Neural, functional, and aesthetic impacts of spatially heterogeneous flicker: A potential role of natural flicker


Autoři: Melisa Menceloglu aff001;  Marcia Grabowecky aff001;  Satoru Suzuki aff001
Působiště autorů: Department of Psychology, Northwestern University, Evanston, Illinois, United States of America aff001;  Interdepartmental Neuroscience, Northwestern University, Evanston, Illinois, United States of America aff002
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0219107

Souhrn

Spatially heterogeneous flicker, characterized by probabilistic and locally independent luminance modulations, abounds in nature. It is generated by flames, water surfaces, rustling leaves, and so on, and it is pleasant to the senses. It affords spatiotemporal multistability that allows sensory activation conforming to the biases of the visual system, thereby generating the perception of spontaneous motion and likely facilitating the calibration of motion detectors. One may thus hypothesize that spatially heterogeneous flicker might potentially provide restoring stimuli to the visual system that engage fluent (requiring minimal top-down control) and self-calibrating processes. Here, we present some converging behavioral and electrophysiological evidence consistent with this idea. Spatially heterogeneous (multistable) flicker (relative to controls matched in temporal statistics) reduced posterior EEG (electroencephalography) beta power implicated in long-range neural interactions that impose top-down influences on sensory processing. Further, the degree of spatiotemporal multistability, the amount of posterior beta-power reduction, and the aesthetic responses to flicker were closely associated. These results are consistent with the idea that the pleasantness of natural flicker may derive from its spatiotemporal multistability that affords fluent and self-calibrating visual processing.

Klíčová slova:

Electroencephalography – Luminance – Perception – Scalp – Sensory perception – Vision – Visual system – Motion detectors


Zdroje

1. Nishida S., & Ashida H. (2000). A hierarchical structure of motion system revealed by interocular transfer of flicker motion aftereffects. Vision Research, 40, 265–278. doi: 10.1016/s0042-6989(99)00176-5 10793900

2. Mather G., Pavan A., Campana G., & Casco C. (2008). The motion aftereffect reloaded. Trends in Cognitive Sciences, 12(12), 481–487. doi: 10.1016/j.tics.2008.09.002 18951829

3. Donner T. H., & Siegel M. (2011). A framework for local cortical oscillation patterns. Trends in Cognitive Sciences, 15(5), 191–199. doi: 10.1016/j.tics.2011.03.007 21481630

4. Bastos A. M., Vezoli J., Bosman C. A., Schoffelen J.-M., Oostenveld R., Dowdall J. R., et al. (2015). Visual areas exert feedforward and feedback influences through distinct frequency channels. Neuron, 85, 390–401. doi: 10.1016/j.neuron.2014.12.018 25556836

5. Michalareas G., Vezoli J., van Pelt S., Schoffelen J.-M., Kennedy H., & Fries P. (2016). Alpha-beta and gamma rhythms subserve feedback and feedforward influences among human visual cortical areas. Neuron, 89, 384–397. doi: 10.1016/j.neuron.2015.12.018 26777277

6. Donner T. H., Siegel M., Oostenveld R., Fries P., Bauer M., & Engel A. K. (2007). Population activity in the human dorsal pathway predicts the accuracy of visual motion detection. Journal of Neurophysiology, 98, 345–359. doi: 10.1152/jn.01141.2006 17493916

7. Siegel M., Donner T. H., Oostenveld R., Fries P., & Engel A. K. (2008) Neuronal synchronization along the dorsal visual pathway reflects the focus of spatial attention. Neuron, 60, 709–719. doi: 10.1016/j.neuron.2008.09.010 19038226

8. Aissani C., Martinerie J., Yahia-Cherif L., Paradis A.-L., & Lorenceau J. (2014). Beta, but not Gamma, band oscillations index visual form-motion integration. PLoS ONE 9(4): e95541. doi: 10.1371/journal.pone.0095541 24781862

9. Meirovitch Y., Harris H., Dayan E., Arieli A., & Flash T. (2015). Alpha and Beta band event-related desynchronization reflects kinematic regularities. Journal of Neuroscience, 35(4), 1627–1637. doi: 10.1523/JNEUROSCI.5371-13.2015 25632138

10. Allison T., Puce A., & McCarthy G. (2000). Social perception from visual cues: Role of the STS region. Trends in Cognitive Sciences, 4, 267–278. 10859571

11. Grossman E., Donnelly M., Price R., Pickens D., Morgan V., Neighbor G., et al. (2000). Brain areas involved in perception of biological motion. Journal of Cognitive Neuroscience, 12, 711–720. 11054914

12. Plass J., Guzman-Martinez E., Ortega L., Grabowecky M., & Suzuki S. (2014). Lip reading without awareness. Psychological Science, 25(9), 1835–1837. doi: 10.1177/0956797614542132 25060525

13. Piantoni G., Kline K. A., & Eagleman D. M. (2010). Beta oscillations correlate with the probability of perceiving rivalrous visual stimuli. Journal of Vision, 10(13):18, 1–11, http://www.journalofvision.org/content/10/13/18, doi: 10.1167/10.13.18 21149311

14. Kim Y.-J., Grabowecky M., & Suzuki S. (2006). Stochastic resonance in binocular rivalry. Vision Research, 46(3), 392–406. doi: 10.1016/j.visres.2005.08.009 16183099

15. Wilson H. R. (2007). Minimal physiological conditions for binocular rivalry and rivalry memory. Vision Research, 47, 2741–2750. doi: 10.1016/j.visres.2007.07.007 17764714

16. Alais D., Cass J., O’Shea R. P., & Blake R. (2010). Visual sensitivity underlying changes in visual consciousness. Current Biology, 20, 1362–1367. doi: 10.1016/j.cub.2010.06.015 20598538

17. van Hateren J. H., & van der Schaaf A. (1996). Temporal properties of natural scenes. Proceedings of SPIE, 2657, 139–143.

