Nicolas Grimault

Marion David

Mathieu Lavandier

Torben Pastore

William A Yost

Willam A Yost

Sequential streaming of speech sounds based on

Keywords: numbers: 43.66.Mk, 43.55.Hy, 43.66.Ba, 43.66.Qp I

come   L'archive ouverte pluridisciplinaire

INTRODUCTION 1

David et al [START_REF] David | Room and head colouration can induce obligatory stream segrega-tion[END_REF][START_REF] David | Sequential streaming, binaural cues and lateralization[END_REF][START_REF] David | Discrimination and streaming of speech sounds based on differences in interaural and spectral cues[END_REF] have already designed several experi-2 ments that reported an effect of position cues (ITD/ILD 6 panel 1 in [START_REF] David | Discrimination and streaming of speech sounds based on differences in interaural and spectral cues[END_REF]). The experimental procedure was to de-16 tect a repetition within a stream of syllables interleaved 17 with an other stream of syllables and these experiments 18 were realized using headphones and virtual angular po-19 sitions (φ2 and φ1) using HRTF. The authors suggest 20 that this effect of position cues on stream segregation of 21 speech sounds probably contributes to the spatial release 22 from masking. In these experiments, virtual environ-23 ments were used and the position of the reference stream was fixed and always equal to 0 • (α = φ1 = 0 • in figure 25 1). Recently, Grange and Culling [START_REF] Grange | The benefit of head orientation to speech intelligibility in noise[END_REF] have suggested that 26 head orientation could benefit to speech intelligibility in noise. Consequently, the first aim of this project is to 28 test the hypothesis that head orientation (i.e. variable α 29 with fixed ∆ in figure 1, panel A) could also benefit to 30 stream segregation of speech sounds. * nicolas.grimault@cnrs.fr; Corresponding author.

In the previous experiment, the use of a virtual en- By observing the effect of α in the rotated-listener conditions, we should observed the potential effect of head orientation for sequential segregation of speech items [START_REF] Grange | The benefit of head orientation to speech intelligibility in noise[END_REF].

According to [START_REF] Grange | The benefit of head orientation to speech intelligibility in noise[END_REF], depending of the spectrum of the speech signal, the best angular difference α of the head from zero azimuth could be different than φ = φ1 + φ2-φ1 2 to better segregate speech coming from one fixed angle φ1 from speech coming from an other fixed angle φ2 (see Fig 1,panel A). Simulations are provided in the next section as an attempt to predict the best position. As the acoustical cues reaching the ears would be the same (in an anechoic room), by comparing the two conditions (rotated-sound versus rotated-listener) we should be able to evidence any effect of spatial attention for sequential segregation of speech items [START_REF] Kidd | The advantage of knowing where to listen[END_REF]. In fact, a larger effect of segregation in the rotated-listener condition than in the rotated-sound condition would be in favor of an effect of spatial attention on stream segregation (i.e. the ability to focus on a particular fixed spatial position to segregate sounds, independently of acoustical cues related to position).

III. SIMULATIONS 92

Previous studies [START_REF] Grange | The benefit of head orientation to speech intelligibility in noise[END_REF][START_REF] Deshpande | Blind localization and segregation of two sources including a binaural head 389 model[END_REF] used binaural simulations to pre-

C. Methods

The same experimental material and protocol than [START_REF] David | Discrimination and streaming of speech sounds based on differences in interaural and spectral cues[END_REF] have been used. Only voluntary segregation is estimated 5 seconds in average. Overall, for each listener, the duration of the experiment is around three hours separated in two sessions (8 and 6 blocks respectively to reach 14 blocks). The loudspeaker at 0 • is visually marked with a red dot to be easy to spot in the rotated-listener condition in order to facilitate the spatial attention.

D. Results

The performances are plotted in Figure 5 function of the position of the loudspeaker related to the head.

In condition rotated-listener, (α-φ1) in the left ear is used for the abscissa (Table I). In condition rotatedsound, (φ1-α) in the right ear is used for the abscissa (Table I). As such, the only remaining acoustic difference across conditions rotated-listener and rotated-sound is the ear of presentation which should made the two conditions comparable for young normal hearing listeners. For each listener, as in the preliminary experiment, d' computation is based on 42 repetitions in each condition. The syllable was repeated in about half the repetitions so, the hit rate is computed as the number of response repeated when there was a repeat in the sequence and the false alarm rate is computed as the number of responses repeated when there was no repeat in the sequence. A repeated measure ANOVA and a Bayesian repeated measure ANOVA has been ap-303 plied to the data using the JASP software [9][START_REF] Dienes | Using Bayes to get the most out of 399 non-significant results[END_REF][11]

3

  and perceived position) to segregate sequences of noise 4 bursts first and, more recently, sequences of conso-5 nant/vowel syllables. For example, to quickly summarize 6 the most recent data [3], David and colleagues have pre-7 sented sequences of phonemes, Tha/Sa/Fi/Shu/... with 8 the bold syllables coming from azimuth φ1 and the other 9 syllables coming from a different azimuth φ2. These se-10 quences can be segregated based only on the azimuth 11 difference ∆ = (φ2 -φ1). For voluntary segregation in 12 the azimuthal plan, performances start around d'=1.8 13 and grow up to d'=3 for ∆=30 • . A ∆ around 8 • leads to 14 intermediate performances (cf figure 5, panel 1 and figure 15

45

  FIG.1. schematic relative position of the head (α) and of the two loudspeakers (φ1, φ2) providing alternating sequences of syllables. By convention, the sequence that starts first and that may contain a repetition always comes from φ1. Panel A: condition Rotated-listener in experience 1. Panel B: condition Rotated-sound in experience 1.

93Figure 3 FIG. 2 .FIG. 4 .

 324 Figure3show the difference between ILDs (panel B)

The results are plotted on figure 4 .

 4 and the listener is instructed to detect a repetition in the sequence of syllables that starts first and that always comes from the 0 • loudspeaker. As such, spatial attention is maximized. An interleaved sequence of syllables is played from 0 • , 15 • , or 30 • . All syllables have been equalized in RMS level and are played at about 65 dB SPL. All three loudspeakers have been calibrated in the room to deliver the same SPL level at head position using a class A sound level meter (Larson Davis 800B). The listener is always facing 0 • and three conditions of angular distance between sources have then been tested (0 • , 15 • , and 30 • ). Each sound sequence duration is 5 sec-179 onds in average. The three angle conditions are repeated 180 three times in a block in random order. Each listener 181 is presented with 14 blocks to reach the same number 182 of repetitions than in David et al [3] and thus compara-183 ble performance levels in terms of d'. Overall, for each 184 listener, the duration of the experiment is less than 15 In average, for these 188 listeners, d' is increasing when increasing the angular dis-189 tance between sources from 0 • to 30 • . The performances 190 start around d'=0.33 and grow up to d'=1.89 for ∆=30

218FIG. 5 .

 5 FIG. 5. Averaged performances with a couple of 15 • distant sources located at different angular positions (abscissa) related to the head. The bars indicate the standard error within subjects. The results from condition rotated-listener are plotted with a continuous line. The results from condition rotated-sound are plotted with a dotted line.

318FIG. 6 .

