Biology subjects

Rousselet, G. A.

Publications and source records attributed to Rousselet, G. A..

4 recordsLinked to original sources

bioRxiv2018

Reaction times and other skewed distributions: problems with the mean and the median

To summarise skewed (asymmetric) distributions, such as reaction times, typically the mean or the median are used as measures of central tendency. Using the mean might seem surprising, given that it provides a poor measure of central tendency for skewed distributions, whereas the median provides a better indication of the location of the bulk of the observations. However, the sample median is biased: with small sample sizes, it tends to overestimate the population median. This is not the case for the mean. Based on this observation, Miller (1988) concluded that \"sample medians must not be used to compare reaction times across experimental conditions when there are unequal numbers of trials in the conditions.\" Here we replicate and extend Miller (1988), and demonstrate that his conclusion was ill-advised for several reasons. First, the medians bias can be corrected using a percentile bootstrap bias correction. Second, a careful examination of the sampling distributions reveals that the sample median is median unbiased, whereas the mean is median biased when dealing with skewed distributions. That is, on average the sample mean estimates the population mean, but typically this is not the case. In addition, simulations of false and true positives in various situations show that no method dominates. Crucially, neither the mean nor the median are sufficient or even necessary to compare skewed distributions. Different questions require different methods and it would be unwise to use the mean or the median in all situations. Better tools are available to get a deeper understanding of how distributions differ: we illustrate a powerful alternative that relies on quantile estimation. All the code and data to reproduce the figures and analyses in the article are available online.

neuroscience↗

bioRxiv2018

Neural Processing of the Same, Behaviourally Relevant Face Features is Delayed by 40 ms in Healthy Ageing

Fast and accurate face processing is critical for everyday social interactions, but it declines and becomes delayed with age, as measured by both neural and behavioural responses. Here, we addressed the critical challenge of understanding how ageing changes neural information processing mechanisms to delay behaviour. Young (20-36 years) and older (60-86 years) adults performed the basic social interaction task detecting a face vs. noise while we recorded their electroencephalogram (EEG). In each participant, using a new information theoretic framework we reconstructed the features supporting face detection behaviour, and also where, when and how EEG activity represents them. We found that occipital-temporal pathway activity dynamically represents the eyes of the face images for behaviour [~]170 ms post-stimulus, with a 40 ms delay in older adults that underlies their 200 ms behavioural deficit of slower reaction times. Our results therefore demonstrate how ageing can change neural information processing mechanisms that underlie behavioural slow down.\n\nAuthor summaryOlder adults are consistently slower than young adults in a variety of behavioural perceptual tasks. So far, it has been unclear if the underlying cause of the behavioural delay relates to attentional or perceptual differences in encoding visual information, or slower neural processing speed, or other neural factors. Our study addresses these questions by showing that in a basic social interaction task (discriminating faces from noise), young and older adults encoded the same visual information (eyes of the face) to perform the task. Moreover, early brain activity (within 200 ms following stimulus onset) encoded the same visual information (again, eyes of the face) in both groups, but was delayed and weaker in older adults. These early delays in information encoding were directly related to the observed behavioural slowing in older adults, showing that differences in early perceptual brain processes can contribute to the motor response.

neuroscience↗

bioRxiv2017

A guide to robust statistical methods in neuroscience

There is a vast array of new and improved methods for comparing groups and studying associations that offer the potential for substantially increasing power, providing improved control over the probability of a Type I error, and yielding a deeper and more nuanced understanding of neuroscience data. These new techniques effectively deal with four insights into when and why conventional methods can be unsatisfactory. But for the non-statistician, the vast array of new and improved techniques for comparing groups and studying associations can seem daunting, simply because there are so many new methods that are now available. The paper briefly reviews when and why conventional methods can have relatively low power and yield misleading results. The main goal is to suggest some general guidelines regarding when, how and why certain modern techniques might be used.

neuroscience↗

bioRxiv2017

Beyond differences in means: robust graphical methods to compare two groups in neuroscience

If many changes are necessary to improve the quality of neuroscience research, one relatively simple step could have great pay-offs: to promote the adoption of detailed graphical methods, combined with robust inferential statistics. Here we illustrate how such methods can lead to a much more detailed understanding of group differences than bar graphs and t-tests on means. To complement the neuroscientists toolbox, we present two powerful tools that can help us understand how groups of observations differ: the shift function and the difference asymmetry function. These tools can be combined with detailed visualisations to provide complementary perspectives about the data. We provide implementations in R and Matlab of the graphical tools, and all the examples in the article can be reproduced using R scripts.

neuroscience↗