Key sentence - "the correct choice of face space axes is critical for achieving a simple explanation of face cells’ responses."
They did PCA over two sets of metrics, taking the top 25 components from each set and then combined that into a 50d space. Using these dimensions and measured responses to fit a model resulted in explaining 57% of variance in real cell firing rates. (Much better than other models including a 5 layer CNN).
This is pretty cool. I'd like to see a follow-up where the chosen dimensions were further refined using something a bit more iterative that an arbitrary PCA cutoff.
Also I really want to know what eye motion was present during each trial. This paper presents a very "instantaneous" recognition perspective and doesn't talk about integration over time or the impact of sequential perception of face components on recognition. (Eg an upside-down face is hard to recognize because your gaze has to move up from the eyes to see the mouth which is a sequence rarely encountered in the real world)
They did PCA over two sets of metrics, taking the top 25 components from each set and then combined that into a 50d space. Using these dimensions and measured responses to fit a model resulted in explaining 57% of variance in real cell firing rates. (Much better than other models including a 5 layer CNN).
This is pretty cool. I'd like to see a follow-up where the chosen dimensions were further refined using something a bit more iterative that an arbitrary PCA cutoff.
Also I really want to know what eye motion was present during each trial. This paper presents a very "instantaneous" recognition perspective and doesn't talk about integration over time or the impact of sequential perception of face components on recognition. (Eg an upside-down face is hard to recognize because your gaze has to move up from the eyes to see the mouth which is a sequence rarely encountered in the real world)