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Visual, motor and attentional influences on proprioceptive contributions to perception of hand path rectilinearity during reaching

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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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References

  • Admiraal MA, Keijsers NL, Gielen CC (2003) Interaction between gaze and pointing toward remembered visual targets. J Neurophysiol 90:2136–2148

    PubMed  Google Scholar 

  • Beers RJv, Sittig AC, Gon JJDvd (1996) How humans combine simultaneous proprioceptive and visual position information. Exp Brain Res 111: 253–261

  • Beers RJv, Sittig AC, Gon JJDvd (1999) Integration of proprioceptive and visual position information: an experimentally supported model. J Neurophysiol 81: 1355–1364

  • Bergenheim M, Johansson H, Pedersen J (1995) The role of the gamma-system for improving information transmission in populations of Ia afferents. Neurosci Res 23:207–215

    PubMed  Google Scholar 

  • Bonnel A, Hafter ER (1998) Divided attention between simultaneous auditory and visual signals. Percept Psychophys 60:179–190

    PubMed  Google Scholar 

  • Capaday C, Cooke JD (1981) The effects of muscle vibration on the attainment of intended final position during voluntary human arm movement. Exp Brain Res 42:228–230

    PubMed  Google Scholar 

  • Chapman CE, Bushnell MC, Miron D, Duncan GH, Lund JP (1987) Sensory perception during movement in man. Exp Brain Res 68:516–524

    PubMed  Google Scholar 

  • Cordo P, Carlton L, Bevan L, Carlton M, Kerr GK (1994) Proprioceptive coordination of movement sequences: role of velocity and position information. J Neurophysiol 71:1848–1861

    PubMed  Google Scholar 

  • Cordo P, Inglis JT, Verschueren S, Collins JJ, Merfeld DM, Rosenblum S, Buckley S, Moss F (1996) Noise in human muscle spindles. Nature 383:769–770

    PubMed  Google Scholar 

  • Cornsweet TN (1962) The staircase method in psychophysics. Am J Psychol 75:485–491

    PubMed  Google Scholar 

  • Crawford JD, Medendorp WP, Marotta JJ (2004) Spatial transformations for eye-hand coordination. J Neurophysiol 92:10–19

    PubMed  Google Scholar 

  • Desmurget M, Grafton S (2000) Forward modeling allows feedback control for fast reaching movements. Tr Cog Sci 4(11):423–431

    Google Scholar 

  • Dewald JP, Pope PS, Given JD, Buchannan TS, Rymer WZ (1995) Abnormal muscle coactivation patterns during isometric torque generation at the elbow and shoulder in hemiparetic subjects. Brain 118:495–510

    PubMed  Google Scholar 

  • Dingwell JB, Mah CD, Mussa-Ivaldi FA (2002) Manipulating objects with internal degrees of freedom: evidence for model-based control. J Neurophysiol 88:222–235

    PubMed  Google Scholar 

  • Dizio P, Lackner JR (2000) Congenitally blind individuals rapidly adapt to Coriolis force perturbations of their reaching movements. J Neurophysiol 84:2175–2180

    PubMed  Google Scholar 

  • Driver J, Spence C (1998) Crossmodal attention. Curr Opin Neurobiol 8:245–253

    PubMed  Google Scholar 

  • Dufresne JR, Soechting JF, Terzuolo CA (1980) Modulation of the myotatic reflex gain in man during intentional movements. Brain Res 193:67–84

    PubMed  Google Scholar 

  • Ernst MO, Banks MS (2002) Humans integrate visual and haptic information in a statistically optimal fashion. Nature 415:429–433

    PubMed  Google Scholar 

  • Flanagan JR, Rao AK (1995) Trajectory adaptation to a nonlinear visuomotor transformation: evidence of motion planning in visually perceived space. J Neurophysiol 74(5):2174–2178

    PubMed  Google Scholar 

  • Flanders M, Soechting JF (1990) Parcellation of sensorimotor transformations for arm movements. J Neurosci 10:2420–2427

