Abstract
Motion perception is theoretically understood as the detection of sequential optical changes at two locations in the visual array. Experiments on generalized apparent motion have demonstrated, however, that sequentiality is not necessarily required for the detection of motion [1] leading to instantaneous counter-change as an alternative theoretical view of motion detection [2]. Here we generalize the counter-change model to spatially and temporally continuous motion. Transients detected within the receptive fields of edge filters are combined in a space-time continuous neural dynamics, in which nonlinear neural interaction leads to the detection decision. We show that the model enables the detection of continuous motion while also accounting for psychophysical results on generalized apparent motion.
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Berger, M., Faubel, C., Norman, J., Hock, H., Schöner, G. (2012). The Counter-Change Model of Motion Perception: An Account Based on Dynamic Field Theory. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds) Artificial Neural Networks and Machine Learning – ICANN 2012. ICANN 2012. Lecture Notes in Computer Science, vol 7552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33269-2_73
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DOI: https://doi.org/10.1007/978-3-642-33269-2_73
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