Neuroscience

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.

eLife Assessment

The authors combine a modeling approach, using a digital twin, with electrophysiological evidence in two species to assess the role of inhibition in shaping selectivity in the visual cortex. The results provide a fundamental advance beyond the classic view of sensory coding by proving compelling evidence that many neurons in visual areas exhibit dual-feature selectivity. Overall, the work compellingly showcases how in silico experiments can generate concrete hypotheses about neuronal coding that are difficult to discover experimentally.

eLife Assessment

This valuable study combines sub-millimeter 7T fMRI, EEG, representational similarity analysis, and deep neural network modeling to investigate layer-specific spatiotemporal dynamics underlying human object processing in early visual cortex and lateral occipital cortex; the authors report temporally distinct signatures in superficial layers of LOC that are interpreted as reflecting sequential feedforward and feedback processing during visual recognition. The multimodal methodological approach and empirical dataset are substantial and will be of broad interest to researchers in visual neuroscience, layer-fMRI methodology, and computational vision. However, the evidence supporting the central interpretation of interareal feedback remains incomplete, as the observed dynamics could also be explained by alternative mechanisms such as within-area recurrent processing, and there are additional concerns regarding several methodological and modeling choices underlying claims about increasing representational complexity at later time points. Overall, the study provides solid evidence for layer- and time-specific neural dynamics during object processing, while the interpretation of these signals as feedback-related remains provisional.

eLife Assessment

This study presents valuable findings on the neuromodulatory underpinnings of adaptive learning in dynamic, probabilistic environments. Solid evidence for these claims comes from showing spatial correlations between model-derived fMRI responses and PET-based receptor density maps. The work will be of interest to cognitive and systems neuroscientists working on decision-making.

eLife Assessment

This useful study combines behavioral reports, EEG decoding, and computational modeling to address an interesting question: how delay-period distractors bias working-memory representations, and how these effects depend on target relevance, distractor location, and the strength of memory maintenance and distractor encoding. However, the supporting evidence is incomplete, as several key claims require clearer statistical tests, better integration of the behavioral and neural results, and more careful consideration of alternative explanations. Stronger engagement with prior literature would also substantially strengthen the manuscript and increase its potential interest to researchers in systems, cognitive, and computational neuroscience.