Eugenio Parise

Electroencephalography (EEG) is an established method for investigating neurocognitive functions during human development. In cognitive neuroscience, time-frequency analysis of the EEG is a widely used analytical approach. This paper introduces WTools, a new MATLAB-based toolbox for time-frequency analysis of EEG signals using complex wavelet transformation. WTools features an intuitive GUI that guides users through the analysis steps, focusing on essential parameters. Being free and open-source, it can be integrated and expanded with new features, making it a handy tool that is growing its popularity in developmental cognitive neuroscience. While the default settings follow established pipelines in developmental research, the toolbox also provides the flexibility to accommodate settings more commonly used in adult neuroscience. Here, we provide a detailed description of the WTools algorithm for wavelet transformation and we compare it with state-of-the-art methods implemented in EEGLAB. Alongside the official tool release, we offer a comprehensive, illustrated tutorial with sample infant EEG data designed to support novice users, enhance accessibility and promote the transparent and reproducible usage of WTools.

Humans categorize objects not only based on perceptual features (e.g. red, rounded), but also function (e.g. used to transport people). Category membership can be communicated via labelling (e.g. ‘apple’, ‘vehicle’). While it is well established that even preverbal infants rely on labels to learn categories, it remains unclear what is the nature of those categories: whether they simply contain sets of visual features diagnostic of category membership, or whether they additionally contain abstract category markers or symbols (e.g. linguistic in the form of category labels or non-linguistic). To address this question, we first used labelling to teach two novel object categories, each composed of unfamiliar visually unrelated objects, to adults and nine-month-olds. Then, we assessed categorization in an electroencephalography category-oddball task. Both adults and infants displayed stronger neural responses to the infrequent category, which, in the absence of visual features shared by all category members, indicates that the categories they set up contained feature-independent category markers. Well before language production starts, labels help infants to discover categories without relying on perceptual similarities across objects and build category representations with summary elements that may be critical for the development of abstract thought.

Electroencephalography (EEG) is an established method for investigating neurocognitive functions during human development. In cognitive neuroscience, time-frequency analysis of the EEG is a widely used analytical approach. This paper introduces WTools, a new MATLAB-based toolbox capable of performing time-frequency analysis of EEG signals using complex wavelet transformation. WTools features an intuitive GUI that guides users through the analysis steps, focusing on essential parameters. Being free and open-source, it can be integrated and expanded with new features, making it a handy tool that is growing its popularity in developmental cognitive neuroscience. Here, we provide a detailed description of the WTools algorithm for wavelet transformation and we compare it with state-of-the-art methods implemented in EEGLAB. Alongside the official tool release, we also offer a comprehensive illustrated tutorial to enhance accessibility and promote the transparent and reproducible usage of WTools by the scientific community. Highlights: WTools is a new MATLAB-based toolbox for time-frequency analysis of the EEG signal It uses wavelet transformation and the algorithm is fully described It is designed to be very user friendly through its simple GUI It is open source and flexible as the code is freely available We provide a step-by-step tutorial and an example dataset to learn the toolbox

When infants start mastering their first language, they may start to notice when words are used incorrectly. Around 14‐months of age, infants detect incorrect labeling when they are presented with an object which is labeled while still visible. However, things that are referred to are often out of sight when we communicate about them. The present study examined infants' detection of semantic mismatch when the object was occluded at the time of labeling. Specifically, we investigated whether mislabeling that referred to an occluded object could elicit a semantic mismatch. We showed 14‐month‐old Danish‐speaking infants events where an onscreen agent showed an object and then hid it in a box. This was followed by another agent's hand pointing at the box, and a concurrent auditory category label played, which either matched or did not match the hidden object. Our results indicate that there is an effect of semantic mismatch with a larger negativity in incongruent trials. Thus, infants detected a mismatch, as indicated by a larger n400, when occluded objects were mislabeled. This finding suggests that infants can sustain an object representation in memory and compare it to a semantic representation of an auditory category label.

