Prosocial behaviors in rodents.

Prosocial behaviors (i.e., actions that benefit others) are central for social interactions in humans and other animals, by fostering social bonding and cohesion. To study prosociality in rodents, scientists have developed behavioral paradigms where animals can display actions that benefit conspecifics in distress or need. These paradigms have provided insights into the role of social interactions and transfer of emotional states in the expression of prosociality, and increased knowledge of its neural bases. However, prosociality levels are variable: not all tested animals are prosocial. Such variation has been linked to differences in animals' ability to process another's state as well as to contextual factors. Moreover, evidence suggests that prosocial behaviors involve the orchestrated activity of multiple brain regions and neuromodulators. This review aims to synthesize findings across paradigms both at the level of behavior and neural mechanisms. Growing evidence confirms that these processes can be studied in rodents, and intense research in the past years is rapidly advancing our knowledge. We discuss a strong bias in the field towards the study of these processes in negative valence contexts (e.g., pain, fear, stress), which should be taken as an opportunity to open new venues for future research.

Multimodal cues displayed by submissive rats promote prosocial choices by dominants.

Animals often display prosocial behaviors, performing actions that benefit others. Although prosociality is essential for social bonding and cooperation, we still know little about how animals integrate behavioral cues from those in need to make decisions that increase their well-being. To address this question, we used a two-choice task where rats can provide rewards to a conspecific in the absence of self-benefit and investigated which conditions promote prosociality by manipulating the social context of the interacting animals. Although sex or degree of familiarity did not affect prosocial choices in rats, social hierarchy revealed to be a potent modulator, with dominant decision-makers showing faster emergence and higher levels of prosocial choices toward their submissive cage mates. Leveraging quantitative analysis of multimodal social dynamics prior to choice, we identified that pairs with dominant decision-makers exhibited more proximal interactions. Interestingly, these closer interactions were driven by submissive animals that modulated their position and movement following their dominants and whose 50-kHz vocalization rate correlated with dominants' prosociality. Moreover, Granger causality revealed stronger bidirectional influences in pairs with dominant focals and submissive recipients, indicating increased behavioral coordination. Finally, multivariate analysis highlighted body language as the main information dominants use on a trial-by-trial basis to learn that their actions have effects on others. Our results provide a refined understanding of the behavioral dynamics that rats use for action-selection upon perception of socially relevant cues and navigate social decision-making.

Open-source, Python-based, hardware and software for controlling behavioural neuroscience experiments.

Laboratory behavioural tasks are an essential research tool. As questions asked of behaviour and brain activity become more sophisticated, the ability to specify and run richly structured tasks becomes more important. An increasing focus on reproducibility also necessitates accurate communication of task logic to other researchers. To these ends, we developed pyControl, a system of open-source hardware and software for controlling behavioural experiments comprising a simple yet flexible Python-based syntax for specifying tasks as extended state machines, hardware modules for building behavioural setups, and a graphical user interface designed for efficiently running high-throughput experiments on many setups in parallel, all with extensive online documentation. These tools make it quicker, easier, and cheaper to implement rich behavioural tasks at scale. As important, pyControl facilitates communication and reproducibility of behavioural experiments through a highly readable task definition syntax and self-documenting features. Here, we outline the system's design and rationale, present validation experiments characterising system performance, and demonstrate example applications in freely moving and head-fixed mouse behaviour.