 6 FIG.6. Averaged performances across all participants and conditions for each block of presentation. The bars indicate the standard error within subjects.

FIG. 7 .

 7 FIG. 7. Averaged performances after training with a couple of 15 • distant sources located at different angular positions (abscissa) related to the head. The bars indicate the standard error within subjects. The results from condition rotated-listener are plotted with a continuous line. The results from condition rotated-sound are plotted with a dotted line.

TABLE I .

 I Values of α, φ1 and φ2 in the rotated-listener and the rotated-sound experimental conditions. The last colums provide the apparent positions of LP 1 and LP 2 in right and left ears. By convention, a positive angle value indicates that the sound is on the same side than the ear.

			Chair LP 1 LP 2 Right ear Left ear
			α	φ1 φ2 φ1-α φ2-α α-φ1 α-φ2
			0	0	15	0	15	0	-15
			5	0	15	-5	10	5	-10
			7	0	15	-7	8	7	-8
			8	0	15	-8	7	8	-7
			10	0	15 -10	5	10	-5
		Rotated	15	0	15 -15	0	15	0
		listener	20	0	15 -20 -5	20	5
			30	0	15 -30 -15 30 15
			45	0	15 -45 -30 45 30
			60	0	15 -60 -45 60 45
			75	0	15 -75 -60 75 60
			90	0	15 -90 -75 90 75
			0	0	-15	0	-15	0	15
			0	15	0	15	0	-15	0
		Rotated Sound	0 0 0	30 45 60	15 30 45	30 15 -30 -15 45 30 -45 -30 60 45 -60 -45
			0	75	60	75 60 -75 -60
			0	90	75	90 75 -90 -75
		start around d'=1,8 and grow up to d'=3 for ∆=30 • . In
	213
	214		V.	EXPERIMENT 1
								192
	215			A. Subjects		193	a significant effect of the angular distance [F(2,8)=41.51,
								p<0.001].
	216	10 young normal hearing and native (4 males and 6
	217	females) English speakers from 18 to 32 (mean age 23.3)
		participated to this experiment. One was research as-

• . 191 A reapeted measure ANOVA applied to the data reveals 194 E. Discussion 195 The results of this preliminary experiment evidence 196 that, when using real sound sources (loudspeakers) in a 197 real room, segregation still occurs for sound sources sep-198 aration equal or over 15 • . In the previous study of David 199 et al [3], when using virtual reality, the performances 200 201 the current experiment, the performances are lower in 202 average but the size of the effect is rougthly similar when 203 increasing ∆ from 0 • to 30 • . This discrepancy can be 204 probably explained by a stimulation with loudspeakers 205 in a real room instead of headphones and a simulated 206 room. Finally, the average performances at 15 • equal 207 d'=1.44 are higher than those at 0 • and lower than those 208 at 30 • . This indicate that the performances at 15 • are 209 probably not saturated. In the next experiment, at 15 • , 210 we expect lower performances when reduced spatial at-211 tention is available and slightly better performances when 212 moving the listener up to the optimal angular position.

  . Nei-

	304
	305	ther the effect of the angular position [F(6,48)=0,87,
	306	p=0,52, BF 01 =10,70] nor the effect of the rotated con-
		dition [F(1,8)=3,14, p=0,11, BF 01 =1.29] are significant.
	308
	309	cant either [F(6,48)=1,44, p=0,22, BF 01 =13.54]. More-
	310	over, based on the simulation, to check for a potential
	311	effect of head position, a dedicated Bayesian repeated
	312	maesure ANOVA has been applied to the data in the
	313	rotated-listener condition restricted to angles from 0 • to
	314	15 • . This analysis reveals not signicicant effet of head
	315	position [F(5,40)=0,17, p=0,97, BF 01 =12.38].
	316	E. Discussion
	317	Based on the preliminary experiment, the expected size
		of the effects is small (around 0.5 variation in d'). This

307

The interaction betwen there two factors is not signifi-

ACKNOWLEDGMENTS

This research has been conducted in the framework of the research network CeLyA (Lyon Acoustics) supported by ANR (ANR-10-LABX-60). This research also benefits from a grant allocated to Bill Yost. The autors also thank Kathryn Pulling and Vivie Truong who have been very helpful during the experimental phase and Andrew Oxenham for helpful comments and discussions about this project.


Gerome Manson
email: gerome.manson@utoronto.ca

Jean Blouin

Animesh S Kumawat

Valentin A Crainic

Luc Tremblay

Gerome A Manson Ab

Rapid online corrections for upper limb reaches to perturbed somatosensory targets: Evidence for non-visual sensorimotor transformation processes

Keywords: reaching, online-control, somatosensory targets, sensorimotor transformations, double-step

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

When performing movements to visual targets, the motor system can successfully correct to changes in target location (Day and Lyon 2000;[START_REF] Johnson | Action and awareness in pointing tasks[END_REF][START_REF] Sarlegna | The influence of visual target information on the online control of movements[END_REF]Smeets et al. 1990). These corrections can occur when perturbations are not consciously detected or when there is no vision of the reaching limb [START_REF] Goodale | Large adjustments in visually guided reaching do not depend on vision of the hand or perception of target displacement[END_REF][START_REF] Heath | Role of limb and target vision in the online control of memory-guided reaches[END_REF][START_REF] Komilis | Error processing in pointing at randomly feedbackinduced double-step stimuli[END_REF][START_REF] Pélisson | Visual control of reaching movements without vision of the limb. II. Evidence of fast unconscious processes correcting the trajectory of the hand to the final position of a double-step stimulus[END_REF][START_REF] Reichenbach | Seeing the hand while reaching speeds up online responses to a sudden change in target position[END_REF][START_REF] Saunders | Humans use continuous visual feedback from the hand to control fast reaching movements[END_REF].

When the reaching limb is not visible, somatosensory information about the limb's position and visual information about the new target location can be used to perform trajectory amendments.

However, because movements to visual targets are hypothesized to be planned in a visual reference frame [START_REF] Ambrosini | Behavioral investigation on the frames of reference involved in visuomotor transformations during peripheral arm reaching[END_REF]Buneo and Andersen 2006;[START_REF] Thompson | Allocentrically implied target locations are updated in an eye-centred reference frame[END_REF][START_REF] Thompson | Visual targets aren't irreversibly converted to motor coordinates: eye-centered updating of visuospatial memory in online reach control[END_REF], previous studies have argued that the reaching limb's position must first be converted into extrinsic visual coordinates prior to the initiation of corrections [START_REF] Prablanc | Automatic control during hand reaching at undetected twodimensional target displacements[END_REF][START_REF] Reichenbach | Seeing the hand while reaching speeds up online responses to a sudden change in target position[END_REF]. It is unknown if such online sensorimotor transformation processes also occur for reaches to non-visual targets. The purpose of the present study was to investigate the sensorimotor transformation processes involved in the online control of reaching movements to somatosensory targets performed without vision of the reaching limb.

