CoActions Lab

Cognition and Actions Lab

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innovationWhen asked to define research, Nobel Prize in Physiology Dr. Albert Szent-Gyorgyi once said: ''Research is four things; Brains with which to think, eyes with which to see, machines with which to measure, and, fourth, money.'' Whilst all of these play a major role, machines and implements play key roles in sculpting (or hindering) the advancement of science. Let’s take for example the 2017 Nobel Prize in Physics. Whom would have thought that one day, it would be possible to measure distances as small as 1/1000 of an atom diameter. The same applies in the field of Neuroscience, where the constant need of both spatial and temporal resolution invites researchers to use their creativity to both improve current techniques and create new ones to further our understanding of the human brain.

In our lab, we are currently developing and validating a double-coil method involving transcranial magnetic stimulation over the primary motor cortex of both hemispheres, allowing one to obtain motor-evoked potentials (MEPs) in muscles of both hands at a near-simultaneous time (1 ms delay). This technique is useful in many ways. Firstly, it allows scientists to acquire double the data in the same amount of time; this is critical as it gives researchers the opportunity to test more conditions than could be done with a regular single-coil method. Secondly, obtaining MEPs in both hands within the same trial allows to investigate the distinct impact of a task on corticospinal excitability of both the dominant and non-dominant sides in the exact same setting. This increases the signal to noise ratio in a significant way. Finally, with this design, one can also make direct comparisons between MEPs elicited in the two hands on a single-trial basis. New dependent measures can be obtained, for example, indexes reflecting the difference (or ratio) between corticospinal excitability of the two hands.

Besides, we also develop statistical and mathematical models (e.g. sequential sampling models) for investigating and understanding human behaviour. Sequential sampling models have been used for a long time to retrieve key features of human behavior, such as speed-accuracy trade-off, urgency, bias in decision making or evidence accumulation rate. We are interested in making these models as hierarchical as they can, by building generic Hierarchical Bayesian Models (HBM) that will allow researchers to make inference about behavioural features (or, more specifically, parameters) from the population level to the trial level.

We are also interested in joining these models with Reinforcement Learning. We are developing models that naturally combine learning and decision making not as distinct mechanisms, but as two parts of the same Bayesian policy optimization mechanism. These models can account for Model-Based/Model-free balance, behavioural automatization and inflexibility.

People involved:

Related publications:

  1. Vassiliadis P*, Grandjean J*, Derosiere G, de Wilde Y, Quemener L, Duque J. Using a double-coil TMS protocol to assess preparatory inhibition. (Submitted).
    *Equal contribution
  2. Grandjean J, Derosiere G, Vassiliadis P, Quemener L, de Wilde Y, Duque J. Towards assessing corticospinal excitability bilaterally: Validation of a double-coil TMS method. Journal of Neuroscience Methods. 2017; 293: 162-168.
  3. Wilhelm, E., Quoilin, C., Petitjean, C., Duque, J. A Double-Coil TMS Method to Assess Corticospinal Excitability Changes at a Near-Simultaneous Time in the Two Hands during Movement Preparation. Front. Hum. Neurosci. 2016; 10: 1–11.


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