18. Yoshimoto S., Garcia J., Jiang F., Wilkins A. J., Takeuchi T., & Webster M. A. (2017). Visual discomfort and flicker. Vision Research, 138, 18–28. doi: 10.1016/j.visres.2017.05.015 28709920

19. Kleiner M., Brainard D., Pelli D., Ingling A., Murray R., & Broussard C. (2007). What’s new in Psychtoolbox-3. Perception, 36(14), 1.

20. Delorme A., & Makeig S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of neuroscience methods, 134(1), 9–21. doi: 10.1016/j.jneumeth.2003.10.009 15102499

21. Lopez-Calderon J., & Luck S. J. (2014). ERPLAB: an open-source toolbox for the analysis of event-related potentials. Frontiers in human neuroscience, 8, 213. doi: 10.3389/fnhum.2014.00213 24782741

22. Hjorth B (1980). Source derivation simplifies topographical EEG interpretation. American Journal of EEG Technology 20, 121–132.

23. Kayser J., Tenke C. E. (2006). Principal components analysis of Laplacian waveforms as a generic method for identifying ERP generator patterns: I. Evaluation with auditory oddball tasks. Clinical Neurophysiology 117, 348–368. doi: 10.1016/j.clinph.2005.08.034 16356767

24. Tenke C. E., & Kayser J. (2012). Generator localization by current source density (CSD): Implications of volume conduction and field closure at intracranial and scalp resolutions. Clinical Neurophysiology, 123, 2328–2345. doi: 10.1016/j.clinph.2012.06.005 22796039

25. Perrin F., Pernier J., Bertrand O., & Echallier J. F. (1989a). Corrigenda EEG 02274. Electroencephalography and Clinical Neurophysiology 76, 565.

26. Perrin F., Pernier J., Bertrand O., & Echallier J. F. (1989b). Spherical Splines for Scalp Potential and Current Density Mapping. Electroencephalography and Clinical Neurophysiology 72(2): 184–187.

27. Perrin F., Pernier J., Bertrand O., Giard M. H., & Echallier J. F. (1987). Mapping of Scalp Potentials by Surface Spline Interpolation. Electroencephalography and Clinical Neurophysiology, 66 (1), 75–81. doi: 10.1016/0013-4694(87)90141-6 2431869

28. Cohen M. X. (2014). Analyzing Neural Time Series Data: Theory and Practice, MIT Press.

29. Hess R. F. & Snowden R. J., (1992) Temporal properties of human visual filters: Number, shapes and spatial covariance, Vision Research, 32, 47–60 doi: 10.1016/0042-6989(92)90112-v 1502811

30. Lui L. L., Bourne J. A., Rosa M. G. P. (2007). Spatial and temporal frequency selectivity of neurons in the middle temporal visual area of new world monkeys (Callithrix jacchus). European Journal of Neuroscience, 25, 1780–1792. doi: 10.1111/j.1460-9568.2007.05453.x 17432965

31. Stromeyer C. F. III, Klein S., Dawson B. M., & Spillmann L. (1982). Low spatial-frequency channel in human vision: adaptation and masking. Vision Research, 22, 225–233. doi: 10.1016/0042-6989(82)90122-5 7101758

32. Hebb D. O. 1949. Organization of Behavior. New York: Wiley.

33. Rumelhart D. E., McClelland J. L. 1986. Parallel Distributed Processing. Cambridge, MA: MIT Press.

34. Palmer S. E., & Schloss K. B. (2010). An ecological valence theory of human color preference. Proceedings of the National Academy of Sciences, U.S.A., 107, 8877–8882.

35. Strauss E. D., Schloss K. B., & Palmer S. E. (2013). Color preferences change after experience with liked/disliked colored objects. Psychonomic Bulletin & Review, 20(5), 935–943.

36. Fründ I., Busch N. A., Kürner U., Schadow J., & Herrmann C. S. (2007). EEG oscillations in the gamma and alpha range respond differently to spatial frequency. Vision Research, 47, 2086–2098. doi: 10.1016/j.visres.2007.03.022 17562345

37. Vannuci M., Gori S., & Kojima H. (2014). The spatial frequencies influence the aesthetic judgment of buildings transculturally. Journal of Cognitive Neuroscience, 5(3–4), 143–149.

38. Fernandez D., & Wilkins A. J. (2008). Uncomfortable images in art and nature. Perception, 37, 1098–1113. doi: 10.1068/p5814 18773732

39. Orgs G., Dombrowski J.-H., Heil M., & Jansen-Osmann P. (2008). Expertise in dance modulates alpha/beta event-related desynchronization during action observation. European Journal of Neuroscience, 27, 3380–3384. doi: 10.1111/j.1460-9568.2008.06271.x 18598273

40. Adelson E. H., & Bergen J. R. (1985). Spatiotemporal energy models for the perception of motion. Journal of the Optical Society of America, A2, 284–299.

41. Georgeson M. A., & Scott-Samuel N. E. (1999). Motion contrast: a new metric for direction discrimination. Vision Research, 39, 4393–4402. doi: 10.1016/s0042-6989(99)00147-9 10789432

42. Orban G. A. (2008). Higher order visual processing in macaque extrastriate cortex. Physiological Review, 88, 59–89.


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2019 Číslo 10