Revealing interpretable object representations from human behavior
To study how mental object representations are related to behavior, we estimated sparse, non-negative representations of objects using human behavioral judgments on images representative of 1,854 object categories. These representations predicted a latent similarity structure between objects, which captured most of the explainable variance in human behavioral judgments. Individual dimensions in the low-dimensional embedding were found to be highly reproducible and interpretable as conveying degrees of taxonomic membership, functionality, and perceptual attributes. We further demonstrated the predictive power of the embeddings for explaining other forms of human behavior, including categorization, typicality judgments, and feature ratings, suggesting that the dimensions reflect human conceptual representations of objects beyond the specific task.
 This is an interesting paper with a new approach to learn a sparse, positive (and hence interpretable) semantic space that maximizes human similarity judgements, by training to specifically maximize the prediction of human similarity judgements. The authors have collected the dataset themselves and have rating of sets of 3 objects from 1854 unique objects. They end up with a space (SPoSE) with relatively low dimensionality with respect to usual word embeddings (49 dimension) but perhaps not surprising when considering the small size of the words to embed. The authors run a set of experiment to show the usefulness of SPoSE. The most interesting one is the prediction of its dimensions by the CSLB features, which reveals a nice clustering in the different SPoSE dimensions. Perhaps the results would be a little more convincing if additional common word embeddings were also tested. Due to the different objects used in the different datasets, some of the experiments have a smaller set of words. A good extension of this work would be to combine a text-derived embedding or the synsets to interpolate the SPoSE dimensions for missing words in the original set. Or perhaps the object similarity ratings could be used in a semi-supervised setting to inform the learning of a co-occurence word embedding. This will allow the model to better describe a larger set of words. Another possible extension is to test this larger set of words on a non-behavioral NLP task to show possible improvements that the behavioral data and the interpretable space give.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Revealing interpretable object representations from human behavior
To study how mental object representations are related to behavior, we estimated sparse, non-negative representations of objects using human behavioral judgments on images representative of 1,854 object categories. These representations predicted a latent similarity structure between objects, which captured most of the explainable variance in human behavioral judgments. Individual dimensions in the low-dimensional embedding were found to be highly reproducible and interpretable as conveying degrees of taxonomic membership, functionality, and perceptual attributes. We further demonstrated the predictive power of the embeddings for explaining other forms of human behavior, including categorization, typicality judgments, and feature ratings, suggesting that the dimensions reflect human conceptual representations of objects beyond the specific task.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 This is an interesting paper with a new approach to learn a sparse, positive (and hence interpretable) semantic space that maximizes human similarity judgements, by training to specifically maximize the prediction of human similarity judgements. The authors have collected the dataset themselves and have rating of sets of 3 objects from 1854 unique objects. They end up with a space (SPoSE) with relatively low dimensionality with respect to usual word embeddings (49 dimension) but perhaps not surprising when considering the small size of the words to embed. The authors run a set of experiment to show the usefulness of SPoSE. The most interesting one is the prediction of its dimensions by the CSLB features, which reveals a nice clustering in the different SPoSE dimensions. Perhaps the results would be a little more convincing if additional common word embeddings were also tested. Due to the different objects used in the different datasets, some of the experiments have a smaller set of words. A good extension of this work would be to combine a text-derived embedding or the synsets to interpolate the SPoSE dimensions for missing words in the original set. Or perhaps the object similarity ratings could be used in a semi-supervised setting to inform the learning of a co-occurence word embedding. This will allow the model to better describe a larger set of words. Another possible extension is to test this larger set of words on a non-behavioral NLP task to show possible improvements that the behavioral data and the interpretable space give.
assistant
 This is an interesting paper with a new approach to learn a sparse, positive (and hence interpretable) semantic space that maximizes human similarity judgements, by training to specifically maximize the prediction of human similarity judgements. The authors have collected the dataset themselves and have rating of sets of 3 objects from 1854 unique objects. They end up with a space (SPoSE) with relatively low dimensionality with respect to usual word embeddings (49 dimension) but perhaps not surprising when considering the small size of the words to embed. The authors run a set of experiment to show the usefulness of SPoSE. The most interesting one is the prediction of its dimensions by the CSLB features, which reveals a nice clustering in the different SPoSE dimensions. Perhaps the results would be a little more convincing if additional common word embeddings were also tested. Due to the different objects used in the different datasets, some of the experiments have a smaller set of words. A good extension of this work would be to combine a text-derived embedding or the synsets to interpolate the SPoSE dimensions for missing words in the original set. Or perhaps the object similarity ratings could be used in a semi-supervised setting to inform the learning of a co-occurence word embedding. This will allow the model to better describe a larger set of words. Another possible extension is to test this larger set of words on a non-behavioral NLP task to show possible improvements that the behavioral data and the interpretable space give.