Eye movements as a model for active inference

Laurent U Perrinet, INT

Acknowledgements:

Rick Adams and Karl Friston @ UCL - Wellcome Trust Centre for Neuroimaging
Mina Aliakbari Khoei, Guillaume Masson and Anna Montagnini - FACETS-ITN Marie Curie Training Network
Jean-Bernard Damasse, Sohir Rahmouni and Laurent Madelain - ANR REM

LAW, Friday, October 14, 2016

(AUTHOR) Hi, I am Laurent Perrinet from (LOGO) the Institute de Neurosciences de la Timone in Marseille, a joint unit from the CNRS and AMU. Using computational models, I am investigating the link between the efficiency of behavioural responses in vision, their underlying neural code and their adaptation to the structure of the world.
(SHOW TITLE - THEME) = In particular, eye movements, that is, motion of the eyeballs targeted at orienting our gaze in the visual field, provide a large repertoire of behaviors at different time scales from fast, reflexive-like saccades to smooth movememnts and even to long-term adaptation.
(SHOW TITLE -OBJECTIVE) today, I will be focusing on the key challenges in modelizing visually guided eye movements and more generally to better understand decision making in biology (the living kingdom). in particular, we will focus on the dynamics of these mechanisms to unravel the sequence of processes which allow the efficient response of motor commands to the environment. As such, my goal here is to show that the solutions proposed by motion perception and EMs provide a generic (and rather unique) framework to finesse models of decision making in the generic theory championned by Karl Friston, active inference (AI)
(ACKNO) this endeavour involves different techniques, tools and... persons. From the head on, I wish to thank the people who collaborated to this project and in particular Rick Adams and Karl Friston and the Wellcome Trust Centre for Neuroimaging for providing the tools for a successful visit / but also Mina, Guillaume and Anna for the great oppportunity to link theories with psychophysical data; Wahiba, Giacomo and Frédéric for the challenging task of linking such models to the neural activity in NHP, and finally Jean-Bernard, Sohir and Laurent Madelain for their essential knowledge in adaptation and reinforcement.

more precisely... what are eye movements and how can they be useful for active inference?

... Eye movements can be of different nature, and I will propose to you now some gentle post-lunch gymnastics by trying to elicit such visually-guided movements with some short movies:

Most simply, saccades are an orienting behavior aiming at catching a specific position in visual space (or vergence movement in depth). if after fixating at the red fixation dot you try to catch the green dot, then you need to do such saccades, which can be very fast (up to 600 deg/s)

When the dot is now moving almong a trajectory, this visual motion can are also responsible for more complex movements such as the reflexive Ocular Following Response or the voluntary Smooth Pursuit EM which aims at stabilizing the visual image on the retina. INterestingly, in this latter case the position of the eye quickly centers on the present, physical position of the dot and we will later see why this is challenging

... Eye movements can be of different nature, and I will propose to you now some gentle post-lunch gymnastics by trying to elicit such visually-guided movements with some short movies:

The Behavioral receptive field

LP and Masson (2012) Bio Behav Reviews, https://laurentperrinet.github.io/publication/masson-12

Montagnini, and Masson (2015) in Biologically Inspired Computer Vision

... we have been busy in the last couple of years to make sense of this repertoire of behaviors, in particular in light with the architecture of the brain areas involved in producing these EMs, for instance for pursuit initiation, ...

the Left-side plot (extracted from this review paper) illustrates in primates a sketch of the hierarchy of neuronal population yielding to ocular following responses from the early visual areas (retina, thalamus, primary visual cortex), finessed by processing in motion specific information in downstream sensory areas (MT for speed or MST for global motion) to the brain stem and the oculomotor muscles which tranform this neural activity in a graded motor output. As such, moving stimuli covering the retina are processed by a cascade of neuronal populations, where information is processed through feedforward, lateral and feedback interactions.
in particular, we took advantage of a Bayesian, probabilistic framework to develop an unified theory, the "behavioral receptive field" which encompasses a large range of psychophysical experiments by modeling behavioral responses from population dynamics. Such cascade implements local, context-dependent extraction of motion information…
today, I would like today to focus on a particular problem which will help us unravel the dynamics of decision making: oculomotor delays. Indeed, one challenge for modelling is to understand EMs using AI as a problem of optimal motor control under axonal delays. The central nervous system has to contend with axonal delays, both at the sensory and the motor levels. For instance, in the human visuo-oculomotor system, it takes approximately $ au_s=50~ms$ for the retinal image to reach the visual areas implicated in motion detection, and a further $ au_m=40~ms $ to reach the oculomotor muscles.

* ... As a consequence, for a tennis player ---here (highly trained) Jo-Wilfried Tsonga at Wimbledon--- trying to intercept a passing-shot ball at a (conservative) speed of $20~m.s^{-1}$, the position sensed on the retinal space corresponds to the instant when its image formed on the photoreceptors of the retina and reaches our hypothetical motion perception area behind: