doi:10.1152/jn.00494.2009 103:164-171, 2010. First published 28 October 2009;J NeurophysiolChristina T. Fuentes and Amy J. BastianAcross Space and TasksWhere Is Your Arm? Variations in Proprioception

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Where Is Your Arm? Variations in Proprioception Across Space and Tasks

Christina T. Fuentes1,2 and Amy J. Bastian1,21Department of Neuroscience, Johns Hopkins School of Medicine; and 2Kennedy Krieger Institute, Baltimore, MarylandSubmitted 5 June 2009; accepted in final form 15 October 2009

Fuentes CT, Bastian AJ. Where is your arm? Variations in propri-oception across space and tasks. J Neurophysiol 103: 164171, 2010.First published October 28, 2009; doi:10.1152/jn.00494.2009. Thesense of limb position is crucial for movement control and environ-mental interactions. Our understanding of this fundamental proprio-ceptive process, however, is limited. For example, little is knownabout the accuracy of arm proprioception: Does it vary with changesin arm configuration, since some peripheral receptors are engagedonly when joints move toward extreme angles? Are these variationsconsistent across different tasks? Does proprioceptive ability changedepending on what we try to localize (e.g., fingertip position vs. elbowangle)? We used a robot exoskeleton to study proprioception in14 arm configurations across three tasks, asking healthy subjects to1) match a pointer to elbow angles after passive movements, 2) matcha pointer to fingertip positions after passive movements, and3) actively match their elbow angle to a pointer. Across all three tasks,subjects overestimated more extreme joint positions; this may be dueto peripheral sensory signals biasing estimates as a safety mechanismto prevent injury. We also found that elbow angle estimates were moreprecise when used to judge fingertip position versus directly reported,suggesting that the brain has better access to limb endpoint positionthan joint angles. Finally, precision of elbow angle estimates im-proved in active versus passive movements, corroborating work show-ing that efference copies of motor commands and alpha-gamma motorneuron coactivation contribute to proprioceptive estimates. In sum, wehave uncovered fundamental aspects of normal proprioceptive pro-cessing, demonstrating not only predictable biases that are dependenton joint configuration and independent of task but also improvedprecision when integrating information across joints.

I N T R O D U C T I O N

Proprioceptionthe sense of position and movement of theparts of the body in the absence of visionplays a crucial rolein daily life, contributing to motor skills and the general abilityto successfully interact with the environment. It is largelyagreed that muscle spindles are the major contributors toproprioception (see Gandevia et al. 1992; Matthews 1988);these receptors increase and decrease activity as a musclestretches and contracts, respectively, thus providing signalsrelating to muscle length and velocity. Studies have shown thatmuscle spindles have preferred sensory directions and weight-ing individual responses based on each spindles preferreddirection results in a population code that accurately representsa limbs movement direction (Bergenheim et al. 2000; Jones etal. 2001; Roll et al. 2000, 2004) and static position (Ribot-Ciscar et al. 2003). Other studies, however, have implicated arole of joint receptors (Ferrell et al. 1987; Macefield et al.1990) and cutaneous receptors (Collins and Prochazka 1996;Collins et al. 2005; Edin and Johansson 1995) in propriocep-

tion; how signals from these additional receptors influenceproprioception remains unclear.

A number of behavioral studies have explored propriocep-tive position estimates. Studies have tested fingertip positionsense across limited areas of Cartesian space (Crowe et al.1987; van Beers et al. 1996, 1998, 1999) and elbow angle senseacross limited ranges of joint space (Darling 1991; Gritsenko etal. 2007; Soechting 1982; Zia et al. 2002). Variations in theseestimates, however, have not been systematically exploredacross joint space. Since joint angles alter the activity ofperipheral sensory signals, exploring variations across jointspace rather than Cartesian space may provide fundamentalinsight into proprioceptive processing. Moreover, although ithas been hypothesized that elbow angle sense should be moreprecise in tasks in which elbow angle estimates are integratedinto the more behaviorally relevant estimates of fingertip po-sition (van Beers et al. 1998), this hypothesis has never beendirectly tested or compared with hypotheses involving efferentinformation in addition to afferent information. Comparing theaccuracy and precision of proprioceptive position estimatesacross tasks can provide unique insight into proprioceptiveprocessing.

