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Sensory transformations and the use of multiple reference frames for reach planning.

McGuire LM, Sabes PN - Nat. Neurosci. (2009)

Bottom Line: This model incorporates the patterns of gaze-dependent errors that we found in our human psychophysics experiment when the sensory signals available for reach planning were varied.These results challenge the widely held ideas that error patterns directly reflect the reference frame of the underlying neural representation and that it is preferable to use a single common reference frame for movement planning.Furthermore, the presence of multiple reference frames allows for optimal use of available sensory information and explains task-dependent reweighting of sensory signals.

View Article: PubMed Central - PubMed

Affiliation: W. M. Keck Center for Integrative Neuroscience, Department of Physiology, and the Neuroscience Graduate Program, University of California, San Francisco, California, USA.

ABSTRACT
The sensory signals that drive movement planning arrive in a variety of 'reference frames', and integrating or comparing them requires sensory transformations. We propose a model in which the statistical properties of sensory signals and their transformations determine how these signals are used. This model incorporates the patterns of gaze-dependent errors that we found in our human psychophysics experiment when the sensory signals available for reach planning were varied. These results challenge the widely held ideas that error patterns directly reflect the reference frame of the underlying neural representation and that it is preferable to use a single common reference frame for movement planning. We found that gaze-dependent error patterns, often cited as evidence for retinotopic reach planning, can be explained by a transformation bias and are not exclusively linked to retinotopic representations. Furthermore, the presence of multiple reference frames allows for optimal use of available sensory information and explains task-dependent reweighting of sensory signals.

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Reach variability. a) Trial-type differences in angular reach variance. Black lines represent mean (standard error) reach variance across subjects. Values are derived from the average variance across subjects and trial conditions within each trial type, with the overall mean subtracted. Colored lines represent model predictions with each readouts fit parameters. b) Mean variability of each model readout scheme, as a function of trial type, using fixed model parameters (INTEG fit).
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Figure 5: Reach variability. a) Trial-type differences in angular reach variance. Black lines represent mean (standard error) reach variance across subjects. Values are derived from the average variance across subjects and trial conditions within each trial type, with the overall mean subtracted. Colored lines represent model predictions with each readouts fit parameters. b) Mean variability of each model readout scheme, as a function of trial type, using fixed model parameters (INTEG fit).

Mentions: In addition to fitting the gaze dependent error patterns, the model predicted the differences in movement variability across trial types (Fig. 5a). Since computations within the model were assumed to be noise-free, model output variability was due entirely to variability in the sensory inputs, shaped by the model computations, and did not require any additional parameter fitting. The INTEG model fit provided an accurate prediction for the changes in output variance across trial types, better than the two single-representation fits. These predictions came from separate parameter fits for each readout. However the model parameters presumably reflect actual variances in the neuronal representations of sensory inputs. Using a single set of variances, e.g. the INTEG fit, we looked at how variability in the movement plan depends on readout. We found that the INTEG readout generally yielded a lower variance estimate (Fig. 5b), since it made better use of all available sensory signals (although the extent of this advantage depended on the statistical properties of the sensory transformations; Supplemental Section 2.5 and Fig. S9, online). In contrast to the idea that a single coordinate frame should dominate movement planning3-10, 15, 18, this analysis illustrates that utilizing multiple representations of a movement plan yields more reliable performance across tasks.


Sensory transformations and the use of multiple reference frames for reach planning.

McGuire LM, Sabes PN - Nat. Neurosci. (2009)

Reach variability. a) Trial-type differences in angular reach variance. Black lines represent mean (standard error) reach variance across subjects. Values are derived from the average variance across subjects and trial conditions within each trial type, with the overall mean subtracted. Colored lines represent model predictions with each readouts fit parameters. b) Mean variability of each model readout scheme, as a function of trial type, using fixed model parameters (INTEG fit).
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Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2749235&req=5

Figure 5: Reach variability. a) Trial-type differences in angular reach variance. Black lines represent mean (standard error) reach variance across subjects. Values are derived from the average variance across subjects and trial conditions within each trial type, with the overall mean subtracted. Colored lines represent model predictions with each readouts fit parameters. b) Mean variability of each model readout scheme, as a function of trial type, using fixed model parameters (INTEG fit).
Mentions: In addition to fitting the gaze dependent error patterns, the model predicted the differences in movement variability across trial types (Fig. 5a). Since computations within the model were assumed to be noise-free, model output variability was due entirely to variability in the sensory inputs, shaped by the model computations, and did not require any additional parameter fitting. The INTEG model fit provided an accurate prediction for the changes in output variance across trial types, better than the two single-representation fits. These predictions came from separate parameter fits for each readout. However the model parameters presumably reflect actual variances in the neuronal representations of sensory inputs. Using a single set of variances, e.g. the INTEG fit, we looked at how variability in the movement plan depends on readout. We found that the INTEG readout generally yielded a lower variance estimate (Fig. 5b), since it made better use of all available sensory signals (although the extent of this advantage depended on the statistical properties of the sensory transformations; Supplemental Section 2.5 and Fig. S9, online). In contrast to the idea that a single coordinate frame should dominate movement planning3-10, 15, 18, this analysis illustrates that utilizing multiple representations of a movement plan yields more reliable performance across tasks.

Bottom Line: This model incorporates the patterns of gaze-dependent errors that we found in our human psychophysics experiment when the sensory signals available for reach planning were varied.These results challenge the widely held ideas that error patterns directly reflect the reference frame of the underlying neural representation and that it is preferable to use a single common reference frame for movement planning.Furthermore, the presence of multiple reference frames allows for optimal use of available sensory information and explains task-dependent reweighting of sensory signals.

View Article: PubMed Central - PubMed

Affiliation: W. M. Keck Center for Integrative Neuroscience, Department of Physiology, and the Neuroscience Graduate Program, University of California, San Francisco, California, USA.

ABSTRACT
The sensory signals that drive movement planning arrive in a variety of 'reference frames', and integrating or comparing them requires sensory transformations. We propose a model in which the statistical properties of sensory signals and their transformations determine how these signals are used. This model incorporates the patterns of gaze-dependent errors that we found in our human psychophysics experiment when the sensory signals available for reach planning were varied. These results challenge the widely held ideas that error patterns directly reflect the reference frame of the underlying neural representation and that it is preferable to use a single common reference frame for movement planning. We found that gaze-dependent error patterns, often cited as evidence for retinotopic reach planning, can be explained by a transformation bias and are not exclusively linked to retinotopic representations. Furthermore, the presence of multiple reference frames allows for optimal use of available sensory information and explains task-dependent reweighting of sensory signals.

Show MeSH
Related in: MedlinePlus