| Publications of year 2020 |
| Books |
| Theses |
| Articles in journals |
| Abstract: | While expressions have traditionally been binarized as compositional and noncompositional in linguistic theory, Multiword Expressions (MWEs) demonstrate finer-grained distinctions. Using Association Measures like Pointwise Mutual Information and Dice's Coefficient, MWEs can be characterized as having different degrees of conventionalization and predictability. Our goal is to investigate how these gradiences could reflect cognitive processes. In this study, fMRI recordings of naturalistic narrative comprehension is used to probe to what extent these computational measures and the cognitive processes they could operationalize are observable during on-line sentence processing. Our results show that Dice's Coefficent, representing lexical predictability, is a better predictor of neural activation for processing MWEs. Overall our experimental approach demonstrates how we can test the cognitive plausibility of computational metrics by comparing it against neuroimaging data. |
| Abstract: | This study examined the brain areas involved in combining words into larger units when there are few or no morphosyntactic cues. We manipulated constituent length in word strings of the same length under two conditions: Mandarin sentence, which had sparse morphosyntactic cues, and nominal phrase that had no morphosyntactic cues [e.g., ((honey mustard) (chicken burger))]. Contrasting sentences to word lists revealed a network that largely overlapped with the one reported in languages with rich morphosyntactic cues, including left IFGorb/IFGtri and areas along left STG/STS. Both conditions showed increased activation in left IFGtri/IFGorb in functional ROIs defined based on previous study in sentence processing, while the nominal phrases additionally revealed a constituent length effect in bilateral dorsal IFGtri, left IFGoper, left pMTG/pSTG, left IPL, and several subcortical areas, which might reflect an increased reliance on semantic and pragmatic information. Moreover, in upper left IFGtri/IFGoper and left thalamus/caudate, this effect increased with the participants' tendency to combine nouns into phrases. The absence of syntactic constraints on linguistic composition might highlight individual differences in cognitive control, which helps to integrate non-syntactic information. |
| Abstract: | Detecting and learning temporal regularities is essential to accurately predict the future. Past research indicates that humans are sensitive to two types of sequential regularities: deterministic rules, which afford sure predictions, and statistical biases, which govern the probabilities of individual items and their transitions. How does the human brain arbitrate between those two types? We used finger tracking to continuously monitor the online build-up of evidence, confidence, false alarms and changes-of-mind during sequence learning. All these aspects of behaviour conformed tightly to a hierarchical Bayesian inference model with distinct hypothesis spaces for statistics and rules, yet linked by a single probabilistic currency. Alternative models based either on a single statistical mechanism or on two non-commensurable systems were rejected. Our results indicate that a hierarchical Bayesian inference mechanism, capable of operating over several distinct hypothesis spaces, underlies the human capability to learn both statistics and rules. |
| Abstract: | Learning in a changing, uncertain environment is a difficult problem. A popular solution is to predict future observations and then use surprising outcomes to update those predictions. However, humans also have a sense of confidence that characterizes the precision of their predictions. Bayesian models use a confidence-weighting principle to regulate learning: for a given surprise, the update is smaller when the confidence about the prediction was higher. Prior behavioral evidence indicates that human learning adheres to this confidence-weighting principle. Here, we explored the human brain dynamics sub-tending the confidence-weighting of learning using magneto-encephalography (MEG). During our volatile probability learning task, subjects' confidence reports conformed with Bayesian inference. MEG revealed several stimulus-evoked brain responses whose amplitude reflected surprise, and some of them were further shaped by confidence: surprise amplified the stimulus-evoked response whereas confidence dampened it. Confidence about predictions also modulated several aspects of the brain state: pupil-linked arousal and beta-range (15-30 Hz) oscillations. The brain state in turn modulated specific stimulus-evoked surprise responses following the confidence-weighting principle. Our results thus indicate that there exist, in the human brain, signals reflecting surprise that are dampened by confidence in a way that is appropriate for learning according to Bayesian inference. They also suggest a mechanism for confidence-weighted learning: confidence about predictions would modulate intrinsic properties of the brain state to amplify or dampen surprise responses evoked by discrepant observations. |
| Abstract: | Nonhuman primate neuroimaging is on the cusp of a transformation, much in the same way its human counterpart was in 2010, when the Human Connectome Project was launched to accelerate progress. Inspired by an open data-sharing initiative, the global community recently met and, in this article, breaks through obstacles to define its ambitions. |
| Abstract: | We present an extension of the Individual Brain Charting dataset -a high spatial-resolution, multi-task, functional Magnetic Resonance Imaging dataset, intended to support the investigation on the functional principles governing cognition in the human brain. The concomitant data acquisition from the same 12 participants, in the same environment, allows to obtain in the long run finer cognitive topographies, free from inter-subject and inter-site variability. This second release provides more data from psychological domains present in the first release, and also yields data featuring new ones. It includes tasks on e.g. mental time travel, reward, theory-of-mind, pain, numerosity, self-reference effect and speech recognition. In total, 13 tasks with 86 contrasts were added to the dataset and 63 new components were included in the cognitive description of the ensuing contrasts. As the dataset becomes larger, the collection of the corresponding topographies becomes more comprehensive, leading to better brain-atlasing frameworks. This dataset is an open-access facility; raw data and derivatives are publicly available in neuroimaging repositories. Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.12958181 |
| Miscellaneous |
| Abstract: | Humans have much more sophisticated communication skills t |