Representation of spatial sequences using nested rules in human prefrontal cortex

Wang, Liping; Amalric, Marie; Fang, Wen; Jiang, Xinjian; Pallier, Christophe; Figueira, Santiago; Sigman, Mariano; Dehaene, Stanislas

doi:10.1016/j.neuroimage.2018.10.061

Cited by 47 publications

(87 citation statements)

References 56 publications

(69 reference statements)

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“…Indeed, this dissociation seems to operate at both syntactic and semantic levels. In the same way that algebraic or geometrical syntax does not seem to recruit linguistic syntax areas [16,55], mathematical semantic content elicits activation that spares classical regions involved in the semantic processing of words or sentences [15].…”

Section: Discussionmentioning

confidence: 95%

Cortical circuits for mathematical knowledge: evidence for a major subdivision within the brain's semantic networks

Amalric

Dehaene

2018

Phil. Trans. R. Soc. B

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Is mathematical language similar to natural language? Are language areas used by mathematicians when they do mathematics? And does the brain comprise a generic semantic system that stores mathematical knowledge alongside knowledge of history, geography or famous people? Here, we refute those views by reviewing three functional MRI studies of the representation and manipulation of high-level mathematical knowledge in professional mathematicians. The results reveal that brain activity during professional mathematical reflection spares perisylvian language-related brain regions as well as temporal lobe areas classically involved in general semantic knowledge. Instead, mathematical reflection recycles bilateral intraparietal and ventral temporal regions involved in elementary number sense. Even simple fact retrieval, such as remembering that 'the sine function is periodical' or that 'London buses are red', activates dissociated areas for math versus non-math knowledge. Together with other fMRI and recent intracranial studies, our results indicated a major separation between two brain networks for mathematical and non-mathematical semantics, which goes a long way to explain a variety of facts in neuroimaging, neuropsychology and developmental disorders.This article is part of a discussion meeting issue 'The origins of numerical abilities'.

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Section: Discussionmentioning

confidence: 95%

Cortical circuits for mathematical knowledge: evidence for a major subdivision within the brain's semantic networks

Amalric

Dehaene

2018

Phil. Trans. R. Soc. B

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“…We merely note here that activation of supra-modal prefrontal cortices has been reported during sequence processing (e.g. Huettel et al, 2002;Wang et al, 2019); that the existence of an automatic visual-toauditory conversion in sequence processing has been challenged (McAuley & Henry, 2010); and that the existence of an abstract representation of sequences as proposed here, allowing a transfer of knowledge across modalities, is already supported by some behavioral data (see Yildirim & Jacobs, 2015).…”

Section: Discussionmentioning

confidence: 59%

“…In this previous work, human participants were presented with a sequence of eight locations on a regular octagon. Using both behavioral and brain-imaging data, we showed the necessity and adequacy of a computer-like language consisting of geometrical primitives of rotation and symmetry plus the ability to repeat them with various variations in starting point or symmetries (Amalric et al, 2017;Romano et al, 2018;Wang et al, 2019). This language was shown to predict which sequences appear as regular, and how educated adults, uneducated Amazon Indians and young children performed an explicit sequence completion task (Amalric et al, 2017) or simply gazed a sequence of dots as fast as possible (Wang et al, 2019).…”

Section: A Short Review Of Sequence Complexitymentioning

confidence: 99%

“…Using both behavioral and brain-imaging data, we showed the necessity and adequacy of a computer-like language consisting of geometrical primitives of rotation and symmetry plus the ability to repeat them with various variations in starting point or symmetries (Amalric et al, 2017;Romano et al, 2018;Wang et al, 2019). This language was shown to predict which sequences appear as regular, and how educated adults, uneducated Amazon Indians and young children performed an explicit sequence completion task (Amalric et al, 2017) or simply gazed a sequence of dots as fast as possible (Wang et al, 2019). Sequence complexity, defined as minimal description length predicted behavioral performance, and could account for variability in brain activation patterns (Amalric et al, 2017;Romano et al, 2018;Wang et al, 2019).…”

