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Exact(27)
There is a strong correlation (R = 0.79) between ∑Kp and HROTI, and HROTI values can be modeled using a linear predictor function (linear regression model).
Based on an existing energy model the location and number of the energy minima along which the hierarchical sampling takes place is predicted from existing data points without any a-priori knowledge, using a predictor function.
The second cognitive predictor function was based, therefore, on how close the probability of target identification was to 50%: this was highest, when the target/distractor decision was at its most ambiguous, and resolution of this ambiguity required maximal processing.
We can regard the linearisation (3) as a result of the first-order Taylor approximation of the predictor function
The minimization of (6) is achieved by stepwise updating the predictor function η τ.
The control bits to select a predictor function in Figure 5 are also listed.
Similar(33)
Given that only the workload values for the training duration are known, the functional risk R(w) cannot be calculated for the candidate predictor functions f (x, w), w ∈ W; hence, the most accurate prediction function cannot be found.
Suppose that we have a set of candidate predictor functions f (x, w), w ∈ W and we want to find the most accurate function among them.
We addressed the first of these questions using two cognitive predictor functions.
The onsets and durations for monitored objects were then weighted according to the two predictor functions, to form the two new regressors.
The behavioural data, averaged across all participants, from the responses to probes was used to plot the predictor functions, separately for each of the three acquisition blocks.
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