Structuring unleashed expression: Developmental foundations of human communication.

The target article highlights the sources of open-endedness of human communication. However, the authors' perspective does not account for the structure of particular communication systems. To this end, we extend the authors' perspective, in the spirit of evolutionary extended synthesis, with a detailed account of the sources of constraints imposed upon expression in the course of child development.

Is love an abstract concept? A view of concepts from an interaction-based perspective.

Research concerning concepts in the cognitive sciences has been dominated by the information-processing approach, which has resulted in a certain narrowing of the range of questions and methods of investigation. Recent trends have sought to broaden the scope of such research, but they have not yet been integrated within a theoretical framework that would allow us to reconcile new perspectives with the insights already obtained. In this paper, we focus on the processes involved in early concept acquisition and demonstrate that certain aspects of these processes remain largely understudied. These aspects include the primacy of movement and coordination with others within a structured social environment as well as the importance of first-person experiences pertaining to perception and action. We argue that alternative approaches to cognition, such as ecological psychology, enactivism and interactivism, are helpful for foregrounding these understudied areas. These approaches can complement the extant research concerning concepts to help us obtain a more comprehensive view of knowledge structures, thus providing us with a new perspective on recurring problems, suggesting novel questions and enriching our methodological toolbox. This article is part of the theme issue 'Concepts in interaction: social engagement and inner experiences'.

Coordinating movements and beliefs: Different facets of doing things together.

This study investigates the relations between two forms of joint action (JA)-movement coordination (MC) and goal attainment-and theory of mind (ToM), contrasting the interactionist and traditional cognitivist views. A custom task was carried out to measure the properties of the JAs between children and their parents, while classical tasks were performed to measure first- and second-order ToM. Thereafter, cross-recurrence quantification analysis was applied to quantify participants' movements. The children were from Poland and were aged 42, 66, and 78 months (N = 297, 133 girls, White, from a large city). The results suggested that the characteristics of dyad MC influence goal attainment and are related to children's first-order ToM (R2  = .447) but not to their second-order ToM.

Dynamics of Remote Communication: Movement Coordination in Video-Mediated and Face-to-Face Conversations.

The present pandemic forced our daily interactions to move into the virtual world. People had to adapt to new communication media that afford different ways of interaction. Remote communication decreases the availability and salience of some cues but also may enable and highlight others. Importantly, basic movement dynamics, which are crucial for any interaction as they are responsible for the informational and affective coupling, are affected. It is therefore essential to discover exactly how these dynamics change. In this exploratory study of six interacting dyads we use traditional variability measures and cross recurrence quantification analysis to compare the movement coordination dynamics in quasi-natural dialogues in four situations: (1) remote video-mediated conversations with a self-view mirror image present, (2) remote video-mediated conversations without a self-view, (3) face-to-face conversations with a self-view, and (4) face-to-face conversations without a self-view. We discovered that in remote interactions movements pertaining to communicative gestures were exaggerated, while the stability of interpersonal coordination was greatly decreased. The presence of the self-view image made the gestures less exaggerated, but did not affect the coordination. The dynamical analyses are helpful in understanding the interaction processes and may be useful in explaining phenomena connected with video-mediated communication, such as "Zoom fatigue".

The cognitive basis of dyslexia in school-aged children: A multiple case study in a transparent orthography.

This study focuses on the role of numerous cognitive skills such as phonological awareness (PA), rapid automatized naming (RAN), visual and selective attention, auditory skills, and implicit learning in developmental dyslexia. We examined the (co)existence of cognitive deficits in dyslexia and assessed cognitive skills' predictive value for reading. First, we compared school-aged children with severe reading impairment (n = 51) to typical readers (n = 71) to explore the individual patterns of deficits in dyslexia. Children with dyslexia, as a group, presented low PA and RAN scores, as well as limited implicit learning skills. However, we found no differences in the other domains. We found a phonological deficit in 51% and a RAN deficit in 26% of children with dyslexia. These deficits coexisted in 14% of the children. Deficits in other cognitive domains were uncommon and most often coexisted with phonological or RAN deficits. Despite having a severe reading impairment, 26% of children with dyslexia did not present any of the tested deficits. Second, in a group of children presenting a wide range of reading abilities (N = 211), we analysed the relationship between cognitive skills and reading level. PA and RAN were independently related to reading abilities. Other skills did not explain any additional variance. The impact of PA and RAN on reading skills differed. While RAN was a consistent predictor of reading, PA predicted reading abilities particularly well in average and good readers with a smaller impact in poorer readers.

