- The pruning method does not appear to offer much beyond the method of feature reweighted representational similarity analysis, which is quite popular (see Kaniuth and Hebert, 2022; NeuroImage). In fact, it is essentially a particular limited case of FR-RSA, where the weights of features are either 0 or 1. The authors do not appear well aware of the literature, as only 20 references are made.
- I found the technique of using multiple different feature spaces (the 25 feature space of Mitchell et. al to fit voxel encoding models, then the full/pruned glove model to analyze similarities within clusters) to be convoluted and potentially circular.
- As the technique is not particularly novel, it is important that the authors deliver some clear novel findings about brain function. The abstract only lists one "From a neurobiological perspective, we find that brain regions encoding social and cognitive aspects
of lexical items consistently also represent their sensory-motor features, though the reverse does not hold." I did not find the case for this finding to be particularly strong. I welcome the authors to make the case more strongly.
- The figures are poorly made, certainly well below the bar of ICLR, and do not communicate much if anything that will affect how researcher's think about semantic organization in the brain. For example, while an interesting approach of clustering brain regions is used, these regions are never visualized. In the one plot that attempts to explain some differences across brain regions, the authors use arbitrary number indices for brain regions; at minimum, anatomical labels are needed. However, in 2024, it is expected that a strong paper on this topic can make elegant visualizations of the cortical surface. Practitioners in this field understand the importance of such visualizations for relating findings to pre-existing conceptual notions of cortical organization, and for driving further intuition that will affect future research.
- The authors waste precious space presenting fits to training data (see Table 1, "complete dataset", which reports the representational similarity after selecting the features that optimize to improve that representational similarity). Only the cross-validated results are worth presenting.
- Focusing on which clusters are "best" rather than what the differences in representation are between them, seems an odd choice given the motivation of the paper.
- Averaging voxels across subjects is likely to drastically reduce the granularity of the possible findings, since there is no expectation in voxel-level alignment of fine-grained conceptual information, but only larger-scale information. I believe it would be better to construct the clusters using all subject's individual data in a group-aligned space, where the same methods can otherwise be used, but individual voxel's are kept independent and not averaged across subjects.
- Focusing on which clusters are "best" rather than what the differences in representation are between them, seems an odd choice given the motivation of the paper.