Capturing Human Category Representations by Sampling in Deep Feature Spaces
Understanding how people represent categories is a core problem in cognitive science, with the flexibility of human learning remaining a gold standard to which modern artificial intelligence and machine learning aspire. Decades of psychological research have yielded a variety of formal theories of categories, yet validating these theories with naturalistic stimuli remains a challenge. The problem is that human category representations cannot be directly observed and running informative experiments with naturalistic stimuli such as images requires having a workable representation of these stimuli. Deep neural networks have recently been successful in a range of computer vision tasks and provide a way to represent the features of images. In this paper, we introduce a method for estimating the structure of human categories that draws on ideas from both cognitive science and machine learning, blending human-based algorithms with state-of-the-art deep representation learners. We provide qualitative and quantitative results as a proof of concept for the feasibility of the method. Samples drawn from human distributions rival the quality of current state-of-the-art generative models and outperform alternative methods for estimating the structure of human categories.
 Quality This paper demonstrates that human category representations can be inferred by sampling deep feature spaces. The idea is an extension of the earlier developed MCMC with people approach where samples are drawn in the latent space of a DCGAN and a BiGAN. The approach is thoroughly validated using two online behavioural experiments. Clarity The rationale is clear and the results are straightforward to interpret. In Section 4.2.1 statements on resemblance and closeness to mean faces could be tested. Last sentences on page 7 are hard to parse. The final sentence probably relates back to the CI approach. A few typos. Originality The approach is a straightforward extension of the MCMCP approach using generative models. Significance The approach improves on previous category estimation approaches by embracing the expressiveness of recent generative models. Extensive experiments demonstrate the usefulness of the approach. Pros Useful extension of an important technique backed up by behavioural experiments. Cons Does not provide new theory but combines existing ideas in a new manner.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Capturing Human Category Representations by Sampling in Deep Feature Spaces
Understanding how people represent categories is a core problem in cognitive science, with the flexibility of human learning remaining a gold standard to which modern artificial intelligence and machine learning aspire. Decades of psychological research have yielded a variety of formal theories of categories, yet validating these theories with naturalistic stimuli remains a challenge. The problem is that human category representations cannot be directly observed and running informative experiments with naturalistic stimuli such as images requires having a workable representation of these stimuli. Deep neural networks have recently been successful in a range of computer vision tasks and provide a way to represent the features of images. In this paper, we introduce a method for estimating the structure of human categories that draws on ideas from both cognitive science and machine learning, blending human-based algorithms with state-of-the-art deep representation learners. We provide qualitative and quantitative results as a proof of concept for the feasibility of the method. Samples drawn from human distributions rival the quality of current state-of-the-art generative models and outperform alternative methods for estimating the structure of human categories.
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>



 Quality This paper demonstrates that human category representations can be inferred by sampling deep feature spaces. The idea is an extension of the earlier developed MCMC with people approach where samples are drawn in the latent space of a DCGAN and a BiGAN. The approach is thoroughly validated using two online behavioural experiments. Clarity The rationale is clear and the results are straightforward to interpret. In Section 4.2.1 statements on resemblance and closeness to mean faces could be tested. Last sentences on page 7 are hard to parse. The final sentence probably relates back to the CI approach. A few typos. Originality The approach is a straightforward extension of the MCMCP approach using generative models. Significance The approach improves on previous category estimation approaches by embracing the expressiveness of recent generative models. Extensive experiments demonstrate the usefulness of the approach. Pros Useful extension of an important technique backed up by behavioural experiments. Cons Does not provide new theory but combines existing ideas in a new manner.
assistant
 Quality This paper demonstrates that human category representations can be inferred by sampling deep feature spaces. The idea is an extension of the earlier developed MCMC with people approach where samples are drawn in the latent space of a DCGAN and a BiGAN. The approach is thoroughly validated using two online behavioural experiments. Clarity The rationale is clear and the results are straightforward to interpret. In Section 4.2.1 statements on resemblance and closeness to mean faces could be tested. Last sentences on page 7 are hard to parse. The final sentence probably relates back to the CI approach. A few typos. Originality The approach is a straightforward extension of the MCMCP approach using generative models. Significance The approach improves on previous category estimation approaches by embracing the expressiveness of recent generative models. Extensive experiments demonstrate the usefulness of the approach. Pros Useful extension of an important technique backed up by behavioural experiments. Cons Does not provide new theory but combines existing ideas in a new manner.