Here we tested whether there are any patterns in the accu-racy and precision of position sense across limb configurationsand, if so, whether these patterns are consistent across differenttasks. Because responses of different sensory signals varydepending on joint angles, we hypothesized that these signalswould differentially influence position sense depending on howthe arm is configured. We also tested whether the accuracy andprecision of position estimates vary depending on whether asubject tries to estimate joint angle versus estimate the end-point position of the limb and whether the limb is movedactively versus passively. Based on evidence suggesting thatthe CNS primarily represents limb position in terms of end-point position (Kalaska et al. 1990; Prudhomme and Kalaska1994; Tillery et al. 1996), we hypothesized that behavioralestimates of fingertip position would produce more preciseelbow angle estimate than would direct estimates of elbowangle.

M E T H O D S

SubjectsWe tested ten right-handed adults (six female, four male; ages

1830 yr, mean 23.6). Two individuals were unable to be tested inTask 3, leaving eight participants in this task. Each subject gavewritten informed consent. All subjects were neurologically healthyand had normal or corrected-to-normal vision. Protocols were ap-proved by the Johns Hopkins Institutional Review Board.

Address for reprint requests and other correspondence: A. Bastian, KennedyKrieger Institute, 707 N. Broadway, G05, Baltimore, MD 21205 (E-mail:bastian@kennedykrieger.org).

J Neurophysiol 103: 164171, 2010.First published October 28, 2009; doi:10.1152/jn.00494.2009.

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Procedure

Experiments were performed on separate days using the KINARM(Kinesiological Instrument for Normal and Altered Reaching Move-ment, BKIN Technologies, Kingston, Ontario, Canada), an exoskel-etal robotic arm that allows for individual application of torques to theelbow and shoulder joints. For all tasks, subjects sat with their rightarm in the KINARM, and the robot was calibrated so that the rightarm moved in a shoulder-level horizontal plane. The left arm re-mained out of the robot. Using a reflected rear-projection system,subjects viewed images that appeared to be in the same plane as theirright arm, and during test blocks vision of their right arm was blockedby a screen (Fig. 1A). Throughout each experiment subjects saw a dotprojected over their elbow joint and a line over their upper arm. At thestart of each trial the elbow dot and upper arm line were red,indicating that the KINARM was moving the subjects right arm intoa new configuration (movements were made along a bell-shapedtrajectory profile with a maximum fingertip velocity of 0.5 m/s). Oncein position for that trial, the KINARM held the elbow and shoulder inplace (only the shoulder in Task 3). The elbow dot and upper arm linethen turned green, and an additional element appeared on the screendepending on the task (Fig. 1B).

Task 1: passive elbow angle matching. A line was projected out ofthe elbow dot at a random angle at least 15 from the forearm butno farther than 30. Subjects used a joystick in their left hand torotate the second line about the elbow dot until they perceived that itwas aligned over their forearm, with the angle of the upper arm lineand the rotated line matching their elbow angle. Trials were all timedat 12 s so that if subjects finished early they were instructed to wait.At completion of the 12 s, the rotating line disappeared and the elbowdot and upper arm line again turned red as the arm was passivelymoved into the next trials configuration. In all, 15 arm configurationswere tested: shoulder angles of 60, 75, and 90 each paired with elbowangles of 30, 45, 60, 75, and 90 (see Fig. 2 for angle definitions).Each configuration was presented twice in a pseudorandom orderwithin a block. Subjects completed four blocks of 30 test trials, for atotal of 8 test trials per arm configuration.

