The Impact of Inter-trial Variability on the Reliability of Interference.

We argue that the relatively low reliability of traditional interference tasks (i.e., Stroop, Simon, & Flanker tasks) is attributed to researchers’ insufficient consideration of inter-trial variability. It is now well-established that in interference tasks, trial transitions exert a profound influence on participants’ final interference effects. For instance, whether the N-1 trial is congruent or incongruent, and the overall probability of congruent trials occurring in the experiment, all modulate interference effects through factors such as conflict monitoring or incidental learning. However, what has been overlooked in previous studies—and what we propose as our hypothesis—is that even when given parameters are constant (e.g., the proportion of N-1 congruency and the proportion of congruent trials are fixed), some incidental and nearly uncontrollable factors in the experiment can still modulate interference effects through trial transitions. Taking attentional fluctuations as an example, it is conceivable that due to pure chance, a participant might complete three consecutive incongruent trials, then become distracted and lose focus during the subsequent two consecutive congruent trials, before correctly completing another two consecutive incongruent trials immediately afterward. This would lead the participant to subjectively perceive that they had completed five consecutive incongruent trials, which is likely to significantly adjust their conflict monitoring level in the short term—exerting an immeasurable impact on subsequent trials, similar to the butterfly effect. Thus, even with the same trial sequence, the final interference effect can vary drastically across participants due to random errors and attentional fluctuations. Therefore, we propose that to enhance the reliability of interference tasks, it is necessary to reduce the impact of trial transitions on interference effects and minimize fluctuations in participants’ conflict monitoring levels. To this end, we adopt a fixed AABB sequence (i.e., con-con-inc-inc). We hypothesize that this sequence can maximize the reduction of random modulation of conflict monitoring levels caused by the interaction between attentional fluctuations and trial transitions, thereby minimizing random variations in interference effects. This approach may increase the stability of interference effects and, consequently, improve statistical reliability.