ABSTRACT
[This corrects the article on p. 612 in vol. 9, PMID: 29765345.].
ABSTRACT
The study of reaction times and their underlying cognitive processes is an important field in Psychology. Reaction times are often modeled through the ex-Gaussian distribution, because it provides a good fit to multiple empirical data. The complexity of this distribution makes the use of computational tools an essential element. Therefore, there is a strong need for efficient and versatile computational tools for the research in this area. In this manuscript we discuss some mathematical details of the ex-Gaussian distribution and apply the ExGUtils package, a set of functions and numerical tools, programmed for python, developed for numerical analysis of data involving the ex-Gaussian probability density. In order to validate the package, we present an extensive analysis of fits obtained with it, discuss advantages and differences between the least squares and maximum likelihood methods and quantitatively evaluate the goodness of the obtained fits (which is usually an overlooked point in most literature in the area). The analysis done allows one to identify outliers in the empirical datasets and criteriously determine if there is a need for data trimming and at which points it should be done.
ABSTRACT
The current work examines the effect of trial-by-trial feedback about correct and error responding on performance in two basic cognitive tasks: a classic Stroop task (n = 40) and a color-word matching task (n = 30). Standard measures of both RT and accuracy were examined in addition to measures obtained from fitting the ex-Gaussian distributional model to the correct RTs. For both tasks, RTs were faster in blocks of trials with feedback than in blocks without feedback, but this difference was not significant. On the other hand, with respect to the distributional analyses, providing feedback served to significantly reduce the size of the tails of the RT distributions. Such results suggest that, for conditions in which accuracy is fairly high, the effect of corrective feedback might either be to reduce the tendency to double-check before responding or to decrease the amount of attentional lapsing.
ABSTRACT
The aim of this work was to study the effects of valence and age on visual image recognition memory. The International Affective Picture System (IAPS) battery was used, and response time data were analyzed using analysis of variance, as well as an ex-Gaussian fit method. Older participants were slower and more variable in their reaction times. Response times were longer for negative valence pictures, however this was statistically significant only for young participants. This suggests that negative emotional valence has a strong effect on recognition memory in young but not in old participants. The τ parameter, often related to attention in the literature, was smaller for young than old participants in an ex-Gaussian fit. Differences on the τ parameter might suggest poorer attentional performance in old participants.
