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What is a Sequence? The Neural Mechanisms of Perceptual, Motor, and Task Sequences Across Species and Their Interaction with Addiction  

Theresa M. Desrochers and Theresa H. McKim

Sequences permeate daily life. They can be defined as a discrete series of items or states that occur in a specific order with a beginning and end. The brain supports the perception and execution of sequences. Perceptual sequences involve tracking regularities in incoming stimuli, such as the series of sounds that make up a word in language. Executed sequences range from the series of muscle activations used by a frog to catch a fly to a chess master mapping her next moves. How the brain controls sequences must therefore scale to multiple levels of control. Investigating how the brain functions to accomplish this task spans from the study of individual cells in the brain to human cognition. Understanding the neural systems that underlie sequential control is necessary to approach the mechanistic underpinnings of complex conditions such as addiction, which may be rooted in difficult-to-extinguish sequential behaviors. Current research focuses on studies in both animal and human models and spans the levels of complexity of sequential control and the brain systems that support it.

Article

Visual Attention  

Sabine Kastner and Timothy J. Buschman

Natural scenes are cluttered and contain many objects that cannot all be processed simultaneously. Due to this limited processing capacity, neural mechanisms are needed to selectively enhance the information that is most relevant to one’s current behavior and to filter unwanted information. We refer to these mechanisms as “selective attention.” Attention has been studied extensively at the behavioral level in a variety of paradigms, most notably, Treisman’s visual search and Posner’s paradigm. These paradigms have also provided the basis for studies directed at understanding the neural mechanisms underlying attentional selection, both in the form of neuroimaging studies in humans and intracranial electrophysiology in non-human primates. The selection of behaviorally relevant information is mediated by a large-scale network that includes regions in all major lobes as well as subcortical structures. Attending to a visual stimulus modulates processing across the visual processing hierarchy with stronger effects in higher-order areas. Current research is aimed at characterizing the functions of the different network nodes as well as the dynamics of their functional connectivity.

Article

Visual Perception in the Human Brain: How the Brain Perceives and Understands Real-World Scenes  

Clemens G. Bartnik and Iris I. A. Groen

How humans perceive and understand real-world scenes is a long-standing question in neuroscience, cognitive psychology, and artificial intelligence. Initially, it was thought that scenes are constructed and represented by their component objects. An alternative view proposed that scene perception starts by extracting global features (e.g., spatial layout) first and individual objects in later stages. A third framework focuses on how the brain not only represents objects and layout but how this information combines to allow determining possibilities for (inter)action that the environment offers us. The discovery of scene-selective regions in the human visual system sparked interest in how scenes are represented in the brain. Experiments using functional magnetic resonance imaging show that multiple types of information are encoded in the scene-selective regions, while electroencephalography and magnetoencephalography measurements demonstrate links between the rapid extraction of different scene features and scene perception behavior. Computational models such as deep neural networks offer further insight by how training networks on different scene recognition tasks results in the computation of diagnostic features that can then be tested for their ability to predict activity in human brains when perceiving a scene. Collectively, these findings suggest that the brain flexibly and rapidly extracts a variety of information from scenes using a distributed network of brain regions.