    PubMed  Google Scholar 

  • Franklin DW, Osu R, Burdet E, Kawato M, Milner TE (2003) Adaptation to stable and unstable dynamics achieved by combined impedance control and inverse dynamics model. J Neurophysiol 90:3270–3282

    PubMed  Google Scholar 

  • Gandevia SC (1996) Kinesthesia: roles for afferent signals and motor commands. In: Rowell LB, Shepherd JT (eds) Handbook of physiology. Oxford, Okford, UK, pp 128–172

    Google Scholar 

  • Gescheider GA (1997) Psychophysics—the fundamentals. Lawrence Erlbaum Associates, New Jersey

    Google Scholar 

  • Ghahramani Z (1995) Computation and psychophysics of sensorimotor integration, PhD Thesis, Dept. of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA

  • Ghahramani Z, Wolpert DM, Jordan MI (1997) Computational models of sensorimotor integration. In: Morasso PG, Sanguineti V (eds) Self-organization, computational maps and motor control. North-Holland Publishing, Amsterdam, pp 117–147

    Google Scholar 

  • Ghez C, Pisa M (1972) Inhibition of afferent transmission in cuneate nucleus during voluntary movement in the cat. Brain Res 40:145–151

    PubMed  Google Scholar 

  • Ghez C, Gordon J, Ghilardi MF (1995) Impairments of reaching movements in patients without proprioception. II Effects of visual information on accuracy. J Neurophysiol 73:361–372

    PubMed  Google Scholar 

  • Ghez C, Krakauer JW, Sainburg RL, Ghilardi M-F (1999) Spatial representations and internal models of limb dynamics in motor learning. In: Gazzaniga MS (ed) The new cognitive neurosciences, 2nd edn. The MIT Press, Cambridge, Mass, pp 501–514

    Google Scholar 

  • Gibson JJ (1933) Adaptation, after-effect and contrast in the perception of curved lines. J Exp Psychology 16:1–31

    Google Scholar 

  • Gordon J, Ghilardi MF, Ghez C (1995) Impairments of reaching movements in patients without proprioception. I. Spatial errors. J Neurophysiol 73:247–360

    Google Scholar 

  • Green DM, Swets JA (1966) Signal detection theory and psychophysics. Wiley, New York

    Google Scholar 

  • Gribble PL, Mullin LI, Cothros N, Mattar A (2003) Role of cocontraction in arm movement accuracy. J Neurophysiol 89:2396–2405

    PubMed  Google Scholar 

  • Gritsenko V, Krouchev NI, Kalaska JF (2007) Afferent input, efference copy, signal noise, and biases in perception of joint angle during active versus passive elbow movements. J Neurophysiol 98:1140–1154

    PubMed  Google Scholar 

  • Hagbarth KE, Valbo AO (1969) Activity in human muscle afferents during muscle stretch and contraction. Elextroencephalogr Clin Neurophysiol 26: 341

    Google Scholar 

  • Hamilton AFdC, Jones KE, Wolpert DM (2004) The scaling of motor noise with muscle strength and motor unit number in humans. Exp Brain Res 157:417–430

    PubMed  Google Scholar 

  • Haruno M, Wolpert DM, Kawato M (2001) MOSAIC model for sensorimotor learning and control. Neural Comput 13:2201–2220

    PubMed  Google Scholar 

  • Hasan Z (1983) A model of spindle afferent response to muscle stretch. J Neurophysiol 49(4):989–1006

    PubMed  Google Scholar 

  • Henriques DYP, Soechting JF (2003) Bias and sensitivity in the haptic perception of geometry. Exp Brain Res 150:95–108

    PubMed  Google Scholar 

  • Houk JC, Henneman E (1967) Responses of Golgi tendon organs to active contractions of the soleus muscle of the cat. J Neurophysiol 30(3):466

