Biology subjects

Dehaene, S.

Publications and source records attributed to Dehaene, S..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

The baboon as a statistician: Can non-human primates perform linear regression on a graph?

Recent studies showed that humans, regardless of age, education, and culture, can extract the linear trend of a noisy graph. Here, we examined whether such skills for intuitive statistics are confined to humans or may also exist in non-human primates. We trained Guinea baboons (Papio papio) to associate arbitrary geometrical shapes with the increasing or decreasing trends of noiseless and noisy scatterplots, while varying the number of points, the noise level, and the regression slope. Many baboons successfully learned this conditional match-to-sample task for both noiseless and noisy plots. Crucially, for successful baboons, accuracy varied as a sigmoid function of the t-value of the regression, the same statistical index upon which humans also base their answers, even after controlling for other variables. These results are compatible with the hypothesis that the human perception of data graphics is based on the pre-emption and recycling of a phylogenetically older competence of the primate visual system for extracting the principal axes of visual displays.

animal behavior and cognition↗

bioRxiv2024

Mapping and Modeling the Semantic Space of Math Concepts

Mathematics is an underexplored domain of human cognition. While many studies have focused on subsets of math concepts such as numbers, fractions, or geometric shapes, few have ventured beyond these elementary domains. Here, we attempted to map out the full space of math concepts and to answer two specific questions: can distributed semantic models, such a GloVe, provide a satisfactory fit to human semantic judgments in mathematics? And how does this fit vary with education? We first analyzed all of the French and English Wikipedia pages with math contents, and used a semi-automatic procedure to extract the 1,000 most frequent math terms in both languages. In a second step, we collected extensive behavioral judgments of familiarity and semantic similarity between them. About half of the variance in human similarity judgments was explained by vector embeddings that attempt to capture latent semantic structures based on cooccurence statistics. Participants self-reported level of education modulated familiarity and similarity, allowing us to create a partial hierarchy among high-level math concepts. Our results converge onto the proposal of a map of math space, organized as a database of math terms with information about their frequency, familiarity, grade of acquisition, and entanglement with other concepts.

neuroscience↗

bioRxiv2024

Two brain systems for the perception of geometric shapes

The perception and production of regular geometric shapes, a characteristic trait of human cultures since prehistory, has unknown neural mechanisms. Behavioral studies suggest that humans are attuned to discrete regularities such as symmetries and parallelism, and rely on their combinations to encode regular geometric shapes in a compressed form. To identify the brain systems underlying this ability, as well as their dynamics, we collected functional MRI in both adults and six-year-olds, and magnetoencephalography data in adults, during the perception of simple shapes such as hexagons, triangles and quadrilaterals. The results revealed that geometric shapes, relative to other visual categories, induce a hypoactivation of ventral visual areas and an overactivation of the intraparietal and inferior temporal regions also involved in mathematical processing, whose activation is modulated by geometric regularity. While convolutional neural networks captured the early visual activity evoked by geometric shapes, they failed to account for subsequent dorsal parietal and prefrontal signals, which could only be captured by discrete geometric features or by bigger deep-learning models of vision. We propose that the perception of abstract geometric regularities engages an additional symbolic mode of visual perception.

neuroscience↗

bioRxiv2023

Dissecting the neuronal mechanisms of invariant word recognition

Learning to read places a strong challenge on the visual system. Years of expertise lead to a remarkable capacity to separate highly similar letters and encode their relative positions, thus distinguishing words such as FORM and FROM invariantly over a large range of sizes and absolute positions. How neural circuits achieve invariant word recognition remains unknown. Here we address this issue through computational modeling and brain imaging. We first trained deep neural network models to recognize written words, then analyzed the reading-specialized units that emerged in deep layers. With literacy, units became sensitive to specific letter identities and their distance from the blank space at the left or right of a word, thus acting as "space bigrams" encoding ordinal position using an approximate number code. Using 7T functional MRI and magnetoencephalography in adults, we localized the predicted ordinal code anatomically (visual word form area) and temporally ([~]220ms). The proposed neuronal mechanism for invariant word recognition can explain reading errors and makes precise predictions about how position-invariant neural codes arise in brains and artificial systems.

neuroscience↗

bioRxiv2023

Transient brain activity dynamics discriminate levels of consciousness during anesthesia

