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Prediction in Human

Prediction in Human. Presented by: Rezvan Kianifar January 2009. Syllabus. Prediction Levels senasorimotor level cognitive level Related brain regions at cognitive level Characteristics which emerge by prediction Discussion. Motor prediction.

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Prediction in Human

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  1. Prediction in Human Presented by: Rezvan Kianifar January 2009

  2. Syllabus • Prediction Levels senasorimotor level cognitive level • Related brain regions at cognitive level • Characteristics which emerge by prediction • Discussion

  3. Motor prediction biological systems need to be able to predict the sensory consequences of their actions to be capable of rapid, robust, and adaptive behavior. Control Strategies: direct directly maps sensations to actions, without meaningful intermediate steps and, in particular, without any attempts to explicitly model the movement system or task. indirect explicitly employs multiple information-processing steps to build the control policy, and in particular it employs internal models.

  4. What is internal model? Internal models are neural substrates that model input/output relationships and their inverses of kinematic and dynamic processes of the motor system and the environment

  5. Why seek for internal model? • Helmholtz observation • Holst and Sperry 1950s(efferent copy) • Other studies

  6. Motor Prediction Influences • State estimation • Sensory confirmation and cancellation • Context estimation

  7. State estimation

  8. Sensory confirmation and cancellation

  9. Context estimation

  10. Mental practice, imitation and socialcognition • Forward model is used to predict the sensory outcome of an action, without actually performing the action. • In perception of action we could usemultiple forward models to make multiple predictions and, based on the correspondence between these predictions and the observed behaviour, we could infer which of our controllers would be used to generate the observed action. • in social interaction, a forward social model could be used to predict the reactions of others to our actions.

  11. How to investigate prediction in cognitive level? • Cognitive Tests • FMRI-Functional Magnetic Resonance Imaging

  12. Related brain regions in cognitive level of prediction • DLPFC- DorsoLateral PreFrontal Cortex • OFC- OrbitoFrontal Cortex • ACC- Anterior Cingulated Cortex

  13. DLPFC- DorsoLateral PreFrontal Cortex • DLPFC- DorsoLateral PreFrontal Cortex is known as a neural substrate for working memory in which a model of environment could exist

  14. OFC- OrbitoFrontal Cortex • OFC provides an updated representation of value through interactions with other brain areas, such as the amygdale, which can affect adaptive behavior

  15. ACC- Anterior Cingulated Cortex • ACC detects the state of conflict and drives control processes to resolve the internal conflict. Because of its anatomical position which receives information from limbic and prefrontal regions as well as having direct access to the motor system, it seems to play a key role in monitoring the outcomes of voluntary choices under uncertainty when the environment is changing.

  16. Midbrain regions • OFC have connections with the amygdala and ventral striatum, both of which have been involved in anticipating the contingencies between environmental stimuli, actions and rewards. • The serial flow of information between the amygdala and ACC is essential for guiding efficient decision making

  17. relations

  18. Characteristics which emerge by prediction • Prediction:capability of predicting future properties • Anticipation:mechanisms that use predictions to improve other mechanisms including learning and behavior

  19. predictive capabilities • (1) the types of predictions represented, • (2) the quality or accuracy of the predictions, • (3) the time scales of the predictions, • (4) the generality of the predictions, • (5) the capability of incorporating context information and action decision information for improving predictions, • (6) the focusing and attentional capabilities of prediction generation, • (7) the capability of predicting inner states .

  20. Anticipatory capabilities • (I) learning, • (II) attention, • (III) action initiation and control, • (IV) decision making.

  21. Epigenetic Robotic • goal of Epigenetic robotics is to understand, and model, the role of development in the emergence of increasingly complex cognitive structures from physical and social interaction. • It is being driven by two main, somewhat parallel, motivations: (a) to understand the brain by constructing embodied systems the so-called synthetic approach, (b) to build better systems by learning from human studies.

  22. Discussion 1- Prediction is a main characteristic of human activity. 2-new modeling approaches should consider prediction aspect of human behavior (model-based control algorithms such as MPC or RL are good candidates) 3- neural substrates under brain prediction is not well understood but it seems it is better to consider a general framework which covers all prediction levels.

  23. thank you

  24. References 1-Wolpert,D.M. & Flanagan,J.R., “Motor prediction” Current Biology Vol 11 No 18,2001 2-Mehta,B. & Schaal,S. “Forward Models in Visuomotor Control” J Neurophysiol88: 942–953, 2002; 3-Web,B. “Neural mechanisms for prediction: do insects have forward models?” Trends in Neurosciences, April 2004. 4-Yoshida,W. & Ishii,S., “Resolution of Uncertainty in Prefrontal Cortex” Neuron 50, 781–789, 2006. 5- Butz,M.V., “MIND RACES: From Reactive to Anticipatory Cognitive Embodied Systems”, Cognitive Systems,2005. 6- Sun,R. & Berthouze,L. & Metta,G., “Epigenetic robotics: modelling cognitive development in robotic systems”, Cognitive Systems Research,2004 7- Polezzi,D. & Lotto,L. & Daum,I. & Sartori,G. & Rumiati,R., “Predicting outcomes of decisions in the brain”, Behavioural Brain Research 187 (2008) 116–122. 8- Tanaka,S.C. & Samejima,K. & Okada,G. & Ueda,K. & Okamoto,Y. & Yamawaki,S. & Doya,K., “Brain mechanism of reward prediction under predictable and unpredictable environmental dynamics” ,Neural Networks 19 (2006)

  25. References 9- Cohen,M.X. & Ranganath,Ch.,“Reinforcement Learning Signals Predict Future Decisions”, J.NeuroSci,27(2)371-378,2007371-378,2007. 10- Amemori,K.I. & Sawaguchi,T.,”Contrasting Effects of Reward Expectation on Sensory and MotorMemories in Primate Prefrontal Neurons”,Cerebral Cortex,16:1002-1015,2006 11- Coricelli,G. & Dolan,R.J. Sirigu,A., “Brain, emotion and decision making: the paradigmatic example of regret”, TRENDS in Cognitive Sciences Vol.11 No.6,2007. 12- Brown,J.W. & Braver,T.S., “A computational model of risk, conflict, and individual difference effects in the anterior cingulate cortex”, Brain Research-37062. (2007) 13- Walton,M.E. & Croxson,P.L. & Behrens,T.E.J. & Kennerley,S.W. & Rushworth,M.F.S., “Adaptive decision making and value in the anterior cingulate cortex”, NeuroImage 36 (2007) T142–T154 14- Floresco,S.B. & Sharifi,S.G., “Amygdala-Prefrontal Cortical Circuitry Regulates Effort-Based Decision Making”, Cerebral Cortex February 2007;17:251—260

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