Task 2: passive fingertip matching. A dot appeared at a randomposition between 8.5 and 17 cm from the subjects right index finger.Subjects used a joystick in their left hand (in a fashion similar to thatin Task 1) to move the dot until they perceived that it was positionedover their index fingertip. Trials were timed at 18 s, which wasslightly longer than the Task 1 trials since the response decisioninvolved two dimensions (i.e., x and y positions of the fingertip) ratherthan one (i.e., elbow angle). At completion of the 18 s, the fingertipdot disappeared and the elbow dot and upper arm line again turned redas the arm was passively moved into the next trials configuration. Thesame 15 arm configurations were tested and presented in the sameorder within each block as in Task 1. Due to the increased length oftrials, subjects completed three rather than four blocks of 30 test trials(for a total of 6 test trials per arm configuration) to keep the total timeof the experimental session comparable to that of the other two tasks.

Task 3: active elbow angle matching. A line was projected out ofthe elbow dot as in Task 1, but instead of rotating the line to match thelocation of their fixed forearm subjects rotated their forearm about theelbow to match the location of the fixed line. Trials were set to 10 seach. The same 15 configurations were presented as were used in thepassive tasks, and both the presentation order and the experimentalprotocol were the same as those in Task 1 (i.e., four blocks of 30 trialswithout vision of the right arm).

The order of Tasks 1 and 2 was randomized across subjects, but allsubjects performed Task 3 last. This design ensured that subjectsresponses in Tasks 1 and 2 were not influenced by rememberedimages from Task 3.

Control trials. Each experiment ended with one block of 30 trials inwhich subjects could see their arm (i.e., two control trials per config-uration). This final block with vision was performed as a control forpotential errors not due to proprioception.

EMG recordings. Across all three tasks, electromyographic (EMG)recordings were collected from five muscles in the right arm: brachia-lis, biceps brachii, triceps brachii, posterior deltoid, and pectoralismajor. Amplifier gains were set to 10,000, and signals were sampledat a rate of 1,000 Hz. During Tasks 1 and 2, in which subjects were

FIG. 1. Setup and tasks. A: positioning in the KINARM.Subjects looked down into a horizontal mirror at chin leveland saw visual elements reflected from a horizontal rear-projection screen above the mirror. Not shown: in all taskssubjects had electromyographs on their right arm, and forthe passive elbow angle task and the passive fingertip tasksubjects held a joystick in their left hand. B: in all taskssubjects arms were blocked from view with a screen, andonly the white elements were visible on the display. In Task1 subjects used a joystick to rotate a line until it matched theorientation of their forearm. In Task 2 subjects moved a dotuntil it matched the position of their fingertip. In Task 3subjects rotated their forearm to match the orientation of aline.

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instructed to keep their right arms passive throughout testing, EMGsignals were monitored on-line to ensure that no muscle activity couldcontribute to proprioceptive estimates. Trials in which activity wasnoted were discarded from later analyses.

AnalysisFor all three tasks, the average error for each configuration from the

two trials with vision was taken as a measure of error not due toproprioception. This error was subtracted from each test trials error,and analyses were conducted on these purely proprioceptive errors.

Task 1. For each trial, the final angle of the rotated line relative tothe upper arm line was taken as measurement of a subjects perceivedangle of his/her elbow. This perceived angle was compared with theactual elbow angle for each trial for a measure of accuracy, and the SDof the perceived angle was used as a measure of precision. For eachsubject an average elbow angle error was calculated for each of the 15arm configurations.

Task 2. The final x and y positions of the fingertip dot on each trialwere taken as measurement of a subjects perceived position of his/herfingertip. This perceived position was compared with the actual

fingertip position for each trial. We then calculated the correspondingelbow angle error, forearm length error, and absolute endpoint errorbased on the known length of the forearm. Because the fingertip wasat the edge of the visual display for arm configuration shoulder90elbow 90, the fingertip dot frequently went off the screen whensubjects were responding; this configuration was therefore droppedfrom this task, leaving 14 configurations.

Task 3. As in Task 1, the angular difference between the displayedforearm line and the actual elbow angle (with the same sign conven-tion as that of Task 1) provided a measure of accuracy for each trial,whereas the SD of the perceived angle provided a measure ofprecision. Because the target forearm line was along the edge of thevisual display for arm configuration shoulder 90elbow 90, subjectsfrequently rotated their arms off the screen; this configuration wastherefore dropped for this task, leaving 14 configurations.

