Difference between reverse inference and decoding (e.g. MVPA) in fMRI Reverse inference, i.e. the use of brain activation (fMRI) data to infer the engagement of a certain mental function, is heavily criticised (e.g., Poldrack 2011, Neuron). 

At the same time, decoding, the process of applying machine learning algorithms to such brain activation patterns in order to infer something about the mental function that elicited this response (e.g. to distinguish between two different conditions in an fMRI experiment), is extensively used and actively developed - multivariate pattern analysis (MVPA) being just the most widespread of these techniques. 

The two concepts appear, however, to be one and the same; or rather, that the second (decoding) is a special case of the first (reverse inference). Is there an additional nuance to their difference? Also, why is decoding not criticised in the same way as reverse inference?
Note. I initially scan read the question, I have rewritten my answer as a consequence, and due to the comments given. 

As highlighted by others here Multi-voxel pattern analysis (MVPA) is an application of machine learning, used for decoding vast quantities of complex information (neural activation patterns to particular asks). This is a form of decoding may be used to infer a cognition, otherwise known as reverse inference. 

The problem of reverse inference is largely summed up by the following comment from Poldrak's (2011)... 


  The use of reasoning from activation to mental functions, known as “reverse inference”, has been previously criticized on the basis that it does not take into account how selectively the area is activated by the mental process in question.


Poldrack (2011) goes on to explain that informal reverse inference, which is based on a researchers knowledge, is flawed because an individuals knowledge is limited by what the remember and have read. Additionally poor interpretations are compounded from one researcher to another.    

The problem with reverse inference comes not from understanding general cognitive processes, such as sight, movement, language, decision-making etc. General patterns for general processes have been established, the problem as Poldrack (2011) points out is when we interpret patterns to more well defined cognitions, for example instead of looking at merely reward processing, we might want to compare patterns of pleasure derived from seeing highly palatable e.g. cake, and less palatable food e.g. fruit. At this level, comparing the data requires far more specific analytical approach. If we were a researcher making an inference based on our knowledge, we would have a strong chance of error.

MVPA deals with a far higher resolution of data than an individual could deal with, and compares data to prior trials or previous experiments, see Fig 1. However it is critical to remember that we are comparing participants within similar contexts. 

Fig 1. MVPA diagram of testing and inference Norman et al (2006)


Poldrack (2011) gives an excellent example of how to use MVPA, which was conducted by Kay et al (2008). Simply Kay et al (2008) scanned participants viewing natural images n= 1750. In the following trial 120 images were added, MVPA of the neural data was able to accurately predict which images were being viewed. This method has seen substantial development to the point that using a similar method researchers have accurately analysed neural patterns observed in a participants waking brain into decoding what participants are dreaming. Basically the quantity and quality of when taking account of context may be used to conduct reverse inference, but this does require previous data, within context, for the MPVA and other machine learning methods to compare too. 

This does not mean that machine learning can not be wrong if incorrectly applied. It is a statistical method in which humans set the parameters, the post below by Arnon Weinberg accurately defines the issues and pot holes that must be avoided to make this method viable for reverse inference.  

Norman, K. A., Polyn, S. M., Detre, G. J., &amp; Haxby, J. V. (2006). Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends in Cognitive Sciences, 10(9), 424–430. http://doi.org/10.1016/j.tics.2006.07.005