INVESTIGATING HUMAN PRIORS FOR PLAYING VIDEO GAMES
What makes humans so good at solving seemingly complex video games? Unlike computers, humans bring in a great deal of prior knowledge about the world, enabling efficient decision making. This paper investigates the role of human priors for solving video games. Given a sample game, we conduct a series of ablation studies to quantify the importance of various priors. We do this by modifying the video game environment to systematically mask different types of visual information that could be used by humans as priors. We find that removal of some prior knowledge causes a drastic degradation in the speed with which human players solve the game, e.g. from 2 minutes to over 20 minutes. Furthermore, our results indicate that general priors, such as the importance of objects and visual consistency, are critical for efficient game-play.
Overall:
I really enjoyed reading this paper and think the question is super important. I have some reservations about the execution of the experiments as well as some of the conclusions drawn. For this reason I am currently a weak reject (weak because I believe the question is very interesting). However, I believe that many of my criticisms can be assuaged during the rebuttal period.
Paper Summary:
For RL to play video games, it has to play many many many many times. In fact, many more times than a human where prior knowledge lets us learn quite fast in new (but related) environments. The authors study, using experiments, what aspects of human priors are the important parts. 
The authors’ Main Claim appears to be: “While common wisdom might suggest that prior knowledge about game semantics such as ladders are to be climbed, jumping on spikes is dangerous or the agent must fetch the key before reaching the door are crucial to human performance, we find that instead more general and high-level priors such as the world is composed of objects, object like entities are used as subgoals for exploration, and things that look the same, act the same are more critical.”
Overall, I find this interesting. However, I am not completely convinced by some of the experimental demonstrations. 
Issue 0: The experiments seem underpowered / not that well analyzed. 
There are only 30 participants per condition and so it’s hard to tell whether the large differences in conditions are due to noise and what a stable ranking of conditions actually looks like. I would recommend that the authors triple the sample size and be more clear about reporting the outcomes in each of the conditions. 
It’s not clear what the error bars in figure 1 represent, are they standard deviations of the mean? Are they standard deviations of the data? Are they confidence intervals for the mean effect? 
Did you collect any extra data about participants? One potentially helpful example is asking how familiar participants are with platformer video games. This would give at least some proxy to study the importance of priors about “how video games are generally constructed” rather than priors like “objects are special”.
Issue 1: What do you mean by “objects”?
The authors interpret the fact that performance falls so much between conditions b and c to mean that human priors about “objects are special” are very important. However, an alternative explanation is that people explore things which look “different” (ie. Orange when everything else is black). 
The problem here comes from an unclear definition of what the authors mean by an “object” so in revision I would like authors to clarify what precisely they mean by a prior about “the world is composed of objects” and how this particular experiment differentiates “object” from a more general prior about “video games have clearly defined goals, there are 4 clearly defined boxes here, let me try touching them.”
This is important because a clear definition will give us an idea for how to actually build this prior into AI systems.
Issue 2: Are the results here really about “high level” priors?
There are two ways to interpret the authors’ main claim: the strong version would maintain that semantic priors aren’t important at all.
There is no real evidence here for the strong version of the claim. A real test would be to reverse some of the expected game semantics and see if people perform just as well as in the “masked semantics” condition.
For example, suppose we had exactly the same game and N different types of objects in various places of the game where N-1 of them caused death but 1 of them opened the door (but it wasn’t the object that looked like a key). My hypothesis would be that performance would fall drastically as semantic priors would quickly lead people in that direction. 
Thus, we could consider a weaker version of the claim: semantic priors are important but even in the absence of explicit semantic cues (note, this is different from having the wrong semantic cues as above) people can do a good job on the game. This is much more supported by the data, but still I think very particular to this situation. Imagine a slight twist on the game:
There is a sword (with a lock on it), a key, a slime and the door (and maybe some spikes). The player must do things in exactly this order: first the player must get the key, then they must touch the sword, then they must kill the slime, then they go to the door. Here without semantic priors I would hypothesize that human performance would fall quite far (whereas with semantics people would be able to figure it out quite well).
Thus, I think the authors’ claim needs to be qualified quite a bit. It’s also important to take into account how much work general priors about video game playing (games have goals, up jumps, there is basic physics) are doing here (the authors do this when they discuss versions of the game with different physics).