Stepwise regression was used for each subject performing each taskto assess which variables best accounted for variability in averageelbow angle errors (i.e., accuracy) and SD of elbow angle estimates(i.e., precision). Included variables were actual elbow angle, shoulderangle, change in elbow angle between the previous trial and thecurrent trial, and distance from fingertip to shoulder. Stepwise regres-sion with the same predictors was also performed for individualsubjects for absolute endpoint errors on Task 2. Repeated-measuresANOVA (3 tasks 3 shoulder angles 4 elbow angles) wasperformed on group data for average errors and SD.

R E S U L T S

In the passive elbow angle task, the accuracy of one of theten subjects correlated positively with time (i.e., performancedegraded over time). This correlation, however, was small (r0.23) and performance in this subject was not related to time inthe other two tasks. No other subjects (in any task) showedsignificantly degraded performance over time. We thereforeincluded all subjects in all tasks since we are confident thatdegrading sensory signals and waning attention did not affectperformance.

Elbow angle accuracyAcross tasks. We found no difference in elbow angle accu-

racy across the three tasksin all tasks average group accuracywas 2 (Fig. 3A). A repeated-measures ANOVA for elbowangle estimate error (3 tasks 3 shoulder angles 4 elbowangles) found no significant effect of task (P 0.66; passiveelbow angle task average error: 0.63 1.85 SE; passivefingertip task: 1.81 2.61; active elbow angle task: 0.54 3.39) or shoulder angle (P 0.09). The repeated-measures

FIG. 2. Angle definitions.

FIG. 3. Group performance across tasks. A: therewere no differences in group accuracy across tasks.B: compared with the passive elbow task (Task 1),subjects were slightly more precise at identifyingthe angle of their elbow in the fingertip position task(Task 2, *P 0.046, planned comparison) andstrongly more precise in the active elbow task (Task3, **P 0.004). Error bars represent SEs.

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ANOVA, however, did find a significant effect of actual elbowangle (P 0.01). This suggests that subjects current elbowangles influence their accuracy, which was further investigatedfor each task.

Within tasks. Within each task a pattern in accuracy wasobserved across joint space: as elbow angles approached thelimits of their range, estimates were biased toward the respec-

tive limit. An example of this behavior is demonstrated in Fig. 4A,which shows an individual subjects responses to two extremeelbow angles in the passive elbow angle task. Figure 4B showsaverage group accuracy for each configuration. Subjects weremost accurate at intermediate elbow angles (60). As the elbowbecame more extended (3045) estimates were biased towardbeing overly extended, and as the elbow became more flexed

FIG. 4. Accuracy across joint space. A: raw traces from a subject in Task 1. Black lines represent average actual arm position (for the particular configuration),blue lines represent perceived positions on individual trials, and red lines represent average perceived position. When the elbow was flexed (left), the subjectperceived the angle as even more flexed, and when the elbow was extended (right), the subject perceived the angle as even more extended. B: the bar graphsshow group errors for each arm configuration tested, for each task (error bars represent SEs). Consistent across shoulder angles, elbow angle estimates wereoverextended as the elbow became more extended and overflexed as the elbow became more flexed. This pattern was consistent across the 3 tasks. C: the heatplots show average group errors across joint space interpolated from the tested configurations. Hotter colors represent flexion errors and colder colors representextension errors. Elbow estimate biases are dependent on elbow angle (i.e., colors change along the y-axis) but not shoulder angle or task.

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(7590) estimates were biased toward being overly flexed.Interpolating between the tested arm configurations, the heatplots in Fig. 4C demonstrate the global pattern in biases acrossjoint space: biases strongly depend on elbow angle but notshoulder angle or task [there was a significant relationshipbetween elbow angle estimates and shoulder angle for only the60 vs. 90 comparison (post hoc Fishers least significantdifference, P 0.03; see Fig. 4B)].