Online-visuomotor transformation processes for movements to visual targets Previous studies have found that rapid reaches to perturbed visual targets are less accurate and have longer correction latencies when there is no vision of the reaching limb compared to when there is vision of the limb. For example, [START_REF] Komilis | Error processing in pointing at randomly feedbackinduced double-step stimuli[END_REF] conducted a study wherein participants reached to visual targets that were perturbed either at movement onset or at peak velocity. Movements were performed with or without vision of the reaching limb and endpoint accuracy was assessed. Similar to previous studies (e.g. [START_REF] Goodale | Large adjustments in visually guided reaching do not depend on vision of the hand or perception of target displacement[END_REF]), the authors found that participants were able to achieve very accurate endpoints in response to target perturbations at movement onset, regardless of whether their limb was visible or not. However, for perturbations at peak velocity, movements that were performed without vision of the limb were slower and slightly less accurate than movements performed with vision of the limb (see also [START_REF] Heath | Role of limb and target vision in the online control of memory-guided reaches[END_REF]. [START_REF] Reichenbach | Seeing the hand while reaching speeds up online responses to a sudden change in target position[END_REF] also noted that the amount of time required to initiate a correction (i.e. the correction latency) was longer (+10 ms based on EMG and +30 ms based on limb kinematics) when participants performed reaches to visual targets that were perturbed shortly after movement onset without vision of their limb compared to when they performed reaches with the vision of their limb. It was reasoned that, when the limb is not visible, corrections are programmed based on the updated visual target location and visual estimates of the current limb position derived using efferent information and somatosensory inputs from the reaching limb (see also: [START_REF] Bard | Deafferentation and pointing with visual double-step perturbations[END_REF]. Reichenbach and colleagues concluded that when the limb is not visible additional time is required for the transformation of somatosensory information about the limb position to a visual estimate prior to the programming of the correction. Together, the results of these studies support the idea that a common visual reference frame is used for the online control of actions irrespective of the sensory modality used to encode hand position (see also: Buneo and Andersen 2006;Buneo et al. 2008).

Sensorimotor transformation processes for movements to somatosensory targets: planning and online control

The idea that a common visual reference frame is used for the online control of goaldirected actions is based primarily on studies involving movements to visual targets. Very little is known about online sensorimotor control processes that occur during reaching movements to somatosensory targets. Research on movement planning, however, has revealed that the reference frame used to plan movements to somatosensory targets could be visual [START_REF] Blangero | Influence of gaze direction on pointing to unseen proprioceptive targets[END_REF][START_REF] Jones | Memory for proprioceptive and multisensory targets is partially coded relative to gaze[END_REF][START_REF] Pouget | Multisensory spatial representations in eyecentered coordinates for reaching[END_REF]) or non-visual [START_REF] Bernier | Evidence for distinct, differentially adaptable sensorimotor transformations for reaches to visual and proprioceptive targets[END_REF][START_REF] Bernier | Direct evidence for cortical suppression of somatosensory afferents during visuomotor adaptation[END_REF]Blouin et al. 2014;[START_REF] Mcguire | Sensory transformations and the use of multiple reference frames for reach planning[END_REF][START_REF] Sarlegna | The influence of target sensory modality on motor planning may reflect errors in sensori-motor transformations[END_REF]Sober and Sabes 2005).

Both [START_REF] Pouget | Multisensory spatial representations in eyecentered coordinates for reaching[END_REF] and [START_REF] Jones | Memory for proprioceptive and multisensory targets is partially coded relative to gaze[END_REF] found that when participants shifted their gaze position prior to reaching to somatosensory targets, participants' endpoint errors were biased in the opposite direction of the gaze-shift (see also [START_REF] Blangero | Influence of gaze direction on pointing to unseen proprioceptive targets[END_REF]. The effects of a change in gaze position on movement endpoint bias was similar to the effects observed when participants performed movements to visual targets (see: Bock, 1986;[START_REF] Henriques | Gaze-centered remapping of remembered visual space in an open-loop pointing task[END_REF]. Thus, these studies concluded that movements to somatosensory targets were also planned in a visual coordinate system (see also [START_REF] Mueller | Effector movement triggers gaze-dependent spatial coding of tactile and proprioceptive-tactile reach targets[END_REF].

In contrast, other studies have found that movements to somatosensory targets can be planned in a non-visual coordinate system. For example, [START_REF] Sarlegna | The influence of target sensory modality on motor planning may reflect errors in sensori-motor transformations[END_REF] found that altering visual information about the initial hand position had no effect on endpoint errors when participants reached to somatosensory targets (see also : Sober and Sabes, 2005). Because both the limb and the target can be represented in somatosensory coordinates (see [START_REF] Battaglia-Mayer | Multiple levels of representation of reaching in the parieto-frontal network[END_REF], and because movements were unaffected by visual perturbations, the authors concluded that the computation of the movement vector was performed in a non-visual reference frame when reaching to somatosensory targets. Other studies have also suggested that using nonvisual sensorimotor transformations for movement planning to somatosensory targets may avoid errors which are associated with the conversion from a somatosensory coordinate system to a visual coordinate system [START_REF] Sarlegna | The influence of target sensory modality on motor planning may reflect errors in sensori-motor transformations[END_REF].

Although sensorimotor transformations in both visual and non-visual coordinate systems have been found for the planning of movements to somatosensory targets, the type of sensorimotor transformation employed for online trajectory amendments remains unclear. In the present study, the latency and magnitude of online trajectory corrections to perturbed visual and somatosensory targets were assessed to investigate the sensorimotor transformation processes used for the online control of an unseen reaching limb. It was hypothesized that if a visual reference frame is employed, longer correction latencies and smaller corrections should be observed for reaches to somatosensory targets as compared to reaches to visual targets. This is because somatosensory cues from both the perturbed somatosensory target position and reaching limb would have to be converted into visual coordinates prior to the initiation of corrections. For movements to visual targets, only the reaching limb position would require a conversion into a visual coordinate system. In contrast, if corrections are programmed in a non-visual reference frame then we would expect faster and more accurate corrections in response to somatosensory target perturbations compared to visual target perturbations. Corrections in a non-visual reference frame would be programmed using the new target and limb position in somatosensory coordinates, without the need for a reference frame transformation.

Methods

Participants

Fourteen participants (10 women, aged 20 -33 years M = 25, SD = 4) took part in the experiment. All participants were right-handed (assessed by the Edinburgh handedness questionnaire, adapted from Oldfield 1971), self-declared neurologically healthy, and had normal or corrected-to-normal vision.

Written informed consent was obtained prior to the experimental protocol and the University of Toronto's Office of Research Ethics approved all procedures. Including the informed consent, breaks, snacks, and debriefing, the experiment lasted approximately 3 hours and participants were compensated with $20 CAD.

Apparatus

A drawn representation of the experimental setup is shown in Fig. 1. The experiment was conducted in a completely dark room, where participants were seated comfortably on a kneeling chair facing a protective cage made of a clear polymer. Participants placed their head on a headrest that was positioned on the outside of the cage and interacted with the experimental materials located inside the cage through a window (80 cm high). A small microphone (FBA_4330948551, Phantom YoYo) that was used for the vocal response time protocol (see below) was placed at the bottom left of the headrest position.

Inside the cage was a Selectively Compliant Assembly Robot Arm (SCARA; Epson E2L853, Seiko Epson Corp.) that was used to position both the visual and the somatosensory target stimuli (see Manson et al. 2014 for details on the robotic device). Located directly below the robot was a table with a custom-built aiming apparatus placed on its surface. Participants saw all the visible stimuli through the clear ploymer cage.