Rhythms of the day: How electronic media and daily routines influence mood during COVID-19 pandemic.

This study aims to investigate how daily activities affect mood in the context of social distancing guidelines enforced during the COVID-19 pandemic. Using Ecological Momentary Assessment (EMA) administered four times a day during a 2-week period, we asked participants (N = 91) about their mood and the activities they engaged in. Seven individuals were selected for a follow-up, open-ended questionnaire. Results show that a stable routine, including physical exercise, hobbies, regular sleep hours, and minimal time spent in front of the computer, helps maintain a good mood. Coping strategies such as planning and scheduling help keep routines and circadian rhythms stable. Face-to-face contact is associated with a more positive mood, while similar interaction through electronic communication has a less positive effect. We observe an effect related to the infodemic phenomenon: Daily reports on COVID-19 cases and deaths affect mood fluctuations. This is an important consideration in shaping public information policies.

Measuring advanced theory of mind: Do story-based tasks work?

INTRODUCTION: Although much is known about theory of mind (ToM) development during childhood, data on how these skills develop in adolescence is scarce. This cavity is due in part to the limited knowledge about measures of advanced theory of mind. METHODS: The study examined the relation among six common story-based tasks designed to measure advanced ToM in two age groups of Polish adolescents: early (13-year-olds; 78 girls) and late (16-year-olds; 143 girls) adolescents. RESULTS: Factor models for individual tasks were constructed, followed by an examination of the underlying structure that explained the variability of factor scores. Only in half of the tasks, the results revealed an age-related increase in advanced ToM. Contrary to expectation, results showed a lack of correlations among story-based advanced ToM tasks in the two adolescent groups. CONCLUSIONS: The results suggest a lack of coherence among advanced story-based ToM tasks and the need for further development of reliable and valid advanced ToM measures which are sensitive enough to show increasingly complex social reasoning abilities in adolescence.

Cultural Artifacts Transform Embodied Practice: How a Sommelier Card Shapes the Behavior of Dyads Engaged in Wine Tasting.

The radical embodied approach to cognition directs researchers' attention to skilled practice in a structured environment. This means that the structures present in the environment, including structured interactions with others and with artifacts, are put at least on a par with individual cognitive processes in explaining behavior. Both ritualized interactive formats and artifacts can be seen as forms of "external memory," usually shaped for a particular domain, that constrain skilled practice, perception, and cognition in online behavior and in learning and development. In this paper, we explore how a task involving the recognition of difficult sensory stimuli (wine) by collective systems (dyads) is modified by a domain-specific linguistic artifact (a sommelier card). We point to how using the card changes the way participants explore the stimuli individually, making it more consistent with culturally accrued sommelier know-how, as well as how it transforms the interaction between the participants, creating specific divisions of labor and novel relations. In our exploratory approach, we aim to integrate qualitative methods from anthropology and sociology with quantitative methods from psychology and the dynamical systems approach using both coded behavioral data and automatic movement analysis.

ShapeGTB: the role of local DNA shape in prioritization of functional variants in human promoters with machine learning.

