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Benjamin, L.

Publications and source records attributed to Benjamin, L..

17 recordsLinked to original sources

Flexible compression of sensory evidence makes human decisions more resilient to noisy neural computations

Categorizing ambiguous stimuli requires accumulating sensory evidence over time through noisy neural computations. Theories disagree on whether this evidence is accumulated in its native sensory space or in an abstract and compressed category space that retains only decision-relevant information. Here we designed a visual categorization task in which revealing decision categories before or after a stimulus sequence determines which space can be used to accumulate evidence, without changing the optimal decision. Humans were more accurate, and their inferences less noisy, when they could accumulate evidence in category space. Concurrent magnetoencephalographic (MEG) recordings revealed flexible and accuracy-predictive representations of evidence compressed onto decision categories in these conditions. Recurrent neural networks (RNNs) trained on the same task developed the same strategy, which made their decisions more resilient to noisy computations. Together, these findings suggest that the human brain mitigates the cost of noisy inferences by focusing limited computational resources on decision-relevant information.

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Neural abstraction of evidence accumulation enables flexible multidimensional perceptual decisions

Human perceptual decisions adapt to rich and changing contexts but are typically tested under impoverished conditions. To probe this flexibility, we examined decisions requiring both the selection and the combination of multiple sensory features across sequences of objects. Here we show that human decision flexibility extends beyond feature selection to combination, even across sensory modalities, supported by the abstraction of underlying neural representations. Rapid magnetoencephalographic (MEG) responses reveal a hierarchy of object representations that become increasingly abstract and gated according to their decision relevance. A shared abstract representation of updates reflects evidence accumulation collapsed onto the relevant axis across unidimensional (selection-based) and multidimensional (combination-based) contexts. Recurrent neural networks tested under the same conditions acquire and benefit from abstract representations only when operating under human levels of computational noise. Together, these findings provide a flexibility through abstraction account of human decisions across a broader range of everyday perceptual conditions.

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An ordinal Language of Thought supports human memory for regular sequences

How do humans store sequences that far exceed working memory capacity? Using visuo-spatial and binary auditory sequences, we previously showed that a Language of Thought (LoT) architecture -- in which simple primitives are recursively combined into hierarchical programs -- enables efficient storage of structured sequences. Here we ask whether this principle extends to purely ordinal structure: sequences defined by how items repeat and in what order, as in AABBCCAABBCC, independently of their spatial content. Across three experiments, participants reproduced 12-item sequences of spatial locations with various ordinal structures. The minimal description length derived from the LoT model predicted recall accuracy with remarkable precision (r = .96), substantially outperforming Shannon entropy, Lempel-Ziv complexity, chunking models and subjective complexity ratings. Critically, fine-grained analyses of participants inter-click intervals during reproduction revealed systematic slowdowns at the hierarchical boundaries predicted by the LoT programs, providing a behavioral signature of the underlying mental syntax. These results identify a compact vocabulary of mental primitives -- repetition, mirroring, and interleaving -- whose composition accounts for the symbolic compression of ordinal structures. For ordinal regularities, human sequence memory operates as a form of program induction, leveraging a domain-general capacity for hierarchical compression to encode complex structured information. Author SummaryHuman short-term memory is heavily limited, holding no more than a few items at once. Yet humans routinely memorize complex sequences that far exceed this capacity. How is this possible? We propose that the brain acts like a programmer: rather than storing each element individually, it compresses sequences into short mental "programs." Just as a programmer writes "repeat ABC four times" instead of typing ABCABCABCABC, the brain leverages regularities such as repetitions (ABC-ABC) or mirror patterns (ABC-CBA) to encode sequences efficiently. We tested this idea across three experiments: two in which participants memorized and reproduced sequences of spatial positions on a screen, one where they only rated their perceived complexity. Sequences described by shorter programs were remembered far better and judged as simpler -- even when they were the same length as less structured sequences. When reproducing sequences, participants paused longer at structural boundaries, revealing the internal organization of their mental programs. Strikingly, program length predicted memory performance better than participants own complexity ratings, suggesting that these mental representations are not fully accessible to conscious awareness. Finally, we identified key new patterns -- including temporal inversion and interleaving -- that extend the Language of Thought framework. Together, these findings suggest that a compositional Language of Thought is a fundamental aspect of how the human brain efficiently store and represent structured information.

