Variance based weighting of multisensory head rotation signals for verticality perception


Autoři: Christopher J. Dakin aff001;  Prateek Kumar aff001;  Patrick A. Forbes aff002;  Amy Peters aff001;  Brian L. Day aff001
Působiště autorů: Institute of Neurology, University College London, London, England aff001;  Department of Neuroscience, Erasmus University Medical Centre, Rotterdam, The Netherlands aff002
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
doi: 10.1371/journal.pone.0227040

Souhrn

We tested the hypothesis that the brain uses a variance-based weighting of multisensory cues to estimate head rotation to perceive which way is up. The hypothesis predicts that the known bias in perceived vertical, which occurs when the visual environment is rotated in a vertical-plane, will be reduced by the addition of visual noise. Ten healthy participants sat head-fixed in front of a vertical screen presenting an annulus filled with coloured dots, which could rotate clockwise or counter-clockwise at six angular velocities (1, 2, 4, 6, 8, 16°/s) and with six levels of noise (0, 25, 50, 60, 75, 80%). Participants were required to keep a central bar vertical by rotating a hand-held dial. Continuous adjustments of the bar were required to counteract low-amplitude low-frequency noise that was added to the bar’s angular position. During visual rotation, the bias in verticality perception increased over time to reach an asymptotic value. Increases in visual rotation velocity significantly increased this bias, while the addition of visual noise significantly reduced it, but did not affect perception of visual rotation velocity. The biasing phenomena were reproduced by a model that uses a multisensory variance-weighted estimate of head rotation velocity combined with a gravito-inertial acceleration signal (GIA) from the vestibular otoliths. The time-dependent asymptotic behaviour depends on internal feedback loops that act to pull the brain’s estimate of gravity direction towards the GIA signal. The model's prediction of our experimental data furthers our understanding of the neural processes underlying human verticality perception.

Klíčová slova:

Acceleration – Head – Motion – Noise reduction – Otolith – Sensory perception – Velocity – Vision


Zdroje

1. Einstein A. Uber das Relativitatsprinzip und die aus demselben gezogenen folgerungen. Jahrb Radioakt Elektron 1907;4:411–462.

2. Angelaki DE, McHenry MQ, Dickman JD, Newlands SD, Hess BJM. Computation of inertial motion: neural strategies to resolve ambiguous otolith information. J Neurosci. 1999;19:316–327. doi: 10.1523/JNEUROSCI.19-01-00316.1999 9870961

3. Angelaki DE, Shaikh AG, Green AM Dickman JD. Neurons compute internal models of the physical laws of motion. Nature 2004;430:560–564. doi: 10.1038/nature02754 15282606

4. Glasauer S. Interaction of semicircular canals and otoliths in the processing structure of the subjective zenith. Ann NY Acad Sci 1992;656:847–849. doi: 10.1111/j.1749-6632.1992.tb25272.x 1599198

5. Green AM, Angelaki DE. Resolution of sensory ambiguities for gaze stabilization requires a second neural integrator. J Neurosci 2003;23:9265–9275. doi: 10.1523/JNEUROSCI.23-28-09265.2003 14561853

6. Green AM, Angelaki DE. An integrative neural network for detecting inertial motion and head orientation. J Neurophysiol 2004;92;905–925. doi: 10.1152/jn.01234.2003 15056677

7. Green AM, Angelaki DE. Multisensory integration: resolving sensory ambiguities to build novel representations. Curr Opin Neurobiol 2010a;20:353–360.

8. Green AM, Angelaki DE. Internal models and neural computation in the vestibular system. Exp Brain Res 2010b;200:197–222.

9. MacNeilage PR, Banks MS, Berger DR, Bülthoff HH. A Bayesian model of disambiguation of gravitoinertial force by visual cues. Exp Brain Res 2007;179:263–290. doi: 10.1007/s00221-006-0792-0 17136526

10. Merfeld DM, Young L, Oman CM, Shelhamer MJ. A multidimensional model of the effect of gravity on the spatial orientation of the monkey. J Vestib Res 1993;3:141–161. 8275250

11. Merfeld DM. Modeling the vestibulo-ocular reflex of the squirrel monkey during eccentric rotation and roll tilt. Exp Brain Res 1995;106:123–134. doi: 10.1007/bf00241362 8542968

12. Merfeld DM, Young LR. The vestibulo-ocular reflex of the squirrel monkey during eccentric rotation and roll tilt. Exp Brain Res 1995;106:111–122. doi: 10.1007/bf00241361 8542967

13. Merfeld DM, Zupan LH, Peterka RJ. Humans use internal models to estimate gravity and linear acceleration. Science 1999;398:615–618.

14. Merfeld DM, Zupan LH. Neural processing of gravitoinertial cues in humans. III. Modeling tilt and translation responses. J Neurophysiol 2002;87:819–833. doi: 10.1152/jn.00485.2001 11826049

15. Zupan LH, Merfeld DM, Darlot C. Using sensory weighting to model the influence of canal, otolith and visual cues on spatial orientation and eye movements. Biol Cybern 2002;86:209–230. doi: 10.1007/s00422-001-0290-1 12068787

16. Merfeld DM. Spatial orientation in the squirrel monkey: an experimental and theoretical investigation (PhD thesis). MIT, Cambridge, Massachusetts; 1990.

