Systems factorial technology (SFT; Townsend & Nozawa, 1995) is regarded as a useful tool to diagnose if features (or dimensions) of the investigated stimulus are processed in a parallel or serial fashion. In order to use SFT, one has to assume the speed to process each feature is influenced by that feature only, termed as selective influence (Sternberg, 1969). This assumption is usually untestable as the processing time for a stimulus feature is not observable. Stochastic dominance is traditionally used as an indirect evidence for selective influence (e.g., Townsend & Fifić, 2004). However, one should keep in mind that selective influence may be violated even when stochastic dominance holds. The current study proposes a trackball movement paradigm for a direct test of selective influence. The participants were shown a reference stimulus and a test stimulus simultaneously on a computer screen. They were asked to use the trackball to adjust the test stimulus until it appeared to match the position or shape of the reference stimulus. We recorded the reaction time, the parameters that defined the reference stimulus (denoted as α and β ), and the parameters that defined the test stimulus (denoted as A and B). It was expected that the participants implemented the serial AND, parallel AND, or coactive manner to adjust A and B, and serial OR and parallel OR strategies were prohibited. We tested selective influence of α and β on the amount of time to adjust A and B through testing selective influence of α and β on the values of A and B using the linear feasibility test (LFT; Dzhafarov & Kujala, 2010). We found that when LFT was passed and stochastic dominance held, the inferred architecture was as expected, which was further confirmed by the trajectory of A and B observed in each trial. However, if stochastic dominance was satisfied but selective influence of α and β on the values of A and B was violated, then SFT could erroneously lead one to infer a prohibited architecture. Our results indicate the proposed method is more reliable for testing selective influence on the processing speed than examining stochastic dominance only.
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