Foveal vision anticipates defining features of eye movement targets
High-acuity foveal processing is vital for human vision. Nonetheless, little is known about how the preparation of large-scale rapid eye movements (saccades) affects visual sensitivity in the center of gaze. Based on findings from passive fixation tasks, we hypothesized that during saccade preparation, foveal processing anticipates soon-to-be fixated visual features. Using a dynamic large-field noise paradigm, we indeed demonstrate that defining features of an eye movement target are enhanced in the pre-saccadic center of gaze. Enhancement manifested as higher Hit Rates for foveal probes with target-congruent orientation and a sensitization to incidental, target-like orientation information in foveally presented noise. Enhancement was spatially confined to the center of gaze and its immediate vicinity, even after parafoveal task performance had been raised to a foveal level. Moreover, foveal enhancement during saccade preparation was more pronounced and developed faster than enhancement during passive fixation. Based on these findings, we suggest a crucial contribution of foveal processing to trans-saccadic visual continuity: Foveal processing of saccade targets commences before the movement is executed and thereby enables a seamless transition once the center of gaze reaches the target.
All data (psychophysical data, timing data, eye movement data, stimulus information) along with all experimental scripts are available on the Open Science Framework: https://osf.io/v9gsq/
Article and author information
Deutsche Forschungsgemeinschaft (RO3579/8-1)
- Martin Rolfs
Deutsche Forschungsgemeinschaft (RO3579/9-1)
- Martin Rolfs
Deutsche Forschungsgemeinschaft (RO3579/12-1)
- Martin Rolfs
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Human subjects: Participants gave written informed consent before the experiments. All studies complied with the Declaration of Helsinki in its latest version and were approved by the Ethics Committee of the Department of Psychology at Humboldt-Universität zu Berlin (reference number: 2018-09).
- Krystel R Huxlin, University of Rochester, United States
- Preprint posted: January 12, 2022 (view preprint)
- Received: February 23, 2022
- Accepted: September 3, 2022
- Accepted Manuscript published: September 9, 2022 (version 1)
- Accepted Manuscript updated: September 13, 2022 (version 2)
- Version of Record published: October 18, 2022 (version 3)
© 2022, Kroell & Rolfs
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
- Page views
Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Comparing connectomes can help explain how neural connectivity is related to genetics, disease, development, learning, and behavior. However, making statistical inferences about the significance and nature of differences between two networks is an open problem, and such analysis has not been extensively applied to nanoscale connectomes. Here, we investigate this problem via a case study on the bilateral symmetry of a larval Drosophila brain connectome. We translate notions of'bilateral symmetry' to generative models of the network structure of the left and right hemispheres, allowing us to test and refine our understanding of symmetry. We find significant differences in connection probabilities both across the entire left and right networks and between specific cell types. By rescaling connection probabilities or removing certain edges based on weight, we also present adjusted definitions of bilateral symmetry exhibited by this connectome. This work shows how statistical inferences from networks can inform the study of connectomes, facilitating future comparisons of neural structures.
Temporal synchrony of signals arriving from different neurons or brain regions is essential for proper neural processing. Nevertheless, it is not well understood how such synchrony is achieved and maintained in a complex network of time-delayed neural interactions. Myelin plasticity, accomplished by oligodendrocytes (OLs), has been suggested as an efficient mechanism for controlling timing in brain communications through adaptive changes of axonal conduction velocity and consequently conduction time delays, or latencies; however, local rules and feedback mechanisms that OLs use to achieve synchronization are not known. We propose a mathematical model of oligodendrocyte-mediated myelin plasticity (OMP) in which OLs play an active role in providing such feedback. This is achieved without using arrival times at the synapse or modulatory signaling from astrocytes; instead, it relies on the presence of global and transient OL responses to local action potentials in the axons they myelinate. While inspired by OL morphology, we provide the theoretical underpinnings that motivated the model and explore its performance for a wide range of its parameters. Our results indicate that when the characteristic time of OL’s transient intracellular responses to neural spikes is between 10 and 40 ms and the firing rates in individual axons are relatively low (⪅ 10 Hz), the OMP model efficiently synchronizes correlated and time-locked signals while latencies in axons carrying independent signals are unaffected. This suggests a novel form of selective synchronization in the CNS in which oligodendrocytes play an active role by modulating the conduction delays of correlated spike trains as they traverse to their targets.