The aiming apparatus included a black tinted Plexiglas aiming surface (60 cm wide by 45 cm long by 0.5 cm thick) mounted 12 cm above a wooden base. Underneath the aiming surface, there was a textured home position (2 cm by 2 cm). On the top of the aiming surface was a blue light emitting diode (LED, 2 mm diameter) that served as the gaze fixation point. The LED was aligned with the participant's midline and was located ~65 cm from the participant's eye position.

In the robot's neutral position (i.e., for no-perturbation trials or at the start of the perturbed trials), the custom end-effector attached to the robot arm was positioned 0.5 cm above the aiming surface and 35 cm to the left of the home position. In the somatosensory target condition, participants grasped the robot's end effector with their left hand and they were instructed to depress an attached micro-switch (B3F-1100, OMRON) with their index finger. The micro-switch served as both a reference for the somatosensory target location and a safety mechanism that would immediately shut off the robot's motors if the button was released (note: no participants released the switch during the study). In the visual target condition, a green LED (~6mm diameter) was attached to the robot's end effector at the same position as the fingertip in the somatosensory target condition (i.e., at the microswitch location). The participant's reaching fingertip and the robot's end effector were both affixed with an infrared light emitting diode (IRED). An Optotrak Certus (Northern Digital Inc.) motion tracking system recorded the position of both IREDs at a sampling rate of 200 Hz. A custom MATLAB (The Mathworks Inc.) program was used to gather data from the Optotrak and microphone, as well as to send outputs to the aiming console and the robotic effector. A piezoelectric buzzer (SC628, Mallory Sonalert Products Inc.) was used to provide brief auditory cues to the participants. A robotic device was used to deliver target perturbations in both somatosensory (left finger) and visual (LED) target conditions. Participants performed reaching movements from the home position located on their left to the target position located close to their midline.

Procedure

Participants completed the experimental tasks over two sessions, one for each target modality. The presentation of target modality was counterbalanced across participants, and the time between sessions was between 5 and 14 days (M = 10.5 days) for most participants.

Each session consisted of two protocols: a vocal response time protocol, and a reaching protocol. In the first protocol, participants were asked to make a vocal response to the perturbation of the target stimulus. In the second protocol, participants were asked to reach to the target stimulus as accurately as possible within a movement time bandwidth (i.e., 450 -600 ms from movement start to movement end). Participants were given the instructions for the reaching protocol only once they had completed the vocal response time protocol.

Vocal Response Time Protocol:

The goal of the vocal response time protocol was to examine whether the modality of the target alters the time taken to detect the onset of target motion. For the somatosensory target session, participants responded to perturbations of their left limb placed on the robot's end effector. For the visual target session, participants responded to the perturbation of the target LED placed on the same position. During both sessions, participants placed their right index finger on the home position located underneath the aiming surface (see Fig. 1a and1b).

At the beginning of each trial, the fixation light was turned on. After the experimenter verified that participants were on the home position, the trial was started. After a random foreperiod, the robot perturbed the visual or somatosensory target either 3 cm toward or away from the participants, or 3 cm to the left of them (i.e. catch trials). The duration (M = 200 ms, SD = 3.8) and velocity of the target perturbations were the same in both the vocal response time protocol and the reaching protocol. Participants were instructed to verbally respond with "Yo!" as soon as the target stimulus moved either toward or away from their body, and to not respond when the target moved to their left (i.e., "catch trials"). Note that participants did react on 0.02% of the catch trials, however none of the participants reacted to more than 1 catch trial. Following the recording of the vocal response, the fixation light turned off and the robot returned to the neutral position. Vocal response times were computed as the time difference between the onset of target displacement (i.e., when the velocity of the robot surpassed 30 mm/s) and the response time recorded by the microphone.

Reaching Protocol:

After participants completed the vocal response time protocol, the experimenter explained the reaching protocol and trial procedures. The reaching protocol consisted of 4 phases: familiarization, perception of target position in the pre-test, reaching trials, and perception of target position in the post-test. All 4 phases were performed for both target modalities.

There were two kinds of reaching trials: no-perturbation and perturbation reaching trials.

In the no-perturbation trials, participants performed movements from the home position to the neutral target position within a movement time bandwidth of 450 -600 ms. Each trial began with the illumination of the fixation LED. In the visual target session, the target LED was also illuminated at the same time as the fixation LED. Four hundred milliseconds after the fixation LED was turned on, an auditory go signal (50 ms beep) cued participants to begin their reaching movement. The time instant at which finger velocity raised above or fell below 30 mm/s for more than 10 ms marked the movement start and end, respectively. Once the reaching movement was completed, participants received auditory feedback about their movement time and the fixation LED was turned off. The auditory feedback indicated to the participants whether their movement time was within the time bandwidth. Participants were presented with two short (50 ms) beeps if their movement time was within the bandwidth; one long (100 ms) beep if their movement time was shorter than the lower limit of the bandwidth; or three short (50 ms) beeps if their movement time was longer than the upper limit of the bandwidth. The beeps also served as a signal to move back to the home position to begin the next trial.

In the perturbation trials, the target was shifted 3 cm away from or towards the participant either 300 ms before the go signal, or ~100 ms or ~200 ms after the movement onset. These perturbation signal times ultimately occurred at 450 ms (SD = 73 ms) before, or 93 ms (SD = 4 ms) or 190 ms (SD = 4 ms) after movement onset. (see Fig. 2 for velocity profiles of hand and robot movements in each perturbation condition). The change of target position required a change in movement direction (with consideration that movements are planned as a vector defined in terms of amplitude and direction; Buneo and Andersen 2006;Dadarlat et al. 2015[START_REF] Desmurget | From eye to hand: planning goaldirected movements[END_REF]. Offline analyses showed that the 100 ms perturbation time occurred prior to peak velocity, at a time during which visual information has been found to be important for online corrections [START_REF] Kennedy | Online vision as a function of real-time limb velocity: Another case for optimal windows[END_REF][START_REF] Tremblay | An optimal velocity for online limb-target regulation processes?[END_REF]. The 200 ms time roughly corresponded with the peak velocity of the aiming movements. This time may be too late to use visual feedback effectively (e.g. [START_REF] Kennedy | Online vision as a function of real-time limb velocity: Another case for optimal windows[END_REF] but may still be viable for the use of somatosensory information [START_REF] Goodman | Amending ongoing upper-limb Reaches: Visual and proprioceptive contributions?[END_REF][START_REF] Redon | Proprioceptive control of goal-directed movements in man, studied by means of vibratory muscle tendon stimulation[END_REF]. Perturbations before movement onset were included as a control condition to compare the corrections that resulted from planning and online control processes between somatosensory and visual conditions.

Participants were asked to reach to the position of the target stimulus (e.g., the surface area of the finger on the button, or target LED) as if projected onto the underneath of the aiming surface. When performing movements to visual targets, participants placed their non-reaching limb on the side of the kneeling chair. To perform the reaching task, participants performed an "underhanded" reaching movements, primarily with muscles in their shoulder joints. Participants were asked to perform their movements without sliding their finger on the aiming surface. Their wrists remained supinated throughout the trajectory. To discourage wrist movements, participants also wore a wrist orthotic (Champion-C218, Champion Health and Sports Supports, Cincinnati, USA). Participants performed 180 reaching trials for each target modality (360 trials total). These trials consisted of 90 no-perturbation trials and 90 perturbation trials (15 trials in each of the 6 perturbation conditions). None of the perturbation conditions were repeated more than twice consecutively.