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From Syllables to Words: EEG Evidence of Different Age Trajectories in Speech Tracking and Statistical Learning in Infants at High and Low Likelihood for Autism

Delayed onset of canonical babbling and first words is often reported in infants later diagnosed with autism spectrum disorder. Identifying the neural mechanisms underlying language acquisition in autism is therefore critical to inform early diagnosis, prognosis, and intervention strategies. In this study, we investigated two speech processing mechanisms previously identified as atypical in children and adults with autism: the neural ability to track syllables; and statistical learning, the capacity to detect speech regularities beneath surface variability. We recorded 83 longitudinal high-density EEGs from 44 infants (2.5-22.6 months) at high (HL) and low (LL) likelihood for autism and assessed their verbal outcomes at 20 months. Neural entrainment was measured at syllable and word frequencies during exposure to a multi-speaker stream of concatenated tri-syllabic words, followed by a word recognition test using ERP recording. Our findings revealed reduced tracking abilities at the syllabic level in HL infants, a measure that correlated with verbal outcomes. While HL infants did not exhibit deficits in statistical learning itself, they displayed reduced novelty orientation during the word recognition test, indicated by a reduced late ERP. By contrast, multi-talker variability temporarily disrupted word segmentation around 12 months in LL infants, but not in HL infants, potentially reflecting decreased sensitivity to human voices variability in the HL group. These results emphasize the importance of longitudinal protocols employing online, implicit measures to track the hierarchical stages of speech processing in both HL and LL infants.

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Sex-Specific Neural Adaptations to Acute and Chronic Restraint Stress in Mice

Stress responses are essential for coping with immediate threats and maintaining physiological homeostasis. While acute stress activates adaptive neuroendocrine and behavioral mechanisms, chronic stress leads to desensitization of these responses, disrupting hormone secretion, neuronal activity, and behavior. Chronic stress is a well-established risk factor for neuropsychiatric disorders, many of which show distinct prevalence and presentation patterns between sexes. However, the neurobiological mechanisms underlying these sex-dependent effects remain poorly understood. This study investigated how acute and chronic stress differentially affect neural activation patterns in male and female mice, with the hypothesis that sex-specific adaptations to chronic stress underlie divergent vulnerabilities to neuropsychiatric disorders. We employed three experimental groups: a control group (no stress), an acute stress group (one hour of restraint stress), and a chronic stress group (one hour of restraint stress daily for ten days). Neural activity was assessed by quantifying c-Fos-positive cells using immunohistochemistry. Acute stress induced widespread neural activation in both sexes, with notable sex differences in c-Fos expression in regions of the hypothalamus, amygdala, and midbrain. Chronic stress led to the desensitization of neuronal activity in most of these regions. Notably, chronically stressed females exhibited more desensitization in specific hypothalamic and amygdaloid regions compared to males. Despite this, corticosterone release remained elevated in stressed females, indicating a decoupling of hormonal and neural responses. These findings suggest that chronic stress elicits distinct neural adaptations in males and females, potentially contributing to the sex-specific vulnerability to neuropsychiatric disorders. Understanding these mechanisms may inform targeted interventions for stress-related pathologies.

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Deep bidirectional interplay between sensory integration and latent rule discovery

Deciphering the structure of variable sensory input is key to building an accurate model of ones environment. Humans can integrate stimulus sequences to estimate their sensory statistics, predict the timing of upcoming stimuli, but also discover rules governing sequence generation. However, whether these three forms of inference operate independently or synergistically remains untested. Here we report selective interactions between sensory integration, temporal prediction, and rule discovery in humans. Participants were exposed to rhythmic sequences of 10 stimuli governed or not by a latent rule - a predictable change in stimulus statistics after 5 stimuli - and then asked to predict the 10th stimulus from incomplete sequences. Individual differences in sensory integration timescale for rule-free sequences predicted efficient rule discovery. Conversely, discovering the latent rule shaped the timescale and format of sensory integration for rule-based sequences. Tampering with the rhythmicity of stimulus presentation impaired rule discovery without affecting sensory integration accuracy. Selective perturbations of recurrent neural networks (RNNs) trained in the same conditions confirmed these specific interactions. Together, these findings provide novel insights into the flexibility of human inferences based on variable yet predictable sensory input.