For each subject four variables were entered as predictors ofelbow angle estimate errors in stepwise multiple regressionsfor each task: actual elbow angle, shoulder angle, change inelbow angle between previous and current trials (i.e., move-ment amount and direction), and distance from fingertip toshoulder. Across-subject averages for each task reveal thatthese variables were significant predictors of elbow angleestimate error (passive elbow angle task average: R2 0.53;passive fingertip task: R2 0.39; active elbow angle task:R2 0.36). In all tasks, actual elbow angle was the bestpredictor of errors: across tasks it was the strongest predictor oferror in the regression for 16/28 subjects and a significantcontributor to the regression for 20/28 subjects. No othervariable contributed as strongly or as frequently to the regres-sion (shoulder angle: strongest predictor for 1/28 subjects anda significant contributor for 9/28 subjects; change in elbowangle: strongest predictor for 4/28 and a significant contributorfor 11/28; distance from fingertip to shoulder: strongest pre-dictor for 6/28 and a significant contributor for 9/28). Of thetested variables, actual elbow angle has the strongest influenceon the accuracy of elbow angle estimates, with estimatesbiased toward the extremes.

Elbow angle precisionAcross tasks. On average, subjects were able to precisely

identify their elbow angle: across-task precision averages were5.5 (Fig. 3B). Although our direct elbow angle matchingtasks differ from previous elbow angle tasks, which had sub-jects match their two elbow angles, our precision values arewithin the range previously reported (3 to 9, single elbowvalues derived by dividing two-elbow estimates by the squareroot of two) (Clark et al. 1995; Darling 1991; Soechting 1982;Soechting and Ross 1984).

Our data show, however, that precision depends on task; arepeated-measures ANOVA for SD of elbow angle estimates(3 tasks 3 shoulder angles 4 elbow angles) found asignificant effect of task (P 0.01). Figure 3B shows thatsubjects were the least precise at identifying their elbow anglewhen their arms were moved passively and they reported theirangle with the visual display (passive elbow angle task).Subjects were more precise at identifying elbow angle whentheir arms were passively moved into configurations but theywere required to identify their fingertip position (passive fin-gertip task) or actively rotate their forearms about the elbow tomatch a particularly displayed angle (active elbow angle task).Averaging over all arm configurations, the group SD of elbowangle estimates in the passive elbow angle task was 5.61 0.49 SE, which is higher than that in the passive fingertip task(average 4.19 0.48; planned comparison P 0.046) and theactive elbow angle task (average 3.79 0.26; planned com-parison P 0.004). There was no significant difference be-tween group SDs of elbow angle estimates in the passive

fingertip task and the active elbow angle task (planned com-parison test P 0.51). Across our three conditions, elbowangle precision was worst in the passive elbow angle task.

Within tasks. There were no significant within-task differ-ences in precision across joint space (repeated-measuresANOVA: effect of shoulder angle; P 0.32, effect of elbowangle, P 0.86). For each subject, actual elbow angle, shoul-der angle, prior movement direction and amount, and distancefrom fingertip to shoulder were tested as predictors of elbowangle judgment precision. No parameters significantly pre-dicted precision of elbow angle estimates in any task. Onaverage, explained variance was low (passive elbow angle taskaverage: R2 0.07; passive fingertip task: R2 0.11; andactive elbow angle task: R2 0.16). Although we cannotdefinitively rule out the possibility that an effect was misseddue to a low number of repetitions per configuration, given thelow average explained variance it appears unlikely that elbowangle precision is dependent on arm configuration.

Fingertip task

Endpoint errors. Task 2 was the only task that involvedlocalizing the fingertip. Subjects average absolute distancebetween their perceived fingertip endpoints and the actualpositions of their index fingertip (i.e., fingertip accuracy)ranged from 3.3 to 12.3 cm, with a group average of 8.0 1.0cm SE. Subjects average SD of perceived endpoint positions(i.e., precision) ranged from 1.3 to 3.9 cm, with a groupaverage of 2.5 0.2 cm SE.