17. Dichgans J, Held R, Young L, Brandt T. Moving visual scenes influence the apparent direction of gravity. Science 1972;178:1217–1219. doi: 10.1126/science.178.4066.1217 4637810

18. Held R, Dichgans J, Bauer J. Characteristics of moving visual scenes influencing spatial orientation. Vis Res 1974;15:357–365.

19. Zupan LH, Merfeld DM. Neural processing of gravito-inertial cues in humans. IV. Influence of visual rotational cues during roll optokinetic stimuli. J Neurophysiol 2003;89: 390–400. doi: 10.1152/jn.00513.2001 12522188

20. Knill DC, Pouget A. The Bayesian brain: The role of uncertainty in neural coding and computation. Trends Neurosci 2004;27:712–719. doi: 10.1016/j.tins.2004.10.007 15541511

21. Ernst MO, Banks M. Humans integrate visual and haptic information in a statistically optimal fashion. Nature 2002;415:429–433. doi: 10.1038/415429a 11807554

22. Ernst MO, Bülthoff HH. Merging the senses into a robust percept. Trends Cogn Sci 2004;8:162–169. doi: 10.1016/j.tics.2004.02.002 15050512

23. ter Horst AC, Koppen M, Selen LPJ, Medendorp WP. Reliability-based weighting of visual vestibular cues in displacement estimation. PLoS One 2015;10:e0145015. doi: 10.1371/journal.pone.0145015 26658990

24. DeAngelis GC, Angelaki DE. Visual-vestibular integration for self-motion perception. In: Murray MM, Wallace Mt, editors. The Neural Basis of Multisensory Processes, Boca Raton(FL): CRC Press; 2012. Chapter 31.

25. Greenlee MW, Frank SM, Kaliuzhna M, Blanke O, Bremmer F, Churan J et al., Multisensory integration in self motion perception. Multisens Res 2016;29(6–7)525–556.

26. Laurens J, Droulez J. Bayesian processing of vestibular information. Biol Cybern 2007;96,389–404. doi: 10.1007/s00422-006-0133-1 17146661

27. Niehof N, Perdreau F, Koppen M, Medendorp P. Time course of the subjective visual vertical during sustained optokinetic and galvanic vestibular stimulation. J Neurophysiol doi: 10.1152/jn.00083.2019 31268803

28. Jürgens R., & Becker W. (2006). Perception of angular displacement without landmarks: evidence for Bayesian fusion of vestibular, optokinetic, podokinesthetic, and cognitive information. Experimental Brain Research, 174(3), 528–543. doi: 10.1007/s00221-006-0486-7 16832684

29. Brainard DH. The psychophysics toolbox. Spat Vis 1997;10:433–436. 9176952

30. Kleiner M, Brainard D, Pelli D, Ingling A, Murray R, Broussard C. What’s new in Psychtoolbox-3. Perception 2007;36(14):1–89.

31. Pelli DG. The videotoolbox software for visual psychophysics: transforming numbers into movies. Spatial Vision, 1997;10(4):437–442. 9176953

32. Dakin CJ, Peters A, Giunti P, Day BL. Cerebellar degeneration increase visual influence on dynamic estimates of verticality. Curr Biol 2018;28(22):3589–3598. doi: 10.1016/j.cub.2018.09.049 30393031

33. R Core Team. R: A language and environment for statistical computing. R Foundation for Statsitical Computing, Vienna, Austria; 2013.

34. Bates D, Maechler M, Bolker B, Walker S. Fitting linear mixed effects models using LME4. J Stat Soft 2015;67(1):1–48.

35. Laurens J, Angelaki DE. The functional significance of velocity storage and its dependence on gravity. Exp Brain Res 2011;210,407–422. doi: 10.1007/s00221-011-2568-4 21293850

36. Laurens J, Meng H, Angelaki DE. Computation of linear acceleration through an internal model in the macaque cerebellum. Nature Neuroscience 2013a;16(11):1701–1708.

37. Laurens J, Meng H, Angelaki DE. Neural representation of orientation relative to gravity in the macaque cerebellum. Neuron 2013b;80;1508–1518.

38. MacNeilage PR, Glasauer S. Gravity perception: The role of the cerebellum. Curr Biol 2018;28(22):1296–1298.

39. Laurens J, Valko Y, Straumann D. Experimental parameter estimation of a visuo-vestibular interaction model in humans. J Vestib Res 2011;21:251–266. doi: 10.3233/VES-2011-0425 22101296

40. Raphan T, Matsuo V, Cohen B. Velocity storage in the vestibule-ocular reflex arc (VOR). Exp Brain Res 1979;35:229–248. doi: 10.1007/bf00236613 108122

41. Glasauer S, Merfeld DM. Modelling three-dimensional vestibular responses during complex motion stimulation. In: Fetter M. Haslwanter T, Misslisch H (eds) Three dimensional kinematics of the eye head and limb movements. Harwood academic, Amsterdam, 1997; pp 387–398.

42. Laurens J, Droulez J. Bayesian modelling of visuo-vestibular interactions. In: Bessière P., Laugier C., Siegwart R. (eds) Probabilistic reasoning and decision making in sensory-motor systems. Springer tracts in advanced robotics, vol 46. Springer, Berlin, Heidelberg; 2008.

43. Kobayashi H, Hayashi Y, Higashino K, Saito A, Kunihiro T, Kanzaki J et al., Dynamic and static subjective visual vertical with aging. Auris Nasus Larynx 2002;29:325–328. doi: 10.1016/s0385-8146(02)00058-5 12393035


Článek vyšel v časopise

PLOS One


2020 Číslo 1