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\"The scientific study of networks - computer, social, and biological - has received an enormous amount of interest in recent years. However, the network approach has been applied to the field of animal behaviour relatively late compared to many other biological disciplines. Understanding social network structure is of great importance for biologists since the structural characteristics of any network will affect its constituent members and influence a range of diverse behaviours. These include finding and choosing a sexual partner, developing and maintaining cooperative relationships, and engaging in foraging and anti-predator behavior. This novel text provides an overview of the insights that network analysis has provided into major biological processes, and how it has enhanced our understanding of the social organisation of several important taxonomic groups. It brings together researchers from a wide range of disciplines with the aim of providing both an overview of the power of the network approach for understanding patterns and process in animal populations, as well as outlining how current methodological constraints and challenges can be overcome. This book is principally aimed at graduate level students and researchers in the fields of ecology, zoology, animal behaviour, and evolutionary biology but will also be of interest to social scientists.\" --Cover.

Deep learning-based methods have advanced animal pose estimation, enhancing accuracy, and efficiency in quantifying animal behavior. However, these methods frequently experience tracking drift, where noise-induced jumps in body point estimates compromise reliability. Here, we present the anti-drift pose tracker (ADPT), a transformer-based tool that mitigates tracking drift in behavioral analysis. Extensive experiments across cross-species datasets—including proprietary mouse and monkey recordings and public Drosophila and macaque datasets—demonstrate that ADPT significantly reduces drift and surpasses existing models like DeepLabCut and SLEAP in accuracy. Moreover, ADPT achieved 93.16% identification accuracy for 10 unmarked mice and 90.36% accuracy for freely interacting unmarked mice, which can be further refined to 99.72%, enhancing both anti-drift performance and pose estimation accuracy in social interactions. With its end-to-end design, ADPT is computationally efficient and suitable for real-time analysis, offering a robust solution for reproducible animal behavior studies. The ADPT code is available at https://github.com/tangguoling/ADPT .

\"Stone tool analysis relies on a strong background in analytical and methodological techniques. However, lithic technological analysis has not been well integrated with a theoretically informed approach to understanding how humans procured, made, and used stone tools. Evolutionary theory has great potential to fill this gap. This collection of essays brings together several different evolutionary perspectives to demonstrate how lithic technological systems are a by-product of human behavior. The essays cover a range of topics, including human behavioral ecology, cultural transmission, phylogenetic analysis, risk management, macroevolution, dual inheritance theory, cladistics, central place foraging, costly signaling, selection, drift, and various applications of evolutionary ecology\"-- Provided by publisher.

Ultrasonic vocalizations (USVs) are used as a phenotypic marker in mouse models of neuropsychiatric disorders. Nevertheless, current methodologies still require time-consuming manual input or sound recordings clean of any background noise. We developed a method to overcome these two restraints to boost knowledge on mouse USVs. The methods are freely available and the USV analysis runs online at https://usv.pasteur.cloud . As little is currently known about usage and structure of ultrasonic vocalizations during social interactions over the long-term and in unconstrained context, we investigated mouse spontaneous communication by coupling the analysis of USVs with automatic labeling of behaviors. We continuously recorded during 3 days undisturbed interactions of same-sex pairs of C57BL/6J sexually naive males and females at 5 weeks and 3 and 7 months of age. In same-sex interactions, we observed robust differences between males and females in the amount of USVs produced, in the acoustic structure and in the contexts of emission. The context-specific acoustic variations emerged with increasing age. The emission of USVs also reflected a high level of excitement during social interactions. We finally highlighted the importance of studying long-term spontaneous communication by investigating female mice lacking Shank3 , a synaptic protein associated with autism. While the previous short-time constrained investigations could not detect USV emission abnormalities, our analysis revealed robust differences in the usage and structure of the USVs emitted by mutant mice compared to wild-type female pairs.