To test whether subjects were worse at locating their finger-tip when their hand was farther from them, errors for each armconfiguration were normalized by arm length and collapsedacross subjects. There was no significant relationship betweenendpoint precision and distance from fingertip to head (r 0.14, P 0.10) or fingertip to shoulder (r 0.08, P 0.35).There was a small but significant relationship between end-point accuracy and distance from fingertip to head (r 0.14,P 0.01) and fingertip to shoulder (r 0.10, P 0.03).Accuracy, but not precision, of fingertip localization maydepend on how far the finger is from the body.

Each subject significantly underestimated the length of his/her forearm across arm configurations (P values of t-tests foreach subject were all 0.01). Length errors were normalizedby each subjects elbow-to-fingertip length and then averagedacross the group; on average, subjects underestimated theirforearm length by 11.4% of the total distance from the elbowto index fingertip (P 0.01).

D I S C U S S I O N

A number of factors influence ones sense of limb position.Here we found that elbow angle estimates are more precisewhen integrated into estimates of fingertip position or based onboth sensory and motor information. These results imply thatthe CNS integrates different information in different tasks toestimate limb position. Across all tasks, we found that elbowangle estimates are biased toward extreme positions, suggest-ing that peripheral signals affect certain aspects of propriocep-tion systematically across tasks.

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Elbow angle estimates are biased toward extremesacross tasks

Across our three tasks we found that position estimates ofelbow angle were biased toward the elbows extremes. Thereare several possible explanations for this finding. First, forwardmodels, which predict the next state of a limb given the currentstate and a motor command (Davidson and Wolpert 2005),could bias position estimates based on movement directione.g., if the elbow is moved from extension to flexion, estimatesmay be biased in the direction of continued movement (Wol-pert et al. 1995). We found biases, however, in both of ourpassive tasks, and even within the active task the direction ofthe preceding movement was not a strong predictor of elbowangle error in seven of eight subjects. Thus forward modelscannot explain the across-task biases in elbow angle estimates.

A second potential explanation of our observed biases couldinvolve the state of muscle spindles. Within intrafusal fibers inspindles, residual cross bridges between the actin and myosinafter muscle contraction can disproportionally increase spindleactivity when the muscle is subsequently stretched; this activitycan lead to overestimations of position toward extremes (An-sems et al. 2006; Gregory et al. 1988; Proske et al. 1993).However, this hypothesis is unable to account for our resultsbecause residual spindle activity is observed only after heldmuscle contractions and in our passive tasks muscles remainedinactive throughout the 0.5-h testing session. Furthermore,change in position between trials was not a strong predictor ofelbow angle error, which demonstrates that subjects errorswere not influenced by residual activity related to their previ-ous arm configuration.

A third possibility is that proprioceptive estimates are biasedtoward frequently held positions as reported by Gritsenko et al.(2007), who proposed that the CNS uses a Bayesian inferenceprocess to estimate elbow position, taking afferent and efferentinput to form a current estimate and combining this estimatewith a kinesthetic prior based on experienced states, whichare predominantly central positions. This hypothesis couldaccount for their results in conditions under which elbowestimates were made during movements. Yet, when subjectsreported stationary postures after active movements, as theydid in our active elbow angle task, elbow angle estimates werenot biased toward central position. Instead they found biases atstatic flexed postures similar to ours, but no biases in extensiontoward overextension. We speculate that during static posturesafferent signals are more stable than when they are sampledduring movements, which may result in perception of staticposition being more greatly influenced by peripheral sensorysignals than priors.

We propose that our results are best explained on a peripherallevel by the proprioceptive contributions of joint and cutaneousreceptors. Joint receptors increase activity as joint angles approachextremes (Burgess and Clark 1969; Burke et al. 1988; Clark andBurgess 1975). Studies have found that when muscle spindles canbe activated, proprioception at the knee and finger joints is largelyunaffected by anesthetizing joint receptors (Clark et al. 1979,1985, 1986), although these studies examined the detection ofonly very small movements and differences in position withinmidrange joint angles. The effects of blocking joint receptorswhen examining proprioception closer to extreme angles remainunknown. Moreover, activity in single-joint afferents can lead to

the perception of movement, whereas for muscle spindles to affectperception there must be population activity (Macefield et al.1990). This suggests that when joint receptors are activemainlytoward extreme anglesthey have the potential to strongly influ-ence proprioceptive perception. Our data suggest that as jointsapproach extremes, joint receptors may bias perception of jointangle.

