Curriculum Vitae

Research accomplishments

1. Effects of hand position on the ocular exploration of the peripersonnal space

Thura D., Hadj-Bouziane F., Meunier M. and Boussaoud D. 

Institut de Neurosciences Cognitives de la Méditerranée (INCM), CNRS & Aix-Marseille Université, Marseille (France) 


The central aim of my Ph.D. research was to test the effects of the signals coding hand position in space (i.e. vision and proprioception) on the neuronal activities related to visual taget selection, saccade preparation and execution in the monkey frontal eye field. Indeed, at the behavioral level, it’s now well documented that eye and hand are highly coupled both in space and time. Accordingly, neurphysiological studies showed that several areas devoted to the planning of arm movements are influenced by eye position signals. Nonetheless, less is known about how oculomotor strucutres intergrate hand position signals. 

Effects of hand position on saccadic reaction times. First, I demonstrated that visible and invisble hand position affect strongly saccadic reaction times, both in humans and in monkeys (Thura et al., J Neurophysiol, 2008).

Hand position and fontal eye field neuronal activity in the monkey. Then, using extracellular single-unit recordings in two monkeys conditionned to execute visually guided saccades (I used two different techniques to acquire and record eye movements: scleral search coil and infra-red camera), I brought evidence that hand position signals are intregrated by FEF visual and saccade neurons (Thura et al., Behav Brain Res, 2008; Thura et al., Cerebral Cortex, 2011).


Hand position and dynamical orientation of visuospatial attention. Finally, recent preliminary data tend to demonstrate that hand affects the dynamical properties of visuospatial attentional exploration when a visual target, located in the perispersonal space, is selected for upcomming saccades. FEF visual neurons might participate to the neural mechanisms allowing this bias. Behavioral experiments in humans and neurophysiological recordings in monkey are performed in order to better understand the implication of hand position in the temporal mechanism of visospatial attention orientation (Thura et al., Society for Neuroscience Abstract, 2007).




2. Neuronal bases of Learning by Observation

Belmalih A., Thura D., Isbaine F., Brovelli A., Demolliens M., Meunier M. and Boussaoud D. 

Institut de Neurosciences Cognitives de la Méditerranée (INCM), CNRS & Aix-Marseille Université, Marseille (France)

Numerous animal species, including human and non human primates, can learn either through their own experience (trial and error learning, TE) or through the observation of conspecifics. Here, we address the question of how the brain of an observer encodes the outcome of others behavior, with particular focus on error and success signals.

Two monkeys were trained, always together, on a visuospatial task where they had to map 2 or 4 visual stimuli (blue patterns) presented at the center of a touch screen on 2 or 4 spatial targets (white squares) occupying the screen corners. The experimental design allowed the monkeys to face each other, and to have access to the touch screen displayed face up between them. Only one of the two monkeys had access to the touch screen at a time (actor), the other could observe but not reach the screen (observer). Monkeys alternated between the two roles. To control that the observer actually looked at to the touch screen, its gaze direction was monitored using an infrared camera. Neural activity was recorded from the dorso-lateral prefrontal cortex of the observer, an area known to play a key role in the processing of action and its outcome during learning. The actor initiated a trial by putting his hand on a lever (white square) close to him. Then a visual cue was presented together with 2 or 4 targets. Pressing the correct target led to a success signal (green ring), followed after a delay by a reward (a drop of fruit juice); Incorrect response led to an error signal (red ring) and no reward was given. We used several conditions to investigate the neural activity: the observation of familiar or new cue-target associations, the execution of new associations that were (observational learning) or were not (TE) observed before. Gaze data showed that the observer did monitor the actor's behavior.

Behavioral data showed that the observation led to performance improvement of up to 13% for associations learned after observation relative to those learned by TE. Preliminary neuronal data indicate that the dorso-lateral prefrontal cortex contains at least two populations of neurons encoding error and/or success signals. One responds similarly to the outcomes of own and other's actions (cognitive mirror properties). The other differentiates the source of the outcome, responding either to signals triggered by the monkey's own actions or only to those triggered by the other monkey's actions.

Taken together, the neuronal properties provide preliminary evidence that the monkey prefrontal cortex integrates signals about own and others’ successful and erroneous behavior.



3. Human decisions in noisy and changing conditions

Thura D., Beauregard-Racine J., Fradet C-W. and Cisek P.

Département de physiologie, Université de Montréal, Montréal (Qc), Canada

The video shows a human subject performing the variable coherence motion discrimination task.


