Humans are particularly efficient at recognising objects – think about what surrounds us: one recognises the object where one is reading the text from as a screen, the place where one sits as a chair, the utensil in which one drinks coffee as a cup, and one does all of this extremely quickly and virtually automatically. One is able to do all this despite the fact that 1) one holds large amounts of information about each object (if one is asked to write down everything you know about a pen, you would certainly have a lot to say); and that 2) there are several exemplars of each object type (a glass can be tall, made out of glass, metal, paper or plastic, it can be different colours, etc. – but despite that, any of them would still be a glass). How does one do this? How one is able to store and process so much information in the process of recognising a glass, and generalise all the different instances of a glass to get the concept “glass”? The goal of the ContentMAP is to understand the processes that lead to successful object recognition.

The answer to these question lies in better understanding of the organisational principles of information in the brain. It is, in fact, the efficient organisation of conceptual information and object representations in the brain that allows one to quickly and efficiently recognise the keyboard that is in front of each of us. To study the neuronal organisation of object knowledge, the project collects large sets of fMRI data from several participants, and then try to decode the organisational principles of information in the brain.

Given the amount of data and the computational requirements of this type of data at the level of pre-processing and post processing, the use of HPC is essential to enable these studies to be conducted in a timely manner. For example, at the post-processing level, the project uses whole brain Support Vector Machine classification algorithms (searchlight procedures) that require hundreds of thousands of classifiers to be trained. Moreover, for each of these classifiers one needs to compute a sample distribution of the average, as well as test the various classifications of interest, and this has to be done per participant.

Because of this, the use of HPC facilities of of the Advanced Computing Laboratory (LCA) at University of Coimbra is crucial. It allows us to actually perform these analyses in one to two weeks – something that on our 14-core computers would take a few months, which in pratice would mean, most probably, that the analysis would not be done. 

By Faculty of Psychology and Educational Sciences, University of Coimbra

Reference 

ProAction Lab http://proactionlab.fpce.uc.pt/ 

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