Familiarization

After receiving the instructions for the reaching task, participants performed 30 trials to familiarize themselves with the experimental task, the auditory feedback, and the movement time bandwidth. Participants were presented with 18 no-perturbation trials followed by 12 perturbation trials (2 trials in every perturbation condition).

Perception of Target Position Pre-and Post-Test

To record the perceptions of the target position, participants were asked to reach to where they perceived each target position was as if it were projected onto the underneath surface of the Plexiglas. Participants first reached to the centre target and adjusted their index finger until they felt it matched the target's position. Participants then verbally indicated when their hand was on the target position, and this position was recorded. Once the reaching hand was returned to the home position, the robotic effector was moved to the 'away' target position and the procedure was repeated. Finally, the entire sequence was repeated for the 'toward' target position.

Participants' perceived target locations were recorded twice during each session: once after the completion of the familiarization trials (i.e. pre-test) and again after the completion of the reaching trials (i.e. post-test). The perceived target locations recorded during these trials were used for endpoint error calculations (see constant and variable errors in the results sections).

Data Analysis

Vocal Response Time Protocol All vocal response time trials, target perception trials, and trajectory data were processed and analyzed using a custom MATLAB program. For vocal response time data, trials with a response time more than 3 standard deviations higher or lower than the participant's mean were removed. These accounted for 5.2% of all vocal response trials. Vocal response data were submitted to a 2-perturbation direction (away, toward) by 2-target modality (somatosensory, visual) repeated-measures ANOVA.

Reaching protocol

Trials with movement times, reaction times, or endpoint errors that were more than 3 standard deviations from the mean were excluded from the analyses. This resulted in the exclusion of 4.5% of all reaching trials. The main dependent variables for this experiment were constant error, variable error, correction magnitude, and the latency of online corrections.

Constant and Variable Errors

Constant error was calculated as the bias in endpoint position relative to the participant's averaged perceived target position (calculated using the pre-and post-target perception trials).

Constant error was computed for both the amplitude and the direction axes (hereafter referred to as amplitude constant errors and direction constant errors, respectively). Variable errors were computed by calculating the standard deviation of these constant errors (hereafter referred to as amplitude variable error and direction variable error).

For amplitude constant errors, positive values indicated an overshoot relative to the target location whereas negative values indicated an undershoot relative to the target location.

Similarly, for direction constant errors, positive values represent an over-correction relative to the new target's position, whereas negative values represent an under-correction relative to the new target's position. Amplitude and direction constant and variable errors were submitted to separate 2-target modality (somatosensory, visual) by 2-perturbation direction (away from the body and toward the body) by 3-perturbation time (before, 100 ms, 200 ms) repeated measures ANOVAs.

Correction Magnitude

Correction magnitude was calculated as the average of the absolute difference between the average end position of the perturbation trials (e.g. before, 100 ms, and 200 ms) and the average end position of the no-perturbation trials. This measure was only computed for the direction axis (i.e. axis of the perturbations). It is worth noting that there were no differences in overall endpoint variability between somatosensory and visual target conditions in the noperturbation conditions t(13) = -1.35 p = 0.20 (see results section: Comparison of noperturbation trials, and Table 2. Correction magnitudes were submitted to a 2-target modality (somatosensory, visual) by 2-perturbation direction (away, toward) by 3-perturbation time (before, 100 ms, and 200 ms) repeated measures ANOVA.

Latency of Online Corrections

The method of determining the latency of online corrections was adapted from Oostwoud [START_REF] Wijdenes | Comparing online adjustments to distance and direction in fast pointing movements[END_REF]. Using this method, correction latency was computed based on a linear extrapolation of the differences in the average acceleration profiles in the movement direction axis (axis of the perturbation) between no-perturbation and perturbation trials (see also [START_REF] Veerman | The latency for correcting a movement depends on the visual attribute that defines the target[END_REF]. When tested on simulated data, this extrapolation method was deemed to be an accurate and precise method for detecting correction latencies (Oostwoud [START_REF] Wijdenes | Analysis of methods to determine the latency of online movement adjustments[END_REF].

Accelerations profiles in the movement direction axis were computed by a double differentiation of the displacement data obtained from sampling the finger IRED and subsequently low pass-filtering these time-series with a second-order recursive bidirectional Butterworth filter at 50Hz. For each participant, the difference in the average acceleration profiles were computed between the no-perturbation and the 100 ms perturbation trials, and the no-perturbation and the 200 ms perturbation trials. These difference profiles were then used to compute the correction latencies.

The no-perturbation trials used for the computation of the average acceleration profile were selected based on the distribution of movement times used to compute the respective profile in the perturbation condition. The number of control trials used to compute the average trajectories thus varied for each condition within each participant. The number of control trials used ranged from 25 to 85 (M = 59 trials, SD = 14). Overall, movement times between perturbation trials (M = 544 ms, SD = 17) and control trials (M = 543 ms, SD = 16) were not significantly different, as indicated by a paired-samples t-test, t(13) = 0.86, p = 0.41.

To determine response latency, the maximum acceleration value occurring after perturbation was first identified. Second, a line was drawn between the points on the acceleration profile corresponding to 25% and 75% of the maximum acceleration. Response latency was defined as the difference between the time of perturbation and the time instant when this line crossed zero (i.e., y value of zero; see Oostwoud [START_REF] Wijdenes | Comparing online adjustments to distance and direction in fast pointing movements[END_REF][START_REF] Wijdenes | Analysis of methods to determine the latency of online movement adjustments[END_REF][START_REF] Veerman | The latency for correcting a movement depends on the visual attribute that defines the target[END_REF]) and Fig. 3 for a graphical representation of the method applied to participants' data from the present study).

Correction latencies were submitted to a 2-target modality (somatosensory, visual) by 2perturbation time (100 ms, 200 ms) by 2-perturbation direction (away, toward) repeated-measures ANOVA. Note that corrections to the target perturbations that occurred before movement onset likely occurred during movement planning and are therefore not reflective of online control processes. Furthermore, accurate correction latencies for the "before" perturbation time are unlikely to be detected by this method. Because of the above-stated reasons, the before perturbation condition was not included in the statistical design.

It is also worth noting that for this analysis, the absolute values of correction latency were not as important as the between-modality differences. In the present study, because of technical limitations between the syncing of the Optotrak and robotic apparatuses, reaching data were sampled at a frequency (200 Hz) lower than what was used in previous studies (500 Hz: Oostwoud [START_REF] Wijdenes | Fast and fine-tuned corrections when the target of a hand movement is displaced[END_REF][START_REF] Wijdenes | Analysis of methods to determine the latency of online movement adjustments[END_REF]. Also, this was the first time such a method was applied to reaching movements performed with a supinated wrist posture to a somatosensory target. Thus, some contrasts between our values and those commonly found in the literature were expected.

