[pymvpa] Interpreting representation similarity results

[Nick Oosterhof]

> On 21 Aug 2015, at 12:45, Vadim Axel <axel.vadim at gmail.com> wrote:
> 
> I think we understood something different by "trivial" term :)
> 
> I observed correlated neural activity for two tasks in some region. For me one trivial explanation would be for example, that all the brain increases its activity during the task (e.g., general arousal or brain vasculature). To rule out this, I show that correlation is only in a specific region. Another trivial explanation, is that anatomical connectivity of my specific region is that it is always activated similarly. To rule out this one, I show that there is a task which does not result in correlated activity in this region. 

That indeed suggests specificity in both location and task, which is good. As I wrote before, however, it may be even more convincing if the control task shows a significant weaker correlated activity than the task of interest (the p=0.049 versus p=0.051 case for alpha=0.05).

> 
> The trivial you refer to are the not-well-controlled experimental manipulation. So, for example, if I hypothetically get correlated activity in the FFA for white faces and white clocks, I cannot say that cognitive processing of  faces and clocks is similar, because it can be processing of white color which is similar. However, the latter one is still "cognitive processing", and it was similar between tasks. It was just not the processing I was interested. Clearly, the control conditions here need to be very specific.
> 
> Makes sense? Do you have more trivial explanations of first type?

That all makes sense and it seems we think along the same lines. Other 'trivial' explanations of the 'first type' did not come to mind. To summarise what we both said earlier, showing spatial specificity and task specificity are important for claims about the involvement of a particular region - and how specific these claims can be depends largely on how good the controls are. 

Good luck with the analysis!