176 research outputs found
The secret life of predictive brains: what's spontaneous activity for?
No full textBrains at rest generate dynamical activity that is highly structured in space and time. We suggest that spontaneous activity, as in rest or dreaming, underlies top-down dynamics of generative models. During active tasks, generative models provide top-down predictive signals for perception, cognition, and action. When the brain is at rest and stimuli are weak or absent, top-down dynamics optimize the generative models for future interactions by maximizing the entropy of explanations and minimizing model complexity. Spontaneous fluctuations of correlated activity within and across brain regions may reflect transitions between ‘generic priors’ of the generative model: low dimensional latent variables and connectivity patterns of the most common perceptual, motor, cognitive, and interoceptive states. Even at rest, brains are proactive and predictive
Acoustic grounding of spatial frames of reference - influence of response actions on space region concepts
No full textde Castro Campos M, Bläsing B, Hermann T, Vorwerg C, Schack T. Acoustic grounding of spatial frames of reference - influence of response actions on space region concepts. In: Butt M, Herbort O, Pezzulo G, Sigaud O, eds. In: M. Butz, O. Herbort, G. Pezzulo & O. Sigaud (Ed), Anticipatory Behavior in Adaptive Learning Systems (ABiALS)- Spatial representation and Dynamic Interactions. 2011: 9-10
Action simulation in the human brain: Twelve questions
No full textAlthough the idea of action simulation is nowadays popular in cognitive science, neuroscience and robotics, many aspects of the simulative processes remain unclear from empirical, computational, and neural perspectives. In the first part of the article, we provide a critical review and assessment of action simulation theories advanced so far in the wider literature of embodied and motor cognition. We focus our analysis on twelve key questions, and discuss them in the context of human and (occasionally) primate studies. In the second part of the article, we describe an integrative neuro-computational account of action simulation, which links the neural substrate (as revealed in neuroimaging studies of action simulation) to the components of a computational architecture that includes internal modeling, action monitoring and inhibition mechanisms. © 2013 Elsevier Ltd
Space Perception through Visuokinesthetic Prediction
No full textSchenck W. Space Perception through Visuokinesthetic Prediction. In: Pezzulo G, Butz M, Sigaud O, Baldassarre G, eds. Anticipatory Behavior in Adaptive Learning Systems: From Psychological Theories to Artificial Cognitive Systems. Lecture Notes in Artificial Intelligence, 5499. Berlin, Heidelberg, New York: Springer; 2009: 247-266
Space Perception by Visuokinesthetic Prediction
No full textSchenck W, Möller R. Space Perception by Visuokinesthetic Prediction. In: Pezzulo G, Butz MV, Sigaud O, Baldassarre G, eds. Proceedings of the Fourth Workshop on Anticipatory Behavior in Adaptive Learning Systems. Munich; 2008
Training and Application of a Visual Forward Model for a Robot Camera Head
No full textSchenck W, Möller R. Training and Application of a Visual Forward Model for a Robot Camera Head. In: Butz MV, Sigaud O, Pezzulo G, Baldassarre G, eds. Anticipatory Behavior in Adaptive Learning Systems: From Brains to Individual and Social Behavior. Lecture Notes in Artificial Intelligence. Berlin, Heidelberg, New York: Springer; 2007: 153-169
Action perception as hypothesis testing
No full textWe present a novel computational model that describes action perception as an active inferential process that combines motor prediction (the reuse of our own motor system to predict perceived movements) and hypothesis testing (the use of eye movements to disambiguate amongst hypotheses). The system uses a generative model of how (arm and hand) actions are performed to generate hypothesis-specific visual predictions, and directs saccades to the most informative places of the visual scene to test these predictions – and underlying hypotheses. We test the model using eye movement data from a human action observation study. In both the human study and our model, saccades are proactive whenever context affords accurate action prediction; but uncertainty induces a more reactive gaze strategy, via tracking the observed movements. Our model offers a novel perspective on action observation that highlights its active nature based on prediction dynamics and hypothesis testing
Learning a Visual Forward Model for a Robot Camera Head
No full textSchenck W, Möller R. Learning a Visual Forward Model for a Robot Camera Head. In: Butz MV, Sigaud O, Pezzulo G, Baldassarre G, eds. Proceedings of the Third Workshop on Anticipatory Behavior in Adaptive Learning Systems (ABIALS 2006). Rome: Instituto di Scienze e Tecnologie della Cognizione, Consiglio Nazionale delle Ricerche (ISTR-CNR); 2006
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