants listened to a hierarchy of sequences of variable complexity, whose minimal description required transition probabilities, chunking, or nested structures. Occasional deviant sounds probed the participants' knowledge of the sequence. We predicted that task difficulty and brain activity would be proportional to the complexity derived from the minimal description length in our formal language. Furthermore, activity should increase with complexity for learned sequences, and decrease with complexity for deviants. These predictions were upheld in both fMRI and MEG, indicating that sequence predictions are highly dependent on sequence structure and become weaker and delayed as complexity increases. The proposed language recruited bilateral superior temporal, precentral, anterior intraparietal, and cerebellar cortices. These regions overlapped extensively with a localizer for mathematical calculation, and much less with spoken or written language processing. We propose that these areas collectively encode regular sequences as repetitions with variations and their recursive composition into nested structures.
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Affiliation(s)
- Fosca Al Roumi
- Cognitive Neuroimaging Unit, Université Paris-Saclay, INSERM, CEA, CNRS, NeuroSpin centerGif/YvetteFrance
| | - Samuel Planton
- Cognitive Neuroimaging Unit, Université Paris-Saclay, INSERM, CEA, CNRS, NeuroSpin centerGif/YvetteFrance
| | - Liping Wang
- Institute of Neuroscience, Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of SciencesShanghaiChina
| | - Stanislas Dehaene
- Cognitive Neuroimaging Unit, Université Paris-Saclay, INSERM, CEA, CNRS, NeuroSpin centerGif/YvetteFrance
- Collège de France, Université Paris Sciences Lettres (PSL)ParisFrance
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Benjamin L, Ana F, Al Roumi F, Dehaene-Lambertz G. Humans parsimoniously represent auditory sequences by pruning and completing the underlying network structure. eLife 2023; 12:86430. [PMID: 37129367 DOI: 10.7554/elife.86430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 04/28/2023] [Indexed: 05/03/2023] Open
Abstract
Successive auditory inputs are rarely independent, their relationships ranging from local transitions between elements to hierarchical and nested representations. In many situations, humans retrieve these dependencies even from limited datasets. However, this learning at multiple scale levels is poorly understood. Here we used the formalism proposed by network science to study the representation of local and higher-order structures and their interaction in auditory sequences. We show that human adults exhibited biases in their perception of local transitions between elements, which made them sensitive to high-order network structures such as communities. This behavior is consistent with the creation of a parsimonious simplified model from the evidence they receive, achieved by pruning and completing relationships between network elements. This observation suggests that the brain does not rely on exact memories but on a parsimonious representation of the world. Moreover, this bias can be analytically modeled by a memory/efficiency trade-off. This model correctly accounts for previous findings, including local transition probabilities as well as high-order network structures, unifying sequence learning across scales. We finally propose putative brain implementations of such bias.
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Affiliation(s)
- Lucas Benjamin
- Cognitive Neuroimaging Unit, CNRS ERL 9003, INSERM U992, Paris-Saclay, France
| | - Fló Ana
- Cognitive Neuroimaging Unit, CNRS ERL 9003, INSERM U992, Paris-Saclay, France
| | - Fosca Al Roumi
- Cognitive Neuroimaging Unit, CNRS ERL 9003, INSERM U992, Paris-Saclay, France
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Dehaene S, Al Roumi F, Lakretz Y, Planton S, Sablé-Meyer M. Symbols and mental programs: a hypothesis about human singularity. Trends Cogn Sci 2022; 26:751-766. [PMID: 35933289 DOI: 10.1016/j.tics.2022.06.010] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 06/22/2022] [Accepted: 06/22/2022] [Indexed: 01/29/2023]
Abstract
Natural language is often seen as the single factor that explains the cognitive singularity of the human species. Instead, we propose that humans possess multiple internal languages of thought, akin to computer languages, which encode and compress structures in various domains (mathematics, music, shape…). These languages rely on cortical circuits distinct from classical language areas. Each is characterized by: (i) the discretization of a domain using a small set of symbols, and (ii) their recursive composition into mental programs that encode nested repetitions with variations. In various tasks of elementary shape or sequence perception, minimum description length in the proposed languages captures human behavior and brain activity, whereas non-human primate data are captured by simpler nonsymbolic models. Our research argues in favor of discrete symbolic models of human thought.
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Affiliation(s)
- Stanislas Dehaene
- Cognitive Neuroimaging Unit, CEA, INSERM, Université Paris-Saclay, NeuroSpin Center, 91191 Gif/Yvette, France; Collège de France, Université Paris-Sciences-Lettres (PSL), 11 Place Marcelin Berthelot, 75005 Paris, France.
| | - Fosca Al Roumi
- Cognitive Neuroimaging Unit, CEA, INSERM, Université Paris-Saclay, NeuroSpin Center, 91191 Gif/Yvette, France
| | - Yair Lakretz
- Cognitive Neuroimaging Unit, CEA, INSERM, Université Paris-Saclay, NeuroSpin Center, 91191 Gif/Yvette, France
| | - Samuel Planton
- Cognitive Neuroimaging Unit, CEA, INSERM, Université Paris-Saclay, NeuroSpin Center, 91191 Gif/Yvette, France
| | - Mathias Sablé-Meyer
- Cognitive Neuroimaging Unit, CEA, INSERM, Université Paris-Saclay, NeuroSpin Center, 91191 Gif/Yvette, France
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Dotan D, Pinheiro-Chagas P, Al Roumi F, Dehaene S. Track It to Crack It: Dissecting Processing Stages with Finger Tracking. Trends Cogn Sci 2019; 23:1058-1070. [DOI: 10.1016/j.tics.2019.10.002] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Revised: 09/12/2019] [Accepted: 10/06/2019] [Indexed: 11/15/2022]
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Al Roumi F, Dotan D, Yang T, Wang L, Dehaene S. Acquisition and processing of an artificial mini-language combining semantic and syntactic elements. Cognition 2019; 185:49-61. [DOI: 10.1016/j.cognition.2018.11.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2018] [Revised: 11/16/2018] [Accepted: 11/19/2018] [Indexed: 01/29/2023]
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Al Roumi F, Buchert T, Wiegand A. Lagrangian theory of structure formation in relativistic cosmology. IV. Lagrangian approach to gravitational waves. Int J Clin Exp Med 2017. [DOI: 10.1103/physrevd.96.123538] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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