The awake mammalian brain is functionally organized in terms of large-scale distributed networks that are constantly interacting. Loss of consciousness might disrupt this temporal organization leaving patients unresponsive. We hypothesized that characterizing brain activity in terms of transient events may provide a signature of consciousness. For this, we analyzed temporal dynamics of spatiotemporally overlapping functional networks obtained from fMRI transient activity across different states of consciousness. We first show a striking homology in spatial organization of networks between monkeys and humans, indicating cross-species similarities in resting- state fMRI structure. We then tracked how network organization shifts under different anesthesia conditions in macaque monkeys. While the spatial aspect of the networks was preserved, their temporal dynamics were highly affected by anesthesia. Networks expressed for longer durations and co-activated in an anesthetic-specific configuration. Additionally, hierarchical brain organization was disrupted with a consciousness-level- signature role of the default mode network. In conclusion, network temporal dynamics is a reliable and robust cortical signature of consciousness, paving the way to its clinical translation.

neuroscience↗

bioRxiv2023

An adversarial collaboration to critically evaluate theories of consciousness

Different theories explain how subjective experience arises from brain activity1,2. These theories have independently accrued evidence, yet, confirmation bias and dependence on design choices hamper progress in the field3. Here, we present an open science adversarial collaboration which directly juxtaposes Integrated Information Theory (IIT)4,5 and Global Neuronal Workspace Theory (GNWT)6-10, employing a theory-neutral consortium approach11,12. We investigate neural correlates of the content and duration of visual experience. The theory proponents and the consortium developed and preregistered the experimental design, divergent predictions, expected outcomes, and their interpretation12. 256 human subjects viewed suprathreshold stimuli for variable durations while neural activity was measured with functional magnetic resonance imaging, magnetoencephalography, and electrocorticography. We find information about conscious content in visual, ventro-temporal and inferior frontal cortex, with sustained responses in occipital and lateral temporal cortex reflecting stimulus duration, and content-specific synchronization between frontal and early visual areas. These results confirm some predictions of IIT and GNWT, while substantially challenging both theories: for IIT, a lack of sustained synchronization within posterior cortex contradicts the claim that network connectivity specifies consciousness. GNWT is challenged by the general lack of ignition at stimulus offset and limited representation of certain conscious dimensions in prefrontal cortex. Beyond challenging the theories themselves, we present an alternative approach to advance cognitive neuroscience through a principled, theory-driven, collaborative effort. We highlight the challenges to change peoples mind 13 and the need for a quantitative framework integrating evidence for systematic theory testing and building.

neuroscience↗

bioRxiv2023

Ultra-high field fMRI of visual mental imagery in typical imagers and aphantasic individuals

Most of us effortlessly describe visual objects, whether seen or remembered. Yet, around 4% of people report congenital aphantasia: they struggle to visualize objects despite being able to describe their visual appearance. What neural mechanisms create this disparity between subjective experience and objective performance? Aphantasia can provide novel insights into conscious processing and awareness. We used ultra-high field 7T fMRI to establish the neural circuits involved in visual mental imagery and perception, and to elucidate the neural mechanisms associated with the processing of internally generated visual information in the absence of imagery experience in congenital aphantasia. Ten typical imagers and 10 aphantasic individuals performed imagery and perceptual tasks in five domains: object shape, object color, written words, faces, and spatial relationships. In typical imagers, imagery tasks activated left-hemisphere frontoparietal areas, the relevant domain-preferring areas in the ventral temporal cortex partly overlapping with the perceptual domain-preferring areas, and a domain-general area in the left fusiform gyrus (the Fusiform Imagery Node). The results were valid for each individual participant. In aphantasic individuals, imagery activated similar visual areas, but there was reduced functional connectivity between the Fusiform Imagery Node and frontoparietal areas. Our results unveil the domain-general and domain-specific circuits of visual mental imagery, their functional disorganization in aphantasia, and support the general hypothesis that conscious visual experience - whether perceived or imagined - depends on the integrated activity of high-level visual cortex and frontoparietal networks.

neuroscience↗

bioRxiv2023

Brain mechanisms of reversible symbolic reference:a potential singularity of the human brain

The emergence of symbolic thinking has been proposed as a dominant cognitive criterion to distinguish humans from other primates during hominization. Although the proper definition of a symbol has been the subject of much debate, one of its simplest features is bidirectional attachment: the content is accessible from the symbol, and vice versa. Behavioural observations scattered over the past four decades suggest that this criterion might not be met in non-human primates, as they fail to generalise an association learned in one temporal order (A to B) to the reverse order (B to A). Here, we designed an implicit fMRI test to investigate the neural mechanisms of arbitrary audio-visual and visual-visual pairing in monkeys and humans and probe their spontaneous reversibility. After learning a unidirectional association, humans showed surprise signals when this learned association was violated. Crucially, this effect occurred spontaneously in both learned and reversed directions, within an extended network of high-level brain areas, including, but also going beyond, the language network. In monkeys, by contrast, violations of association effects occurred solely in the learned direction and were largely confined to sensory areas. We propose that a human-specific brain network may have evolved the capacity for reversible symbolic reference.

