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Our central reverse processing hypothesis was tested by an interaction contrast between the factors type of task and question type.
The mean interaction contrast (MIC) is one measure designed to improve discriminability of serial parallel model properties.
For the interaction analysis in (i), we also had to apply a linear transformation to the data, because the d-values during encoding and retrieval (which are compared directly in the interaction contrast) differed too much in scale.
These time points were entered into a 2 × 2 within-subjects ANOVA with the factors type of feature (perceptual or semantic), and type of task (encoding or retrieval), with the only planned comparison in this analysis being the interaction contrast.
The Survivor Interaction Contrast (SIC) and the Capacity Coefficient are effective measures in determining these underlying properties, based on response-time data.
The Survivor Interaction Contrast (SIC) is a powerful tool for assessing the architecture and stopping rule of a model of mental processes (Townsend & Nozawa, 1995).
The Survivor Interaction Contrast (SIC) is a distribution-free measure for assessing the fundamental properties of human information processing such as architecture (i.e., serial or parallel) and stopping rule (i.e., minimum time or maximum time).
Interaction contrast.
Adjusted interaction contrast.
Fig. 1 Predicted survivor interaction contrast for parallel, serial, and coactive models with both AND and OR stopping rules.
Hence, in some cases it is advantageous to analyze the mean interaction contrast: text{MIC}(t)= left[M_{text{LL}}(t -M_{text{LH}}(t -M_{text{LH}M_{text{HL}}(t)-M_{text{HH}}(t)right].
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