A Reverse Stroop Task with Mouse Tracking

Abstract

In a reverse Stroop task, observers respond to the meaning of a color word irrespective of the color in which the word is printed—for example, the word red may be printed in the congruent color (red), an incongruent color (e.g., blue), or a neutral color (e.g., white). Although reading of color words in this task is often thought to be neither facilitated by congruent print colors nor interfered with incongruent print colors, this interference has been detected by using a response method that does not give any bias in favor of processing of word meanings or processing of print colors. On the other hand, evidence for the presence of facilitation in this task has been scarce, even though this facilitation is theoretically possible. By modifying the task such that participants respond to a stimulus color word by pointing to a corresponding response word on a computer screen with a mouse, the present study investigated the possibility that not only interference but also facilitation would take place in a reverse Stroop task. Importantly, in this study, participants’ responses were dynamically tracked by recording the entire trajectories of the mouse. Arguably, this method provided richer information about participants’ performance than traditional measures such as reaction time and accuracy, allowing for more detailed (and thus potentially more sensitive) investigation of facilitation and interference in the reverse Stroop task. These trajectories showed that the mouse’s approach toward correct response words was significantly delayed by incongruent print colors but not affected by congruent print colors, demonstrating that only interference, not facilitation, was present in the current task. Implications of these findings are discussed within a theoretical framework in which the strength of association between a task and its response method plays a critical role in determining how word meanings and print colors interact in reverse Stroop tasks.

Validating Mouse-Tracking: How Design Factors Influence Action Dynamics in Intertemporal Decision Making

“…Hence, in the last ten years, mouse-tracking flourished in many fields of psychological research (for a review, see Erb, 2018;Freeman, 2018;Freeman, Dale, & Farmer, 2011), finding applications in studies of phonological and semantic processing (Dale et al, 2007;Dshemuchadse, Grage, & Scherbaum, 2015;Spivey et al, 2005), cognitive control (Dignath, Pfister, Eder, Kiesel, & Kunde, 2014;Scherbaum, Dshemuchadse, Fischer, & Goschke, 2010;Yamamoto, Incera, & McLennan, 2016), selective attention (Frisch, Dshemuchadse, Görner, Goschke, & Scherbaum, 2015), numerical cognition (Szaszi, Palfi, Szollosi, Kieslich, & Aczel, 2018), perceptual choices (Quinton, Volpi, Barca, & Pezzulo, 2014), moral decisions (Koop, 2013), preferential choices (Koop & Johnson, 2013;O'Hora, Dale, Piiroinen, & Connolly, 2013), lexical decisions (Barca & Pezzulo, 2012, 2015, and value-based decisions (Calluso, Committeri, Pezzulo, Lepora, & Tosoni, 2015;Dshemuchadse et al, 2013;Kieslich & Hilbig, 2014;Koop & Johnson, 2011;O'Hora, Carey, Kervick, Crowley, & Dabrowski, 2016;Scherbaum et al, 2016;Scherbaum, Dshemuchadse, Leiberg, & Goschke, 2013;Scherbaum, Frisch, & Dshemuchadse, 2018b, 2018avan Rooij, Favela, Malone, & Richardson, 2013).…”

A Brain-Inspired Cognitive System that Mimics the Dynamics of Human Thought

“…The trained MLP was run repeatedly to generate the output time, which was determined when the accumulated output of one of the output vectors exceeded a response threshold. The system was able to reproduce the basic Stroop effect and predicted a reverse-Stroop effect that has been recently observhed [45].…”

Measuring the Timing of the Bilingual Advantage

“…Instead, we performed 50 within-participants t -tests (one every 20 ms) for the first 1,000 ms of the mouse trajectories. To maintain the overall Type-I error rate below 0.05, we used Monte Carlo simulations to calculate the minimum threshold of contiguous t -tests that had to be significant in order to consider the effect real (for a detailed explanation of this approach, see Dale et al, 2007 ; Yamamoto et al, 2016 ). Using this threshold, we observed that interference emerged 420 ms after stimulus onset in the within-language condition and 500 ms after stimulus onset in the between-language condition, which led us to conclude that the difference is 80 ms.…”