Cutaneous receptors also play a role in proprioception. Mech-anoreceptors in the hairy nonglabrous skin of the hand (Edin1992; Edin and Abbs 1991), ankle (Aimonetti et al. 2007), andthigh (Edin 2001) are activated by movements at nearby jointsthat result in skin stretch. Cells in the primary somatosensorycortex of monkeys discharge in meaningful patterns, dependingon arm movement direction and posture, and the sensitivity ofthese cortical cells to skin stretch is correlated with how well theyencode position information (Cohen et al. 1994). Experimentsalso show that different skin stretch patterns affect the perceptionof finger, elbow, and knee position and movement (Collins andProchazka 1996; Collins et al. 2005; Edin and Johansson 1995).Cutaneous afferent activity shows a predominantly linear relation-ship to skin stretch (Edin 1992, 2001; Edin and Abbs 1991),suggesting that for many cutaneous receptors activity increases asa joint approaches an extreme position (i.e., where the skin is moststretched).

By biasing the perception of joint angles toward extremes,these signals could help prevent movements beyond a jointsrange of motion. It follows from this hypothesis that subjectswould sense their elbow as more extended when their shoulderis abducted versus adducted: as the arm moves farther awayfrom the central visual workspace it becomes harder to judgeelbow angle extensions. In other words, there is a greater riskof misperceiving elbow angle when the shoulder is abducted,and thus at these shoulder angles the system should protec-tively bias estimates even more to prevent dangerous exten-sions. This could explain the only elbow angle bias we saw thatdepended on shoulder angle: for extended elbow angles, sub-jects perceived their elbow as significantly more extendedwhen the shoulder was configured at 60 versus 90 (Fig. 4B).

If these biases indeed reflect a protective mechanism, theirstrength in these experiments may appear surprising consideringthat the most extended elbow angle tested was 30 and the mostflexed was 90. However, under natural conditions individuals donot perceive their limbs to be in configurations outside a jointsrange of motion. In other words, unless illusory effects are in-duced (Craske 1977), we do not perceive our elbow as flexed tothe point of hitting us or extended to a negative angle. Theminimum and maximum angles tested in our experiments thusprovided ample room to observe biases toward extremes. Futurestudies should test joint angle perception near extremes while skinand joint receptors are anesthetized; if no bias toward extremes isobserved under these conditions then this would support theproposed role of joint and skin receptors in protectively biasingjoint angle perception.

Precision of elbow angle estimates depends ontask conditions

We found that the precision of elbow angle estimates variesacross tasks. First, we observed that elbow angle estimateswere more precise in our fingertip localization task than theywere in our passive elbow angle task, supporting the hypoth-

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esis that the CNS can more precisely estimate joint angleswhen those estimates are integrated into limb endpoint posi-tion. A previous study derived elbow angle precision fromfingertip matching data and reported elbow angle SDs in therange of 0.6 to 1.1 (van Beers et al. 1998), which is smallerthan the range we observed in our fingertip task (2.1 to 6.1).In their analysis, however, subjects elbow positions and armlengths were based on the recorded positions of the shoulder,elbow, and hand of one subject and one degree of freedom wasassumed for the shoulder, although it could move in three. Inour fingertip localization study all variables were known foreach subject, for each trial, and we controlled arm configura-tions, allowing us to directly measure elbow angle estimates.

