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Justin Ales, Thom Carney, Stanley Klein; Contrast masking using VEP state triggered kernel estimation (STKE). Journal of Vision 2007;7(9):387. doi: 10.1167/7.9.387.
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© ARVO (1962-2015); The Authors (2016-present)
White noise stimuli have been very useful in studying nonlinear system properties. The white noise method assumes that the system studied is stationary, that is, the responses do not change during the course of the experiment. However we are interested in studying system dynamics during state transitions between response regimes. Specifically, how does the system impulse response change at different time delays after a sudden contrast change? The sudden contrast change violates the stationarity assumption and would have been impossible to analyze with previous white noise methods. The system dynamics were analyzed by implementing a novel state triggered kernel estimation (STKE) method that allowed us to trigger the kernel estimation to a change in the stimulus (Menz, Menz & Sutter IOVS 2004;45:E-Abstract 4228).
A standard 30 Hz pattern reversing m-sequence stimulus was presented to one eye, while the stimulus in the other eye alternated at 0.5 Hz between a mean luminance field and a high contrast pattern. The experiment was also performed with the m-sequence and the slowly alternating pattern both presented binocularly. The STKE method was used to show how the presentation of a contrast mask dynamically changes the system response kernels.
Both dichoptic and binocular high contrast mask conditions rapidly (∼64 ms) reduced the VEP. The suppression was sustained over the 1 sec presentation. However, when the mask was presented dichoptically there was an early component that was not masked as much as the binocular presentation. While in the binocular presentation a late component appears that was not present in the response during the unmasked interval.
The STKE method is easily extendable to other system state transitions such as changes of attention, figure/ground, or color, thereby extending the power of nonlinear systems analysis to stimulus changes that were previously unmeasurable.
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