REVEALING INTERPRETABLE OBJECT REPRESENTA- TIONS 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 a paper that communicates a large scale experiment on human object/semantic representations and a model of such representations.   The experiment could have been more carefully controlled (and described in the paper) and the modeling work is inconclusive.
Quality, 
The experiment design is conventional, based on rating pair-wise similarity among triplets. Compared to earlier experiments, this data has more objects and more triplets.  Additional control experiments on smaller subsets have been carried out to further address hypotheses.  The description of the experiment could have been more careful: What are the precise instructions, how are the object/images presented (it is well known that relative positions, asymmetry, etc can play an important role), are there any temporal/learning effects (how clear is the task to the workers?).
The modeling work is basic and contains a number of steps that have unknown influence on the final outcome. For example model dimension: Is you claim that "D=49" is a law of human nature?  Model predictive performance seems excellent, that is interesting! But we do not know how robust this is to the many heuristics
Clarity, 
The presentation of the inference process is clear. Not so clear what the uncertainties are
Originality 
Limited. Mainly related to scale. But the data quality is unclear. The modeling approach involves a number of untested heuristics (non-negative, exponentiation etc). 
Significance 
Mostly related to the data.  I did not understand if it is planned to release the data.
Pros and cons 
+Large scale experiment
+simple model, seem to have good accuracy
-experiment needs more careful description
-too many heuristics in model and inference, unclear how general the conclusions are
Other comments:
References have many issues
The authors have done a good job in the revision and have clarified points that were unclear in the first version. 
I have remaining reservations on significance, but move rating up a notch to reflect the extensive improvements and  the authors' confirmation that they will release the data.