    PubMed  Google Scholar 

  • Houk JC, Rymer WZ (1981) Neural control of muscle length and tension. In: Brooks VB (ed) Handbook of physiology. American Physiological Society, Baltimore, pp 257–323

    Google Scholar 

  • Houk JC, Rymer WZ, Crago PE (1981) Dependence of dynamic response of spindle receptors on muscle length and velocity. J Neurophysiol 46(1):143–166

    PubMed  Google Scholar 

  • Hultborn H (2001) State-dependent modulation of sensory feedback. J Physiol 533:5–13

    PubMed  Google Scholar 

  • Ingram HA, van Donkelaar P, Cole J, Vercher JL, Gauthier GM, Miall RC (2000) The role of proprioception and attention in a visuomotor adaptation task. Exp Brain Res 132(1):114–126

    PubMed  Google Scholar 

  • Jones KE, Hamilton AFdC, Wolpert DM (2002) Sources of signal-dependent noise during isometric force production. J Neurophysiol 88:1533–1544

    PubMed  Google Scholar 

  • Kakuda N, Nagaoka M (1998) Dynamic response of human muscle spindle afferents to stretch. J Physiol 513(2):621–628

    PubMed  Google Scholar 

  • Kearney RE, Lortie M, Stein RB (1999) Modulation of stretch reflexes during imposed walking movements of the human ankle. J Neurophysiol 81:2893–2902

    PubMed  Google Scholar 

  • Koerding KP, Wolpert DM (2004) Bayesian integration in sensorimotor learning. Nature 427:244–247

    Google Scholar 

  • Koerding KP, Beierholm U, Ma WJ, Quartz S, Tenenbaum JB, Shams L (2007) Causal inference in multisensory perception. PLoS ONE 2(9):e943. doi:10.1371/journal.pone.0000943

    Google Scholar 

  • Kontsevich LL, Tyler CW (1999) Distraction of attention and the slope of the psychometric function. J Opt Soc Am A 16(2):217–222

    Google Scholar 

  • Krakauer JW, Pine ZM, Ghilardi M-F, Ghez C (2000) Learning of visuomotor transformations for vectorial planning of reaching trajectories. J Neurosci 20(23):8916–8924

    PubMed  Google Scholar 

  • Lackner JR, Dizio P (1994) Rapid adaptation to Coriolis force perturbations of arm trajectory. J Neurophysiol 72:299–313

    PubMed  Google Scholar 

  • Lillis KP, Scheidt RA (2003) Sensitivity to hand path curvature during reaching. IEEE EMBS Soc. Cancun MX

  • Matthews BHC (1933) Nerve endings in mammalian muscle. J Physiol Lond 78:1–53

    PubMed  Google Scholar 

  • Matthews PBC (1963) The response of de-efferented muscle spindle receptors to stretching at different velocities. J Physiol 168:660–678

    PubMed  Google Scholar 

  • Matthews PBC (1986) Observations on the automatic compensation of reflex gain on varying the pre-existing level of motor discharge in man. J Physiol 374:73–90

    PubMed  Google Scholar 

  • McCloskey DI (1973) Differences between the senses of movement and position shown by the effects of loading and vibration of muscles in man. Brain Res 61:119–131

    PubMed  Google Scholar 

  • McIntyre J, Stratta F, Lacquaniti F (1997) Viewer-centered frame of reference for pointing to memorized targets in three-dimensional space. J Neurophysiol 78:1601–1618

    PubMed  Google Scholar 

  • Miall RC, Weir DJ, Stein JF (1985) Visuomotor tracking with delayed visual feedback. J Neurosci 16:511–522

    Google Scholar 

  • Miall RC, Weir DJ, Wolpert DM, Stein JF (1993) Is the cerebellum a Smith predictor? J Mot Behav 25:203–216

    PubMed  Google Scholar 

  • Milner TE, Cloutier C (1993) Compensating for mechanically unstable loading in voluntary wrist movement. Exp Brain Res 94:522–532

