6

Here's a quick answer from general background knowledge, not from any specific knowledge of "Bayesian Program Synthesis (BPS)" In general, Bayesian models can use strongly informed priors or diffuse "could be anything" priors. Strong priors specify that a lot of parameter values are very unlikely, while a few other parameter values are possible descriptions ...


4

The number of samples that are necessary for a good parameter estimation does indeed depend on the estimation method. I am not aware of a simple rule of thumb to determine an optimal sample size, but there has been a lot of literature on this topic. A paper that might be a good starting point for a literature search is Van Zandt T. (2000) How to fit a ...


4

Disclaimer: I'm not generally doing experiments where reaction time is the primary DV. But I thought I'd look at this issue and explored RTs from a neuroimaging dataset, and I think the findings are relevant to the question. I think without further qualification, this question doesn't have an answer. Here I've plotted the estimation of reaction time/RT over ...


2

It depends what you mean by a biological mechanism. If you mean that there should be a protein cascade that implements normalization, that doesn't seem plausible, in my opinion. Normalization in probabilistic population codes is just one of many computations that can be performed in a neural system. If you're okay with the notion that there's nothing ...


1

Correct me if I'm wrong here, but after some more reading and thinking this is what I took away: both the term 'prediction' and 'cause' here refer to the maximum likelihood of a distribution ($Y$), meaning the probability of an event ($X$), given sensory data ($Y_{1}$) and the likelihood given prior data ($Y_{2}$) . Even though maybe seemingly orthogonal to ...


Only top voted, non community-wiki answers of a minimum length are eligible