The emergence of structure from continuous speech: Multiple cues and constraints for speech segmentation and its neural bases

This thesis studies learning mechanisms and cognitive biases present from birth involved in language acquisition, in particular in speech segmentation and the extraction of linguistic regularities. Due to the sequential nature of speech, uncovering language structure is closely related with how infants segment speech. We investigated infant abilities to track distributional properties on the stimuli, and the role of prosodic cues and of memory constraints. In two experiments we investigated neonates\u2019 capacities to segment and extract words from continuous speech by using fNIRS. Experiment 1 demonstrates that neonates can segment and extract words from continuous speech based on distributional cues alone; whereas Experiment 2 shows that newborns can extract words when they are marked only by prosodic contours. Additionally we implemented a method for the study of the dynamics of the functional connectivity of the neonatal brain during speech segmentation tasks. We identi\ufb01ed stable and reproducible functional networks with small-world properties that were task independent. Moreover, we observed periods of high global and low global connectivity, which remarkably, were task dependent, with stronger values when neonates listen to speech with structure. In another set of experiments we studied memory constraints on the encoding of six-syllabic words in newborns using fNIRS. Experiment 4 demonstrates that the edge syllables of a sequence are better encoded, and Experiment 5 goes beyond by showing that a subtle pause enhances the encoding of intermediate syllables, which evidences the role of prosodic cues in speech processing. A \ufb01nal group of experiments explore how information is encoded when it is presented continuously across different modalities; speci\ufb01cally if an abstract encoding of the sequences\u2019 constituents is generated. Experiments 6-9 suggest that adults form an abstract representation of words based on the position of the syllables, but only in the speech modality. In Experiments 10 and 11 we used pupillometry to test the same in 5-month-old infants. Nevertheless results were not conclusive, we did not \ufb01nd evidence of an abstract encoding

Graph analysis of functional brain networks: practical issues in translational neuroscience

The brain can be regarded as a network: a connected system where nodes, or units, represent different specialized regions and links, or connections, represent communication pathways. From a functional perspective communication is coded by temporal dependence between the activities of different brain areas. In the last decade, the abstract representation of the brain as a graph has allowed to visualize functional brain networks and describe their non-trivial topological properties in a compact and objective way. Nowadays, the use of graph analysis in translational neuroscience has become essential to quantify brain dysfunctions in terms of aberrant reconfiguration of functional brain networks. Despite its evident impact, graph analysis of functional brain networks is not a simple toolbox that can be blindly applied to brain signals. On the one hand, it requires a know-how of all the methodological steps of the processing pipeline that manipulates the input brain signals and extract the functional network properties. On the other hand, a knowledge of the neural phenomenon under study is required to perform physiological-relevant analysis. The aim of this review is to provide practical indications to make sense of brain network analysis and contrast counterproductive attitudes

Nonparametric Two-Sample Test for Networks Using Joint Graphon Estimation

This paper focuses on the comparison of networks on the basis of statistical inference. For that purpose, we rely on smooth graphon models as a nonparametric modeling strategy that is able to capture complex structural patterns. The graphon itself can be viewed more broadly as density or intensity function on networks, making the model a natural choice for comparison purposes. Extending graphon estimation towards modeling multiple networks simultaneously consequently provides substantial information about the (dis-)similarity between networks. Fitting such a joint model - which can be accomplished by applying an EM-type algorithm - provides a joint graphon estimate plus a corresponding prediction of the node positions for each network. In particular, it entails a generalized network alignment, where nearby nodes play similar structural roles in their respective domains. Given that, we construct a chi-squared test on equivalence of network structures. Simulation studies and real-world examples support the applicability of our network comparison strategy.Comment: 25 pages, 6 figure

What does semantic tiling of the cortex tell us about semantics?

Recent use of voxel-wise modeling in cognitive neuroscience suggests that semantic maps tile the cortex. Although this impressive research establishes distributed cortical areas active during the conceptual processing that underlies semantics, it tells us little about the nature of this processing. While mapping concepts between Marr's computational and implementation levels to support neural encoding and decoding, this approach ignores Marr's algorithmic level, central for understanding the mechanisms that implement cognition, in general, and conceptual processing, in particular. Following decades of research in cognitive science and neuroscience, what do we know so far about the representation and processing mechanisms that implement conceptual abilities? Most basically, much is known about the mechanisms associated with: (1) features and frame representations, (2) grounded, abstract, and linguistic representations, (3) knowledge-based inference, (4) concept composition, and (5) conceptual flexibility. Rather than explaining these fundamental representation and processing mechanisms, semantic tiles simply provide a trace of their activity over a relatively short time period within a specific learning context. Establishing the mechanisms that implement conceptual processing in the brain will require more than mapping it to cortical (and sub-cortical) activity, with process models from cognitive science likely to play central roles in specifying the intervening mechanisms. More generally, neuroscience will not achieve its basic goals until it establishes algorithmic-level mechanisms that contribute essential explanations to how the brain works, going beyond simply establishing the brain areas that respond to various task conditions