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Full-Text Articles in Cognitive Psychology
Visual Entrainment Of Perception-Related Neural Oscillations As A Mechanism For Maintaining Rhythmic Temporal Expectations Across A Wide Range Of Frequencies, Michael James Gray
Visual Entrainment Of Perception-Related Neural Oscillations As A Mechanism For Maintaining Rhythmic Temporal Expectations Across A Wide Range Of Frequencies, Michael James Gray
Dissertations, Theses, and Capstone Projects
Visual sensitivity fluctuates rhythmically, in-synch with ongoing, EEG-recorded neural oscillations across a wide range of frequencies (~1-25hz). Some recent work has suggested that these perception-related neural oscillations can be entrained by rhythmic visual stimulation. Evidence is also emerging that the entrainment of ongoing oscillations in visual and auditory cortices is involved in rhythmic temporal expectations. In the introduction chapter, I attempt to bridge these bodies of literature and hypothesize that rhythmic visual stimuli automatically entrain ongoing, perception-related neural oscillations and that this mechanism supports the maintenance of rhythmic temporal expectations. Chapters 2 and 3 address this hypothesis from different angles. …
The Interaction Of Attention And Memory On The Reorienting Negativity, John C. Moses
The Interaction Of Attention And Memory On The Reorienting Negativity, John C. Moses
Dissertations, Theses, and Capstone Projects
The three-stage model of distraction asserts that when we are presented with salient but task-irrelevant information, our sensory systems first detect the distracting stimulus by way of sensory memory buffers, which is indicated electrophysiologically by the mismatch negativity (MMN). Following detection, attentional resources are involuntarily allocated towards the processing of the distraction, as represented by the P3a. Finally, attentional resources are shifted away from the distracting stimulus and returned to the task-relevant information, as indicated by the reorienting negativity (RON). A great deal of research has focused on this last step in the model, largely centering around defining the mechanisms …
A Defense Of Pure Connectionism, Alex B. Kiefer
A Defense Of Pure Connectionism, Alex B. Kiefer
Dissertations, Theses, and Capstone Projects
Connectionism is an approach to neural-networks-based cognitive modeling that encompasses the recent deep learning movement in artificial intelligence. It came of age in the 1980s, with its roots in cybernetics and earlier attempts to model the brain as a system of simple parallel processors. Connectionist models center on statistical inference within neural networks with empirically learnable parameters, which can be represented as graphical models. More recent approaches focus on learning and inference within hierarchical generative models. Contra influential and ongoing critiques, I argue in this dissertation that the connectionist approach to cognitive science possesses in principle (and, as is becoming …