Comparison of no-perturbation trials

To examine the effect of target modality on reaching performance and kinematics, paired samples t-tests were performed (effect sizes reported with Cohen's dz) on reaction time, movement time, total movement amplitude, amplitude and direction constant and variable errors, as well as on time to, and time after peak velocity. Note that for these comparisons, direction constant errors were defined using a coordinate system relative to the home and target positions.

Negative values indicated deviations closer to the body with respect to the target, and positive values indicated endpoints further away from the body with respect to the target.

Statistics and post-hoc tests

All statistical analyses were performed using the Statistical Package for Social Scientists (SPSS: IBM Inc. version 20). For all t-tests and repeated-measures ANOVA's, alpha was set to p = 0.05. For clarity and brevity, only the significant main effects and interactions were reported. Also, when main effects could be solely explained by a higher order interaction, only the breakdown of the interaction was reported. The Hyunh-Feldt correction was used to correct the degrees of freedom (corrected to 1 decimal place) when the assumption of sphericity was violated. The Tukey's Honestly Significant Differences (HSD) test was used to decompose all significant interactions. Fig 3 . The extrapolation method for determining the latency of online corrections. For each participant average acceleration profiles in the direction axis were computed for both perturbation and no-perturbation trials. The acceleration difference between these profiles (Accel Difference) was then plotted to calculate correction latency. Correction latencies were computed by drawing a line (Extrapolation Line) between 75% and 25% of the maximum difference in the Accel Difference profile (Extrapolation points) and extrapolating the line to the first zero crossing. The time between the perturbation and the zero crossing was defined as the correction latency. Panel (a) shows this method applied to averaged data for somatosensory target perturbations and panel (b) shows the method applied to averaged data for visual target perturbations.

Results

Vocal Response Protocol

For perturbation detection times, the analysis yielded a significant main effect of stimulus modality, F(1,13) = 5.04, p < 0.05, ηP 2 = 0.28, and a significant stimulus modality by perturbation direction interaction, F(1,13) = 5.56, p < 0.05, ηP 2 = 0.30, HSD = 22 ms. Overall, somatosensory targets perturbations were detected faster (M = 452 ms, SD = 95) than visual targets perturbations (M = 486 ms, SD = 104). Breaking down the interaction between modality and perturbation direction revealed that, when the stimulus was perturbed away from the body, there was a larger difference in detection times between somatosensory and visual stimuli (somatosensory away: M = 445 ms, SD = 100; visual away: M = 491ms, SD = 102) than when the stimuli were perturbed toward the body (somatosensory towards: M = 459 ms, SD = 92; visual towards: M = 481 ms, SD = 108).

Reaching Protocol

Normalized trajectory profiles for each condition in the reaching protocol are displayed in Fig. 4. Also, a summary of the average temporal and kinematic characteristics of the movements produced in each condition are shown in Table 1, and a summary of the movement endpoint characteristics are displayed in Table 2.

Comparison of no-perturbation trials

First, no-perturbation trials were analyzed to determine whether the sensory modality of the target had significant effects on the different reaching variables. The analyses revealed significant effects of target modality on the reaction time, t(13) = 2.98, p < 0.05, dz = 0.8 movement amplitude, t(13) = 4.12, p < 0.001, dz = 1.1 and direction constant error, t(13) = -3.15, p < 0.01, dz = 0.8. Participants took more time to initiate movements to somatosensory targets (mean reaction time = 330 ms, SD = 60) compared to visual targets (mean reaction time = 253 ms, SD = 71). Also, movements had larger total movement amplitudes when participants reached to somatosensory targets (M = 35.2 ms, SD = 3.4) as compared to visual targets (M = 31.2 ms, SD = 5.0). Finally, the analysis of direction constant error revealed that participant's endpoints were distributed further away from the body when reaching to visual targets (M = 1.31 cm, SD = 1.19) compared to somatosensory targets (M = 0.16 cm, SD = 1.07). 5) 37 (4) 37 ( 5) 37 ( 5) 37 ( 6) 37 (4) Somato 38 ( 5) 36 ( 5) 37 ( 5) 36 ( 5) 40 ( 7) 37 ( 6) 38 ( 6) Time after Peak Velocity(%) Visual 61 ( 5) 64 ( 5) 63 ( 4) 63 ( 5) 63 ( 5) 63 ( 6) 63 (4) Somato 62 ( 5) 64 ( 5) 63 ( 5) 64 ( 5) 60 ( 7) 63 ( 6

Constant and Variable Errors

For direction constant error, the ANOVA yielded a significant main effect of perturbation time, F(2,26) = 40.85, p < .001, ηP 2 = 0.76, HSD = 0.41 cm, and a significant modality by perturbation time interaction, F(2,26) = 11.94, p < .001, ηP 2 = 0.48, HSD = 0.64 cm. Breaking down the 2-way interaction revealed that participants showed smaller direction constant errors in response to target perturbations in both the 100 ms and 200 ms perturbation conditions when they reached to somatosensory targets as compared to when they reached to visual targets. No difference in direction constant error between target modalities was observed in response to perturbations before movement onset (see Fig. 5a and Table 2). For the trials with target perturbations, the analyses yielded no significant main effects or interactions for amplitude constant and amplitude variable errors (Fs > 0.09 & ps > 0.098).

For direction variable error, the analysis yielded a significant main effect of perturbation direction, F(1,13) = 16.80, p < .001, ηP 2 = 0.62; perturbation time, F(2,26) = 21.00, p < .001, ηP 2 = 0.56, HSD = 0.20; and a target modality by perturbation direction interaction, F(1,13) = 11.54, p <.01, , ηP 2 = 0.47, HSD = 0.25 cm. Participants' endpoint variability was significantly higher when they reached to targets perturbed 200 ms after movement onset (M = 1.71 cm, SD = 0.62) compared to when they reached to targets perturbed 100 ms after movement onset (M = 1.51 cm, SD= 0.56). Direction variable errors were also significantly higher in response to both 100 ms and 200 ms perturbations than in response to perturbations before movement onset (M = 1.20 cm, SD = 0.39). Breaking down the target modality by perturbation direction interaction revealed that, when moving to a somatosensory target, participants' direction variable errors were significantly lower when the target was perturbed towards the body (M = 1.16 cm, SD = 0.37) compared to when the target was perturbed away from the body (M =1.66 cm, SD = 0.56). There were no differences between perturbation directions for reaches to visual targets (global M = 1.62 cm SD = 0.58).

Correction Magnitude

The analysis of correction magnitude yielded a significant main effect of target modality, Decomposing the target modality by perturbation time interaction revealed that, overall participants performed larger corrections in response to somatosensory target perturbations compared to visual target perturbations and that, compared to the before condition, the increase in correction magnitudes observed in the 200 ms condition was significantly larger for movement to visual targets (1.63 cm increase) compared to movements to somatosensory targets (0.64 cm increase: see Fig. 5b). Participants exhibited larger corrections in response to somatosensory target perturbations than in response to visual target perturbations at all perturbation times. Furthermore, for both modalities, participants exhibited smaller corrections in response to perturbations at 200 ms than in response to the before and 100 ms perturbation times.