MOTIVATION: The identification of functional sequence variations in regulatory DNA regions is one of the major challenges of modern genetics. Here, we report results of a combined multifactor analysis of properties characterizing functional sequence variants located in promoter regions of genes. RESULTS: We demonstrate that GC-content of the local sequence fragments and local DNA shape features play significant role in prioritization of functional variants and outscore features related to histone modifications, transcription factors binding sites, or evolutionary conservation descriptors. Those observations allowed us to build specialized machine learning classifier identifying functional single nucleotide polymorphisms within promoter regions-ShapeGTB. We compared our method with more general tools predicting pathogenicity of all non-coding variants. ShapeGTB outperformed them by a wide margin (average precision 0.93 vs. 0.47-0.55). On the external validation set based on ClinVar database it displayed worse performance but was still competitive with other methods (average precision 0.47 vs. 0.23-0.42). Such results suggest unique characteristics of mutations located within promoter regions and are a promising signal for the development of more accurate variant prioritization tools in the future.

Social adaptation in multi-agent model of linguistic categorization is affected by network information flow.

This paper explores how information flow properties of a network affect the formation of categories shared between individuals, who are communicating through that network. Our work is based on the established multi-agent model of the emergence of linguistic categories grounded in external environment. We study how network information propagation efficiency and the direction of information flow affect categorization by performing simulations with idealized network topologies optimizing certain network centrality measures. We measure dynamic social adaptation when either network topology or environment is subject to change during the experiment, and the system has to adapt to new conditions. We find that both decentralized network topology efficient in information propagation and the presence of central authority (information flow from the center to peripheries) are beneficial for the formation of global agreement between agents. Systems with central authority cope well with network topology change, but are less robust in the case of environment change. These findings help to understand which network properties affect processes of social adaptation. They are important to inform the debate on the advantages and disadvantages of centralized systems.

Performance of Language-Coordinated Collective Systems: A Study of Wine Recognition and Description.

Most of our perceptions of and engagements with the world are shaped by our immersion in social interactions, cultural traditions, tools and linguistic categories. In this study we experimentally investigate the impact of two types of language-based coordination on the recognition and description of complex sensory stimuli: that of red wine. Participants were asked to taste, remember and successively recognize samples of wines within a larger set in a two-by-two experimental design: (1) either individually or in pairs, and (2) with or without the support of a sommelier card-a cultural linguistic tool designed for wine description. Both effectiveness of recognition and the kinds of errors in the four conditions were analyzed. While our experimental manipulations did not impact recognition accuracy, bias-variance decomposition of error revealed non-trivial differences in how participants solved the task. Pairs generally displayed reduced bias and increased variance compared to individuals, however the variance dropped significantly when they used the sommelier card. The effect of sommelier card reducing the variance was observed only in pairs, individuals did not seem to benefit from the cultural linguistic tool. Analysis of descriptions generated with the aid of sommelier cards shows that pairs were more coherent and discriminative than individuals. The findings are discussed in terms of global properties and dynamics of collective systems when constrained by different types of cultural practices.

PDP-CON: prediction of domain/linker residues in protein sequences using a consensus approach.

The prediction of domain/linker residues in protein sequences is a crucial task in the functional classification of proteins, homology-based protein structure prediction, and high-throughput structural genomics. In this work, a novel consensus-based machine-learning technique was applied for residue-level prediction of the domain/linker annotations in protein sequences using ordered/disordered regions along protein chains and a set of physicochemical properties. Six different classifiers-decision tree, Gaussian naïve Bayes, linear discriminant analysis, support vector machine, random forest, and multilayer perceptron-were exhaustively explored for the residue-level prediction of domain/linker regions. The protein sequences from the curated CATH database were used for training and cross-validation experiments. Test results obtained by applying the developed PDP-CON tool to the mutually exclusive, independent proteins of the CASP-8, CASP-9, and CASP-10 databases are reported. An n-star quality consensus approach was used to combine the results yielded by different classifiers. The average PDP-CON accuracy and F-measure values for the CASP targets were found to be 0.86 and 0.91, respectively. The dataset, source code, and all supplementary materials for this work are available at https://cmaterju.org/cmaterbioinfo/ for noncommercial use.

Computational inference of H3K4me3 and H3K27ac domain length.