The manual categorization of behavior from sensory observation data to facilitate further analyses is a very expensive process. To overcome the inherent subjectivity of this process, typically, multiple domain experts are involved, resulting in increased efforts for the labeling. In this work, we investigate whether social behavior and environments can automatically be coded based on uncontrolled everyday audio recordings by applying deep learning. Recordings of daily living were obtained from healthy young and older adults at randomly selected times during the day by using a wearable device, resulting in a dataset of uncontrolled everyday audio recordings. For classification, a transfer learning approach based on a publicly available pretrained neural network and subsequent fine-tuning was implemented. The results suggest that certain aspects of social behavior and environments can be automatically classified. The ambient noise of uncontrolled audio recordings, however, poses a hard challenge for automatic behavior assessment, in particular, when coupled with data sparsity.

Oxytocin/vasopressin-like peptides are important regulators of physiology and social behavior in vertebrates. However, the function of inotocin, the homologous peptide in arthropods, remains largely unknown. Here, we show that the level of expression of inotocin and inotocin receptor are correlated with task allocation in the ant Camponotus fellah. Both genes are up-regulated when workers age and switch tasks from nursing to foraging. in situ hybridization revealed that inotocin receptor is specifically expressed in oenocytes, which are specialized cells synthesizing cuticular hydrocarbons which function as desiccation barriers in insects and for social recognition in ants. dsRNA injection targeting inotocin receptor, together with pharmacological treatments using three identified antagonists blocking inotocin signaling, revealed that inotocin signaling regulates the expression of cytochrome P450 4G1 (CYP4G1) and the synthesis of cuticular hydrocarbons, which play an important role in desiccation resistance once workers initiate foraging.

As cities strive for greater liveability, data-driven methods can enhance our understanding of public space behaviours and social interactions. Recent developments in computer vision technologies have significantly advanced the accuracy of micro-scale human behaviour detection, but there is a lack of methodologies that capture relational, nuanced behaviours within specific spatial and temporal environments. This paper presents the development of a computer vision and machine learning-based methodology to analyse co-presence and micro-social interactions in urban spaces, introducing new metrics for spatial behavioural analysis. The methodology was tested on a 22.5-min video dataset obtained at a university campus, demonstrating its capacity for trajectory analysis and detecting nuanced interpersonal behaviours including encountering, congregating, approaching and avoiding. Human observers validated the computer-generated behaviour classifications, achieving high agreement levels and demonstrating the system's accuracy in detecting diverse pedestrian interactions. The approach successfully offers fine-grained analysis of social behaviours and spatial patterns of co-presence, revealing how urban morphology influences social interaction hotspots. It advances environment-behaviour research by providing scalable, automated tools for detailed, data-driven analysis of public space vitality, with potential applications in urban design, social sciences, and policy-making.

Previous research, that showed that corporate social responsibility (CSR) had positive effects on the corporate image and performance, has attracted much attention and resulted in an increasing number of follow-up studies. However, CSR-related activities are focused on their effect on external stakeholders, although they are social service activities geared towards internal and external stakeholders, thus showing a research gap regarding the effects of internal stakeholders on organizational effectiveness. Therefore, this study investigated the mediating effects of the value congruence climate and prosocial behavior among the team members in the relationship between leader’s CSR perception and team members’ psychological well-being, using a multilevel analysis of the relationship between the team and individual level factors. For the empirical analysis, 69 teams (334 employees) were sampled from 23 Korean small and medium-sized enterprises (SMEs). Analyses revealed a positive effect of a leader’s CSR perception on the team members’ psychological well-being. Furthermore, a leader’s CSR perception had a positive effect on his/her team’s value congruence environment and team members’ prosocial behavior. The team’s value congruence environment and team members’ prosocial behavior were found to mediate the relationship between the leader’s CSR perception and team members’ psychological well-being. The relationships among these variables were investigated using a multilevel analysis model capable of simultaneous validation of team- and individual-level factors associated with team members’ psychological well-being. Future research directions were then discussed based on the theoretical and practical implications and limitations of the study results.

