Animal Behavior and Cognition

eLife Assessment

This study presents vassi, a Python package that streamlines the preparation of training data for machine-learning-based classification of social behaviors in animal groups. This package is a valuable resource for researchers with computational expertise, implementing a framework for the detection of directed social interactions within a group and an interactive tool for reviewing and correcting behavior detections. However, the strength of evidence that the method is widely applicable remains incomplete, performance on benchmark dyadic datasets is comparable to existing approaches, and performance scores on collective behavioral datasets are low. While the package can analyze behavior in large groups of animals, it only outputs dyadic interactions within these groups and does not account for behaviors where more than two animals may be interacting.

eLife Assessment

These valuable studies explore the consequences of exposure to the toxin hydrogen sulfide (H2S) on the behavior and physiology of C. elegans. The work finds that behavioral changes evoked by H2S exposure are modulated by several regulatory pathways known to influence chemosensory-evoked locomotor behavior, but there is incomplete data to support the authors' claim of comprehensive mechanistic insight into the consequences of H2S exposure. Nevertheless, the findings may be informative for those studying organismal stress responses and the effects of mitochondrial ROS on behavior and physiology.

eLife Assessment

This study introduces Megabouts, a transformer-based classifier for larval zebrafish movement bouts. This useful tool is thoughtfully implemented and has clear potential to unify analyses across labs. However, the evidence supporting its robustness is incomplete. How the method generalizes across datasets, how sensitive it is to noise, and the specific sources of misclassification are unclear. The method would also be strengthened by providing options for users to fine-tune the clusters under different experimental conditions, which would further enhance reliability and flexibility.

eLife Assessment

This important work develops the C. elegans as a model organism for studying effort-based discounting by asking the worms to choose between patches of easy and hard to digest bacteria. The authors provide convincing evidence that the nematodes are effort discounting. They also provide solid evidence of involvement of dopamine in the food preference and that the finding is not restricted to lab-acclimated strains.

eLife Assessment

This valuable study addresses the effects of selection on aggression on fitness and life-history trade-offs in Drosophila melanogaster. However, the evidence presented is incomplete and does not support the claims proposed in the study of increased survival of highly aggressive males at the expense of reproductive success and shorter mating duration. The main limitation of the study is the choice to use males from only one aggressive Drosophila line in combination with CantonS females, that do not allow disambiguation between nonaggression-related factors, such as hybrid vigor and aggression-related factors influencing mating and lifespan.

eLife Assessment

This important study shows that visual search for upright and rotated objects is affected by rotating participants in a VR and gravitational reference frame. However, the evidence supporting this conclusion is incomplete, given the authors' use of normalized response time and the assumption that object recognition across rotations requires mental rotation.

eLife Assessment

This paper provides important insight into how early life experience shapes adult behavior in fruit bats. The authors raised juvenile bats either in an impoverished or enriched environment and studied their foraging behaviors. The evidence is convincing that bats raised in enriched environments are more active, bold, and exploratory. The work will be of interest to ethologists and developmental psychologists.

eLife Assessment

In this important study, the authors provide compelling evidence that the likelihood of looking behaviour is predicted by the expected information gain, hence constituting an invaluable formal model and explanation of habituation. Such modelling represents a crucial advance in explanation, over-and-above less specified models that can be fitted post hoc to any empirical pattern. The findings would be of interest to researchers studying cognitive development, and perception and learning more broadly.

eLife Assessment

This paper reports a useful low-cost platform for studying mosquito behaviors such as flight activity, sugar feeding, and host-seeking responses over the course of several weeks, and demonstrates key applications of this platform. While the authors provide a biological proof of principle, the evidence that supports the validation of the tracking algorithm is incomplete; it lacks biological replicates, independent confirmation of the tracking algorithm, and data on mosquito survival.

eLife Assessment

Shen et al. present a computational account of individual differences in mouse exploration when faced with a novel object in an open field from a previously published study (Akiti et al.) that relates subject-specific intrinsic exploration and caution about potential hazards to the spectrum of behaviors observed in this setting. Overall, this computational study is an important contribution that leverages a very general modeling framework (a Bayes Adaptive Markov Decision Process) to quantify and interrogate distinct drivers of exploratory behavior under potential threat. Given their assumptions, the modeling results are convincing: the authors are able to describe a substantial amount of the behavioral features and idiosyncracies in this dataset, and their model affords a normative interpretation related to inherent risk aversion and predation hazard "flexibility" of individual animals and should be of broad interest to researchers working to understand open-ended exploratory behaviors.