Cognitive neuroscience: Neural mechanisms for the recognition of biological movements

By Martin A. Giese, Tomaso A. Poggio. 2003

978 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Social Psychology, Neuroscience

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Citations in the corpus

An Incremental Self-Organizing Architecture for Sensorimotor Learning and Prediction (2017, background): “When body motion sequences are provided, the response of the neurons in the architecture roughly resembles the neural selectivity toward temporally ordered body pose snapshots in the human brain [40].”
Lifelong learning of human actions with deep neural network self-organization (2017, background): “…of body action features in two distinct neural pathways and their subsequent integration in the STS are in line with neurophysiological evidence (Giese & Poggio, 2003; Giese & Rizzolatti, 2015), our model is a strong simplification with respect to biological mechanisms where the two streams…”
A self-organizing neural network architecture for learning human-object interactions (2017, methodology): “tructs invariant object representations [8] and how neurons in the uperior temporal sulcus (STS) area encode actions in terms of paterns of body posture and motion [9,10] .”
The global landscape of cognition: hierarchical aggregation as an organizational principle of human cortical networks and functions (2015, methodology): “General algorithmic procedure for plotting and sorting the behavioral databases upon connectivity matrices can be outlined in four steps: (1) Compute connectivity order, (2) Compute behavioral element frequencies, (3) Bin connectivity ordering, (4) Regress behavioral element frequency across bins, (”
Emergence of multimodal action representations from neural network self-organization (2017, methodology): “…actions with high accuracy from video sequences with the use of spatiotemporal hierarchies that functionally resemble the organization of earlier areas of the visual cortex (e.g. Giese & Poggio, 2003; Jung, Hwang, & Tani, 2015; Layher, Giese, & Neumann, 2014; Parisi, Weber, & Wermter, 2015).”
Self-organizing neural integration of pose-motion features for human action recognition (2015, methodology): “Our recognition scheme for action sequences is in line with a number of studies demonstrating that action discrimination is selective to temporal order (Bertenthal and Pinto, 1993; Giese and Poggio, 2003; Jastorﬀ et al., 2006).”
A Biologically Inspired System for Action Recognition (2007, methodology): “As in [ 10 ], S1 unit responses were obtained by applying the following equation:”
Towards the neurobiology of emotional body language (2006, neutral): “So far, this hypothesis seems consistent with results from motor control studies showing that the control of multi-joint movements is based on synergies between simpler components that encompass only a limited number of joint”
Human Neuroscience (neutral): “Because the outputs of action-tuned high-level neurons are believed to be constructed from the outputs of the low-level neurons (Jellema et al., 2000; Giese and Poggio, 2003), a critical factor is the order of the stimuli in the cross-adaptation trials.”
Physiologically Inspired Model for the Visual Recognition of Transitive Hand Actions (2013, neutral): “(3) As for previous models for the recognition of non-transitive actions (Giese and Poggio, 2003; Jhuang et al., 2007; Escobar et al., 2009) one might consider a second primary visual pathway that processes local motion and optic flow features instead of form features.”
Specificity of Action Representations in the Lateral Occipitotemporal Cortex (2006, background): “Action recognition likely includes multiple stages, from the perceptual categorization of complex motion patterns to the conceptual understanding of the goal of an action or the intention of the agent (e.g., see the recent model in Giese & Poggio, 2003 ).”
Human Action Recognition with Hierarchical Growing Neural Gas Learning (2014, methodology): “In the biological visual system, dynamic scenes are analyzed in parallel by two separate channels [5].”
Augmented visual, auditory, haptic, and multimodal feedback in motor learning: A review (2012, background): “Since humans can recognize complex biological motions by observing only a few point lights placed on a moving body (Giese & Poggio, 2003), point lights can also provide effective feedback, as has been shown for dancing (Eaves et al., 2011).”
Self-Supervised Spatio-Temporal Representation Learning for Videos by Predicting Motion and Appearance Statistics (2019, background): “The idea is inspired by Giese and Poggio’s work on human visual system [14], in which the representation of motion is found to be based on a set of learned patterns.”
Self-supervised Video Representation Learning by Pace Prediction (2020, background): “Such a property has also been revealed in neuroscience studies [17, 54].”