Joanna Duda-Goławska

Early speech development involves learning complex motor actions under constantly changing body proportions and biomechanics. However, the motor underpinnings of infant vocal production are largely unknown. The key question is whether vocal production (a complex motor action) is related to limb movements at the early stages of learning to speak. We tracked the development of coupling between limb movements and vocalisations in infants at 4, 6, 9 and 12 months of age. Limb movements were tightly coupled with vocalisation onsets at all time points across infancy. The initiation of speech-like vocalisations in early life is accompanied by movements of legs and arms. These findings suggest that the coupling of limb movements with vocalisation could be a potential mechanism supporting vocal production in early infancy, as well as a likely precursor to the emergence of gestures.

The efficient classification of body position is crucial for monitoring infants’ motor development. It may fast-track the early detection of developmental issues related not only to the acquisition of motor milestones but also to postural stability and movement patterns. In turn, this may facilitate and enhance opportunities for early intervention that are crucial for promoting healthy growth and development. The manual classification of human body position based on video recordings is labour-intensive, leading to the adoption of Inertial Motion Unit (IMU) sensors. IMUs measure acceleration, angular velocity, and magnetic field intensity, enabling the automated classification of body position. Many research teams are currently employing supervised machine learning classifiers that utilise hand-crafted features for data segment classification. In this study, we used a longitudinal dataset of IMU recordings made in the lab in three different play activities of infants aged 4–12 months. The classification was conducted based on manually annotated video recordings. We found superior performance of the CatBoost Classifier over the Random Forest Classifier in the task of classifying five positions based on IMU sensor data from infants, yielding excellent classification accuracy of the Supine (97.7%), Sitting (93.5%), and Prone (89.9%) positions. Moreover, using data ablation experiments and analysing the SHAP (SHapley Additive exPlanations) values, the study assessed the importance of various groups of features from both the time and frequency domains. The results highlight that both accelerometer and magnetometer data, especially their statistical characteristics, are critical contributors to improving the accuracy of body position classification.

Efficient classification of body position is crucial for monitoring infants’ motor development. It may fast-track the early detection of developmental issues related not only to the acquisition of motor milestones but also to postural stability and movement patterns. In turn, this may facilitate and enhance opportunities for early intervention that are crucial for promoting healthy growth and development. Manual classification of human body position based on video recordings is labour-intensive, leading to the adoption of Inertial Motion Unit (IMU) sensors. IMUs measure acceleration, angular velocity, and magnetic field intensity, enabling automated classification of body position. Many research teams are currently employing supervised machine learning classifiers that utilise hand-crafted features for data segment classification. In this study, we used a longitudinal dataset of IMU recordings made in the lab in three different play activities of infants aged 4-12 months. The classification was conducted based on manually annotated video recordings. We found superior performance of the CatBoost Classifier over the Random Forest Classifier in the task of classifying five positions based on IMU sensor data from infants, yielding excellent classification accuracy of Supine (97.7%), Sitting (93.5%) and Prone (89.9%) position. Moreover, using data ablation experiments and analysing the SHAP values, the study assessed the importance of various groups of features from both time and frequency domains. The results highlight that both accelerometer and magnetometer data, especially their statistical characteristics, are critical contributors to improving the accuracy of body position classification.

Warmth and competence are fundamental dimensions of social cognition. This also applies to the interpretation of ambiguous symbolic stimuli in terms of their relation to warmth or competence. The affective state of an individual may affect the way people interpret the neutral stimuli in the environment. As previous findings have shown, it is possible to alter the perception of neutral social stimuli in terms of warmth vs. competence by eliciting an incidental affect with the use of emotion-laden words. In the current experiment, we expected the valence and origin of an affective state, factors ascribing emotionally laden words, to be able to switch the interpretation of the neutral objects. We have shown in behavioural results that negative valence and reflective origins promote the interpretation of unknown objects in terms of competence rather than warmth. Furthermore, electrophysiological-response-locked analyses revealed differences specific to negative valence while making the decision in the ambiguous task and while executing it. The results of the current experiment show that the usage of warmth and competence in social cognition is susceptible to affective state manipulation. In addition, the results are coherent with the evolutionary perspective on social cognition (valence effects) as well as with predictions of the dual mind model of emotion (origin effects).

In this study, we propose a new algorithm for analysing event-related components observed in EEG signals in psychological experiments. We investigate its capabilities and limitations. The algorithm is based on multivariate matching pursuit and clustering. It is aimed to find patterns in EEG signals which are similar across different experimental conditions, but it allows for variations in amplitude and slight variability in topography. The method proved to yield expected results in numerical simulations. For the real data coming from an emotional categorisation task experiment, we obtained two indications. First, the method can be used as a specific filter that reduces the variability of components, as defined classically, within each experimental condition. Second, equivalent dipoles fitted to items of the activity clusters identified by the algorithm localise in compact brain areas related to the task performed by the subjects across experimental conditions. Thus this activity may be studied as candidates for hypothetical latent components. The proposed algorithm is a promising new tool in ERP studies, which deserves further experimental evaluations.

The emotional properties of words, such as valence and arousal, influence the way we perceive and process verbal stimuli. Recently, subjective significance was found to be an additional factor describing the activational aspect of emotional reactions, which is vital for the cognitive consequences of emotional stimuli processing. Subjective significance represents the form of mental activation specific to reflective mind processing. The Lexical Decision Task (LDT) is a paradigm allowing the investigation of the involuntary processing of meaning and differentiating this processing from the formal processing of the perceptual features of words. In this study, we wanted to search for the consequences of valence, arousal, and subjective significance for the involuntary processing of verbal stimuli meaning indexed by both behavioral measures (reaction latencies) and electrophysiological measures (Event-Related Potentials: ERPs). We expected subjective significance, as the reflective form of activation, to shorten response latencies in LDT. We also expected subjective significance to modulate the amplitude of the ERP FN400 component, reducing the negative-going deflection of the potential. We expected valence to shape the LPC component amplitude, differentiating between negative and positive valences, since the LPC indexes the meaning processing. Indeed, the results confirmed our expectations and showed that subjective significance is a factor independent from the arousal and valence that shapes the involuntary processing of verbal stimuli, especially the detection of a link between stimulus and meaning indexed by the FN400. Moreover, we found that the LPC amplitude was differentiated by valence level.