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Competition with and without priority control: linking rivalry to attention through winner-take-all networks with memory.

Marx S, Gruenhage G, Walper D, Rutishauser U, Einhäuser W - Ann. N. Y. Acad. Sci. (2015)

Bottom Line: This model introduces a form of memory by forming discrete states and explains experimental data better than competitive models of rivalry without memory.This result supports the crucial role of memory in rivalry specifically and in competitive processes in general.Our approach unifies the seemingly distinct phenomena of rivalry, memory, and attention in a single model with competition as the common underlying principle.

View Article: PubMed Central - PubMed

Affiliation: Neurophysics, Philipp-University of Marburg, Marburg, Germany.

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Levelt's propositions. (A) Levelt's first proposition tested for the three models and data of experiment 1; relative dominance is color coded individually per panel. In the panel for model 1, some simulations are stuck within the same state throughout, and—as for all analysis the last period is excluded—no data is available, indicated in gray. (B) Levelt's second proposition: log dominance duration for one eye (ipsilateral eye) while input strength to this eye and to the other eye (contralateral eye) are varied independently. Data are collapsed over both eyes (left/right) or units (i1/i2, p1/p2). Log scale is used for illustration, and correlations are computed on the original data. (C) Levelt's third and fourth propositions: dependence of switch rate on input strength to either eye.
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fig04: Levelt's propositions. (A) Levelt's first proposition tested for the three models and data of experiment 1; relative dominance is color coded individually per panel. In the panel for model 1, some simulations are stuck within the same state throughout, and—as for all analysis the last period is excluded—no data is available, indicated in gray. (B) Levelt's second proposition: log dominance duration for one eye (ipsilateral eye) while input strength to this eye and to the other eye (contralateral eye) are varied independently. Data are collapsed over both eyes (left/right) or units (i1/i2, p1/p2). Log scale is used for illustration, and correlations are computed on the original data. (C) Levelt's third and fourth propositions: dependence of switch rate on input strength to either eye.

Mentions: For all models and the experimental data, we calculate the relative dominance of each combination of input strengths (input currents or contrast levels, respectively). By definition, a relative dominance of 0 corresponds to equal dominance of either percept, positive values dominance of percept 2 or right eye, negative values of percept 1 or left eye. Consistent with Levelt's first proposition, we find relative dominance to increase when input to the right eye or the corresponding input unit u2 or i2 is increased, to decrease when input to the left eye (or unit u1 or i1) is increased, and to fall around 0 when the input to both is the same (Fig.4A). Quantitatively, however, there are substantial differences: model 1, the single WTA circuit, only has a narrow band around equal input strength in which dominance does not get stuck at the extreme. When input is applied asymmetrically, there is no mechanism to release the nondominant state from suppression as soon as noise becomes negligible. Adaption in model 2 counters this effect, and the extremes are approached in a more shallow fashion. Importantly, a qualitatively very similar behavior is observed for the double WTA network of model 3, even though there is no explicit adaptation mechanism at the level of an individual unit. The experimental data also show a broad range and smooth variation as do models 2 and 3. Unlike those models, however, experimental data reach the extremes of full dominance, while these models do not exceed a relative dominance of about ±0.5 (i.e., one input dominating for 75% of time) for the input range tested. Nonetheless, the double WTA (model 3) and the single WTA with adaptation (model 2) similarly capture the smooth transition of relative dominance from one eye to the other when input strength is changed.


Competition with and without priority control: linking rivalry to attention through winner-take-all networks with memory.

Marx S, Gruenhage G, Walper D, Rutishauser U, Einhäuser W - Ann. N. Y. Acad. Sci. (2015)

Levelt's propositions. (A) Levelt's first proposition tested for the three models and data of experiment 1; relative dominance is color coded individually per panel. In the panel for model 1, some simulations are stuck within the same state throughout, and—as for all analysis the last period is excluded—no data is available, indicated in gray. (B) Levelt's second proposition: log dominance duration for one eye (ipsilateral eye) while input strength to this eye and to the other eye (contralateral eye) are varied independently. Data are collapsed over both eyes (left/right) or units (i1/i2, p1/p2). Log scale is used for illustration, and correlations are computed on the original data. (C) Levelt's third and fourth propositions: dependence of switch rate on input strength to either eye.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4376592&req=5

fig04: Levelt's propositions. (A) Levelt's first proposition tested for the three models and data of experiment 1; relative dominance is color coded individually per panel. In the panel for model 1, some simulations are stuck within the same state throughout, and—as for all analysis the last period is excluded—no data is available, indicated in gray. (B) Levelt's second proposition: log dominance duration for one eye (ipsilateral eye) while input strength to this eye and to the other eye (contralateral eye) are varied independently. Data are collapsed over both eyes (left/right) or units (i1/i2, p1/p2). Log scale is used for illustration, and correlations are computed on the original data. (C) Levelt's third and fourth propositions: dependence of switch rate on input strength to either eye.
Mentions: For all models and the experimental data, we calculate the relative dominance of each combination of input strengths (input currents or contrast levels, respectively). By definition, a relative dominance of 0 corresponds to equal dominance of either percept, positive values dominance of percept 2 or right eye, negative values of percept 1 or left eye. Consistent with Levelt's first proposition, we find relative dominance to increase when input to the right eye or the corresponding input unit u2 or i2 is increased, to decrease when input to the left eye (or unit u1 or i1) is increased, and to fall around 0 when the input to both is the same (Fig.4A). Quantitatively, however, there are substantial differences: model 1, the single WTA circuit, only has a narrow band around equal input strength in which dominance does not get stuck at the extreme. When input is applied asymmetrically, there is no mechanism to release the nondominant state from suppression as soon as noise becomes negligible. Adaption in model 2 counters this effect, and the extremes are approached in a more shallow fashion. Importantly, a qualitatively very similar behavior is observed for the double WTA network of model 3, even though there is no explicit adaptation mechanism at the level of an individual unit. The experimental data also show a broad range and smooth variation as do models 2 and 3. Unlike those models, however, experimental data reach the extremes of full dominance, while these models do not exceed a relative dominance of about ±0.5 (i.e., one input dominating for 75% of time) for the input range tested. Nonetheless, the double WTA (model 3) and the single WTA with adaptation (model 2) similarly capture the smooth transition of relative dominance from one eye to the other when input strength is changed.

Bottom Line: This model introduces a form of memory by forming discrete states and explains experimental data better than competitive models of rivalry without memory.This result supports the crucial role of memory in rivalry specifically and in competitive processes in general.Our approach unifies the seemingly distinct phenomena of rivalry, memory, and attention in a single model with competition as the common underlying principle.

View Article: PubMed Central - PubMed

Affiliation: Neurophysics, Philipp-University of Marburg, Marburg, Germany.

Show MeSH
Related in: MedlinePlus