Applying Systems Factorial Technology to Accumulators with Varying Thresholds

Abstract Since its inception, Systems Factorial Technology (SFT; Townsend & Nozawa, 1995 ) has been used alongside many research paradigms to detect the characteristics underlying a cognitive process. Here, we show how thresholds variability in a coactive architecture can result in an ambiguous diagnosis even when all SFT assumptions are met. We implemented two independent race models: the well-known Linear Ballistic Accumulator (LBA; Brown & Heathcote, 2008 ) and a discrete accumulator model with varying thresholds (DAVT), a suitable model for demonstration purposes. When threshold variability increases in both models, all architectures other than coactive can be correctly identified by SFT. The coactive SIC curve is affected by the magnitude of the variability and converges towards a parallel self-terminating SIC curve. To avoid possible misdiagnoses, we show the importance of exhausting the entire SFT toolbox, including the capacity curve. We also present the SIC centerline, which can be used to discriminate architectures when threshold variability is suspected.