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Testing neuronal accounts of anisotropic motion perception with computational modelling.

Wong W, Chiang Price NS - PLoS ONE (2014)

Bottom Line: We show in psychophysical experiments that reference repulsion and the oblique effect do not depend on the duration of a moving stimulus, and that brief adaptation to a single direction simultaneously causes a reference repulsion in the orientation domain, and the inverse of the oblique effect in the direction domain.We attempted to link these results to underlying neuronal anisotropies by implementing a large family of neuronal decoding models with parametrically varied levels of anisotropy in neuronal direction-tuning preferences, tuning bandwidths and spiking rates.We argue that the oblique effect arises from the anisotropic distribution of preferred directions evident in V1 and MT, but that reference repulsion occurs separately, perhaps reflecting a process of categorisation occurring in higher-order cortical areas.

View Article: PubMed Central - PubMed

Affiliation: Department of Physiology, Monash University, Victoria, Australia.

ABSTRACT
There is an over-representation of neurons in early visual cortical areas that respond most strongly to cardinal (horizontal and vertical) orientations and directions of visual stimuli, and cardinal- and oblique-preferring neurons are reported to have different tuning curves. Collectively, these neuronal anisotropies can explain two commonly-reported phenomena of motion perception - the oblique effect and reference repulsion - but it remains unclear whether neuronal anisotropies can simultaneously account for both perceptual effects. We show in psychophysical experiments that reference repulsion and the oblique effect do not depend on the duration of a moving stimulus, and that brief adaptation to a single direction simultaneously causes a reference repulsion in the orientation domain, and the inverse of the oblique effect in the direction domain. We attempted to link these results to underlying neuronal anisotropies by implementing a large family of neuronal decoding models with parametrically varied levels of anisotropy in neuronal direction-tuning preferences, tuning bandwidths and spiking rates. Surprisingly, no model instantiation was able to satisfactorily explain our perceptual data. We argue that the oblique effect arises from the anisotropic distribution of preferred directions evident in V1 and MT, but that reference repulsion occurs separately, perhaps reflecting a process of categorisation occurring in higher-order cortical areas.

Show MeSH
Isolated effects of population anisotropy.Maximum likelihood (A–C) and vector averaging (D–F) decoding of simulated anisotropic neuronal populations at integration times that produce similar perceptual precision to humans. Each coloured point quantifies the mean cardinal repulsion (Eq. 3) and oblique effect (Eq. 4) observed in 1000 instantiations of a neuronal population with a defined level of anisotropy. The colour of the point defines the level of anisotropy: mid-level (grey) RGB values correspond to neuronal populations with no anisotropies; higher blue values correspond to populations with more cardinal-preferring neurons; higher red values to lower spiking rates for neurons with cardinal preferences; and higher green values to narrower tuning curves for cardinal-preferring neurons. In each panel, the effect of varying a single anisotropy metric in isolation is shown: (A,D) variable anisotropy in direction preference (k) while enforcing no anisotropy in spiking rate or bandwidth (wR = 0; wBW = 0); (B,E) variable anisotropy in spiking rate (wR) while enforcing no anisotropy in direction preference or bandwidth (k = 0; wBW = 0); (C,F) variable anisotropy in bandwidth (wBW) while enforcing no anisotropy in direction preference or spiking rate (k = 0; wR = 0).
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pone-0113061-g010: Isolated effects of population anisotropy.Maximum likelihood (A–C) and vector averaging (D–F) decoding of simulated anisotropic neuronal populations at integration times that produce similar perceptual precision to humans. Each coloured point quantifies the mean cardinal repulsion (Eq. 3) and oblique effect (Eq. 4) observed in 1000 instantiations of a neuronal population with a defined level of anisotropy. The colour of the point defines the level of anisotropy: mid-level (grey) RGB values correspond to neuronal populations with no anisotropies; higher blue values correspond to populations with more cardinal-preferring neurons; higher red values to lower spiking rates for neurons with cardinal preferences; and higher green values to narrower tuning curves for cardinal-preferring neurons. In each panel, the effect of varying a single anisotropy metric in isolation is shown: (A,D) variable anisotropy in direction preference (k) while enforcing no anisotropy in spiking rate or bandwidth (wR = 0; wBW = 0); (B,E) variable anisotropy in spiking rate (wR) while enforcing no anisotropy in direction preference or bandwidth (k = 0; wBW = 0); (C,F) variable anisotropy in bandwidth (wBW) while enforcing no anisotropy in direction preference or spiking rate (k = 0; wR = 0).

