Brain-imaging evidence for compression of binary sound sequences in human memory

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 MR...

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Bibliographic Details
Published in:eLife 2023-11, Vol.12
Main Authors: Al Roumi, Fosca, Planton, Samuel, Wang, Liping, Dehaene, Stanislas
Format: Article
Language:English
Subjects:
auditory processing
Brain
Brain - diagnostic imaging
Brain Mapping
Cerebellum
compression
EEG
Experiments
Functional magnetic resonance imaging
Humans
Language
language of thought
Learning
Memory
Neuroimaging
Neuroscience
sequence
syntax
Violations
working memory
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Summary: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.
ISSN:2050-084X
2050-084X
DOI:10.7554/eLife.84376