talk: 97%

did a dry run today for my FVM talk. i think it went well, but there was a good amount of feedback. (incidentally, earlier this week i came to the lab, and passed my preceptor e* talking with a familiar old guy in the hall; a few minutes later, e* brings the guy to my office and asks me to show him my work. the old guy was l.s., one of the elder statesmen of european psychophysics. turns out he had been a postdoc at the instutute more than 40 years ago, and was in town, and had just dropped in to see old friends.. i took him through my presentation at quarter speed, and he was very enthusiastic. made some suggestions about controlling for the 'knowledge' aspect of my stimuli and experiment design. took notes. had a good talk with him, he seems to know my grad school mentor well, knows all his students. so i didn't go to ECVP this week, but i got to spend a morning with one of its founders...)

anyways, the dry run: p* was the only one, as i guess i expected, to make real comments on the substance of the talk. he had two points/questions:

1. what happens if the two images are different, i.e. if they have different phase spectra? i have not tried to do this experiment, or to predict the result. i guess that technically, the model that i am evaluating would make clear predictions in such an experiment, and the perceptual process i am claiming to occur would be equally applicable. but, really, i am tacitly assuming that the similarity of the two images is tamping down noise that would otherwise be there, somehow in the spatial summation, that isn't actually reflected in the model but that would be there for the humans. but, it might work just fine. i should really try it out, just to see what happens... (*edit: i tested it in the afternoon, and the result is exactly the same. experiment is harder, and the normalization is wacky, but seems clear it works...)

2. don't the weighting functions look just like CSFs? isn't this what would happen if perceived contrasts were just CSF-weighted image contrasts? yeah, sure, but there's no reason to believe that this is how perceived contrast is computed. the flat-GC model is close to this. i wonder if i shouldn't just show a family of flat-GC models instead of a single one, with one of them having 0-weighted GC...

the other main criticism was of the slide with all the equations. this is the main thing i think i need to address. i need to remake that slide so it more naturally presents the system of processes that the equations represent. some sort of flow or wiring diagram, showing how the equations are nested...

also need to modify the explanation of the contrast randomization; not add information, but make clearer that the two contrast weighting vectors are indeed random and (basically) independent.