neuroscience↗

bioRxiv2023

Dimensionality and ramping: Signatures of sentence integration in the dynamics of brains and deep language models

A sentence is more than the sum of its words: its meaning depends on how they combine with one another. The brain mechanisms underlying such semantic composition remain poorly understood. To shed light on the neural vector code underlying semantic composition, we introduce two hypotheses: First, the intrinsic dimensionality of the space of neural representations should increase as a sentence unfolds, paralleling the growing complexity of its semantic representation, and second, this progressive integration should be reflected in ramping and sentence-final signals. To test these predictions, we designed a dataset of closely matched normal and Jabberwocky sentences (composed of meaningless pseudo words) and displayed them to deep language models and to 11 human participants (5 men and 6 women) monitored with simultaneous magneto-encephalography and intracranial electro-encephalography. In both deep language models and electrophysiological data, we found that representational dimensionality was higher for meaningful sentences than Jabberwocky. Furthermore, multivariate decoding of normal versus Jabberwocky confirmed three dynamic patterns: (i) a phasic pattern following each word, peaking in temporal and parietal areas, (ii) a ramping pattern, characteristic of bilateral inferior and middle frontal gyri, and (iii) a sentence-final pattern in left superior frontal gyrus and right orbitofrontal cortex. These results provide a first glimpse into the neural geometry of semantic integration and constrain the search for a neural code of linguistic composition. Significance statementStarting from general linguistic concepts, we make two sets of predictions in neural signals evoked by reading multi-word sentences. First, the intrinsic dimensionality of the representation should grow with additional meaningful words. Second, the neural dynamics should exhibit signatures of encoding, maintaining, and resolving semantic composition. We successfully validated these hypotheses in deep Neural Language Models, artificial neural networks trained on text and performing very well on many Natural Language Processing tasks. Then, using a unique combination of magnetoencephalography and intracranial electrodes, we recorded high-resolution brain data from human participants while they read a controlled set of sentences. Time-resolved dimensionality analysis showed increasing dimensionality with meaning, and multivariate decoding allowed us to isolate the three dynamical patterns we had hypothesized.

neuroscience↗

bioRxiv2023

Why do young readers vary in reading fluency? The impact of word length and frequency in French 6th graders

PurposeWhich processes induce variations in reading speed in young readers with the same amount of education, but different levels of reading fluency? Here, we tested a prediction of the dual-route model: as fluency increases, these variations may reflect a decreasing reliance on decoding and an increasing reliance on the lexical route. Method1,500 French 6th graders passed a one-minute speeded reading-aloud task evaluating fluency, and a 10-minute computerized lexical decision task evaluating the impact of word length, word frequency and pseudoword type. ResultsAs predicted, the word length effect varied dramatically with reading fluency, with the least fluent group showing a length effect even for frequent words. The frequency effect also varied, but solely in proportion to overall slowness, suggesting that frequency affects the decision stage in all readers, while length impacts poor readers disproportionately. Response times and errors were also affected by pseudoword type (e.g. letter substitutions or transpositions), but these effects did not vary much with fluency. Overall, lexical decision variables were excellent predictors of reading fluency (r=0.62). ConclusionOur results call attention to middle-school reading difficulties and encourage the use of lexical decision as a test of students mental lexicon and the automatization of reading.

neuroscience↗

bioRxiv2022

Does the visual word form area split in bilingual readers? A millimeter-scale 7T fMRI study

In expert readers, a brain region known as the visual word form area (VWFA) is highly sensitive to written words, exhibiting a posterior-to-anterior gradient of increasing sensitivity to orthographic stimuli whose statistics match those of real words. Using high-resolution 7T fMRI, we ask whether, in bilingual readers, distinct cortical patches specialize for different languages. In 21 English-French bilinguals, unsmoothed 1.2 mm fMRI revealed that the VWFA is actually composed of several small cortical patches highly selective for reading, with a posterior-to-anterior word similarity gradient, but with near-complete overlap between the two languages. In 10 English-Chinese bilinguals, however, while most word-specific patches exhibited similar reading specificity and word-similarity gradients for reading in Chinese and English, additional patches responded specifically to Chinese writing and, surprisingly, to faces. Our results show that the acquisition of multiple writing systems can indeed tune the visual cortex differently in bilinguals, sometimes leading to the emergence of cortical patches specialized for a single language.

neuroscience↗

bioRxiv2022

Graphicacy across age, education, and culture: a new tool to assess intuitive graphics skills