    PubMed  Google Scholar 

  • Morasso P (1981) Spatial control of arm movements. Exp Brain Res 42:223–227

    PubMed  Google Scholar 

  • Nelson W (1983) Physical principles for economies of skilled movements. Biol Cybern 46:135–147

    PubMed  Google Scholar 

  • Nelson RJ (1996) Interactions between motor commands and somatic perception in sensorimotor cortex. Curr Opin Neurobiol 6:801–810

    PubMed  Google Scholar 

  • Nielsen JB (2004) Sensorimotor integration at spinal level as a basis for muscle coordination during voluntary movement in humans. J Appl Physiol 96:1961–1967

    PubMed  Google Scholar 

  • Perreault EJ, Chen K, Trumbower RD, Lewis G (2008) Interactions with compliant loads alter stretch reflex gains but not intermuscular coordination. J Neurophysiol 99:2101–2113

    PubMed  Google Scholar 

  • Pine ZM, Krakauer JW, Gordon J, Ghez C (1996) Learning of scaling factors and reference axes for reaching movements. NeuroReport 7:2357–2361

    PubMed  Google Scholar 

  • Poladia C, Scheidt RA, Beardsley S (2008) Systems identification of sensory-motor control for visually guided wrist movements. Abstr Soc Neurosci 32. Washington, DC

  • Prochazka A (1996) Proprioceptive feedback and movement regulation. In: Rowell LB, Shepard JT (eds) Handbook of physiology—section 12. Oxford University Press, New York, pp 89–127

    Google Scholar 

  • Rack PMH (1981) Limitations of somatosensory feedback in control of posture and movement. In: Handbook of physiology. The nervous system. Bethesda: Am Physiol Soc, sect. 1, vol II, part 1, chap. 7, pp 229–256

  • Redding GM, Rader SD, Lucas DR (1992) Cognitive load and prism adaptation. J Mot Behav 24:238–246

    PubMed  Google Scholar 

  • Ribot-Ciscar E, Rossi-Durand C, Roll J-P (2000) Increased muscle spindle sensitivity to movement during reinforcement manoeuvres in relaxed human subjects. J Physiol 523(1):271–282

    PubMed  Google Scholar 

  • Roll JP, Gilhodes JC (1995) Proprioceptive sensory codes mediating movement trajectory perception: human hand vibration-induced drawing illusions. Can J Physiol Pharmacol 73:295–305

    PubMed  Google Scholar 

  • Rowland LP (1985) Clinical syndromes of the spinal cord. In: Kandel ER, Schwartz JH (eds) Principles of neuroscience, 2nd edn. Elsevier, New York, pp 469–477

  • Sainburg RL, Ghilardi MF, Poizner H, Ghez C (1995) Control of limb dynamics in normal subjects and patients without proprioception. J Neurophysiol 73:820–835

    PubMed  Google Scholar 

  • Salinas E (2004) Fast remapping of sensory stimuli onto motor actions on the basis of contextual modulation. J Neurosci 24:1113–1118

    PubMed  Google Scholar 

  • Scheidt RA, Kertesz AE (1992) Temporal and spatial aspects of sensory interactions during human fusional response. Vis Res 33:1259–1270

    Google Scholar 

  • Scheidt RA, Rymer WZ (2000) Control strategies for the transition from multijoint to single-joint arm movements studied using a simple mechanical constraint. J Neurophysiol 83:1–12

    PubMed  Google Scholar 

  • Scheidt RA, Stoeckmann T (2007) Reach adaptation and final position control amid environmental uncertainty following stroke. J Neurophysiol 97:2824–2836

    PubMed  Google Scholar 

  • Scheidt RA, Conditt MA, Reinkensmeyer DJ, Mussa-Ivaldi FA (2000) Persistence of motor adaptation during constrained, multi-joint, arm movements. J Neurophysiol 84:853–862

    PubMed  Google Scholar 

  • Scheidt RA, Dingwell J, Mussa-Ivaldi FA (2001) Learning to move amid uncertainty. J Neurophysiol 86:971–985

