Comunicação apresentada na "Joint-Action Science and Technology (JAST) Opening Conference", em Atenas, Grécia, em 2005.European Grant JAST (IST-2-003747-IP)

Bicho, E.

Erlhagen, Wolfram

Mukovskiy, Albert

Universidade do Minho: RepositoriUM

Neural CircuitsUnderlying Action UnderstandingWolfram Erlhagen, Albert Mukovskiy, Estela BichoDepartment of Mathematics for Science and TechnologyDepartment of Industrial ElectronicsUniversity of Minho, PortugalJoint-ActionScience  and Technology6th Framework Programme - Priority 2“Information Society Technologies”FP6/2003/IST/2JAST Opening Conference15-16 February 2005A. Knoll et al,TU MünchenE. Bicho et al, UMinhoG. Rizzolatti et al, Uni ParmaP. Thier et al, Uni TübingenH. Bekkering et alUni NijmegenReaction Time Single Unit Activity, EEG, etc.Control of Arm and Hand Movement, Goal-directed ImitationAction UnderstandingModeling/TheoryCognitive Psychology ElectrophysiologyRoboticsDynamic Field Theory, OptimizationUni MinhoArtefact Structural LearningThrough ImitationDutch Bridge Paradigm• Motor action composed of two motor acts:1) grasping an object (proximate goal),2) placing the object at one of two possible target positions(ultimate goal) thereby avoiding an obstacle.• Trajectory above (AT) or below (BT) the bridge• Grasping from the side (FG) or from above (PG)• Imitation is fundamentally goal-directed, (as opposed to trajectory-oriented „replay“ over via points.(Bekkering and colleagues)• Action understanding based on “motor simulation”,existence of an action observation/execution matching system(Rizzolatti and colleagues)However……very often, the matching cannot be automatic and directdue to differences• in embodiment (child-adult, robot-human)• environmental constraints (obstacles), and/or• motor skills.Main hypotheses which guided our workStructure of the talk• Neural circuits underlying goal inferenceand imitation• The dynamic model• Simulation examples (bridge paradigm)• The real artifact in actionThe mirror neuron circuitMirror neuron in the premotor cortex Mirror neuron in the parietal cortexExperimenter grasps an object Monkey grasps a piece of food (right hand)Simple presentation of an object Monkey grasps a piece of food (left hand)(Rizzolatti et al, 2001)Motor responses of parietal neuronsMotor taskObservation task Visual responses of parietal mirror neuronsFogassi et al, Science, in press• Neurons of inferior parietal cortex appear to be organizedin chains, each of which is aimed to a final action goal.Action organization in the parietal cortex(Fogassi et al, in press)Beyond the mirror circuit• Integration of contextual informtion• Establishing a link between  means and goal (physical outcome) of an action sequence.Role of Prefrontal Cortex (PFC)•Combine sensory and contextual information to organize the meansrepresented in other brain areas to achieve an intentional goal.⇒ strong connections to IPL (areas PF/PFG)•Cognitive control, for instance, to override prepotent responses(e.g., a direct matching).•Form associations between events separated in time(“Learning object meaning”).•Learning novel complex actions by combining existingmotor primitives. Model architecturePFC:Goal representationsTask inputObject properties (e.g. colour)STS:Visual desciption of grip and trajectoryPF:Sequence of meansF5:Movement primitivesBridge Paradigm(Erlhagen et al., 2005)Basic Concepts of the Dynamic Model( ) ( )( ) '),'()'(),(),(),(),('),'()'(),(),(),(dxtxuFxxWtxvdttxdvtxvtxShdxtxuFxxWtxuGtxudttxduvu−+−=−++−+−=∫∫• in each layer, neuronal activation patterns encode task relevant informationType of grasping behaviour• Integration: representations evolve underthe influence of multiple information sourcesS(x,t) (visual input, input from other layers...)• Decision making in ambigious situationsthrough recurrent inhibitionMean-Field Rate Model(e.g., Erlhagen & Schöner, 2002)Path planning in posture spaceActive NodeInhibited NodeTarget Node• Planning provides a posture sequence linking the initial postureto the desired end-posture.• Movement primitives in F5 serve to pre-select relevant parts ofthe posture space.