Abstract It is increasingly appreciated that a complete description of brain functioning will necessarily involve the characterization large‐scale interregional temporal synchronization neuronal assemblies. The need to capture dynamic formation such networks has yielded renewed interest in human EEG combination with suite methods for estimating functional connectivity along graph theoretical approaches characterizing network structure. While initial work established generally good...

Abstract Contemporary neuroscience suggests that perception is perhaps best understood as a dynamically iterative process does not honor cleanly segregated “bottom-up” or “top-down” streams. We argue there substantial empirical support for the idea affective influences infiltrate earliest reaches of sensory processing and even primitive internal dimensions (e.g., goodness-to-badness) are represented alongside physical external world.

In recent years, multivariate pattern analysis (MVPA) has been hugely beneficial for cognitive neuroscience by making new experiment designs possible and increasing the inferential power of functional magnetic resonance imaging (fMRI), electroencephalography (EEG), other neuroimaging methodologies. a similar time frame, “deep learning” (a term use artificial neural networks with convolutional, recurrent, or similarly sophisticated architectures) produced parallel revolution in field machine...

The rhythmic delivery of visual stimuli evokes large-scale neuronal entrainment in the form steady-state oscillatory field potentials. spatiotemporal properties stimulus drive appear to constrain relative degrees entrainment. Specific frequency ranges, for example, are uniquely suited enhancing strength stimulus-driven brain oscillations. When it comes nature itself, studies have used a plethora inputs ranging from spatially unstructured empty fields simple contrast patterns (checkerboards,...

Previous attempts to classify task from eye movement data have relied on model architectures designed emulate theoretically defined cognitive processes and/or that been processed into aggregate (e.g., fixations, saccades) or statistical fixation density) features. Black box convolutional neural networks (CNNs) are capable of identifying relevant features in raw and minimally images, but difficulty interpreting these has contributed challenges generalizing lab-trained CNNs applied contexts....

Abstract In recent years, multivariate pattern analysis (MVPA) has been hugely beneficial for cognitive neuroscience by making new experiment designs possible and increasing the inferential power of functional magnetic resonance imaging (fMRI), electroencephalography (EEG), other neuroimaging methodologies. a similar time frame, “deep learning” (a term use artificial neural networks with convolutional, recurrent, or similarly sophisticated architectures) produced parallel revolution in field...

It is well established that emotional stimuli capture attention, but there are a variety of contexts in which ignoring distractions and maintaining focus on the task at hand critical (e.g. emergency responders). In present study, we investigate a) whether task-irrelevant would influence performance primary visual search as function difficulty b) individual differences personality characteristics may provide insight into how irrelevant attention. Participants performed standard they...

Since Yarbus (1967) wrote the book on examining eye movements, researchers have tracked movements associated with various tasks and mindsets. This line of research has consistently shown that can be indicative task at hand (Einhauser et al., 2008; Yarbus, 1967). Recently, theoretically informed computational models been able to categorize levels significantly above chance (e.g., MacInnes 2018). The purpose present study was design a neural network alternative previously implemented tracking...

Previous attempts to classify task from eye movement data have relied on model architectures designed emulate theoretically defined cognitive processes, and/or that has been processed into aggregate (e.g., fixations, saccades) or statistical fixation density) features. _Black box_ convolutional neural networks (CNNs) are capable of identifying relevant features in raw and minimally images, but difficulty interpreting these contributed challenges generalizing lab-trained CNNs applied...