A Neural-Dynamic Model of Counterchange Motion Detection in One Dimension
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1
Ruhr-Universität, Institut für Neuroinformatik, Germany
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2
Florida Atlantic University, Department of Psychology, United States
Although models based on motion energy provide the standard approach to understanding how motion is detected, recent experimental work on generalized apparent motion is in conflict with this class of models [1]. The counterchange model was developed to account for this new data [2]. It postulates that the instantaneous detection of a luminance or contrast change toward the background at one location and a change away from the background at another location signals motion. The model has been formulated and tested only for apparent motion between two discrete spatial locations. We extend the model to account for real motion along one spatial dimension. This requires us to specify the spatio-temporal filters, on which counterchange motion detection is based. The model takes the spatio-temporal stimuli as input and generates an activation pattern representing motion at any location along the spatial dimension into one of two motion directions. We focus on edge-based motion by defining an appropriate spatial filter. Transient detectors are formulated as pairs of excitatory and inhibitory neurons. Generalizing the model to continuous motion also requires us to address the "sampling" problem, that is, to specify the distances over which transients are combined to a motion signal. We hypothesize that a single such sampling for one distance is sufficient to detect continuous motion over a range of speeds. The motion signal is computed as a product of the two half-way rectified transient signals and fed into two neural fields, one for each motion direction, defined over the single spatial dimension.
We demonstrate, that continuous motion at a range of speeds can indeed be detected. We explore how the speed of the stimulus is reflected in the total amount of motion detector activation. Finally, we demonstrate that the results of the original model for apparent motion can be reproduced in the generalized model. We also consider stimuli of varying spatial characteristics.
References
[1] H S Hock, L Gilroy, and G Harnett. Counter-changing luminance: A non-fourier, nonattentional basis for the perception of single-element apparent motion. Journal of Experimental Psychology: Human Perception and Performance, 28(1):93-112, 2002.
[2] H S Hock, G Schöner, and L Gilroy. A counterchange mechanism for the perception of motion. Acta Psychologica, 132:1-21, 2009.
Keywords:
computational model,
Continuous motion,
Counterchange,
motion,
Vision
Conference:
BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011, Freiburg, Germany, 4 Oct - 6 Oct, 2011.
Presentation Type:
Abstract
Topic:
neurons, networks and dynamical systems (please use "neurons, networks and dynamical systems" as keywords)
Citation:
Berger
M,
Schöner
G and
Hock
H
(2011). A Neural-Dynamic Model of Counterchange Motion Detection in One Dimension.
Front. Comput. Neurosci.
Conference Abstract:
BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011.
doi: 10.3389/conf.fncom.2011.53.00218
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Received:
16 Aug 2011;
Published Online:
04 Oct 2011.
*
Correspondence:
Mr. Michael Berger, Ruhr-Universität, Institut für Neuroinformatik, Bochum, 44801, Germany, Michael.Berger@ini.rub.de