Data plots are widely used in science, journalism and politics, since they efficiently allow to depict a large amount of information. Graphicacy, the ability to understand graphs, thus became a fundamental cultural skill. Here, we introduce a new measure of graphicacy that assesses the ability to detect a trend in noisy scatterplots ("does this graph go up or down?"). In 3943 educated participants, responses vary as a sigmoid function of the t-value that a statistician would compute to detect a significant trend. We find a minimum level of core graphicacy even in unschooled participants living in remote Namibian villages (N=87) and 6-year-old 1st-graders who never read a graph (N=27). However, the sigmoid slope (the "graphicacy index") varies across participants, increases with education, and tightly correlates with statistical knowledge, showing that experience contributes to refining graphical intuitions. Our tool is publicly available online and allows to quickly evaluate intuitive graphics skills. STATEMENT OF RELEVANCEThe rising cost of gas, the number of Covid deaths, the evolution of temperatures during the summer months: we often face graphs depicting these phenomena. The scientific literature has shown that human adults can intuit, within milliseconds, the statistical trend of these graphs. However, we do not know if these intuitions generalized to unschooled people and, most importantly, how to measure their variations in the population. In this study we show that intuitive graphics skills are present even in 6-year-old children who never saw a graph and in the Himba of Namibia, an indigenous people with no access to formal schooling. Furthermore, we developed a quantitative assessment of such intuitive graphics skills (which we called the "graphicacy index"), that everyone can easily obtain for free, through a short (10 minutes) online test: https://neurospin-data.cea.fr/exp/lorenzo-ciccione/graphicacy-index/. In summary, our study provides the first attempt to formally quantify human intuitions of statistical graphs.

scientific communication and education↗

bioRxiv2022

Compression of binary sound sequences in human working 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 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.

neuroscience↗

bioRxiv2022

Can a short math video enhance the brain's mathematical networks?

Many teaching websites, such as the Khan Academy, propose vivid videos illustrating a mathematical concept. Using fMRI, we asked whether watching such a video suffices to rapidly change the brain networks for mathematical knowledge. We capitalized on the finding that, when judging the truth of short spoken statements, distinct semantic regions activate depending on whether the statements bear on mathematical knowledge or on other domains of semantic knowledge. Here, participants answered such questions before and after watching a lively five-minute video which taught them the rudiments of a new domain. During the video, a distinct math-responsive network, comprising anterior intraparietal and inferior temporal nodes, showed inter-subject synchrony when viewing mathematics course rather than control courses in biology or law. However, this experience led to minimal subsequent changes in the activity of those domain-specific areas when answering questions on the same topics a few minutes later. All taught facts, whether mathematical or not, led to domain-general repetition enhancement, particularly prominent in the cuneus, posterior cingulate and posterior parietal cortices. We conclude that short videos do not suffice to induce a meaningful lasting change in the brains math-responsive network, but merely engage domain-general regions possibly involved in episodic short-term memory. Significance StatementTeaching mathematical concepts is difficult. To facilitate the comprehension and appeal of mathematics, several teaching websites provide vivid videos illustrating math concepts. Here, however, we show that merely watching such videos fails to improve the brain networks for mathematics. During the video itself, these networks are transiently engaged - but a few minutes later, when we ask questions about the taught concepts, performance is only minimally improved, and the participants engage generic regions thought to be involved in short-memory and language, rather than the targeted math-responsive regions. Brief video watching is therefore insufficient as a pedagogical device, probably because it misses ingredients such as teacher-pupil interactions, explicit teaching, active engagement, retrieval practice, repetition, and sleep.

neuroscience↗

bioRxiv2022

Disentangling Hierarchical and Sequential Computations during Sentence Processing.

Sentences in natural language have a hierarchical structure, that can be described in terms of nested trees. To compose sentence meaning, the human brain needs to link successive words into complex syntactic structures. However, such hierarchical-structure processing could co-exist with a simpler, shallower, and perhaps evolutionarily older mechanism for local, word-by-word sequential processing. Indeed, classic work from psycholinguistics suggests the existence of such non-hierarchical processing, which can interfere with hierarchical processing and lead to sentence-processing errors in humans. However, such interference can arise from two, non mutually exclusive, reasons: interference between words in working memory, or interference between local versus long-distance word-prediction signals. Teasing apart these two possibilities is difficult based on behavioral data alone. Here, we conducted a magnetoen-cephalography experiment to study hierarchical vs. sequential computations during sentence processing in the human brain. We studied whether the two processes have distinct neural signatures and whether sequential interference observed behaviorally is due to memory-based interference or to competing word-prediction signals. Our results show (1) a large dominance of hierarchical processing in the human brain compared to sequential processing, and (2) neural evidence for interference between words in memory, but no evidence for competing prediction signals. Our study shows that once words enter the language system, computations are dominated by structure-based processing and largely robust to sequential effects; and that even when behavioral interference occurs, it need not indicate the existence of a shallow, local language prediction system.

