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Jean-Bernard Damasse, Anna Montagnini, Laurent Perrinet; Dynamic modulation of volatility by reward contingencies: effects on anticipatory smooth eye movement. Journal of Vision 2017;17(10):273. doi: 10.1167/17.10.273.
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By using a visual tracking task, where we manipulated the probability for the target to move in a direction (Right) or another (Left) in three different direction-biased blocks (with 50%, 75% and 90% of rightward trials respectively), we observed a systematic and graded anticipatory smooth pursuit eye movements (aSPEM) in human volunteers, suggesting that probabilistic information about the a priori direction of future motions is inferred to optimize visuomotor tracking. Smooth eye movements are known to be sensitive to reward contingencies both during the visually guided phase (Schütz et al, 2015), maintained pursuit during blanking (Madelain & Krauzlis, 2003) and anticipation, where aSPEM could be enhanced or reduced by reward in a velocity criterion-matching protocol (Damasse et al, 2016). Optimal decision-making results from the weight given to the outcomes of possible decisions. These weights reflect their relevance in predicting future outcomes, which itself is related to the volatility of the environment (Behrens et al, 2007). In our situation, indeed, the way each past outcome is included to infer decision-making in the present is quite complex, as it has to account both for an evolving reward schedule and on sensorimotor regularities (probability of motion direction). To analyze this, we implemented an agent that produces aSPEM velocities and parameterized by a characteristic memory decay time (i.e. the number of past trials used to estimate the likelihood of a particular motion direction –similarly to Anderson & Carpenter, 2006). We challenged this model by comparing its predictions to the experimental aSPEM velocity changes associated to specific trial-sequences (tested across many subjects). Results suggest that aSPEM reflect an estimation of the volatility of predictive information that may be dynamically biased by the reinforcement program. This dynamical bias was consistent with our previously reported block-based results.
Meeting abstract presented at VSS 2017
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