Should I stay or should I go? Adaption of human observers to the volatility of visual inputs


Animal behavior has to constantly adapt to changes, for instance when unexpectedly switching the state of an environmental context. For an agent interacting with this kind of volatile environment, it is important to respond to such switches accurately and with the shortest delay. However, this operation has in general to be performed in presence of noisy sensory inputs and solely based on the accumulated information. It has already been shown that human observers can accurately anticipate the motion direction of a visual target with their eye movements when this random sequence of rightward/leftward motions is defined by a bias in direction probability. Here, we generalized the capacity of these observers to anticipate different random biases within random-length contextual blocks. Experimental results were compared to those of a probabilistic agent which is optimal with respect to this switching model. We found a better fit between the behaviorally observed anticipatory response with that of the probabilistic agent compared to other models such as a leaky integrator model. Moreover, we could similarly fit the level of confidence reported by human observers with that provided by the model and derive a common marker for subject inter-variability, titrating their level of preference between exploration and exploitation. Such results provide evidence that in such a volatile environment human observers may still efficiently represent an internal belief, along with its precision, and use this representation for sensorimotor control as well as for explicit judgments. This work proposes a novel approach to more generically test human cognitive abilities in uncertain and dynamic environments.

Apr 5, 2019 3:45 PM
CausaL Kick-off
INT, Marseille (France)
Laurent U Perrinet
Laurent U Perrinet
Researcher in Computational Neuroscience

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