Physiologically Inspired Model for the Visual Recognition of Transitive Hand Actions

By Falk Fleischer, Vittorio Caggiano, Peter Thier, Martin A. Giese. 2013

48 citations recorded by the source corpus.

The visual recognition of actions is an important visual function that is critical for motor learning and social communication. Action-selective neurons have been found in different cortical regions, including the superior temporal sulcus, parietal and premotor cortex. Among those are mirror neurons, which link visual and motor representations of body movements. While numerous theoretical models for the mirror neuron system have been proposed, the computational basis of the visual processing of goal-directed actions remains largely unclear. While most existing models focus on the possible role of motor representations in action recognition, we propose a model showing that many critical properties of action-selective visual neurons can be accounted for by well-established visual mechanisms. Our model accomplishes the recognition of hand actions from real video stimuli, exploiting exclusively mechanisms that can be implemented in a biologically plausible way by cortical neurons. We show that the model provides a unifying quantitatively consistent account of a variety of electrophysiological results from action-selective visual neurons. In addition, it makes a number of predictions, some of which could be confirmed in recent electrophysiological experiments.

Subfields: Cognitive Neuroscience, Social Psychology

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Citations in the corpus

A self-organizing neural network architecture for learning human-object interactions (2017, methodology): “often been the focus of research in neuroscience and psychology [1,2] .”
Neural and Computational Mechanisms of Action Processing: Interaction between Visual and Motor Representations. (2015, background): “One model of this type reproduces a variety of electrophysiological results from action-selective neurons in the STS and premotor area F5, at the same time accomplishing (Fleischer et al., 2013) (see Figure 2B).”
Recognition of Transitive Actions with Hierarchical Neural Network Learning (2016, unknown): “Given a quadruple of body joints positions in real-world coordinates X = [x1, x2, x3, x4] with x ∈ R(3), a local coordinate system is built by making x1 the origin and mapping x2 onto the vector [1, 1, 1] .”