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Annual Review of Vision Science - Early Publication
Reviews in Advance appear online ahead of the full published volume. View expected publication dates for upcoming volumes.
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SPIDER 2.0: Driver Distraction and Visual Attention
First published online: 11 April 2025More LessDriving is a complex multisensory experience that requires the integration of various sensory inputs to maintain effective situational awareness, with vision and visual attention being paramount for safe driving. However, multitasking significantly degrades a driver's situational awareness and causes them to overlook or misjudge important aspects of their environment, such as pedestrians, road signs, or other vehicles. It also impairs a driver's ability to visually scan for hazards and process vital information, reducing their capacity to notice and respond to changes on the roadway. Multitasking can also induce inattentional blindness, causing drivers to miss important information directly in their line of sight. Beyond diminished visual attention, multitasking also slows reaction times to detected events, increasing the likelihood and severity of crashes. This article discusses the central role that visual attention plays in a driver's situational awareness, examines common methods for assessing visual attention while driving, and presents an updated review of the SPIDER (scanning, predicting, identification, decision-making, and executing a response) model of driver awareness with a focus on visual distraction.
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Hierarchical Vector Analysis of Visual Motion Perception
First published online: 31 March 2025More LessVisual scenes are often populated by densely layered and complex patterns of motion. The problem of motion parsing is to break down these patterns into simpler components that are meaningful for perception and action. Psychophysical evidence suggests that the brain decomposes motion patterns into a hierarchy of relative motion vectors. Recent computational models have shed light on the algorithmic and neural basis of this parsing strategy. We review these models and the experiments that were designed to test their predictions. Zooming out, we argue that hierarchical motion perception is a tractable model system for understanding how aspects of high-level cognition such as compositionality may be implemented in neural circuitry.
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