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Frontiers Commentary ARTICLE

Front. Neurosci., 11 October 2012 |

What silly postures tell us about the brain

  • 1Department of Biology, Emory University, Atlanta, GA, USA
  • 2Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, USA
  • 3Department of Physiology, Northwestern University, Chicago, IL, USA
  • 4Department of Applied Mathematics, Northwestern University, Chicago, IL, USA
  • 5The Rehabilitation Institute of Chicago, Chicago, IL, USA

A commentary on

Multi-sensory weights depend on contextual noise in reference frame transformations
by Blohm, G., and Burns, J. K. (2010). Front. Hum. Neurosci. 4:221. doi: 10.3389/fnhum.2010.00221

Because it looks funny and is pretty useless, we do not frequently touch our shoulder with our ear. It turns out that this insight has led to an exciting discovery in the field of cue combination. To interpret our senses, we rely on other senses. For example, to know how the image on our retina relates to the outside world, we need to know the alignment of our eyeball with respect to the outside world. To know this alignment, in turn, we need to know the orientation of our head relative to the outside world. Similar problems occur in other sensory and motor contexts. For example, the perceived direction of sound depends on the orientation of our head and movements of our body rely on the alignment of our body relative to the outside world. One way of formalizing these problems uses the idea of a coordinate system: every sensor provides information in a particular coordinates (e.g., retinal coordinates for visual input or joint angle coordinates for proprioceptive information).

The fact that sensory information arrives in many different coordinate systems produces a challenge for the brain: how to combine information that is encoded in different coordinate systems for successful movements. Both behavioral (Soechting and Flanders, 1989; van Beers et al., 1999; Sober and Sabes, 2003) and neural recording (Wallace et al., 1998; Fetsch et al., 2007) studies have explored how the brain “transforms” information across coordinate systems. It is generally understood that information degrades as it is transformed across coordinate systems.

Another important fact about information integration is that the brain must make all of its decisions based on unreliable and sometimes conflicting sensory data. Crucially, sensory uncertainty can be reduced by combining information from multiple sensors. Any combination needs to assign weights to different inputs. Should each sensor have the same importance or are some sensors more important than others? Based on studies showing that the brain often weights sensory inputs in a near-optimal fashion (Yuille and Kersten, 2006; Trommershauser et al., 2010), it is generally understood that the brain is exquisitely sensitive to statistical issues that occur when combining information.

That coordinate transforms are costly and sensory information is uncertain sets the stage for a remarkably interesting problem. Tasks require precision in the relevant coordinate system. Imprecise sensory information arrives from multiple sources. How should the brain combine information from multiple sources to move successfully? One intuition is that information that requires less coordinate transformation should be trusted more than information that requires more transformation – it should get more weight. The current study by Blohm and Burns (2010) tests a crucial prediction of this intuition: if one sensor is made more difficult to transform into the required coordinate system then it should be assigned a lower weight.

The authors begin with an established cue-conflict paradigm that allows the experimenter to measure the weight subjects put on vision versus proprioception (Sober and Sabes, 2003, 2005). Then, in a variant of the experiment, the subjects performed the task while holding their heads at a 30° angle. The rationale was that in this very unusual posture, we should have more uncertainty about the alignment of the head-centric and the body-centric coordinate systems. If this increases the difficulty of coordinate transformations then subjects should show less reliance on signals that must be transformed across coordinate systems.

The authors beautifully show that changing the difficulty of the transformation affects weights. For example, in the experiment, proprioceptive information about the hand relative to the body should be independent of head tilt. Visual information about the same variable, on the other hand, should be affected by the tilt as it requires converting information from retinal to joint coordinates. Indeed, the authors find that head tilt leads to smaller visual weights (see Figure 8A, light blue line). However, at some level the paper raises as many questions as it answers, since there seem to be two completely independent (but not mutually exclusive) interpretations of the results.

One interpretation, provided by the authors, is that the precision of our sense of head rotation is probably good when the head is held normally and less good when the head is rotated. This should be true due to the Weber-law properties of many sensors, which can result from the signal-dependent noise that has been characterized in the relevant orientation sensors. In this case, it is uncertainty about the relative orientation of the head to the body that gives rise on uncertainty about the alignment of the coordinate systems (Kording and Tenenbaum, 2006). In this interpretation transformed signals are given less weight during head rotation simply because our position sense from the eyes is made less reliable because of uncertainty about the head position.

Another interpretation is that the precision of computations in the nervous system should be better for coordinate transformations that happen more often. As the head is usually roughly at a 90° angle relative to the shoulders, the nervous system has more experience and might devotes more neural resources to the associated coordinate transformation, independent of any signal-dependent noise arising from peripheral sensors. In this interpretation transformed signals are given less weight because the brain avoids the neural noise associated with performing a less-familiar version of the transformation.

The paper thus opens up a new set of questions that should also be asked about previous studies (Sober and Sabes, 2003, 2005). How much of cross-modal weighting is determined by neural noise in coordinate transfers and how much is due to uncertainty about the coordinate system alignments? Future variants of the experiment could give additional cues about the relative alignment of the head or remove such cues. Such a manipulation should have no effect if neural noise dominates and should have a strong effect if alignment uncertainty dominates.


Blohm, G., and Burns, J. K. (2010). Multi-sensory weights depend on contextual noise in reference frame transformations. Front. Hum. Neurosci. 4:221. doi: 10.3389/fnhum.2010.00221

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

Fetsch, C. R., Wang, S., Gu, Y., Deangelis, G. C., and Angelaki, D. E. (2007). Spatial reference frames of visual, vestibular, and multimodal heading signals in the dorsal subdivision of the medial superior temporal area. J. Neurosci. 27, 700–712.

Pubmed Abstract | Pubmed Full Text | CrossRef Full Text

Kording, K. P., and Tenenbaum, J. B. (2006). Causal inference in sensorimotor integration. Adv. Neural Inf. Process. Syst. 19, 737–744.

Sober, S. J., and Sabes, P. N. (2003). Multisensory integration during motor planning. J. Neurosci. 23, 6982–6992.

Pubmed Abstract | Pubmed Full Text

Sober, S. J., and Sabes, P. N. (2005). Flexible strategies for sensory integration during motor planning. Nat. Neurosci. 8, 490–497.

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Soechting, J. F., and Flanders, M. (1989). Errors in pointing are due to approximations in sensorimotor transformations. J. Neurophysiol. 62, 595–608.

Pubmed Abstract | Pubmed Full Text

Trommershauser, J., Kording, K., and Landy, M. S. (2010). Sensory Cue Integration. Oxford: Oxford University Press.

van Beers, R. J., Sittig, A. C., and Denier van der Gon, J. J. (1999). Integration of proprioceptive and visual position-information: an experimentally supported model. J. Neurophysiol. 81, 1355–1364.

Pubmed Abstract | Pubmed Full Text

Wallace, M. T., Meredith, M. A., and Stein, B. E. (1998). Multisensory integration in the superior colliculus of the alert cat. J. Neurophysiol. 80, 1006–1010.

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Yuille, A., and Kersten, D. (2006). Vision as Bayesian inference: analysis by synthesis? Trends Cogn. Sci. 10, 301–308.

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Citation: Sober SJ and Körding KP (2012) What silly postures tell us about the brain. Front. Neurosci. 6:154. doi: 10.3389/fnins.2012.00154

Received: 11 September 2012; Accepted: 24 September 2012;
Published online: 11 October 2012.

Edited by:

Francisco Barcelo, University of Illes Balears, Spain

Copyright: © 2012 Sober and Körding. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.