Neuroscience

eLife Assessment

This study by Roseby and colleagues shows that region-specific mechanosensation - especially anterior-dorsal inputs - controls larval self-righting, and links this to Hox gene function in sensory neurons. The work is important for understanding how body plan cues shape sensorimotor behaviour, and the experimental toolkit will be of use to others. The strength of evidence is compelling with respect to the assays developed and the involvement of the anterior region, the evidence is more limited with respect to the dorso-ventral organization of sensory inputs in that region and the mechanism by which Hox genes contribute to the process. These findings will be of broad interest to researchers studying neural circuits, developmental genetics, and the evolution of behaviour.

eLife Assessment

The authors have presented a study which addresses a recognised gap in the literature, the emergence of the neural correlates of cognitive and affective empathy in children; they introduce a task for measuring both positive and negative empathy in a relatively large group of children aged 3-5. The task was combined with functional near-infrared spectroscopy to examine brain regions involved in the task. The findings are interpreted as providing evidence for the earlier emergence of cognitive than affective empathy. The study represents a valuable contribution to understanding the development of cognitive function, but in its current form, the strength of support for the conclusion is incomplete due to limited support for the comparison to the adult literature and a need to more clearly justify the pre-selected brain regions, their links to empathy and the justification of the hypotheses.

eLife Assessment

This manuscript represents a valuable contribution to understanding motion processing in the visual cortex. Based on a heterogeneous collection of previous empirical findings, the authors show that the diversity of tuning curves in the middle temporal (MT) area, in response to moving center-surround images, can be explained by Bayesian inference combined with neural sampling. The model rests on strong and solid assumptions about the prior and likelihood; independent evidence that neither of these factors is misspecified would strengthen the work.