Mentions: Figure 10 illustrates the dependence of the oblique effect and cardinal repulsion on the level of anisotropy in preferred direction, spiking rate and bandwidth in isolation for both ML and VA decoding of neuronal activity. Each panel shows the performance of seven models corresponding to variations in only a single anisotropy metric while the other two anisotropy metrics are held constant. The level of neuronal anisotropy for each model is indicated by each data point's colour value, where each weighting variable corresponds to a primary colour component in the sRGB colour space (IEC 61966-2-1:1999) with linear spacing; k =  blue (preferred direction), wR =  red (spiking rate), wBW =  green (bandwidth). To reiterate: positive k concentrates neurons with preferred directions around the cardinals; positive wR concentrates minimum peak firing rates around the cardinals; and positive wBW concentrates narrow bandwidths around the cardinals. This modelling allows us to examine if there are specific forms of anisotropy that reproduce our perceptual data. Qualitative matches to our perceptual data required positive values for both the cardinal repulsion and oblique effect metrics, corresponding to data points falling in the upper-right quadrants of each panel. We further expect that as colour saturation increases, corresponding to increased strength of anisotropy, data points should move further into the upper-right quadrant. While this preliminary analysis indicates that a maximum likelihood decoder with anisotropy in spiking rate is the most realistic, this does not incorporate the possibility of a neural population with multiple anisotropies. Figure 11 addresses this by showing data for all combination of each anisotropy (243 models) and four different integration times. The average orientation precision produced by isotropic populations at each integration time is indicated in each panel.


Testing neuronal accounts of anisotropic motion perception with computational modelling.

Wong W, Chiang Price NS - PLoS ONE (2014)

Isolated effects of population anisotropy.Maximum likelihood (A–C) and vector averaging (D–F) decoding of simulated anisotropic neuronal populations at integration times that produce similar perceptual precision to humans. Each coloured point quantifies the mean cardinal repulsion (Eq. 3) and oblique effect (Eq. 4) observed in 1000 instantiations of a neuronal population with a defined level of anisotropy. The colour of the point defines the level of anisotropy: mid-level (grey) RGB values correspond to neuronal populations with no anisotropies; higher blue values correspond to populations with more cardinal-preferring neurons; higher red values to lower spiking rates for neurons with cardinal preferences; and higher green values to narrower tuning curves for cardinal-preferring neurons. In each panel, the effect of varying a single anisotropy metric in isolation is shown: (A,D) variable anisotropy in direction preference (k) while enforcing no anisotropy in spiking rate or bandwidth (wR = 0; wBW = 0); (B,E) variable anisotropy in spiking rate (wR) while enforcing no anisotropy in direction preference or bandwidth (k = 0; wBW = 0); (C,F) variable anisotropy in bandwidth (wBW) while enforcing no anisotropy in direction preference or spiking rate (k = 0; wR = 0).
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4237403&req=5