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A proposal for unifying statistical learning at different scales: Long-Horizon Associative Learning

Sensory inputs are rich with temporal patterns that unfold across multiple timescales. Discovering these regularities is essential for predicting future events and navigating the environment efficiently. Numerous models have been proposed to account for learning at specific temporal scales; however, they are often designed in isolation and rely on narrowly tuned statistical measures, limiting their generalizability to other paradigms. In contrast, humans typically learn without prior knowledge of the underlying structure or the relevant timescale at which regularities occur. Here, we present a unifying account of statistical learning that spans a wide range of temporal dependencies, from adjacent and non-adjacent transitions to complex network structures. This model, Long-Horizon Associative Learning (L-HAL), offers a biologically grounded implementation of the successor representation, or equivalently, the Free Energy Minimization Model. Reanalyzing data from 11 previously published studies, we show that a single neural mechanism, based on graded temporal overlap of associative traces and governed by a single free parameter ({beta}), captures both local statistical regularities and higher-order structural properties. This initial domain-general associative learning process, emerging from the graded structure of associations, may later scaffold to higher-level operations such as grouping, categorization, rule abstraction, and memory formation. Overall, this framework offers a conceptual synthesis that bridges disparate strands of the statistical learning literature and reframes apparent paradigm-specific effects as different expressions of a common underlying computation

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Statistical learning beyond words in human neonates

Interest in statistical learning in developmental studies stems from the observation that 8-month-olds were able to extract words from a monotone speech stream solely using the transition probabilities (TP) between syllables (Saffran et al., 1996). A simple mechanism was thus part of the human infants toolbox for discovering regularities in language. Since this seminal study, observations on statistical learning capabilities have multiplied across domains and species, challenging the hypothesis of a dedicated mechanism for language acquisition. Here, we leverage the two dimensions conveyed by speech -speaker identity and phonemes- to examine (1) whether neonates can compute TPs on one dimension despite irrelevant variation on the other and (2) whether the linguistic dimension enjoys an advantage over the voice dimension. In two experiments, we exposed neonates to artificial speech streams constructed by concatenating syllables while recording EEG. The sequence had a statistical structure based either on the phonetic content, while the voices varied randomly (Experiment 1) or on voices with random phonetic content (Experiment 2). After familiarisation, neonates heard isolated duplets adhering, or not, to the structure they were familiarised with. In both experiments, we observed neural entrainment at the frequency of the regularity and distinct Event-Related Potentials (ERP) to correct and incorrect duplets, highlighting the universality of statistical learning mechanisms and suggesting it operates on virtually any dimension the input is factorised. However, only linguistic duplets elicited a specific ERP component, potentially an N400 precursor, suggesting a lexical stage triggered by phonetic regularities already at birth. These results show that, from birth, multiple input regularities can be processed in parallel and feed different higher-order networks. HIGHLIGHTSO_LIHuman neonates are sensitive to regularities in speech, encompassing both phonetic content and voice dimension. C_LIO_LIThere is no observed advantage for statistical computations of linguistic content over voice content. C_LIO_LIBoth speech dimensions are processed in parallel by distinct networks. C_LIO_LIPhonetic regularities evoked a specific ERP component in the post-learning phase, suggesting activations along the pathway to the lexicon. C_LI

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Influence of environmental and biological factors on functional connectivity of the superior temporal sulcus in neonates scanned at term age

