Friday, September 21, 2012

grant, presentation, paper, model

Been trying to skip between several jobs: grant proposal with a looming deadline, modeling experiments for a paper revision with a looming deadline, looming conference presentation... well, the conference is over, and the grant is coming along, though I still do not believe I will make it.

The paper..  okay, another paper: poked an editor yesterday, and he came back with a 'minor revision' request, which I fulfilled by late afternoon today. So, finally, we have a journal article - in a 1.0 impact factor journal - to show for a 3 year postdoc. Sigh. Another in revision, in a better journal, but that's the big problem: I'm doing all these model tests, but I can't get any real momentum because I keep flipping back to the grant. Sigh. I keep complaining about the same thing. Need to set a deadline - 3 more years? - after which if I'm still making the same complaint, something needs to change.

Let's talk about the model stuff. I've talked about it already in the past few posts: in the original paper, I proposed a modification to an existing model, a minor modification, which was able to closely fit our data, but which was a bit complexified, and difficult to explain exactly why it worked as well as it did, and also unable to show how varying its parameters explained the variance in our data, etc. So, it "worked", but that's about all it did. It didn't explain much.

The existing model we call the "simple model". The simple model is indeed simple. It's so simple that it's almost meaningless, which is what frustrates me. Of course it's not that simple; you can interpret its components in very simplified, but real, visual system terms. And, it basically can describe our data, even when I complexify it just a bit to handle the extra complexity of our stimuli. And this complexification is fine, because it works best if I remove an odd hand-waving component that the original author had found it necessary to include to explain his data. Only... it doesn't quite work. The matching functions that make up the main set of data have slopes that are different in a pattern that is replicated by the simple model, but overall the model slopes are too shallow. I spent last week trying to find a dimension of the model that I could vary in order to shift the slopes up and down without destroying other aspects of its performance..  no dice.. fail fail fail.

So, I'm thinking that I can present a 'near miss': the model gets a lot of things right, and it fails to get everything right for reasons that I haven't thought hard enough about just yet. I really need to sit some afternoon and really think it out. Why, for the normal adaptor, is the matching function slope steeper than the identity line, but never steep enough? What is missing? Is it really the curvature of the CSF? How do I prove it?

Now, out of some horrible masochistic urge, I'm running the big image-based version of the "simple model". This version doesn't collapse the input and adaptation terms into single vectors until the 'blur decoding' stage. It seems like, really, some version of this has to work, but it hasn't come close yet. Looking at it now, though, I see that I did some strange things that are kind of hard to explain... Gonna give it another chance overnight.