In recent years, significant progress has been made toward understanding the neural basis of primate decision-making. Most decision-making studies and models have suggested that simple decisions are made through a process of ‘bounded integration’, in which neurons integrate sensory evidence until a threshold is reached. However, nearly all of the results supporting this theory have been obtained in tasks where sensory evidence was constant during the course of each trial. In this particular situation, behavioral and neural data are also compatible with a model in which there is no integration of sensory evidence, but instead a multiplication of current evidence by a growing ‘urgency’ signal. In a recent study, Cisek et al. (2009) presented human subjects with a task (‘tokens’ task) in which evidence changed over time. Results were more consistent with the ‘urgency-gating’ model than with “integrator” models, but it was not clear whether this was task-dependent. 

Here, 11 human subjects performed a new task, conceptually similar to the ‘tokens’ task but perceptually close to the well-known ‘direction-discrimination’ task, in which subjects perform two-alternative perceptual decisions about the direction of motion in a dynamic random-dot display. In our task, each trial began with a centrally-located visual stimulus consisting of 200 dots moving in random directions. After 200ms, 6 of the dots began to move coherently in one of two opposite directions (right or left). After another 200ms, another 6 of the random dots began to move coherently in one of the two directions, and so on for a total of 15 steps. The subject’s task was to select, as soon as they felt confident enough, the target corresponding to the net direction of motion they predicted to see at the end of the trial. Once the choice was made, the remaining steps of coherence were reduced to a 20ms duration. To distinguish the models, we embedded in a full pseudorandom sequence some trials whose first 6 motion steps provided either a perceptual bias for or against the correct target.

Consistent with the ‘urgency-gating’ model, decision times as well as success probabilities of 8/11 subjects were not significantly influenced by these early biases, suggesting that noisy sensory evidence in favor of a given choice was not integrated with a long time-constant. Additional analyses suggested that the level of certainty at which most subjects made their decisions decreased significantly during the course of a trial (Thura et al., 2012).

Our results suggest that humans form decisions by comparing to a threshold the product of the momentary information provided by the environment with a growing signal related to elapsed time (‘urgency’).



4. Temporal mechanims of decision-making in the dorsal premotor cortex of the macaque monkey

Thura D. and Cisek P.

Département de physiologie, Université de Montréal, Montréal (Qc), Canada


Multi-electrode recordings of single units (blue curves) and local field potentials LFPs (red curves) in the dorsal premotor cortex.


Many decision-making studies and models have suggested that simple decisions are made through a process of “bounded integration”, in which neurons accumulate sensory evidence until a threshold is reached. However, when human subjects were presented with tasks in which evidence changed over time (Cisek et al., 2009; Thura et al., SfN 2009), their behavior was better explained with an “urgency-gating” model, in which the current evidence is multiplied by a growing “urgency” signal. In the present study, we examine neural correlates of this process in monkey frontal cortex. 

A monkey was trained to perform a two-alternative reach decision task (the “tokens” task). The animal began each trial by moving a handle into a central circle in which 15 small circular tokens were randomly arranged. Next, the tokens began to jump, one-by-one every 200 ms, from the central circle to one or the other of two target circles placed 180° apart. The monkey’s task was to move the cursor, as soon as he felt sufficiently confident, to the target that he believed would ultimately receive the majority of the tokens. Once the choice was made, the timing of the remaining token movements was accelerated to either 50ms (fast block) or to 150ms (slow block) in separate blocks. 

Analysis of behavioral variables (performance, decision time and probability of success at decision time) shows that the monkey acts very similarly to humans. For instance, he behaves more hastily in the fast blocks and more conservatively in the slow blocks. Moreover, in trials incorporating an early bias in favor of or against the correct target, decision times as well as success probabilities are not influenced by these early biases in a way predicted by most “integrator” models. Ninety-three task related neurons were recorded from the dorsal premotor and the prefrontal cortex of the monkey performing the “tokens” task. Among a population of 72 cells showing spatial tuning during at least one epoch of the task, 24 predicted the monkey’s choices (pre-decision target selectivity) and reached a fixed threshold at the time of decisions. Prior to the choice commitment, these decision-related activities reflect the profile of evidence presented to the monkey with target selectivity emerging earlier in easy than ambiguous trials. Consistent with behavioral data, preliminary analyses show that neural activity at a given moment is not significantly influenced by the information presented earlier in the trial. Overall, our results suggest that neural activity in frontal cortex combines current sensory information provided by the environment with a growing urgency signal, and decisions are made when this quantity reaches a threshold.

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