A new tool for equating lexical stimuli across experimental conditions.

In cognitive psychology and psycholinguistics, lexical characteristics can drive large effects, which can create confounds when word stimuli are intended to be unrelated to the effect of interest. Thus, it is critical to control for these potential confounds. As an alternative to randomly assigning word bank items to stimulus lists, we present LIBRA (Lexical Item Balancing & Resampling Algorithm), a MATLAB-based toolbox for quickly generating stimulus lists of user-determined length and number that can be closely equated on any number of lexical properties.

Deep-Learning-Based Multivariate Pattern Analysis (dMVPA): A Tutorial and a Toolbox.

In recent years, multivariate pattern analysis (MVPA) has been hugely beneficial for cognitive neuroscience by making new experiment designs possible and by increasing the inferential power of functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and other neuroimaging methodologies. In a similar time frame, "deep learning" (a term for the use of artificial neural networks with convolutional, recurrent, or similarly sophisticated architectures) has produced a parallel revolution in the field of machine learning and has been employed across a wide variety of applications. Traditional MVPA also uses a form of machine learning, but most commonly with much simpler techniques based on linear calculations; a number of studies have applied deep learning techniques to neuroimaging data, but we believe that those have barely scratched the surface of the potential deep learning holds for the field.

Variability in the analysis of a single neuroimaging dataset by many teams.

Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data.

Not-so-working Memory: Drift in Functional Magnetic Resonance Imaging Pattern Representations during Maintenance Predicts Errors in a Visual Working Memory Task.

Working memory (WM) is critical to many aspects of cognition, but it frequently fails. Much WM research has focused on capacity limits, but even for single, simple features, the fidelity of individual representations is limited. Why is this? One possibility is that, because of neural noise and interference, neural representations do not remain stable across a WM delay, nor do they simply decay, but instead, they may "drift" over time to a new, less accurate state.