Section: A Short Review Of Sequence Complexitymentioning

confidence: 99%

“…This language was shown to predict which sequences appear as regular, and how educated adults, uneducated Amazon Indians and young children performed an explicit sequence completion task (Amalric et al, 2017) or simply gazed a sequence of dots as fast as possible (Wang et al, 2019). Sequence complexity, defined as minimal description length predicted behavioral performance, and could account for variability in brain activation patterns (Amalric et al, 2017;Romano et al, 2018;Wang et al, 2019).…”

Section: A Short Review Of Sequence Complexitymentioning

confidence: 99%

See 2 more Smart Citations

Mental compression of binary sequences in a language of thought

Planton¹,

T²,

Abbih³

et al. 2020

Preprint

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The capacity to store information in working memory strongly depends upon the ability to recode the information in a compressed form. Here, we tested the theory that human adults encode binary sequences of stimuli in memory using a recursive compression algorithm akin to a “language of thought”, and capable of capturing nested patterns of repetitions and alternations. In five experiments, we probed memory for auditory or visual sequences using both subjective and objective measures. We used a sequence violation paradigm in which participants detected occasional violations in an otherwise fixed sequence. Both subjective ratings of complexity and objective sequence violation detection rates were well predicted by complexity, as measured by minimal description length (also known as Kolmogorov complexity) in the binary version of the “language of geometry”, a formal language previously found to account for the human encoding of complex spatial sequences in the proposed language. We contrasted the language model with a model based solely on surprise given the stimulus transition probabilities. While both models accounted for variance in the data, the language model dominated over the transition probability model for long sequences (with a number of elements far exceeding the limits of working memory). We use model comparison to show that the minimal description length in a recursive language provides a better fit than a variety of previous encoding models for sequences. The data support the hypothesis that, beyond the extraction of statistical knowledge, human sequence coding relies on an internal compression using language-like nested structures.

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A multimodal cortical network of sensory expectation violation revealed by fMRI

Grundei,

Schmidt,

Blankenburg

2023

Human Brain Mapping

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The brain is subjected to multi‐modal sensory information in an environment governed by statistical dependencies. Mismatch responses (MMRs), classically recorded with EEG, have provided valuable insights into the brain's processing of regularities and the generation of corresponding sensory predictions. Only few studies allow for comparisons of MMRs across multiple modalities in a simultaneous sensory stream and their corresponding cross‐modal context sensitivity remains unknown. Here, we used a tri‐modal version of the roving stimulus paradigm in fMRI to elicit MMRs in the auditory, somatosensory and visual modality. Participants (N = 29) were simultaneously presented with sequences of low and high intensity stimuli in each of the three senses while actively observing the tri‐modal input stream and occasionally reporting the intensity of the previous stimulus in a prompted modality. The sequences were based on a probabilistic model, defining transition probabilities such that, for each modality, stimuli were more likely to repeat (p = .825) than change (p = .175) and stimulus intensities were equiprobable (p = .5). Moreover, each transition was conditional on the configuration of the other two modalities comprising global (cross‐modal) predictive properties of the sequences. We identified a shared mismatch network of modality general inferior frontal and temporo‐parietal areas as well as sensory areas, where the connectivity (psychophysiological interaction) between these regions was modulated during mismatch processing. Further, we found deviant responses within the network to be modulated by local stimulus repetition, which suggests highly comparable processing of expectation violation across modalities. Moreover, hierarchically higher regions of the mismatch network in the temporo‐parietal area around the intraparietal sulcus were identified to signal cross‐modal expectation violation. With the consistency of MMRs across audition, somatosensation and vision, our study provides insights into a shared cortical network of uni‐ and multi‐modal expectation violation in response to sequence regularities.

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Representation of spatial sequences using nested rules in human prefrontal cortex

Cited by 47 publications

References 56 publications

Cortical circuits for mathematical knowledge: evidence for a major subdivision within the brain's semantic networks

Cortical circuits for mathematical knowledge: evidence for a major subdivision within the brain's semantic networks

Mental compression of binary sequences in a language of thought

A multimodal cortical network of sensory expectation violation revealed by fMRI

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