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Long-Horizon Associative Learning Explains Human Sensitivity to Statistical and Network Structures in Auditory Sequences.
- Source :
-
The Journal of neuroscience : the official journal of the Society for Neuroscience [J Neurosci] 2024 Apr 03; Vol. 44 (14). Date of Electronic Publication: 2024 Apr 03. - Publication Year :
- 2024
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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.<br />Competing Interests: The authors declare no competing financial interests.<br /> (Copyright © 2024 the authors.)
Details
- Language :
- English
- ISSN :
- 1529-2401
- Volume :
- 44
- Issue :
- 14
- Database :
- MEDLINE
- Journal :
- The Journal of neuroscience : the official journal of the Society for Neuroscience
- Publication Type :
- Academic Journal
- Accession number :
- 38408873
- Full Text :
- https://doi.org/10.1523/JNEUROSCI.1369-23.2024