    PubMed  Google Scholar 

  • Scheidt RA, Conditt M, Secco EL, Mussa-Ivaldi FA (2005) Interaction of visual and proprioceptive feedback during adaptation of human reaching movements. J Neurophysiol 93:3200–3213

    PubMed  Google Scholar 

  • Schmidt RA, Zelaznik H, Hawkins B, Frank JS, Quinn JTJ (1979) Motor-output variability: a theory for the accuracy of rapid motor acts. Psychol Rev 47:415–451

    PubMed  Google Scholar 

  • Seki K, Perlmutter SI, Fetz EE (2003) Sensory input to primate spinal cord is presynaptically inhibited during voluntary movement. Nat Neurosci 6:1309–1316

    PubMed  Google Scholar 

  • Shadmehr R, Mussa-Ivaldi FA (1994) Adaptive representation of dynamics during learning of a motor task. J Neurosci 14:3208–3224

    PubMed  Google Scholar 

  • Sinkjaer T, Andersen JB, Larsen B (1996) Soleus stretch reflex modulation during gait in humans. J Neurophysiol 76:1112–1120

    PubMed  Google Scholar 

  • Sittig AC, Gon JJDvd, Gielen CC (1987) The contribution of afferent information on position and velocity to the control of slow and fast human forearm movements. Exp Brain Res 67:33–40

    PubMed  Google Scholar 

  • Slifkin AB, Newell KM (2000) Variability and noise in continuous force production. J Mot Behav 32(2):141–150

    PubMed  Google Scholar 

  • Smith MA, Ghazizadeh A, Shadmehr R (2006) Interacting adaptive processes with different time scales underlie short-term motor learning. PLoS Biol 4:e179

    PubMed  Google Scholar 

  • Sober SJ, Sabes PN (2003) Multisensory integration during motor planning. J Neurosci 23:6982–6992

    PubMed  Google Scholar 

  • Soechting JF, Flanders M (1989) Sensorimotor representations for pointing to targets in three-dimensional space. J Neurophysiol 62:582–594

    PubMed  Google Scholar 

  • Soso MJ, Fetz EE (1980) Responses of identified cells in postcentral cortex of awake monkeys during comparable active and passive joint movements. J Neurophysiol 43(4):1090–1111

    PubMed  Google Scholar 

  • Stoeckmann T, Sullivan K, Scheidt RA (2009) Elastic, viscous, and mass load effects on post-stroke muscle recruitment and cocontraction during reaching: a pilot study. Phys Ther 89(7):665–678

    PubMed  Google Scholar 

  • Sutton G, Sykes K (1967) The variation of hand tremor with force in healthy subjects. J Physiol (Lond) 191:699–711

    Google Scholar 

  • Takahashi C, Scheidt RA, Reinkensmeyer DJ (2001) Impedance control and internal model formation when reaching in a randomly varying dynamical environment. J Neurophysiol 86:1047–1051

    PubMed  Google Scholar 

  • Taylor JA, Thoroughman KA (2006) Divided attention impairs human motor adaptation but not feedback control. J Neurophysiol 98:317–326

    Google Scholar 

  • Taylor JA, Thoroughman KA (2007) Motor adaptation scaled by the difficulty of a secondary cognitive task. PLoS ONE 3(6):e2485. doi:10.1371/journal.pone.0002485

    Google Scholar 

  • Thoroughman KA, Shadmehr R (1999) EMG correlates of learning internal models of reaching movements. J Neuroscience 19:8573–8588

    Google Scholar 

  • Thoroughman KA, Shadmehr R (2000) Learning of action through adaptive combination of motor primitives. Nature 407:742–747

    PubMed  Google Scholar 

  • Tillery SI, Flanders M, Soechting JF (1991) A coordinate system for the synthesis of visual and kinesthetic information. J Neurophysiol 11:770–778

    Google Scholar 

  • Todorov E, Jordan MI (2002) Optimal feedback control as a theory of motor coordination. Nat Neurosci 5:1226–1235