• Obstacles are mapped into posture space.Model Simulations¾Goal inference and choice of means¾Growth of cognitive skills through learning“I know what you are doing”(Umiltà et al, 2001)Goal Inference: Bridge Paradigm• Combination of partial visual information (grasping) with prior task information.• Constant task input  results in  a “preshaping” of neural  populations representing goals (in PFC) and  associated sequences of   means (in PF).Goal Inference TaskGoal-directed imitation: Conflict in the grip typeConstraints allow to copy the meansPurely temporal mechanism:Change in baseline firing rateaffects time course(e.g., Asaad, Rainer & Miller, 2000)Time course in PFCLearning the synaptic links betweenDynamic Fields• Learning results from modifyingsynaptic connectionsbetween neurons (Hebb 1949).• The modification of synapses isslow compared with thecaracteristic time scale of neuronaldynamics.• Internally generated reinforcementsignal representinga successful planning definesepoches of learning (goal-directed).Mathematical formalization• Learning the connections between Field 1 and Field 2:phase. relaxation  theof solutions mequilibriu ~,~  and  .with  '))'(~())'(~(),,(),,(2121uuconstdyyxufyyugyxsyxs=−−+−=∂∂ ∫αατττ• Total input to Field 2  after learning (equivalent for Field 1):∫ −= ')',())'(~(),( 1 dyyxsyyugyxSLearning object meaningExample: color↔ goalA Hebbian perspective on how mirror properties evolve• First learning phase (correct alignment): Pay attention to your own arm/hand, motor system provides stimulus for the visual system.• Second learning phase (mirror properties): Generalization to goal-directed actions of others. ⇒Action understanding requires high level of abstraction⇒Learning a goal-directed matchingUnderstanding actions made with tools• Tool-use not in the repertoire of the observer• Long visual exposure to represent the hand-tool motion in STS• End-state/goal should be observableInference task:Only the tool-grip “IG” is observable,color information isambigious.Tool-use task: Overt behaviorTool-responding mirror neurons(Ferrari et al., 2005)Experimental conditions:• Long exposure to actions made with tools.• Tool can be associated by the monkeyto the possibility to receive food.F5Learning the link between goal and means(PFC-PF)Copying the organizationalstructure of actions• New goal parametrizedby an intermediate gap.• Trying to copy thedemonstrator’s means(covert planning).• If sucessful, associationis learned between PFCand PF.Learning of a new action sequenceExample: object not in upright positionAfter learning (no teacher)ConclusionsExperimental and modelling results suggest that¾action understanding is a continuous process which combines sensory evidence, prior task knowledge and a goal-directed matching,¾ a goal-directed matching between  action observation and actionexecution may develop during practice using a biologicallyplausible learning rule.The model architecture may be extended to allow also for inferring higher intentional goals.ReferencesW F Asaad, G Rainer, and E K Miller. Task-specific neural activity in the primate prefrontal cortex. Journal of Neurophysiology, 84(1):451–459, 2000.H Bekkering, A Wohlschläger, and M Gattis. Imitation of gestures in children is goal-directed. The Quartely Journal of Experimental Psychology, 53A:153–164, 2000.W Erlhagen and G Schöner. Dynamic field theory of movement preparation. Psychological Review, 109:545–572, 2002.W  Erlhagen, A Mukovskiy, and E Bicho. A dynamic model for action understanding and goal-directed imitation, Cognitive Brain research, accepted for publicationP F Ferrari, S Rozzi, and L Fogassi. Mirror neurons responding to observation of actions made with tools in monkey ventral premotor cortex. Journal of Cognitive Neuroscience, 17:1–15, 2005.L Fogassi, P F Ferrari, B Gesierich, S Rozzi, and G Rizzolatti. Parietal lobe: from action organization to intention understanding. Science, in pressG Rizzolatti, L Fogassi, and V Gallese. Neurophysiological mechanisms underlying the understanding and imitation of action. Nature Reviews, 2:661–670, 2001.

Neural circuits underlying action understanding

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Neural circuits underlying action understanding

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