A key assumption of models of human cognition is that there is variability in information processing. Evidence accumulation models (EAMs) commonly assume two broad variabilities in information processing: within-trial variability, which is thought to reflect moment-to-moment fluctuations in perceptual processes, and between-trial variability, which is thought to reflect variability in slower-changing processes like attention, or systematic variability between the stimuli on different trials. Recently, Ratcliff, Voskuilen, and McKoon (2018) claimed to “provide direct evidence that external noise is, in fact, required to explain the data from five simple two-choice decision tasks” (p. 33), suggesting that at least some portion of the between-trial variability in information processing is due to “noise”. However, we argue that Ratcliff et al. (2018) failed to distinguish between two different potential sources of between-trial variability: random (i.e., “external noise”) and systematic (e.g., item effects). Contrary to the claims of Ratcliff et al. (2018), we show that “external noise” is not required to explain their findings, as the same trends of data can be produced when only item effects are present. Furthermore, we contend that the concept of “noise” within cognitive models merely serves as a convenience parameter for sources of variability that we know exist, but are unable to account for. Therefore, we question the usefulness of experiments aimed at testing the general existence of “random” variability, and instead suggest that future research should attempt to replace the random variability terms within cognitive models with actual explanations of the process.
A systematic comparison and reliability analysis of formal measures of sentence acceptability