    PubMed  Google Scholar 

  • Treisman AM, Gelade G (1980) A feature-integration theory of attention. Cogn Psychol 12:97–136

    PubMed  Google Scholar 

  • van den Dobbelsteen JJ, Brenner E, Smeets JB (2004) Body-centered visuomotor adaptation. J Neurophysiol 92:416–423

    PubMed  Google Scholar 

  • Verschueren SMP, Swinnen SP, Cordo PJ, Dounskaia NV (1999) Proprioceptive control of multijoint movement: unimanual circle drawing. Exp Brain Res 127:171–181

    PubMed  Google Scholar 

  • Vetter P, Goodbody SJ, Wolpert DM (1999) Evidence for an eye-centered spherical representation of the visuomotor map. J Neurophysiol 81:935–959

    PubMed  Google Scholar 

  • Voss M, Ingram JN, Haggard P, Wolpert DM (2006) Sensorimotor attenuation by central motor command signals in the absence of movement. Nat Neurosci 9(1):26–27

    PubMed  Google Scholar 

  • Voss M, Ingram JN, Wolpert DM, Haggad P (2008) Mere expectation to move causes attenuation of sensory signals. PLOS One 3(8):e2866

    PubMed  Google Scholar 

  • Welch RB (1978) Perceptual modification: adapting to altered sensory environments. Academic Press, New York

    Google Scholar 

  • Wickens TD (2002) Elementary signal detection theory. Oxford University Press, New York

    Google Scholar 

  • Williams SR, Chapman CE (2000) Time course and magnitude of movement-related gating of tactile detection in humans. II. Effects of stimulus intensity. J Neurophysiol 84:863–875

    PubMed  Google Scholar 

  • Wolpert DM, Kawato M (1998) Multiple paired forward and inverse models for motor control. Neur Net 11:1317–1329

    Google Scholar 

  • Wolpert DM, Ghahramani Z, Jordan MI (1994) Perceptual distortion contributes to the curvature of human reaching movements. Exp Brain Res 98:153–156

    PubMed  Google Scholar 

  • Wolpert DM, Ghahramani Z, Jordan MI (1995a) An internal model for sensorimotor integration. Science 269:1880–1882

    PubMed  Google Scholar 

  • Wolpert DM, Ghahramani Z, Jordan MI (1995b) Are arm trajectories planned in kinematic or dynamic coordinates? An adaptation study. Exp Brain Res 103:460–470

    PubMed  Google Scholar 

  • Wong T, Henriques DYP (2009) Visuomotor adaptation does not recalibrate kinesthetic sense of felt hand path. J Neurophysiol 101:614–623

    PubMed  Google Scholar 

  • Zackowski K, Dromerick A, Sahrmann S, Thach W, Bastian A (2004) How do strength, sensation, spasticity and joint individuation relate to the reaching deficits of people with chronic hemiparesis? Brain 127:1035–1046

    PubMed  Google Scholar 

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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.

Fig. 5
figure 5

The equal-variance Gaussian model of curvature discrimination used in this study. Top the nominal case where proprioceptive signals providing evidence for curvature (red, ‘C’) exceed those supporting a ‘straight’ response (black, ‘S’) by an unknown critical proportion at detection threshold, κt. Bottom Applying the same criterion to the case where inherent variability has increased by the same amount in both C* and S* distributions results in an increase in κt (i.e., the ratio of red:black shaded areas is the same for both sets of curves)

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):

$$ d^{\prime} = {\frac{{\mu_{\text{c}} }}{{\sigma_{\text{s}} }}}. $$
(2)

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:

$$ \% \Updelta \sigma_{\text{prop}} \cong 100\left( {{\frac{{\kappa_{{{\text{t}}_{\text{treatment}} }} }}{{\kappa_{{{\text{t}}_{{{\text{no\_treatment}}}} }} }}} - 1} \right). $$
(3)

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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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