PACE-ITN (2015/2019)

The PACE ITN project involves over 50 researchers spread across 10 full and 5 associated partners, from academia and the private sector, established in 7 different European and Associated countries, the PACE network gathers a broad range of expertise from experimental psychology, cognitive neurosciences, brain imaging, technology and clinical sciences.

The PACE Project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 642961

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Laurent U Perrinet
Researcher in Computational Neuroscience

My research interests include Machine Learning and computational neuroscience applied to Vision.

Publications

Humans adapt their eye movements to the volatility of visual motion properties, and know about it

Humans are able to accurately track a moving object with a combination of saccades and smooth eye movements. These movements allow …

A low-cost, accessible eye tracking framework

Recording eye movements is a technique that attracts an increasing number of scientists, but also in the general public. Indeed, this …

Estimating and anticipating a dynamic probabilistic bias in visual motion direction

Humans are able to accurately track a moving object with a combination of saccades and smooth eye movements. These movements allow …

Estimating and anticipating a dynamic probabilistic bias in visual motion direction

Humans are able to accurately track a moving object with a combination of saccades and smooth eye movements. These movements allow …

Selectivity to oriented patterns of different precisions

The selectivity of the visual system to oriented patterns is very well documented in a wide range of species, especially in mammals. In …

Expériences autour de la perception de la forme en art et science

La vision utilise un faisceau d’informations de différentes qualités pour atteindre une perception unifiée du monde environnant. …

Voluntary tracking the moving clouds : Effects of speed variability on human smooth pursuit

The properties of motion processing for driving smooth eye movements have bee investigated using simple, artificial stimuli such as …

Talks

Should I stay or should I go? Humans adapt to the volatility of visual motion properties, and know about it

Animal behavior must constantly adapt to changes, for example when the state of an environmental context changes unexpectedly. For an agent that interacts with this volatile setting, it is important to react accurately and as quickly as possible. For example, it has already been shown that when a random sequence of directions of motion to the right or left of a visual target is suddenly biased to one direction, human observers adapt to accurately anticipate it with their eye movements. Here, we prove that this ability extends to a volatile environment where probability biases could change at random switching times. In addition, we also recorded the level of confidence reported by human observers. These results were compared to those of a probabilistic agent that is optimal in relation to the event switching generating model. Compared to other models such as the leaky integrator, we found a better match between the behavioral response observed and that given by this agent. Furthermore, we were also able to fit the experimental data with different levels of switching volatility in the model and derive a common marker for the inter-variability of participants, by titrating their level of preference between exploration and exploitation. Such results prove that in such an unstable environment, human observers can still effectively represent an internal belief, and use this representation in their sensory-motor control system and for explicit judgments. This work offers an innovative approach to more generically test human cognitive abilities in uncertain and dynamic environments.

Reinforcement contingencies modulate anticipatory smooth eye movements

Natural environments potentially contain several interesting targets for goal-directed behavior. Thus sensorimotor systems need to operate a competitive selection based on behaviorally meaningful parameters. Recently, it has been observed that voluntary eye movements such as saccades and smooth pursuit can be considered as operant behaviors (Madelain et al, 2011). Indeed, parameters of saccades such as peak-velocity or latency (Montagnini et al, 2005) as well as smooth pursuit behavior during transient blanking (Madelain et al, 2003) or visually-guided pursuit of ambiguous stimuli (Schutz et al, 2015) can be modified by reinforcement contingencies. Here we address the question of whether expectancy-based anticipatory smooth pursuit can be modulated by reinforcement contingencies. When predictive information is available, anticipatory smooth pursuit eye movements (aSPEM) is frequently observed before target appearance. Actions that occur at some distance in time from the reinforcement outcome, such as aSPEM -which occurs without any concurrent sensory feedback suffer of the well-known credit assignment problem (Kaelbling et al, 1996). We designed a direction-bias task as a baseline and modified it by setting an implicit eye velocity criterion during anticipation. The nature of the following trial-outcome (reward or punishment) was contingent to the online criterion matching. We observed a dominant graded effect of motion-direction bias and a small modulational effect of reinforcement on aSPEM velocity. A yoked-control paradigm corroborated this result showing a strong reduction in anticipatory behavior when the reward/punishment schedule was not contingent to behavior. An additional classical conditioning paradigm confirmed that reinforcement contingencies have to be operant to be effective and that they have a role in solving the credit assignment problem during aSPEM.