pone-0113061-g010: Isolated effects of population anisotropy.Maximum likelihood (A–C) and vector averaging (D–F) decoding of simulated anisotropic neuronal populations at integration times that produce similar perceptual precision to humans. Each coloured point quantifies the mean cardinal repulsion (Eq. 3) and oblique effect (Eq. 4) observed in 1000 instantiations of a neuronal population with a defined level of anisotropy. The colour of the point defines the level of anisotropy: mid-level (grey) RGB values correspond to neuronal populations with no anisotropies; higher blue values correspond to populations with more cardinal-preferring neurons; higher red values to lower spiking rates for neurons with cardinal preferences; and higher green values to narrower tuning curves for cardinal-preferring neurons. In each panel, the effect of varying a single anisotropy metric in isolation is shown: (A,D) variable anisotropy in direction preference (k) while enforcing no anisotropy in spiking rate or bandwidth (wR = 0; wBW = 0); (B,E) variable anisotropy in spiking rate (wR) while enforcing no anisotropy in direction preference or bandwidth (k = 0; wBW = 0); (C,F) variable anisotropy in bandwidth (wBW) while enforcing no anisotropy in direction preference or spiking rate (k = 0; wR = 0).
Mentions: Figure 10 illustrates the dependence of the oblique effect and cardinal repulsion on the level of anisotropy in preferred direction, spiking rate and bandwidth in isolation for both ML and VA decoding of neuronal activity. Each panel shows the performance of seven models corresponding to variations in only a single anisotropy metric while the other two anisotropy metrics are held constant. The level of neuronal anisotropy for each model is indicated by each data point's colour value, where each weighting variable corresponds to a primary colour component in the sRGB colour space (IEC 61966-2-1:1999) with linear spacing; k =  blue (preferred direction), wR =  red (spiking rate), wBW =  green (bandwidth). To reiterate: positive k concentrates neurons with preferred directions around the cardinals; positive wR concentrates minimum peak firing rates around the cardinals; and positive wBW concentrates narrow bandwidths around the cardinals. This modelling allows us to examine if there are specific forms of anisotropy that reproduce our perceptual data. Qualitative matches to our perceptual data required positive values for both the cardinal repulsion and oblique effect metrics, corresponding to data points falling in the upper-right quadrants of each panel. We further expect that as colour saturation increases, corresponding to increased strength of anisotropy, data points should move further into the upper-right quadrant. While this preliminary analysis indicates that a maximum likelihood decoder with anisotropy in spiking rate is the most realistic, this does not incorporate the possibility of a neural population with multiple anisotropies. Figure 11 addresses this by showing data for all combination of each anisotropy (243 models) and four different integration times. The average orientation precision produced by isotropic populations at each integration time is indicated in each panel.

Bottom Line: We show in psychophysical experiments that reference repulsion and the oblique effect do not depend on the duration of a moving stimulus, and that brief adaptation to a single direction simultaneously causes a reference repulsion in the orientation domain, and the inverse of the oblique effect in the direction domain.We attempted to link these results to underlying neuronal anisotropies by implementing a large family of neuronal decoding models with parametrically varied levels of anisotropy in neuronal direction-tuning preferences, tuning bandwidths and spiking rates.We argue that the oblique effect arises from the anisotropic distribution of preferred directions evident in V1 and MT, but that reference repulsion occurs separately, perhaps reflecting a process of categorisation occurring in higher-order cortical areas.

View Article: PubMed Central - PubMed

Affiliation: Department of Physiology, Monash University, Victoria, Australia.

ABSTRACT
There is an over-representation of neurons in early visual cortical areas that respond most strongly to cardinal (horizontal and vertical) orientations and directions of visual stimuli, and cardinal- and oblique-preferring neurons are reported to have different tuning curves. Collectively, these neuronal anisotropies can explain two commonly-reported phenomena of motion perception - the oblique effect and reference repulsion - but it remains unclear whether neuronal anisotropies can simultaneously account for both perceptual effects. We show in psychophysical experiments that reference repulsion and the oblique effect do not depend on the duration of a moving stimulus, and that brief adaptation to a single direction simultaneously causes a reference repulsion in the orientation domain, and the inverse of the oblique effect in the direction domain. We attempted to link these results to underlying neuronal anisotropies by implementing a large family of neuronal decoding models with parametrically varied levels of anisotropy in neuronal direction-tuning preferences, tuning bandwidths and spiking rates. Surprisingly, no model instantiation was able to satisfactorily explain our perceptual data. We argue that the oblique effect arises from the anisotropic distribution of preferred directions evident in V1 and MT, but that reference repulsion occurs separately, perhaps reflecting a process of categorisation occurring in higher-order cortical areas.

Show MeSH