Literature DB >> 22275469

A motion pooling model of visually guided navigation explains human behavior in the presence of independently moving objects.

Oliver W Layton1, Ennio Mingolla, N Andrew Browning.   

Abstract

Humans accurately judge their direction of heading when translating in a rigid environment, unless independently moving objects (IMOs) cross the observer's focus of expansion (FoE). Studies show that an IMO on a laterally moving path that maintains a fixed distance with respect to the observer (non-approaching; C. S. Royden & E. C. Hildreth, 1996) biases human heading estimates differently from an IMO on a lateral path that gets closer to the observer (approaching; W. H. Warren & J. A. Saunders, 1995). C. S. Royden (2002) argued that differential motion operators in primate brain area MT explained both data sets, concluding that differential motion was critical to human heading estimation. However, neurophysiological studies show that motion pooling cells, but not differential motion cells, in MT project to heading-sensitive cells in MST (V. K. Berezovskii & R. T. Born, 2000). It is difficult to reconcile differential motion heading models with these neurophysiological data. We generate motion sequences that mimic those viewed by human subjects. Model MT pools over V1; units in model MST perform distance-weighted template matching and compete in a recurrent heading representation layer. Our model produces heading biases of the same direction and magnitude as humans through a peak shift in model MSTd without using differential motion operators, maintaining consistency with known primate neurophysiology.

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Mesh:

Year:  2012        PMID: 22275469     DOI: 10.1167/12.1.20

Source DB:  PubMed          Journal:  J Vis        ISSN: 1534-7362            Impact factor:   2.240


  16 in total

1.  The temporal dynamics of heading perception in the presence of moving objects.

Authors:  Oliver W Layton; Brett R Fajen
Journal:  J Neurophysiol       Date:  2015-10-28       Impact factor: 2.714

2.  Going with the Flow: The Neural Mechanisms Underlying Illusions of Complex-Flow Motion.

Authors:  Junxiang Luo; Keyan He; Ian Max Andolina; Xiaohong Li; Jiapeng Yin; Zheyuan Chen; Yong Gu; Wei Wang
Journal:  J Neurosci       Date:  2019-02-18       Impact factor: 6.167

3.  Efficient spiking neural network model of pattern motion selectivity in visual cortex.

Authors:  Michael Beyeler; Micah Richert; Nikil D Dutt; Jeffrey L Krichmar
Journal:  Neuroinformatics       Date:  2014-07

4.  A Neural Model of MST and MT Explains Perceived Object Motion during Self-Motion.

Authors:  Oliver W Layton; Brett R Fajen
Journal:  J Neurosci       Date:  2016-08-03       Impact factor: 6.167

5.  A simple approach to ignoring irrelevant variables by population decoding based on multisensory neurons.

Authors:  HyungGoo R Kim; Xaq Pitkow; Dora E Angelaki; Gregory C DeAngelis
Journal:  J Neurophysiol       Date:  2016-06-22       Impact factor: 2.714

6.  Computational Mechanisms for Perceptual Stability using Disparity and Motion Parallax.

Authors:  Oliver W Layton; Brett R Fajen
Journal:  J Neurosci       Date:  2019-11-07       Impact factor: 6.167

7.  Modeling heading and path perception from optic flow in the case of independently moving objects.

Authors:  Florian Raudies; Heiko Neumann
Journal:  Front Behav Neurosci       Date:  2013-04-01       Impact factor: 3.558

8.  Multisensory Integration of Visual and Vestibular Signals Improves Heading Discrimination in the Presence of a Moving Object.

Authors:  Kalpana Dokka; Gregory C DeAngelis; Dora E Angelaki
Journal:  J Neurosci       Date:  2015-10-07       Impact factor: 6.167

9.  Recurrent competition explains temporal effects of attention in MSTd.

Authors:  Oliver W Layton; N Andrew Browning
Journal:  Front Comput Neurosci       Date:  2012-10-05       Impact factor: 2.380

10.  A unified model of heading and path perception in primate MSTd.

Authors:  Oliver W Layton; N Andrew Browning
Journal:  PLoS Comput Biol       Date:  2014-02-20       Impact factor: 4.475

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