Animal Behavior and Cognition

eLife Assessment

This manuscript makes a valuable contribution to understanding learning in multidimensional environments with spurious associations, which is critical for understanding learning in the real world. The evidence is based on model simulations and a preregistered human behavioral study, but remains incomplete because of inconclusive empirical results and insufficiencies in the modeling. Moreover, there are open questions about the nature and extent to which the behavioral task induced semantic congruency.

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

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 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

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.