During rapid eye movements, we are not aware of the motion of the retinal image, nor do we perceive the world to be in a different place after every eye movement. This shows that, somehow, the brain corrects for the movement of the eyes when translating the retinal input into a visual percept. This project combines cellular approaches in monkeys, with behavioral studies, and functional imaging in humans to investigate the neural mechanisms underlying perceptual stability in the presence of eye movements. We propose and test a specific implementation of the dual mechanism theory of perceptual stability. In this theory, one mechanism uses eye-position information to transform eye-centered retinal information into stable, world-centered information. This transform is assumed to be imperfect while rapid eye movements are underway. Another mechanism -called saccadic suppression- is therefore invoked to blunt visual perception and hide imperfections in the coordinate transform during saccades. Our hypothesis is that both mechanisms are implemented in early visual cortical areas. As others have pointed out, the presence of eye-position signals in those areas in principle provides the information needed to perform the required coordinate transform. The presence of sufficient information, however, does not necessarily mean that the signal is actually used for the coordinate transform. In our first specific aim we test a strong prediction of the hypothesis, namely that errors in the eye-position signal should cause errors in perception. We will use single cell recordings to test whether, around the time of saccades, there is a mismatch between the true eye-position and the eye-position signals in early visual areas (V1, MT). Crucially, this mismatch should match the perceptual errors in localization that are known to occur around saccades.
Our second aim i s to measure changes in the visual response of these neurons around the time of a saccade. Our hypothesis predicts first, that these changes explain why errors in localization and detection of visual stimuli occur in the temporal vicinity of eye movements. Second, we will test whether random trial-by-trial variations in the neural response are correlated with trial-by-trial variation in the behavioral response of the animal. Together these behavioral and electrophysiological experiments would provide strong evidence for our hypothesis. The significance of this project is that it will enhance our understanding of the neural mechanisms that solve a fundamental problem in visual perception. This will help to develop treatment programs for neurological disorders of vision and rehabilitation after trauma and disease.
| Kar, Kohitij; Duijnhouwer, Jacob; Krekelberg, Bart (2017) Transcranial Alternating Current Stimulation Attenuates Neuronal Adaptation. J Neurosci 37:2325-2335 |
| Klingenhoefer, Steffen; Krekelberg, Bart (2017) Perisaccadic visual perception. J Vis 17:16 |
| Joukes, Jeroen; Yu, Yunguo; Victor, Jonathan D et al. (2017) Recurrent Network Dynamics; a Link between Form and Motion. Front Syst Neurosci 11:12 |
| Quiroga, Maria Del Mar; Morris, Adam P; Krekelberg, Bart (2016) Adaptation without Plasticity. Cell Rep 17:58-68 |
| Kar, Kohitij; Krekelberg, Bart (2016) Testing the assumptions underlying fMRI adaptation using intracortical recordings in area MT. Cortex 80:21-34 |
| Morris, Adam P; Bremmer, Frank; Krekelberg, Bart (2016) The Dorsal Visual System Predicts Future and Remembers Past Eye Position. Front Syst Neurosci 10:9 |
| Duijnhouwer, Jacob; Krekelberg, Bart (2016) Evidence and Counterevidence in Motion Perception. Cereb Cortex 26:4602-4612 |
| Kar, Kohitij; Krekelberg, Bart (2014) Transcranial alternating current stimulation attenuates visual motion adaptation. J Neurosci 34:7334-40 |
| Patterson, Carlyn A; Duijnhouwer, Jacob; Wissig, Stephanie C et al. (2014) Similar adaptation effects in primary visual cortex and area MT of the macaque monkey under matched stimulus conditions. J Neurophysiol 111:1203-13 |
| Joukes, Jeroen; Hartmann, Till S; Krekelberg, Bart (2014) Motion detection based on recurrent network dynamics. Front Syst Neurosci 8:239 |
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