The superior temporal sulcus (STS), one of the first sulci visible during brain development, is a key region for human communication, notably hosting linguistic functions in the left hemisphere. Fetuses and premature newborns already process external sound, but the auditory environment is vastly different in-utero and ex-utero. Does this have an impact on the development of the auditory and linguistic networks? To answer this question, we studied the functional connectivity of regions bordering the STS, delimited in each individual including full-term and preterm male and female neonates born at different gestational ages but all scanned at term. We found that in addition to the expected contralateral connectivity, various STS parts were more strongly connected to specific distant regions, revealing the typical auditory/linguistic division across the two banks of the STS reported in adults. Furthermore, the right posterior STS was more connected to the contralateral hemisphere than the left. Finally, sex and premature birth had effects on both STS volume and connectivity. Female neonates displayed a lesser left-right asymmetry in STS depth, and heightened connectivity from the left posterior STS compared to males. Most importantly, despite equivalent scan age, full-term newborns had deeper sulci compared with preterms, and local connectivity in the right temporal region increased linearly with gestation length. These results emphasize the impact of both sex and early auditory environment on the setting up of the cerebral networks that might contribute to explain the milder impact of premature birth on language in females.

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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.

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Long-horizon associative learning explains human sensitivity to statistical and network structures in auditory sequences

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 non-adjacent elements with a memory decay. In the present study, we explored the neural correlates of this hypothesis via magnetoencephalography (MEG). Participants 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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The role of conscious attention in statistical learning: evidence from patients with impaired consciousness

The debate over whether conscious attention is necessary for statistical learning has produced mixed and conflicting results. Testing individuals with impaired consciousness may provide some insight, but very few studies have been conducted due to the difficulties associated with testing such patients. In this study, we examined the ability of patients with varying levels of consciousness disorders (DOC), including coma, unresponsive wakefulness syndrome, minimally conscious patients, and emergence from minimally conscious state patients, to extract statistical regularities from an artificial language composed of four randomly concatenated pseudowords. We used a methodology based on frequency tagging in EEG, which was developed in our previous studies on speech segmentation in sleeping neonates. Our study had two main objectives: firstly, to assess the automaticity of the segmentation process and explore correlations between the level of covert consciousness and the abilities to extract statistical regularities, second, to explore a potential new diagnostic indicator to aid in patient management by examining the correlation between successful statistical learning markers and consciousness level. We observed that segmentation abilities were preserved in some minimally conscious patients, suggesting that statistical learning is an inherently automatic low-level process. Due to significant inter-individual variability, word segmentation may not be a sufficiently robust candidate for clinical use, unlike temporal accuracy of auditory syllable responses, which correlates strongly with coma severity. Therefore, we propose that frequency tagging of an auditory stimulus train, a simple and robust measure, should be further investigated as a possible metric candidate for DOC diagnosis.

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Humans parsimoniously represent auditory sequences by pruning and completing the underlying network structure

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 structures such as network 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 compressed representations of the world. Moreover, this bias can be analytically modeled by a memory/efficiency trade-off. This model correctly account for previous findings, including local transition probabilities as well as high order network structures, unifying statistical learning across scales. We finally propose putative brain implementations of such bias.

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Tracking transitional probabilities and segmenting auditory sequences are dissociable processes in adults and neonates

Since speech is a continuous stream with no systematic boundaries between words, how do pre-verbal infants manage to discover words? A proposed solution is that they might use the transitional probability between adjacent syllables, which drops at word boundaries. Here, we tested the limits of this mechanism by increasing the size of the word-unit to 4 syllables, and its automaticity by testing asleep neonates. Using markers of statistical learning in neonates EEG, compared to adult behavioral performances in the same task, we confirmed that statistical learning is automatic enough to be efficient even in sleeping neonates. But we also revealed that: 1) Successfully tracking transition probabilities in a sequence is not sufficient to segment it 2) Prosodic cues, as subtle as subliminal pauses, enable to recover segmenting capacities 3) Adults and neonates capacities are remarkably similar despite the difference of maturation and expertise. Finally, we observed that learning increased the similarity of neural responses across infants, providing a new neural marker to monitor learning. Thus, from birth, infants are equipped with adult-like tools, allowing to extract small coherent word-like units within auditory streams, based on the combination of statistical analyses and prosodic cues.

