two out of three ain't enough

okay, so, really, i spent the labor day weekend watching youtube videos, looking at funny gifs, reading the news, and other random things, while running half-baked model simulations for the blur adaptation revision.

first thing i did was to run the video-based model through the experiment on the same three adaptation levels used in the original experiment. it worked at an operational level, i.e. it matched sharper things with sharper things and blurrier things with blurrier things, and the effects of the adaptors were correctly ordered - it didn't do anything crazy. on an empirical level, though, it was wrong.

for the original subjects, and most of the replication subjects, the perceived normal after blank adaptation should be matched to a slightly sharpened normal-video-adapted test; the simulation did the opposite. not a huge problem, but like i said, against the trend.

bigger problem is that the simulation failed to get the 'gain' peak for the normal adaptation condition; instead, gain just increased with sharpness of the adaptor. now i'm rerunning the simulation with some basic changes (adding white noise to the spatial inputs, which i don't think will work - might make it worse by increasing the effective sharpness of all inputs - but might have something of a CSF effect; and windowing the edges, which i should have done from the start).

one funny thing: even though the gain for the sharp adaptor is too high (being higher than for the normal adaptor), the gains for the normal and blurred adaptors are *exactly* the same as the means for the original three subjects: enough to make me think i was doing something horribly weirdly wrong in the spreadsheet, but there it is:



weird, but too good to be true. undoubtedly, every change to the model will change all of the simulation measurements, and the sim is definitely as noisy as the humans - even the same one run again would not get the same values.