| Publications of year 2026 |
| Articles in journals |
| Abstract: | Sensory inputs are rich with temporal patterns that unfold across multiple timescales. Uncovering these regularities is essential for anticipating 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 nonadjacent transitions to complex network structures. This model, long-horizon associative learning, 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 captures both local statistical regularities and higher-order structural properties. This mechanism rests on graded temporal overlap of associative traces and is governed by a single free parameter (?). 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. |
| Abstract: | This repository contains recordings from 56 human participants, collected during quiet wakefulness using magnetoencephalography (MEG). Participants were asked to remain quietly awake with their eyes open, fixating on a monitor screen for a few minutes. Following the resting-state MEG recording, participants were unexpectedly asked to estimate its duration as precisely as possible in minutes and seconds. MEG resting-state durations ranged from 2 to 5 minutes. The experimenter further ensured participants did not guess the timing nature of the task beforehand, so that this task is a pure retrospective duration estimation (episodic time). Additionally, they provided a judgment of the passage of time using a Likert scale ranging from 1 (very slow) to 5 (very fast). See the associated publication (Azizi et al., 2023) for details. Repository structure Raw data. We provide the raw MEG and anatomical MRI data in the archive files named rs-meg\_sub* by batches of 10 participants. Each resting-state is associated with an empty-room MEG recording located in the archive rs-meg\_emptyrooms, organized by session date. Anonymized session dates that match resting-state and empty-room recordings are available in the tsv sidecar files for each MEG recording. Empty-room recordings were already SSS-, Maxwell-, and notch-filtered, and downsampled to 500 Hz. Derivatives. The derivatives archive contains the transformation file (*-trans.fif), the cortical reconstruction files from FreeSurfer, and the MEG continuous raw data, preprocessed using MNE-Python and following the analyses described in Azizi et al. (2023). Data exclusion. We also provide the raw MEG data of participants excluded from Azizi et al. (2023). For simplicity, participant numbers were remapped: sub-e57 and onward are excluded. Exclusion details are in the participants.tsv file. Download Decompressing all files into the same root directory yields a BIDS-compliant data structure. On Linux and macOS, data extraction of *.tar.gz files can be performed with tar -xzf derivatives.tar.gz -C /path/to/output-directory. On Windows, it can be done with tools such as 7-Zip or WinRAR. MEG recording Electromagnetic brain activity was recorded using a whole-head Elekta Neuromag Vector View 306 MEG system (Neuromag Elekta LTD) equipped with 102 triple-sensor elements (one magnetometer and two orthogonal planar gradiometers) in a magnetically shielded room. Data were sampled at 2 kHz or 1 kHz, as indicated in the .json file associated with each MEG recording. An online low-pass filter was set at 500 Hz for data sampled at 2 kHz and 330 Hz for data sampled at 1 kHz. No high-pass filter (DC recordings) was applied. Horizontal and vertical electrooculograms (EOG) and electrocardiogram (ECG) were also recorded. Participants' head position was measured before MEG recording by means of four head position coils (HPI) placed over the frontal and mastoid areas. Anatomical MRI recording The T1-weighted aMRI was recorded using a 3-T Siemens Trio MRI scanner. Parameters of the sequence were: voxel size: 1.0 x 1.0 x 1.1 mm, acquisition time: 466 s, repetition time TR: 2300 ms, and echo time TE: 2.98 ms. |
| Abstract: | In the first months postterm, human infants are traditionally viewed as passive to their environment, unable to focus or sustain attention on specific objects. Here, we asked whether 4-mo-old infants could engage their attention strongly enough in a visual task to block the perception of a subsequent interesting stimulus--a phenomenon known as attentional blink, which in adults reflects a serial processing bottleneck for accessing a central workspace. We presented three successive visual events: a central teddy bear (T1) followed by a lateralized face and scrambled face (T2) at varying stimulus onset asynchrony (SOAs: 400, 800, and 1,200 ms), then the same face reappeared after 1,600 ms (T3). We monitored saccades toward the face, pupil size, and electroencephalic (EEG) responses to the flickering background. Our behavioral and EEG results showed that infants missed the lateralized face at the shortest SOA (400 ms). At 800 ms, detection occurred only when the face appeared in the left hemifield, while detection in the right hemifield required 1,200 ms, suggesting a hemispheric asymmetry in face processing. Furthermore, at SOAs where T2 should be visible, a trial-by-trial metric based on event-related variability revealed that the depth of attentional engagement to T1 predicted access to T2, despite identical visual input. These findings support the presence of a global workspace in early infancy, though with slower dynamics. |
| Abstract: | 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 6-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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Note that this is not the exhaustive list of publications, but only a selection. Contact the individual authors for complete lists of references.
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