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From computing transition probabilities to word recognition in sleeping neonates, a two-step neural tale

Extracting statistical regularities from the environment is a primary learning mechanism, which might support language acquisition. While it is known that infants are sensitive to transition probabilities between syllables in continuous speech, the format of the encoded representation remains unknown. Here we used electrophysiology to investigate how 31 full-term neonates process an artificial language build by the random concatenation of four pseudo-words and which information they retain. We used neural entrainment as a marker of the regularities the brain is tracking in the stream during learning. Then, we compared the evoked-related potentials (ERP) to different triplets to further explore the format of the information kept in memory. After only two minutes of familiarization with the artificial language, we observed significant neural entrainment at the word rate over left temporal electrodes compared to a random stream, demonstrating that sleeping neonates automatically and rapidly extracted the word pattern. ERPs significantly differed between triplets starting or not with the correct first syllable in the test phase, but no difference was associated with later violations in transition probabilities, revealing a change in the representation format between segmentation and memory processes. If the transition probabilities were used to segment the stream, the retained representation relied on syllables ordinal position, but still without a complete representation of the words at this age. Our results revealed a two-step learning strategy, probably involving different brain regions.

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Automated Pipeline for Infants Continuous EEG (APICE): a flexible pipeline for developmental studies

Infant electroencephalography (EEG) presents several challenges compared with adult data. Recordings are typically short. Motion artifacts heavily contaminate the data. The EEG neural signal and the artifacts change throughout development. Traditional data preprocessing pipelines have been developed mainly for event-related potentials analyses, and they required manual steps, or use fixed thresholds for rejecting epochs. However, larger datasets make the use of manual steps infeasible, and new analytical approaches may have different preprocessing requirements. Here we propose an Automated Pipeline for Infants Continuous EEG (APICE). APICE is fully automated, flexible, and modular. Artifacts are detected using multiple algorithms and adaptive thresholds, making it suitable to different age groups and testing procedures without redefining parameters. Artifacts detection and correction of transient artifacts is performed on continuous data, allowing for better data recovery and providing flexibility (i.e., the same preprocessing is usable for different analyses). Here we describe APICE and validate it using two infant datasets of different ages tested in different experimental paradigms. We also tested the combination of APICE with common data cleaning methods such as Independent Component Analysis and Denoising Source Separation. APICE uses EEGLAB and compatible custom functions. It is freely available at https://github.com/neurokidslab/eeg_preprocessing, together with example scripts.

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Rapid ocular responses are a robust marker for bottom-up driven auditory salience

Despite the prevalent use of alerting sounds in alarms and human-machine interface systems, we have only a rudimentary understanding of what determines auditory salience - the automatic attraction of attention by sound - and the brain mechanisms which underlie this process. A major roadblock to understanding has been the lack of a robust, objective means of quantifying sound-driven attentional capture. Here we demonstrate that microsaccade inhibition - an oculomotor response arising within 300ms of sound onset - is a robust marker for the salience of brief sounds. Specifically, we show that a crowd sourced (N=911) subjective salience ranking correlated robustly with the superior colliculus (SC) mediated ocular freezing response measured in naive, passively listening participants (replicated in two groups of N=15 each). More salient sounds evoked earlier microsaccadic inhibition, consistent with a faster orienting response. These results establish that microsaccade-indexed activity within the SC is a practical objective measure for auditory salience.\n\nSignificance statementMicrosaccades are small, rapid, fixational eye movements, measurable with sensitive eye-tracking equipment. We reveal a novel, robust link between microsaccade dynamics and the subjective salience of brief sounds (salience ranking obtained from a large number of participants in an online experiment): Within 300 ms of sound onset, the eyes of naive, passively listening participants demonstrate different microsaccade patterns as a function of the sounds perceptual salience. These results position the Superior Colliculus (the generator of microsaccades) as an important brain area to investigate in the context of a putative multi-modal salience-hub and establish a robust objective means for quantifying auditory salience, critical in the development of sound-based human machine interfaces.

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