PhD defense

29 Nov 2017

Today I defended my PhD dissertation entitled Characterizing the neurocognitive mechanisms of arithmetic, supervised by Stanislas Dehaene and co-supervised by Manuela Piazza (Trento University, Italy) at the CEA-NeuroSpin Center in the outskirts of Paris! Download here the slides of my presentation.

The amazing members of the jury were:

How are two numbers combined into a third? Arithmetic is one of the most important cultural inventions of humanity, however we still lack a comprehensive understanding of how the brain computes additions and subtractions. The main goal of my dissertation is to better understand the temporal and spatial dynamics of the neurocognitive mechanisms underlying mental calculation. In the first study, I used a novel behavioral method based on trajectory tracking capable of dissecting the succession of processing stages involved in arithmetic computations. Results supported a model whereby single-digit arithmetic is computed by a stepwise displacement on a spatially organized mental number line, starting with the larger operand and incrementally adding or subtracting the smaller operand. In a second study, I analyzed electrophysiological signals recorded directly from the human cortex while subjects solved addition problems. I found that the overall activity in the intraparietal sulcus monotonically increased as the operands got larger, providing evidence for its involvement in arithmetic computation and decision-making. Surprisingly, sites within the posterior inferior temporal gyrus showed an initial burst of activity that monotonically decreased as a function of problem-size, suggesting that the ventral temporal cortex contain neuronal populations specifically involved in arithmetic processing, possibly engaged in the early identification of the difficulty or amount of evidence available for a calculation. In the last study, I recorded magnetoencephalography signals while subjects verified simple additions and subtractions in the form of 3+2=9, with each successive symbol presented sequentially. By applying machine learning techniques, I investigated the temporal evolution of the representational codes of the operands and provided a first comprehensive picture of a cascade of unfolding processing stages underlying arithmetic calculation and decision-making at a single-trial level. Overall, this dissertation provides several contributions to our knowledge about how elementary mathematical concepts are implemented in the brain and shows that a multimethod approach including continuous behavioral measures and time-resolved neuroimaging can help us identify and characterize the mental processes of high-level symbolic cognition.