F

Latency of Online Corrections Following Target Perturbation

The analysis of correction latencies yielded a significant main effect of target modality, F(1,13) = 501.00, p < .001, ηP 2 = 0.96, perturbation direction, F(1,13) = 18.11, p < .001, ηP 2 = 0.58 and a target modality by perturbation direction interaction, F(1,13) = 11.62, p <.01, ηP 2 = 0.47, HSD = 30 ms. The main effect of target modality revealed that correction latencies in response to somatosensory target perturbations were significantly shorter (M = 68 ms, SD = 20 ) than correction latencies to visual target perturbations (M = 188 ms SD = 46). Breaking down the interaction revealed that, for visual targets, correction latencies were significantly shorter when the target was perturbed away from the body (M = 164 ms, SD = 30) as compared to when the target was perturbed towards the body (M = 213 ms, SD = 46). There were no significant differences in correction latency between directions for movements to somatosensory targets (see Fig. 6).

A supplementary analysis was performed to investigate if the differences between modalities in correction latencies could be explained by the differences in perturbation detection times. It was found that the differences in vocal response time between visual and somatosensory target modalities (M = 34 ms, SD = 61) were much lower than differences between modalities in correction latency (M = 120 ms, SD = 41) (see Online Resource 1 for computation and statistics). Overall, the results of this analysis revealed that between modality differences in target shift detection could not fully explain the differences in correction latency observed between the visual and somatosensory target conditions.

Discussion

The goal of the present study was to investigate whether the online sensorimotor transformations for movements to somatosensory targets, performed without vision of the limb, occurred in a visual or non-visual reference frame. Participants performed reaches to both somatosensory and visual targets that were either stationary or perturbed either before (~ 450 ms)

or after (~ 100 ms or ~200 ms) movement onset. If sensorimotor transformations for the online control of movements to perturbed somatosensory targets employed a visual reference frame, then higher endpoint errors and longer correction latencies were expected in response to such perturbations. In contrast to this hypothesis, participants produced larger corrections and were more accurate when reaching to perturbed somatosensory targets compared to when reaching to perturbed visual targets. Also, correction latencies were shorter in response to somatosensory target perturbations than in response to visual target perturbations for perturbations that occurred after movement onset. Taken together, these results provide evidence that non-visual sensorimotor transformations are employed for the online control of movements to somatosensory targets when reaching with an unseen limb.

Participants were able to implement adjustments in response to somatosensory target perturbations (average correction latency = 68 ms) more rapidly than in response to visual target perturbations (average correction latency = 188 ms). These differences in latency were observed even though the amplitude, the speed and timing of target displacements were the same for both conditions. Correction latencies in response to the shift of visual target position (e.g. 120 -300 ms) were in the range of what is typically found in other studies that examined reaching movements performed without vision of the reaching limb (Day and Lyon 2000; [START_REF] Komilis | Error processing in pointing at randomly feedbackinduced double-step stimuli[END_REF][START_REF] Prablanc | Automatic control during hand reaching at undetected twodimensional target displacements[END_REF][START_REF] Reichenbach | Seeing the hand while reaching speeds up online responses to a sudden change in target position[END_REF][START_REF] Saunders | Humans use continuous visual feedback from the hand to control fast reaching movements[END_REF].

Furthermore, the noted correction latencies were longer than the online visual feedback processing times observed when vision of the reaching limb is available (e.g. ~100 ms: Carlton 1992; Oostwoud [START_REF] Wijdenes | Comparing online adjustments to distance and direction in fast pointing movements[END_REF][START_REF] Zelaznik | Rapid visual feedback processing in singleaiming movements[END_REF]. These findings suggest that, similar to previous studies, corrections in response to perturbed visual targets likely involved the online remapping of the unseen reaching hand position to a visual reference frame.

In contrast, correction latencies in response to somatosensory target perturbations were much shorter than those commonly found when examining movements to visual targets. As mentioned above (see above section: Latency of Online Corrections), technical differences between the current study and previous work could account for some of these discrepancies.

However, the much shorter latencies in response to somatosensory target perturbations as compared to visual target perturbations does provide evidence against the hypothesis that the online control of reaching movements occur in a common visual reference frame. For upper-limb reaches to perturbed somatosensory targets, both the unseen target location and the unseen limb position would have to be remapped onto an extrinsic coordinate system prior to the initiation of online trajectory amendments. These transformations would likely require more time [START_REF] Reichenbach | Seeing the hand while reaching speeds up online responses to a sudden change in target position[END_REF]) and result in greater errors [START_REF] Sarlegna | The influence of target sensory modality on motor planning may reflect errors in sensori-motor transformations[END_REF]) than transformations required when reaching to a visual target with the unseen hand position, where only the latter must be remapped in visual coordinates. Thus, in the present study, it is likely that online sensorimotor transformations occurred in a non-visual reference frame for planning and correcting reaches to somatosensory targets.

The use of non-visual sensorimotor transformations for reaches to somatosensory targets with the unseen hand would support the more rapid and more accurate corrections found in the present study. For this type of sensorimotor transformation, corrections would be computed in somatosensory coordinates based on inputs from both the target and the reaching limb [START_REF] Battaglia-Mayer | Multiple levels of representation of reaching in the parieto-frontal network[END_REF]Burnod et al. 1999;[START_REF] Sarlegna | The influence of target sensory modality on motor planning may reflect errors in sensori-motor transformations[END_REF]. Previous studies investigating connections of the posterior parietal cortex in macaque monkeys have revealed that neural networks capable of performing computations in somatosensory coordinates [START_REF] Prevosto | Proprioceptive pathways to posterior parietal areas MIP and LIPv from the dorsal column nuclei and the postcentral somatosensory cortex[END_REF]. Specifically, the medial intraparietal area of the posterior parietal cortex, which is implicated in sensorimotor transformations during the planning and control of arm movements (Buneo and Andersen 2006;Desmurget et al. 1999;[START_REF] Reichenbach | A key region in the human parietal cortex for processing proprioceptive hand feedback during reaching movements[END_REF]) was shown to receive direct projections from both the somatosensory cortex (area 3a) and the dorsal column nuclei [START_REF] Prevosto | Cerebellar inputs to intraparietal cortex areas LIP and MIP: functional frameworks for adaptive control of eye movements, reaching, and arm/eye/head movement coordination[END_REF][START_REF] Prevosto | Proprioceptive pathways to posterior parietal areas MIP and LIPv from the dorsal column nuclei and the postcentral somatosensory cortex[END_REF]. It is thus possible that when reaching to a somatosensory target, updates to target location and reaching limb positions are processed directly through these network connections. Some support for this hypothesis could be drawn from previous studies which showed that disrupting processing in medial intraparietal area through transcranial magnetic stimulation impaired somatosensory-based corrections during the control of goaldirected actions [START_REF] Reichenbach | A key region in the human parietal cortex for processing proprioceptive hand feedback during reaching movements[END_REF].

The short correction latencies noted when participants reached to somatosensory targets may also suggest the use of predictive mechanisms for correcting the movements. The fact that the target finger was always displaced by 3 cm may have contributed to the speed of movement corrections. That is, a priori knowledge of the final finger target position might have facilitated processes responsible for the trajectory corrections and thus reducing the correction latency.

Although the visual target was always similarly displaced by 3 cm, the latencies for correcting the movements were much longer in the visual target condition. Together, these observations might suggest that predictive mechanisms are facilitated for controlling goal-directed arm movements on-line when both the target and the reaching hand can be encoded in a common sensory modality.

