Testing Selective Influence Directly Using Trackball Movement Tasks

TL;DR

This paper introduces a trackball movement task to directly test the assumption of selective influence in systems factorial technology, providing a more reliable method than traditional stochastic dominance tests.

Contribution

The study proposes a novel trackball-based paradigm for directly testing selective influence, improving reliability over indirect stochastic dominance methods.

Findings

The trackball task effectively tests selective influence assumptions.

Results align with expected architectures when stochastic dominance holds.

The method outperforms stochastic dominance in reliability for testing selective influence.

Abstract

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 & Fifi\'c, 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 defined the reference stimulus (denoted as \alpha and \beta ), and the parameters defined the test stimulus (denoted as A and B). We tested selective influence of \alpha and \beta on the amount of time to adjust A and B through testing selective influence of \alpha and \beta on the values of A and B using the linear feasibility test (Dzhafarov & Kujala, 2010). We found that when the test 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, with stochastic dominance only SFT can suggest 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.