Convolutional neural networks can decode eye movement data: A black box approach to predicting task from eye movements.

Previous attempts to classify task from eye movement data have relied on model architectures designed to emulate theoretically defined cognitive processes and/or data that have been processed into aggregate (e.g., fixations, saccades) or statistical (e.

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.

Parallel effects of retrieval ease on attributions about the past and the future.

The present study explored the role of task difficulty in judgments about the past and the future. Participants recalled events from childhood and imagined future events. The difficulty of the task was manipulated by asking participants to generate either four or twelve events.

A practical comparison of algorithms for the measurement of multiscale entropy in neural time series data.

There is a broad family of statistical methods for capturing time series regularity, with increasingly widespread adoption by the neuroscientific community. A common feature of these methods is that they permit investigators to quantify the entropy of brain signals - an index of unpredictability/complexity. Despite the proliferation of algorithms for computing entropy from neural time series data there is scant evidence concerning their relative stability and efficiency.

Entrainment of visual steady-state responses is modulated by global spatial statistics.

The rhythmic delivery of visual stimuli evokes large-scale neuronal entrainment in the form of steady-state oscillatory field potentials. The spatiotemporal properties of stimulus drive appear to constrain the relative degrees of neuronal entrainment. Specific frequency ranges, for example, are uniquely suited for enhancing the strength of stimulus-driven brain oscillations.

Reliability of graph metrics derived from resting-state human EEG.

It is increasingly appreciated that a complete description of brain functioning will necessarily involve the characterization of large-scale interregional temporal synchronization of neuronal assemblies. The need to capture the dynamic formation of such large-scale networks has yielded a renewed interest in the human EEG in combination with a suite of methods for estimating functional connectivity along with the graph theoretical approaches for characterizing network structure. While initial work has established generally good reproducibility for a limited selection of these graph theoretical measures, there remains an obvious need to document the reproducibility of a more extensive array of commonly used graph metrics.

Charting moment-to-moment brain signal variability from early to late childhood.

Large-scale brain signals exhibit rich intermittent patterning, reflecting the fact that the cortex actively eschews fixed points in favor of itinerant wandering with frequent state transitions. Fluctuations in endogenous cortical activity occur at multiple time scales and index a dynamic repertoire of network states that are continuously explored, even in the absence of external sensory inputs. Here, we quantified such moment-to-moment brain signal variability at rest in a large, cross-sectional sample of children ranging in age from seven to eleven years.

Representation of affect in sensory cortex.

Contemporary neuroscience suggests that perception is perhaps best understood as a dynamically iterative process that does not honor cleanly segregated "bottom-up" or "top-down" streams. We argue that there is substantial empirical support for the idea that affective influences infiltrate the earliest reaches of sensory processing and even that primitive internal affective dimensions (e.g.