Given our greater behavioral need to estimate hand positionrather than joint angles, the CNS may directly calculate handposition from peripheral sensory signals, whereas isolated jointangle estimates may need to be extracted from these represen-tations. Indeed, along the dorsal columnmedial lemniscalpathwaythe pathway most consider to be the primary meansby which proprioceptive information reaches the cerebral cor-tex (cf. Wall and Noordenbos 1977)most proprioceptiveneurons in the primary somatosensory cortex are tuned to armmovement direction (Prudhomme and Kalaska 1994) andactivity is related best to linear combinations of joint andsegment angles rather than to single-joint angles (Tillery et al.1996). Along the dorsal spinocerebellar tract, the other path-way by which proprioceptive information reaches the CNS,cells also encode endpoint position of limbs (Bosco et al.2000). It therefore appears that the CNS optimizes estimates oflimb endpoint positions rather than conscious estimates of jointangles, a design that likely contributes to our behavioralresults.

Subjects strategies may have also contributed to ourfinding that elbow angle estimates are more precise in thefingertip versus passive elbow angle task. After the passiveelbow angle task some subjects reported that they had beentrying to point the response line toward their fingertip. Thusdespite encouraging a focus on elbow angle rather thanfingertip position, identifying fingertip position may havebeen the goal in both tasks for some subjects. In this caseone would predict better precision in the task directly re-quiring identification of finger position versus the task re-quiring the distant pointing to finger position.

In addition to proprioceptive differences driven by whetherthe fingertip or elbow angle was reported, we also observeddifferences driven by whether the arm was actively or pas-sively moved. Many studies have found that proprioceptiveestimates are more precise when subjects report positions afteractive rather than passive movements (Adamovich et al. 1998;Brouchon and Paillard 1966; Craske and Crawshaw 1975;Gritsenko et al. 2007; Gurfinkel et al. 1985; Laufer et al. 2001;Zia et al. 2002), a trend thought to result from the additionalposition information available in active movements from ef-ference copies of motor commands and alpha-gamma motorneuron coactivation. Direct effects of motor commands onproprioception have been demonstrated in studies showing thatwhen movement of a limb is blocked, efforts to move stillresult in a perceived change in position, with the amplitude ofthe perceived position change dependent on the amount ofmovement effort (Gandevia et al. 2006; Smith et al. 2009).

There was no significant group difference between precisionon the active elbow angle task and the passive fingertip task.Since the improved precision in these tasks appears to resultfrom distinct mechanisms, these benefits could be additive.

Fingertip position estimates

In Task 2, where subjects reported the perceived x and ypositions of their fingertip, additional variables other thanelbow angle estimates could be explored. The range of oursubjects fingertip estimation precision across 14 arm config-urations was 1.3 to 3.9 cm, which is within the range previ-ously reported in other studies (Crowe et al. 1987; van Beers etal. 1996, 1998, 1999). Unlike our results, a previous studyfound a significant relationship between precision of fingertipestimates and distance between the hand and shoulder, withestimates becoming less precise the farther the finger was fromthe shoulder (van Beers et al. 1998). This study, however, hadsubjects report their perceived fingertip position by matchingthe position of their two index fingertips. The observed in-crease in variance with farther distances could therefore reflectless motor precision for larger movements rather than a de-crease in the precision of the sensory estimate. Since subjectsin our study reported the perceived position of their fingertipwithout making arm movements to that position, our measuresare more likely to reflect precision of the position estimate.While no relationship between precision and distance fromhand to shoulder was observed, it is possible that an effect wasmissed due to an insufficient number of repetitions.

Conclusion

Our results show that there are systematic biases in positionsense that are independent of task demands and are thus likelyexplained by changes in peripheral proprioceptive signals.Other aspects, like the precision of estimates, are affected bytask demands and thus likely depend on high-level integrationof sensory and motor signals. In addition to furthering ourknowledge of sensory processing, understanding the funda-mental properties of proprioception will help us appreciatewhich specific aspects of this sense are impaired in differentpatient populations and how best to go about assisting thesepopulations.

A C K N O W L E D G M E N T S

We thank all participants in this research study; S. Bensmaia, A. Okamura,S. Hsiao, and C. Connor for discussion regarding the methods and results; D.Grow for help with adapting the joystick used in Tasks 1 and 2; and N.Bhanpuri for general assistance in implementing the experimental design.

G R A N T S

This research was supported by an Autism Speaks predoctoral fellowship.

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