Background. Recent epigenomic studies have shown that the length of a DNA region covered by an epigenetic mark is not just a byproduct of the assaying technologies and has functional implications for that locus. For example, expanded regions of DNA sequences that are marked by enhancer-specific histone modifications, such as acetylation of histone H3 lysine 27 (H3K27ac) domains coincide with cell-specific enhancers, known as super or stretch enhancers. Similarly, promoters of genes critical for cell-specific functions are marked by expanded H3K4me3 domains in the cognate cell type, and these can span DNA regions from 4-5kb up to 40-50kb in length. These expanded H3K4me3 domains are known as buffer domains or super promoters. Methods. To ask what correlates with-and potentially regulates-the length of loci marked with these two important histone marks, H3K4me3 and H3K27ac, we built Random Forest regression models. With these models, we computationally identified genomic and epigenomic patterns that are predictive for the length of these marks in seven ENCODE cell lines. Results. We found that certain epigenetic marks and transcription factors explain the variability of the length of H3K4me3 and H3K27ac marks across different cell types, which implies that the lengths of these two epigenetic marks are tightly regulated in a given cell type. Our source code for the regression models and data can be found at our GitHub page: https://github.com/zubekj/broad_peaks. Discussion. Our Random Forest based regression models enabled us to estimate the individual contribution of different epigenetic marks and protein binding patterns to the length of H3K4me3 and H3K27ac deposition patterns, therefore potentially revealing genomic signatures at cell specific regulatory elements.

Multi-level machine learning prediction of protein-protein interactions in Saccharomyces cerevisiae.

Accurate identification of protein-protein interactions (PPI) is the key step in understanding proteins' biological functions, which are typically context-dependent. Many existing PPI predictors rely on aggregated features from protein sequences, however only a few methods exploit local information about specific residue contacts. In this work we present a two-stage machine learning approach for prediction of protein-protein interactions. We start with the carefully filtered data on protein complexes available for Saccharomyces cerevisiae in the Protein Data Bank (PDB) database. First, we build linear descriptions of interacting and non-interacting sequence segment pairs based on their inter-residue distances. Secondly, we train machine learning classifiers to predict binary segment interactions for any two short sequence fragments. The final prediction of the protein-protein interaction is done using the 2D matrix representation of all-against-all possible interacting sequence segments of both analysed proteins. The level-I predictor achieves 0.88 AUC for micro-scale, i.e., residue-level prediction. The level-II predictor improves the results further by a more complex learning paradigm. We perform 30-fold macro-scale, i.e., protein-level cross-validation experiment. The level-II predictor using PSIPRED-predicted secondary structure reaches 0.70 precision, 0.68 recall, and 0.70 AUC, whereas other popular methods provide results below 0.6 threshold (recall, precision, AUC). Our results demonstrate that multi-scale sequence features aggregation procedure is able to improve the machine learning results by more than 10% as compared to other sequence representations. Prepared datasets and source code for our experimental pipeline are freely available for download from: http://zubekj.github.io/mlppi/ (open source Python implementation, OS independent).

Ensemble learning prediction of protein-protein interactions using proteins functional annotations.

Protein-protein interactions are important for the majority of biological processes. A significant number of computational methods have been developed to predict protein-protein interactions using protein sequence, structural and genomic data. Vast experimental data is publicly available on the Internet, but it is scattered across numerous databases. This fact motivated us to create and evaluate new high-throughput datasets of interacting proteins. We extracted interaction data from DIP, MINT, BioGRID and IntAct databases. Then we constructed descriptive features for machine learning purposes based on data from Gene Ontology and DOMINE. Thereafter, four well-established machine learning methods: Support Vector Machine, Random Forest, Decision Tree and Naïve Bayes, were used on these datasets to build an Ensemble Learning method based on majority voting. In cross-validation experiment, sensitivity exceeded 80% and classification/prediction accuracy reached 90% for the Ensemble Learning method. We extended the experiment to a bigger and more realistic dataset maintaining sensitivity over 70%. These results confirmed that our datasets are suitable for performing PPI prediction and Ensemble Learning method is well suited for this task. Both the processed PPI datasets and the software are available at .