Related Experiment Video
Updated: May 28, 2026

Examining Bilingual Language Control Using the Stroop Task
Published on: February 26, 2020
When the tail counts: the advantage of bilingualism through the ex-gaussian distribution analysis
Marco Calabria1, Mireia Hernández, Clara D Martin
1Department of Technology, Universitat Pompeu Fabra Barcelona, Spain.
Abstract:
Several studies have documented the advantage of bilingualism with respect to the development of the executive control (EC) system. Two effects of bilingualism have been described in conflict resolution tasks: (a) bilinguals tend to perform the tasks faster overall, and (b) bilinguals tend to experience less interference from conflicting information, compared to monolinguals. The precise way in which the bilingual advantage relies on different EC mechanisms is still not well understood. The goal of the present article is to further explore how bilingualism impacts the EC system by performing a new analysis (Ex-Gaussian) of already reported data in which bilinguals and monolinguals performed a flanker task. Ex-Gaussian distribution analysis allows us to partial out the contribution of the normal and the exponential components of the RT distribution of the two groups. The fit of the raw data to the ex-Gaussian distribution showed two main results. First, we found that the bilingualism advantage in the overall speed of processing is captured by group differences in the normal (μ) and the exponential (τ) components of the distribution. Second, the bilingual advantage in the magnitude of the conflict effect is captured by group differences only in the exponential component. The results are discussed in terms of: (a) usefulness of the ex-Gaussian analysis as a tool to better describe the RT distribution, and (b) a new approach to explore the cognitive processes purportedly involved in instantiating the bilingualism advantage with respect to EC.
Related Concept Videos
Language and Cognition
Distributions to Estimate Population Parameter
Choosing Between z and t Distribution
Testing a Claim about Mean: Unknown Population SD
Estimating a population mean requires the samples to be approximately normally distributed. The data should be collected from the randomly selected samples having no sampling bias. There is no specific requirement for sample size. But if the sample size is less than 30, and we don't know the population standard deviation, a different approach is used; instead...
Chi-square Distribution
Testing a Claim about Population Proportion
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...