neuroscience↗

bioRxiv2022

Evolution of reading and face circuits during the first three years of reading acquisition

Although words and faces activate neighboring regions in the fusiform gyrus, we lack an understanding of how such category selectivity emerges during development. To investigate the organization of reading and face circuits at the earliest stage of reading acquisition, we measured the fMRI responses to words, faces, houses, and checkerboards in three groups of 60 French children: 6-year-old pre-readers, 6-year-old beginning readers and 9-year-old advanced readers. The results showed that specific responses to written words were absent prior to reading, but emerged in beginning readers, irrespective of age. Likewise, specific responses to faces were barely visible in pre-readers and continued to evolve in the 9-year-olds, yet primarily driven by age rather than by schooling. Crucially, the sectors of ventral visual cortex that become specialized for words and faces harbored their own functional connectivity prior to reading acquisition: the VWFA with left-hemispheric spoken language areas, and the FFA with the contralateral region and the amygdalae. The results support the view that reading acquisition occurs through the recycling of a pre-existing but plastic circuit which, in pre-readers, already connects the VWFA site to other distant language areas. We argue that reading acquisition does not compete with the face system directly, through a pruning of preexisting face responses, but indirectly, by hindering the slow growth of face responses in the left hemisphere, thus increasing a pre-existing right hemispheric bias. HighlightsO_LIWritten words and faces activate neighboring areas of the fusiform gyri, but their developmental trajectory is different. C_LIO_LIThe growth of word-induced activation in VWFA is primarily due to schooling. C_LIO_LIThe growth of face responses is primarily affected by age rather than by schooling. C_LIO_LIWord and face-related areas exhibit distinct functional connectivity even prior to reading acquisition C_LIO_LIVWFA is initially functionally connected with left-hemisphere spoken language areas, and FFA with amygdala and contralateral FFA. C_LI

neuroscience↗

bioRxiv2022

A connectome-based model of conscious access in monkey cortex

A growing body of evidence suggests that conscious perception of a sensory stimulus coincides with all-or-none activity across multiple cortical areas, a phenomenon called ignition. In contrast, the same stimulus, when undetected, induces only transient activity. In this work, we report a large-scale model of the macaque cortex based on recently quantified structural mesoscopic connectome data. We use this model to simulate a detection task, and demonstrate how a dynamical bifurcation mechanism produces ignition-like events in the model network. The model predicts that feedforward excitatory transmission is primarily mediated by the fast AMPA receptors to ensure rapid signal propagation from sensory to associative areas. In contrast, a greater proportion of the inter-areal feedback projections and local recurrent excitation depend on the slow NMDA receptors, to ensure ignition of distributed frontoparietal activity. Our model predicts, counterintuitively, that fast-responding sensory areas contain a higher ratio of NMDA to AMPA receptors compared to association cortical areas that show slow, sustained activity. We validate this prediction using cortex-wide in-vitro receptor autoradiography data. Finally, we show how this model can account for various behavioral and physiological effects linked to consciousness. Together, these findings clarify the neurophysiological mechanisms of conscious access in the primate cortex and support the concept that gradients of receptor densities along the cortical hierarchy contribute to distributed cognitive functions.

neuroscience↗

bioRxiv2021

Prefrontal neural ensembles encode an internal model of visual sequences and their violations

When exposed to sensory sequences, do macaque monkeys spontaneously form abstract internal models that generalize to novel experiences? Here, we show that neuronal populations in macaque ventrolateral prefrontal cortex encode visual sequences by factorizing them into separate codes for the specific pictures presented and for their abstract sequential structure. Ventrolateral prefrontal neurons were recorded while macaque monkeys passively viewed visual sequences and sequence mismatches in the local-global paradigm. Even without any overt task or response requirements, prefrontal populations spontaneously built up representations of sequence structure, serial order, and image identity within distinct but superimposed neuronal subspaces. Representations of sequence structure rapidly updated following single exposure to a mismatch sequence, while orthogonal populations represent mismatches for sequences of different complexity. Finally, those representations generalized across sequences following the same structure but comprising different images. These results suggest that prefrontal populations spontaneously encode rich internal models of visual sequences that reflect both content-specific and abstract information.

neuroscience↗