Thèse de doctorat
Résumé : In this thesis we pitted two views of action selection. On the one hand, a traditional view suggesting that action selection emerges from a sequential process whereby perception, cognition and action proceed serially and are subtended by distinct brain areas. On the other hand, an ecological view (formalized in the affordance competition hypothesis) advocating that action selection stems from the parallel implementation of potential action plans. In parallel, the competition between these action plans would be biased by relevant task factors. We first addressed the issue of the temporal dynamics of action selection processes in Chapter 2. We built a reaching task design that crucially gave equal opportunities for serial and parallel processing of cognitive and motor processes to occur. In our study, we first cued participants with probabilities associated to upcoming potential reaches. After several hundreds of milliseconds, participants were given a deterministic go signal indicating which target to reach for. They had to reach for the signaled target as fast as possible. Importantly, our design tries to cope with the biases involved in previous reaching tasks, allowing for a much more informative way to tackle the issue of serial versus parallel processing in action selection. We show that effects of action probability are not only present in the initiation time (i.e. the time it takes to initiate the movement), but crucially also in the movement time (i.e. the time interval between movement initiation and target reaching). Furthermore, an analysis of the movement trajectories showed that reach probability influenced the trajectories according to the predicted pattern. Thus, these results back up a system where cognitive and motor processes continuously interact with one another to come up with a decision. After clarifying the temporal dynamics, we concentrate our efforts on exposing the neural architecture of processes subtending action selection in Chapter 3. In a two-choice button press task, participants were first cued with predictive information regarding upcoming button presses. Crucially, we experimentally manipulated the amount of information in favor of specific button presses whilst adopting a design as similar as possible to those used in monkey neurophysiology (e.g., Cisek & Kalaska, 2005). Using fMRI, our results showed that as information in favor a button press increases, so does activity in the contralateral primary motor cortex, while activity in the ipsilateral primary motor cortex decreases. Moreover, we observed that primary motor regions are more tightly coupled with fronto-parietal areas in a condition involving a decision compared with a situation not implicating a decision between two button presses. Our results are compatible with an account predicting that decision-making emerges from motor areas, and therefore suggest that the architecture presented in the affordance competition hypothesis is not only valid in monkeys but also humans. In Chapter 4, we combine the findings acquired in the studies of chapter 2 and 3 with recent neurophysiological insights to develop a neuro-computational model capable of grasping the continuous interaction between cognitive and motor processes, responsible for the behavioral pattern in reach selection tasks. Our model functions on the principles of cascade forward models whereby activation at one stage of processing systematically spills to the next one, thereby substantially blurring the boundaries between perceptive, cognitive and motor processes. Contrary to most computational models confining action selection processes prior to action execution, our model allows for these processes to leak into action execution. Moreover, the threshold for action execution is not fixed, but rather dynamic and crucially depends on the activity pattern of the model’s primary motor neurons. We propose that the modification of the threshold is governed by the subthalamic nucleus, receiving direct input signals from the primary motor cortex and in turn imposing a dynamical brake on action execution. By including this dynamical threshold, our model has the advantage that it can release movement execution either rapidly or slowly depending on the context. Our model accounts not only for initiation times, but also movement times in reaching task studies. Furthermore, it can grasp the qualitative pattern of movement trajectories. This study suggests that to explain unfolding actions a classical fixed threshold is not sufficient, but rather an execution threshold level that is continuously being updated depending on the context is required.