Abstract
We examined how proprioceptive contributions to perception of hand path straightness are influenced by visual, motor and attentional sources of performance variability during horizontal planar reaching. Subjects held the handle of a robot that constrained goal-directed movements of the hand to the paths of controlled curvature. Subjects attempted to detect the presence of hand path curvature during both active (subject driven) and passive (robot driven) movements that either required active muscle force production or not. Subjects were less able to discriminate curved from straight paths when actively reaching for a target versus when the robot moved their hand through the same curved paths. This effect was especially evident during robot-driven movements requiring concurrent activation of lengthening but not shortening muscles. Subjects were less likely to report curvature and were more variable in reporting when movements appeared straight in a novel “visual channel” condition previously shown to block adaptive updating of motor commands in response to deviations from a straight-line hand path. Similarly, compromised performance was obtained when subjects simultaneously performed a distracting secondary task (key pressing with the contralateral hand). The effects compounded when these last two treatments were combined. It is concluded that environmental, intrinsic and attentional factors all impact the ability to detect deviations from a rectilinear hand path during goal-directed movement by decreasing proprioceptive contributions to limb state estimation. In contrast, response variability increased only in experimental conditions thought to impose additional attentional demands on the observer. Implications of these results for perception and other sensorimotor behaviors are discussed.
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Acknowledgments
This work was supported by: Whitaker Foundation RG010157, National Science Foundation BES 0238442, National Institutes of Health R24 HD39627 and R01 HD053727, the Alvin W. and Marion Birnschein Foundation and the Falk Foundation Medical Trust. We also thank members of the Neuromotor Control Lab at Marquette University for helpful comments on a previous version of this manuscript.
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Appendix: The ideal observer and decision process model
Appendix: The ideal observer and decision process model
In the experiments described here, proprioception alone is informative of whether the hand path is actually straight or curved. Each movement produces proprioceptive signals providing information the subject uses to classify his or her percept. We model the percept as a point on an underlying continuous dimension with units of curvature [m−1] (the discrimination axis; Fig. 5, top). We assume that sensation is imperfect and influenced by noises that are Gaussian with zero mean. By definition, the evidence provided by curved trials is, on average, greater than that provided by straight trials. Figure 5 (top) shows two distribution functions indicating the likelihood of evidence under the two alternatives (curved: C, red; straight: S, black). The subject’s task is to decide which distribution the evidence was drawn from. We assume that subjects are stationary in their understanding of “straight” and therefore align the mean of the S distribution with the origin. It seems reasonable to expect that the same noises affect proprioceptive cues regardless of path. We therefore model the task as an equal-variance Gaussian signal discrimination process (Wickens 2002) wherein σ 2C = σ 2S . Finally, we allow that the magnitude of noises influencing perception may vary across experimental treatments.
An ideal observer chooses the distribution (S or C) from which a sensation originated based on whether the ratio of evidence for the two alternatives exceeds some critical criterion value (cf. Green and Swets 1966). Under an equal-variance Gaussian model, the criterion corresponds to a distance from the origin (measured in units of standard deviations) above which the presence of signal is indicated reliably. When fit to experimental data, Eq. 1 (Fig. 5 top, dashed trace) provides an estimate of \( \kappa_{\text{t}} \), the value above which movements are identified as curved. The likelihood ratio that defines criterion depends on the unknown variability of the S and C curves, and so the actual criterion is unknown. However, as there is no a priori reason to prefer signal or noise responses, we assume that criterion does not change from one trial block to the next (i.e., the subject does not need more evidence for the presence of curvature in one case over another). As shown below, a change in the underlying distribution variance will shift \( \kappa_{\text{t}} \) proportionally (Fig. 5, bottom), allowing a comparison of relative impact of experimental treatment on the inherent variability of proprioceptive contribution to limb state estimation.
Signal detection theory provides a measure of signal discriminability (d’) relating pairs of distributions as in Fig. 5. d’ is 0 when the distributions are identical and large when widely separated. For the equal-variance model (Wickens 2002):
μ C corresponds to the expected value of evidence observed when the hand path is curved and σ 2S is the variance of noises influencing the proprioceptive estimate of limb state. d’ is determined only by the signal strength and the subject’s receptivity to that signal; it is not influenced by subjective decision criteria (Green and Swets 1966; Wickens 2002). Because of this invariance, we equate the value of d’ across experimental conditions. Under the stationary criterion assumption, we estimate μ C as the value κ t for each experimental condition because this is the stimulus intensity sufficient for the signal-to-noise likelihood ratio to just exceed criterion. Using this decision process model, it is easy to show that changes in threshold reflect changes in how subjects use proprioceptive information to detect curvature; the effective change in the subject’s internal estimate of the variability of proprioception (%Δσprop) caused by a treatment relative to the variability observed without the treatment is proportional to the ratio of thresholds obtained in the two cases:
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Scheidt, R.A., Lillis, K.P. & Emerson, S.J. Visual, motor and attentional influences on proprioceptive contributions to perception of hand path rectilinearity during reaching. Exp Brain Res 204, 239–254 (2010). https://doi.org/10.1007/s00221-010-2308-1
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DOI: https://doi.org/10.1007/s00221-010-2308-1