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Neural model of biological motion recognition based on shading cues
BMC Neuroscience volume 16, Article number: P81 (2015)
Point-light or stick-figure biological motion stimuli, due to the absence of depth cues, can induce bistable perception, where the walker is perceived as heading in two alternating directions [1, 2]. Psychophysical studies suggested an importance of depth cues for biological motion perception . However, neural models of biological motion perception so far have focused on the processing of features that characterize the 2D structure and motion of the human body [4, 5]. We extend such models for the processing of shading cues in order to analyze the three-dimensional structure of walkers from monocular stimuli.
As extension of a learning-based neural model , we add a 'shading pathway' that computes the internal contrast gradients that vary with the 3D view of the walker, even if the silhouette information remains identical (Figure 1A-C). The model exploits physiologically plausible operations. After suppression of strong external luminance gradients caused by the boundaries of the silhouette, internal luminance gradient features are extracted by a hierarchy of neural detectors. These gradient features, combined with the shape features extracted by the form pathway of the model in , are used as input for 'snapshot neurons', RBF units that detect 3D body shapes (Figure 1D). These model neurons are embedded within a two-dimensional recurrent neural field  that jointly represents the sequential temporal structure of the stimulus and the view of the walker.
The neural field dynamics reproduces perceptual multi-stability and spontaneous perceptual switching between stimulus views, observed for silhouette stimuli in psychophysical experiments [1, 2]. It also reproduces the disambiguation by addition of shading information and a new perceptual illusion, which illustrates a lighting-from-above prior in the processing of biological motion stimuli.
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Supported by EC FP7 ABC PITN-GA-011-290011, HBP FP7-604102, Koroibot FP7-611909, COGIMON H2020-644727, DFG GI 305/4-1, DFG GZ: KA 1258/15-1, and BMBF, FKZ: 01GQ1002A.