Currently, online social networks and social media have become increasingly popular showing an exponential growth. This fact have attracted increasing research interest and, in turn, facilitating the emergence of new interdisciplinary research directions, such as social network analysis. In this scenario, link prediction is one of the most important tasks since it deals with the problem of the existence of a future relation among members in a social network. Previous techniques for link prediction were based on structural (or topological) information. Nevertheless, structural information is not enough to achieve a good performance in the link prediction task on large-scale social networks. Thus, the use of additional information, such as interests or behaviors that nodes have into their communities, may improve the link prediction performance. In this paper, we analyze the viability of using a set of simple and non-expensive techniques that combine structural with community information for predicting the existence of future links in a large-scale online social network, such as Twitter. Twitter, a microblogging service, has emerged as a useful source of informative data shared by millions of users whose relationships require no reciprocation. Twitter network was chosen because it is not well understood, mainly due to the occurrence of directed and asymmetric links yet. Experiments show that our proposals can be used efficiently to improve unsupervised and supervised link prediction task in a directed and asymmetric large-scale network.

Few studies have examined the relationship between interpersonal functioning and coping, two constructs that have been empirically linked to depression. This study examined the association between the coping strategies most commonly used by individuals with major depressive disorder and their interpersonal functioning. These processes were examined at the beginning of a 20-session cognitive-behavioral therapy for depression and at the end of treatment. Psychotherapy transcripts of 42 participants were rated for coping strategies using the Coping Pattern Rating System (Perry, Drapeau, & Dunkley, 2007) and for interpersonal functioning using the Structural Analysis of Social Behavior (Benjamin & Cushing, 2000). Early in therapy, a significant association was found between the escape coping strategy and interpersonal behaviors involving seeking distance from one's therapists, the assertion and separation from others and lesser use of the information seeking coping strategy, blaming others and more aggressive forms of coping, and self-criticism and escape coping. Later in therapy, patients who expressed themselves and connected with their therapists were more likely to use coping strategies such as self-reliance and information seeking to cope with stressors and to rely on their personal resources in relationships. These results are discussed in the context of tracking psychotherapy process and enhancing treatment outcome. Très peu d'études ont examiné la relation entre le fonctionnement interpersonnel et l'adaptation, deux concepts empiriquement liés à la dépression. La présente étude a examiné l'association entre les stratégies d'adaptation les plus couramment utilisées par les personnes atteintes de troubles dépressifs graves et leur fonctionnement interpersonnel. Ces processus ont été examinés dans les premiers stades d'une thérapie cognitivo-comportementale de la dépression s'échelonnant sur 20 séances et lors des derniers stades du traitement. Les transcriptions de psychothérapie de 42 participants ont été évaluées en fonction de leurs stratégies d'adaptation à l'aide du Système de classement de la capacité d'adaptation (Perry, Drapeau et Dunkley, 2007) et de leur fonctionnement interpersonnel à l'aide de l'Analyse structurelle du comportement social (Benjamin et Cushing, 2000). Dans les premiers stades de la thérapie, une association significative a été trouvée entre la stratégie d'adaptation par l'évitement et les comportements interpersonnels incluant la recherche d'une distance de leurs thérapeutes, l'affirmation, la séparation d'autres personnes et une utilisation réduite de la stratégie d'adaptation par la recherche d'information, jeter le blâme sur autrui et d'autres formes d'adaptation plus agressives, ainsi que l'autocritique et l'adaptation par l'évitement. Dans les stades plus avancés de la thérapie, les patients qui s'exprimaient et qui avaient établi un lien avec leurs thérapeutes étaient plus susceptibles d'utiliser des stratégies d'adaptation comme l'autonomie et la recherche d'information pour faire face au stress et de se fier à leurs ressources personnelles dans leurs relations. Ces constats sont abordés dans un contexte de suivi du processus psychothérapeutique et de l'amélioration des résultats de traitement. Public Significance Statement Within the context of depression, patient coping strategies and interpersonal behaviors are highly associated. Throughout psychotherapy treatment, coping strategies and interpersonal behaviors should be tracked to enhance treatment outcomes. More specifically, clinicians should be attentive to patients seeking distance within their therapeutic relationship as this may be associated with maladaptive coping strategies. Moreover, clinicians should encourage patients toward developing a balance between support seeking behaviors and self-reliant behaviors.