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Cinématique du Mouvement. Quels sont les indicateurs qui permettent de lire l’état émotionel du sujet ?. Daniel Lewkowicz. Introduction.
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Cinématique du Mouvement Quelssont les indicateurs qui permettent de lire l’étatémotionel du sujet ? Daniel Lewkowicz
Introduction Heider & Simmel (1944) showed to their participants a visual scene on which different figures moved in various directions and at various speeds. They observed that, even when they asked their participants to describe this scene in geometrical terms, the movements were described in terms of intentions and emotions were attributed to the figures.
Introduction Michotte (1950) showed that different interpersonal emotions are evoked by the mere movements of geometric figures. Rimé, Boulanger, Laubin, Richir & Stroobants (1985) find similar results and even went further by showing that certain interpersonal emotions evoked by the kinematics of geometrical figures are crosscultural. A series of developmental studies have shown that the ability to interpret simple geometrical shapes as intentional agents simply from their kinematic properties is also present in young infants (see Scholl & Tremoulet, 2000, for a review). One set of stimuli, the Frith–Happé animations, have been widely used in neuroimaging studies (Castelli, Happé, Frith, & Frith, 2000; Gobbini, Koralek, Bryan, Montgomery, & Haxby, 2007; Moriguchi, Ohnishi, Mori, Matsuda, & Komaki, 2007), => Autism (Castelli, Frith, Happé, & Frith, 2002; Kana, Keller, Cherkassky, Minshew, & Just, 2009), => Schizophrenia (Horan et al., 2009; Koelkebeck et al., 2010), => various other psychopathological conditions (Bird, Castelli, Malik, Frith, & Husain, 2004; Fyfe, Williams, Mason, & Pickup, 2008; Lawrence et al., 2007; Moriguchi et al., 2006; Rosenbaum, Stuss, Levine, & Tulving, 2007).
Analysecinématique des animations Frith-Happé Roux et al. (2013)
Analysecinématique des animations Frith-Happé Roux et al. (2013)
Analysecinématique des animations Frith-Happé Roux et al. (2013)
Résultats en oculométrie Conclusion : Il est possible d’obtenirunemesureimplicite de l’attributiond’intention aux animations Frith-Happé Roux et al. (2013)
L’exemple de la marche • Wallbott (1998) observed that there seem to be distinctive patterns of movement and postural behavior associated with certain emotions. “Non-fluent body movements express anger, fear, and joy, while fluent ones express sadness, boredom, and happiness.” (Johansson, 73, 76; Barclay, Cutting, & Kozlowski, 1978; Blakemore & Decety, 2001; Dittrich, Troscianko, Lea, & Morgan, 1996; Mather & Murdoch, 1994; Pollick, Paterson, Bruderlin, & Sanford, 2001; Runeson, 1994; Troje, 2002a, 2002b). Conclusion : Il est possible à partir de la cinématique de discriminer le genre, maisaussil’âge, l’état mental, les actions ou les intentions. Comment le vérifier ?
A social interactive game… adapted « Jungle Speed » Ecological interactive protocol Standardized relative positions (start and object) Realtime control (Qualysis 4 camera-system) Lewkowicz, D., Delevoye-Turrell, Y., Bailly, D., Andry, P., & Gaussier, P. (2013). Reading motor intention through mental imagery. Adaptive Behavior, 21(5), 315-327.
Experimental procedure • 26 participants viewed 192 trials (48*4 blocks) • Task: judge the agent’s intention • 3 different key presses to select: • Initiate the Game (Play) • Place in my workspace (Me) • Place in yourworkspace (You) • At the end of each block : • Self-Evaluation (Analogical Scales)
Results : Conclusions: Humanparticipants canread intentions fromearlymovementkinematics. Under-estimatedability close to chance level.
A complex problem… Max : 24 inputs 3 hidden units 3 outputs 1 2 3 4 (…) 24 • 480 different networks • (24 input sizes * 20 networks) (…) Quantity of movement information Artificial Neural Network Classifier Learning algorithm : backpropagation (FANN, Nissen 2005) 10000 epochs, goal : MSE < 10-5 Database : 192 sequences
A simple solution… End of first motor element (video stop) 20 different networks for each input size Conclusions : A simple neural network classifier cansucessfullycategorizemotor intentions - as well as humans- withonlylow-levelkinematics.
Variability is not RANDOMLY DISTRIBUTED ! Conclusion : Meilleure reconnaissance si l’exemple est « éloigné » de la solution optimale. Overlap area : 31,4% No Overlap : 68,6%
Computational Model for Mental State Inference (Oztop et al. 2005)
HypothèsesNeurophysiologiques Grosbras et al. 2006
Grosbras et al. 2006; Grezès et al. 2007; Pichon et al. 2008; Pichon et al. 2009
Quelques exemples pour terminer… • Emotions possibles : • Joie • Peur • Tristesse • Colère • Dégout https://community.dur.ac.uk/a.p.atkinson/Stimuli.html Source : Atkinson et al. (2004, 2007)
Conclusions • Au moins 4 niveaux d’observation du mouvement biologique (Troje, 2008): • 1) Life Detector • 2) Structure from motion • 3) Action recognition • 4) Style recognition • 5) Social coningencies ?