Reinforcement contingencies modulate anticipatory smooth eye movements

Abstract

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.

Date
Nov 3, 2016 12:00 AM
Event
GDR Vision, Toulouse, Nov 3rd, 2016
Jean-Bernard Damasse
Jean-Bernard Damasse
Phd in Computational Neuroscience

During my PhD, I focused on Gaze orientation and reinforcemnet learning.

Laurent U Perrinet
Laurent U Perrinet
Researcher in Computational Neuroscience

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

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