Covert visual search has been studied extensively in humans, and has been used as a tool for understanding visual attention and cueing effects. In contrast, much less is known about covert search performance in monkeys, despite the fact that much of our understanding of the neural mechanisms of attention is based on these animals. In this study, we characterize the covert visual search performance of monkeys by training them to discriminate the orientation of a briefly-presented, peripheral Landolt-C target embedded within an array of distractor stimuli while maintaining fixation. We found that target discrimination performance declined steeply as the number of distractors increased when the target and distractors were of the same color, but not when the target was an odd color (color pop-out). Performance was also strongly affected by peripheral spatial precues presented before target onset, with better performance seen when the precue coincided with the target location (valid precue) than when it did not (invalid precue). Moreover, the effectiveness of valid precues was greatest when the delay between precue and target was short (∼80-100 ms), and gradually declined with longer delays, consistent with a transient component to the cueing effect. Discrimination performance was also significantly affected by prior knowledge of the target location in the absence of explicit visual precues. These results demonstrate that covert visual search performance in macaques is very similar to that of humans, indicating that the macaque provides an appropriate model for understanding the neural mechanisms of covert search.
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http://dx.doi.org/10.1016/j.visres.2012.10.007 | DOI Listing |
J Rehabil Med
January 2025
Department of Clinical Sciences, Division of Rehabilitation Medicine, Karolinska Institutet, Danderyd Hospital, Stockholm, Sweden; Department of Rehabilitation Medicine, Danderyd Hospital, Stockholm, Sweden.
Objective: To investigate if eye tracking can support detection of covert voluntary eye movements and to compare these findings with a simultaneously performed clinical assessment according to the Coma Recovery Scale manual regarding visual stimuli.
Design: Observational case series.
Subjects: Twelve outpatients with prolonged disorders of consciousness recruited from the rehabilitation clinic of a regional rehabilitation unit.
Biol Psychol
December 2024
Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian 116029, China; Key Laboratory of Brain and Cognitive Neuroscience, Liaoning Province, Dalian 116029, China. Electronic address:
J Vis
December 2024
School of Psychological Science, University of Bristol, Bristol, UK.
Being able to detect changes in our visual environment reliably and quickly is important for many daily tasks. The motion silencing effect describes a decrease in the ability to detect feature changes for faster moving objects compared with stationary or slowly moving objects. One theory is that spatiotemporal receptive field properties in early vision might account for the silencing effect, suggesting that its origins are low-level visual processing.
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December 2024
Institut national de la santé et de la recherche médicale, U1028, Centre National de Recherche Scientifique, UMR5292, Lyon Neuroscience Research Center, Integrative Multisensory Perception and ACTion Team, Lyon, France.
Objectives: Catch-up saccades help to compensate for loss of gaze stabilization during rapid head rotation in case of vestibular deficit. While overt saccades observed after head rotation are obviously visually guided, some of these catch-up saccades occur with shorter latency while the head is still moving, anticipating the needed final eye position. These covert saccades seem to be generated based on the integration of multisensory inputs.
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December 2024
UCL Queen Square Institute of Neurology, London, UK.
Objective: Magnetic resonance imaging (MRI) is a crucial tool for identifying brain abnormalities in a wide range of neurological disorders. In focal epilepsy, MRI is used to identify structural cerebral abnormalities. For covert lesions, machine learning and artificial intelligence (AI) algorithms may improve lesion detection if abnormalities are not evident on visual inspection.
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