When infants start mastering their first language, they may start to notice when words are used incorrectly. Around 14-months of age, infants detect incorrect labeling when they are presented with an object which is labeled while still visible. However, things that are referred to are often out of sight when we communicate about them. The present study examined infants’ detection of semantic mismatch when the object was occluded at the time of labeling. Specifically, we investigated whether mislabeling that referred to an occluded object could elicit a semantic mismatch. We showed 14-month-old Danish-speaking infants events where an onscreen agent showed an object and then hid it in a box. This was followed by another agent’s hand pointing at the box, and a concurrent auditory category label played, which either matched or did not match the hidden object. Our results indicate that there is an effect of semantic mismatch with a larger negativity in incongruent trials. Thus, infants detected a mismatch, as indicated by a larger n400, when occluded objects were mislabeled. This finding suggests that infants can sustain an object representation in memory and compare it to a semantic representation of an auditory category label.

Electroencephalogram (EEG) data are typically affected by artifacts. The detection and removal of bad channels (i.e., with poor signal-to-noise ratio) is a crucial initial step. EEG data acquired from different populations require different cleaning strategies due to the inherent differences in the data quality, the artifacts’ nature, and the employed experimental paradigm. To deal with such differences, we propose a robust EEG bad channel detection method based on the Local Outlier Factor (LOF) algorithm. Unlike most existing bad channel detection algorithms that look for the global distribution of channels, LOF identifies bad channels relative to the local cluster of channels, which makes it adaptable to any kind of EEG. To test the performance and versatility of the proposed algorithm, we validated it on EEG acquired from three populations (newborns, infants, and adults) and using two experimental paradigms (event-related and frequency-tagging). We found that LOF can be applied to all kinds of EEG data after calibrating its main hyperparameter: the LOF threshold. We benchmarked the performance of our approach with the existing state-of-the-art (SoA) bad channel detection methods. We found that LOF outperforms all of them by improving the F1 Score, our chosen performance metric, by about 40% for newborns and infants and 87.5% for adults.

This study measured mu rhythm desynchronization (MRD), while nine-month-old infants observed an agent extend her arm and hand, palm up (‘back-of-hand action’) either in social (object and recipient present), individual (object present, recipient absent), or social object-absent (recipient present, object absent) situations across two experiments. In addition, infants’ MRD was measured as they reached for objects. Results revealed significant mu desynchronization in the right centro-parietal region selectively for the social group, indicating that infants processed the back-of-hand action as an object-directed request. Findings suggest to extend the action reconstruction account to object-directed communicative actions as well.

A recently discovered electrophysiological response, the social N400, suggests that we use our language system to track how social partners comprehend language. Listeners show an increased N400 response, when themselves not, only a communicative partner experiences a semantic incongruity. Does the N400 reflect purely semantic or mentalistic computations as well? Do we attribute language comprehension to communicative partners using our semantic systems? In five electrophysiological experiments we identified two subcomponents of the social N400. First, we manipulated the presence-absence of an Observer during object naming: the semantic memory system was activated by the presence of a social partner in addition to semantic predictions for the self. Next, we induced a false belief—and a consequent miscomprehension—in the Observer. Participants showed the social N400, over and above the social presence effect, to labels that were incongruent for the Observer, even though they were congruent for them. This effect appeared only if participants received explicit instructions to track the comprehension of the Observer. These findings suggest that the semantic systems of the brain are not merely sensitive to social information and contribute to the attribution of comprehension, but they appear to be mentalistic in nature.

Gaze following is an early-emerging skill in infancy argued to be fundamental to joint attention and later language development. However, how gaze following emerges is a topic of great debate. Representational theories assume that in order to follow adults’ gaze, infants must have a rich sensitivity to adults’ communicative intention from birth. In contrast, learning-based theories hold that infants may learn to gaze follow based on low-level social reinforcement, without the need to understand others’ mental states. Nagai et al. (2006) successfully taught a robot to gaze follow through social reinforcement and found that the robot learned in stages: first in the horizontal plane, and later in the vertical plane—a prediction that does not follow from representational theories. In the current study, we tested this prediction in an eye-tracking paradigm. Six-month-olds did not follow gaze in either the horizontal or vertical plane, whereas 12-month-olds and 18-month-olds only followed gaze in the horizontal plane. These results confirm the core prediction of the robot model, suggesting that children may also learn to gaze follow through social reinforcement coupled with a structured learning environment.