Some thoughts on writing ‘Bayes Glaze’ theoretical papers.

[This was a twitter navel-gazing thread someone ‘unrolled’. I was really surprised that it read basically like a blog post, so I thought why not post it here directly! I’ve made a few edits for readability. So consider this an experiment in micro-blogging ….]

In the past few years, I’ve started and stopped a paper on metacognition, self-inference, and expected precision about a dozen times. I just feel conflicted about the nature of these papers and want to make a very circumspect argument without too much hype. As many of you frequently note, we have way too many ‘Bayes glaze’ review papers in glam mags making a bunch of claims for which there is no clear relationship to data or actual computational mechanisms.

It has gotten so bad, I sometimes see papers or talks where it feels like they took totally unrelated concepts and plastered “prediction” or “prediction error” in random places. This is unfortunate, and it’s largely driven by the fact that these shallow reviews generate a bonkers amount of citations. It is a land rush to publish the same story over and over again just changing the topic labels, planting a flag in an area and then publishing some quasi-related empirical stuff. I know people are excited about predictive processing, and I totally share that. And there is really excellent theoretical work being done, and I guess flag planting in some cases is not totally indefensible for early career researchers. But there is also a lot of cynical stuff, and I worry that this speaks so much more loudly than the good, careful stuff. The danger here is that we’re going to cause a blowback and be ultimately seen as ‘cargo cult computationalists’, which will drag all of our research down both good and otherwise.

In the past my theoretical papers in this area have been super dense and frankly a bit confusing in some aspects. I just wanted to try and really, really do due-diligence and not overstate my case. But I do have some very specific theoretical proposals that I think are unique. I’m not sure why i’m sharing all this, but I think because it is always useful to remind people that we feel imposter syndrome and conflict at all career levels. And I want to try and be more transparent in my own thinking – I feel that the earlier I get feedback the better. And these papers have been living in my head like demons, simultaneously too ashamed to be written and jealous at everyone else getting on with their sexy high impact review papers.

Specifically, I have some fairly straightforward ideas about how interoception and neural gain (precision) inter-relate, and also have a model i’ve been working on for years about how metacognition relates to expected precision. If you’ve seen any of my recent talks, you get the gist of these ideas.

Now, I’m *really* going to force myself to finally write these. I don’t really care where they are published, it doesn’t need to be a glamour review journal (as many have suggested I should aim for). Although at my career stage, I guess that is the thing to do. I think I will probably preprint them on my blog, or at least muse openly about them here, although i’m not sure if this is a great idea for theoretical work.

Further, I will try and hold to three key promises:

  1. Keep it simple. One key hypothesis/proposal per paper. Nothing grandiose.
  2. Specific, falsifiable predictions about behavioral & neurophysiological phenomenon, with no (minimal?) hand-waving
  3. Consider alternative models/views – it really gets my goat when someone slaps ‘prediction error’ on their otherwise straightforward story and then acts like it’s the only game in town. ‘Predictive processing’ tells you almost *nothing* about specific computational architectures, neurobiological mechanisms, or general process theories. I’ve said this until i’m blue in the face: there can be many, many competing models of any phenomenon, all of which utilize prediction errors.

These papers *won’t* be explicitly computational – although we have that work under preparation as well – but will just try to make a single key point that I want to build on. If I achieve my other three aims, it should be reasonably straight-forward to build computational models from these papers.

That is the idea. Now I need to go lock myself in a cabin-in-the-woods for a few weeks and finally get these papers off my plate. Otherwise these Bayesian demons are just gonna keep screaming.

So, where to submit? Don’t say Frontiers…

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