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Séminaire - Ignasi Cos When to dwell and when to move: finding comfort in variability

Abstract :

 Understanding how the CNS predicts the duration of time intervals and defines their motor content is a non-trivial task. For example, professional baseball batters adjust the duration and speed of their strike movement according to their prediction of ball movement time, as expert waltz dancers do to synchronize the end of each loop on beat with the music, as do orchestra players guided by the tempo dictated by their director. Irrespective of the differences in behaviour, these tasks highlight the common problem of predicting a temporal interval and of producing a set of controlled movements that fits therein.

Although experimental evidence shows that the CNS can estimate time intervals, these predictions become increasingly inaccurate as the duration of the interval expands (Ivry, 1996; Hazeltine et al., 1997). Despite this limitation, skilled musicians or dancers seldom fail in their predictions and on defining the content of their intervals. How does the CNS do to make this possible? On the way to answer this question, I will show the results of a recent set of experiments in which subjects were instructed to tap repetitively at different frequencies and amplitudes, synchronized with a metronome or in an autonomous fashion.

Our main result shows that subjects predicted the duration of intervals within cycle by trading-off the variability of intervals of movement and dwell during each cycle to minimize the error of the overall estimate. I will first describe the experimental results, focusing on the subjects’ behavioural distribution of variability. Second, I will introduce a theoretical model that reproduces these results by seeking the optimization of variability across each tapping cycle.

Although further analyses are required, the average subjects’ behaviour was compliant with the principle of overall variability minimization implemented by the model. This suggests that subjects are subjectively knowledgeable about the variability of their temporal estimates, both for movement and rest intervals, and that they exploit the statistical specificities of each of them to predictively improve the overall performance of their temporal estimates.

Selected publications

1. Marcos, E., Cos, I., Girard, B., and Verschure, P.M.J. (2014) The influence of biomechanics on visual decision-making. Under review at Nature Neuroscience.

2. Cos, I., Girard, B., and Guigon, E. (2014) When to dwell and when to move: finding comfort in variability. Under review at PLOS Computational Biology.

3. Cos, I., Duque, J., and Cisek, P. (2014). Rapid prediction of biomechanics during action decisions. Journal of Neurophysiology, 112(6), 1256-1266.

4. Thura, D., Cos, I., Trung, J., Cisek, P. (2014). Relationships between speed-accuracy trade-offs in decision-making and movement execution. Journal of Neuroscience, In Press.

5. Cos, I., Khamassi, M. and Girard, B. (2013). Modelling the Learning of Biomechanics and Visual Planning for Decision-Making of Motor Actions. Journal of Physiology - P, 107(5) 399-408.

6. Cos, I., Cañamero, L., Hayes, G.M. and Gillies, A. (2013). Hedonic value: enhancing adaptation for motivated agents. Adaptive Behavior, 21(6) 465--483. pdf

7. Cos, I., Cisek, P. and Girard, B. (2012). A Modelling Perspective on the Role of Biomechanics in Motor Decision-Making. In Proceedings of the NEUROCOMP Conference, Bordeaux, July 2012.

8. Cos, I., Medleg, F. and Cisek, P. (2012). The modulatory influence of end-point controllability on decision-making of motor actions. Journal of Neurophysiology 105(6) 1764--1780.

9. Cos, I., Bélanger, N. and Cisek, P. (2011). The influence of predicted arm biomechanics on decision-making. Journal of Neurophysiology, 105(6) 3022—3033, March 2011.

10. Cos, I., Cañamero, L. and Hayes, G.M. (2010). Learning Affordances of Consummatory Behaviors: Motivation-Driven Adaptive Perception. Adaptive Behavior, 18(3-4), June 2010.

Scientific focus :

Ever since early 2012 my research has been based at the CNRS / ISIR Institute of the UMPC, within the Computational Neuroscience Group, in the group of Dr. E. Guigon and B. Girard. Specifically, my research focuses on the characterization of the neural dynamics of decisions between movements, and on the laws of selection of movement parameters. My main scientific goal is to gain a normative understanding of decision-making, with special emphasis in the dissociation of motivational and motor costs. Furthermore, I am particularly interested in this normative knowledge for the characterization of motor disorders, such as Parkinson’s Disease (PD).


Institut des Systèmes Intelligents et de Robotique

La modélisation et l'analyse des systèmes dynamiques artificiels et naturels
La conception optimale de systèmes robotiques interactifs
La commande des systèmes interactifs
La conception et le traitement du signal de systèmes perceptifs multimodaux
La modélisation des interactions homme - système
Les modèles neuro-computationnels pour l’autonomie
L'apprentissage artificiel
L'adaptation bio-inspirée des systèmes et de leur commande.