Neural dynamics of stimulus-response representations during inhibitory control

The investigation of action control processes is one major field in cognitive neuroscience, and several theoretical frameworks have been proposed. One established framework is the "Theory of Event Coding" (TEC). However, only rarely, this framework has been used in the context of response inhibition and how stimulus-response association or binding processes modulate response inhibition performance. Particularly the neural dynamics of stimulus-response representations during inhibitory control are elusive. To address this, we examined N=40 healthy controls and combined temporal EEG signal decomposition with source localization and temporal generalization multivariate pattern analysis (MVPA). We show that overlaps in features of stimuli used to trigger either response execution or inhibition compromised task performance. According to TEC, this indicates that binding processes in event file representations impact response inhibition through partial repetition costs. In the EEG data, reconfiguration of event files modulated processes in time windows well-known to reflect distinct response inhibition mechanisms. Crucially, event file coding processes were only evident in a specific fraction of neurophysiological activity associated with the inferior parietal cortex (BA40). Within that specific fraction of neurophysiological activity, the decoding of the dynamics of event file representations using temporal generalization MVPA suggested that event file representations are stable across several hundred milliseconds, and that event file coding during inhibitory control is reflected by a sustained activation pattern of neural dynamics.

MVPAlab: A Machine Learning decoding toolbox for multidimensional electroencephalography data

MVPAlab is a MATLAB-based and very flexible decoding toolbox for multidimensional electroencephalography and mag-netoencephalography data. The MVPAlab Toolbox implements several machine learning algorithms to compute multivari-ate pattern analyses, cross-classification, temporal generalization matrices and feature and frequency contribution anal-yses. It also provides access to an extensive set of preprocessing routines for, among others, data normalization, data smoothing, dimensionality reduction and supertrials generation. To draw statistical inferences at the group level, MVPAlab includes a non-parametric cluster-based permutation approach. This toolbox has been designed to include an easy-to-use and very intuitive graphic user interface and data representation software, which makes MVPAlab a very convenient tool for those users with few or no previous coding experience. However, MVPAlab is not for beginners only, as it implements several high and low-level routines allowing more experienced users to design their own projects in a highly flexible manner.

Empirical Insights from a Study on Outlier Preserving Value Generalization in Animated Choropleth Maps

Time series animation of choropleth maps easily exceeds our perceptual limits. In this empirical research, we investigate the effect of local outlier preserving value generalization of animated choropleth maps on the ability to detect general trends and local deviations thereof. Comparing generalization in space, in time, and in a combination of both dimensions, value smoothing based on a first order spatial neighborhood facilitated the detection of local outliers best, followed by the spatiotemporal and temporal generalization variants. We did not find any evidence that value generalization helps in detecting global trends.

Field Geometry and the Spatial and Temporal Generalization of Crop Classification Algorithms—A Randomized Approach to Compare Pixel Based and Convolution Based Methods

With the ongoing trend towards deep learning in the remote sensing community, classical pixel based algorithms are often outperformed by convolution based image segmentation algorithms. This performance was mostly validated spatially, by splitting training and validation pixels for a given year. Though generalizing models temporally is potentially more difficult, it has been a recent trend to transfer models from one year to another, and therefore to validate temporally. The study argues that it is always important to check both, in order to generate models that are useful beyond the scope of the training data. It shows that convolutional neural networks have potential to generalize better than pixel based models, since they do not rely on phenological development alone, but can also consider object geometry and texture. The UNET classifier was able to achieve the highest F1 scores, averaging 0.61 in temporal validation samples, and 0.77 in spatial validation samples. The theoretical potential for overfitting geometry and just memorizing the shape of fields that are maize has been shown to be insignificant in practical applications. In conclusion, kernel based convolutions can offer a large contribution in making agricultural classification models more transferable, both to other regions and to other years.

Studying language in context using the temporal generalization method

The temporal generalization method (TGM) is a data analysis technique that can be used to test if the brain’s representation for particular stimuli (e.g. sounds, images) is maintained, or if it changes as a function of time (King J-R, Dehaene S. 2014 Characterizing the dynamics of mental representations: the temporal generalization method. Trends Cogn. Sci. 18 , 203–210. ( doi:10.1016/j.tics.2014.01.002 )). The TGM involves training models to predict the stimuli or condition using a time window from a recording of brain activity, and testing the resulting models at all possible time windows. This is repeated for all possible training windows to create a full matrix of accuracy for every combination of train/test window. The results of a TGM indicate when brain activity patterns are consistent (i.e. the trained model performs well even when tested on a different time window), and when they are inconsistent, allowing us to track neural representations over time. The TGM has been used to study the representation of images and sounds during a variety of tasks, but has been less readily applied to studies of language. Here, we give an overview of the method itself, discuss how the TGM has been used to analyse two studies of language in context and explore how the TGM could be applied to further our understanding of semantic composition. This article is part of the theme issue ‘Towards mechanistic models of meaning composition’.

Tracking the Canonical Computations of Perceptual Processing in a Quasi-Continuous Sensory Flow

The canonical computations involved in sensory processing, such as neural adaptation and prediction-error signals, have mainly derived from studies investigating the neural responses elicited by a single stimulus. Here, we test whether these computations can be tracked in a quasi-continuous flow of visual stimulation, by correlating scalp electroencephalography (EEG) recordings to simulations of neuronal populations. Fifteen subjects were presented with ~5,000 visual gratings presented in rapid sequences. Our results show that we can simultaneously decode, from the EEG sensors, up to 4 visual stimuli presented sequentially. Temporal generalization and source analyses reveal that the information contained in each stimulus is processed by a “visual pipeline”: a long cascade of transient processing stages, which can overall encode multiple stimuli at once. Importantly, our data suggest that the early feedforward activity but not the late feedback responses are marked by an adaptation phenomenon. Overall, our approach demonstrates how theoretically-derived computations, as isolated in single-stimulus paradigms, can be generalized to conditions of a continuous flow of sensory information.