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The curve was re-sampled to give 60 values as opposed to the original 12 so that learning within a block was modelled as smoothly increasing during blocks rather than increasing stepwise from block to block.
Block was modelled as the random independent variable.
Each block was modelled as box car function (representing the full length of a video block, 20 s) convolved with a standard canonical haemodynamic response function as implemented in SPM5.
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Infection status was modeled as a fixed effect, while block was modeled as a random effect.
The BOLD response during each experimental task block was modeled as a boxed-car covariate of variable length in SPM8 using a human canonical hemodynamic function.
In this second GLM, the entire duration of each block was modeled as an epoch, from the onset of the first trial to the offset of the last trial.
In the proposed modelling approach each stone block is modelled as a discrete element which is discretized by triangular finite elements.
The instruction screen between blocks was modelled as a separate regressor.
Consequently, a given pattern observation, numbered i, at some position x i in a given block, is modeled as: y ( x i ) = y B ( x i ) + a i b i p + ε i + n i (2).
The channel impulse response (CIR) of the j th information block is modeled as an FIR filter with coefficients h j = [h j [0],..., h j [L]]T, where L denotes the corresponding channel memory length.
Each mini-block was modeled as an epoch of 12 s and convolved with a canonical hemodynamic response function.
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