Similar to previous studies (e.g., [START_REF] Saunders | Humans use continuous visual feedback from the hand to control fast reaching movements[END_REF][START_REF] Reichenbach | Seeing the hand while reaching speeds up online responses to a sudden change in target position[END_REF] there was no effect of perturbation time on correction latency for movements to both visual and somatosensory targets. This finding supports those of previous studies that showed that online corrections occurred at roughly the same time relative to the onset of visual target perturbations (i.e., 163 ms after) in response to both early (25% of movement distance) and mid (50% of movement distance) perturbation conditions [START_REF] Saunders | Humans use continuous visual feedback from the hand to control fast reaching movements[END_REF]. This result was taken as evidence for the pseudo-continuous use of visual feedback throughout the reaching trajectory (see also [START_REF] Elliott | Discrete vs. continuous visual control of manual aiming[END_REF]). In the present study, our observation that correction latencies were not significantly altered by perturbation time when reaching to both visual and somatosensory targets may indicate that somatosensory information can be used in a pseudo-continuous manner at least in the first 200 ms of a movement lasting at least 500 ms (see also [START_REF] Tremblay | An optimal velocity for online limb-target regulation processes?[END_REF].

It is also important to note that the aforesaid differences in correction latencies between movements to visual and somatosensory targets were not attributable to differences in the detection of the target displacement. Although participants detected perturbations to somatosensory targets faster than they detected perturbations to visual targets, the between modality differences in detection time was much lower than the between modality differences in correction latencies (see Online Resource 1).

The response times in both detection tasks (i.e., visual and somatosensory) may appear longer (i.e. >440 ms) compared to previous studies. However, it is important to note that, in the present study, participants had to respond only if the visual or somatosensory targets moved toward or away from them and had to refrain from responding when the targets moved to their left. Therefore, the detection tasks used here can be considered as the go/no-go tasks which are known to increase the latency of the go response compared to the response latency obtained through simple reaction tasks [START_REF] Miller | Motor processes in simple, go/no-go, and choice reaction time tasks: a psychophysiological analysis[END_REF]. Moreover, the longer detection times in the visual target condition could also be explained by the fact that it takes less time to detect motion onset than to detect motion direction (Blouin et al. 2010).

In agreement with the correction latency results, the analyses of direction constant errors revealed that participants were more effective at implementing corrections in response to perturbed somatosensory targets compared to perturbed visual targets. Participants exhibited larger corrections when reaching to perturbed somatosensory targets compared to perturbed visual targets for all perturbation times. Moreover, the constant error analyses revealed that participants were more accurate when correcting for somatosensory target perturbations as compared to visual target perturbations after movement onset (i.e. 100 ms and 200 ms conditions). No differences in accuracy were noted with respect to target modality for target perturbations that occurred before movement onset.

Similar to previous studies [START_REF] Goodale | Large adjustments in visually guided reaching do not depend on vision of the hand or perception of target displacement[END_REF][START_REF] Komilis | Error processing in pointing at randomly feedbackinduced double-step stimuli[END_REF], the current results

showed that, if the target was perturbed during movement planning, participants were able to fully correct for changes in target location. In the present study, the time available to implement trajectory amendments in response to perturbations that occurred before movement onset (> 900 ms) could explain the absence of differences in movement accuracy between target modalities.

In contrast, when target perturbations occurred after movement onset, movement endpoints were more accurate for reaches to somatosensory targets compared to visual targets. This finding could have two possible explanations. First, because corrections latencies were ~120 ms shorter in the somatosensory target condition, participants had more time to implement corrections when reaching to somatosensory targets than to visual targets. Second, when both the target and the hand are mapped in the same sensory coordinate system, sensorimotor transformations leading to the movements would be more accurate compared to when a reference frame conversion is necessary (Blouin et al. 2014;[START_REF] Sarlegna | The influence of target sensory modality on motor planning may reflect errors in sensori-motor transformations[END_REF]Sober and Sabes 2005).

To investigate whether target information obtained from somatosensory sources provides a better estimate of new target position, future studies should examine how disruptions in the accuracy of somatosensory information from the target limb (e.g., via tendon vibration) affects endpoint accuracy. If more accurate reference frame transformations are responsible for endpoint accuracy, then disrupting somatosensory target information should decrease movement accuracy.

This decrease in accuracy would also be independent of the time required to implement corrections.

In the present study, differences were found when targets were perturbed away vs.

towards the body, and that was the case for both the detection and control processes. Participants were quicker at detecting perturbed somatosensory targets compared to perturbed visual targets, but the difference between the two detection times was smaller when the targets were perturbed toward the body. This result could be explained by the physical attributes of the experimental setup. In the somatosensory target condition, the limb was already in slight extension. Thus, further extension may be more easily sensed than flexion due to the increased loading of muscle spindles [START_REF] Hulliger | The mammalian muscle spindle and its central control[END_REF]. A further extension also shifts the limb further away from the body's centre of gravity likely invoking a greater postural response that could have been more salient due to greater activation of vestibular and cutaneous receptors [START_REF] Lacquaniti | EMG responses to load perturbations of the upper limb: effect of dynamic coupling between shoulder and elbow motion[END_REF].

For the visual target, the physical setup of the experiment could have also played a role. Because of the position of the eyes and the fixation LED, the angular displacement of the target resulting from the 3 cm perturbations was greater when the target moved towards than away from the body. Even though all perturbations took place in the lower visual field, it is possible that this wider change in angle could have facilitated perturbation detection.

For somatosensory targets, the shorter detection times appeared to have a positive impact on performance as movements to somatosensory targets perturbed away from the body were significantly more precise than movements to somatosensory targets perturbed towards the body.

In contrast, the pattern of results in detection times was not consistent with the results obtained for correction latency for visual targets. For example, it was found that correction latency was significantly longer for movements to visual targets perturbed towards the body compared to visual targets perturbed away from the body. These findings are therefore consistent with the hypothesis that detection processes have very little influence on correction processes during ongoing reaching movements [START_REF] Smeets | Movement adjustments have short latencies because there is no need to detect anything[END_REF].

Conclusions

The results of the present study suggest that corrections to perturbed somatosensory targets are faster and more accurate than corrections to perturbed visual targets when reaching with an unseen limb. Thus, in contrast to movements to external visual targets, movements to somatosensory targets can be controlled using non-visual transformation processes based on somatosensory information about the reaching limb and target positions.

Fig 1 .

 1 Fig 1. A drawn representation (not to scale) of the experimental setup in the Somatosensory target condition is shown in panel (a). Panel (b) is a drawn representation (not to scale) of the aiming console and stimuli positions. Participants sat facing the aiming apparatus in a dark room. A robotic device was used to deliver target perturbations in both somatosensory (left finger) and visual (LED) target conditions. Participants performed reaching movements from the home position located on their left to the target position located close to their midline.

Fig 2 .

 2 Fig 2. Examples of velocity profiles of the reaching hand and the robotic effector (Target) for each perturbation time. Panel (a) shows a perturbation occurring before movement onset; panel (b) shows a perturbation occurring ~100 ms after movement onset, and panel (c) shows a perturbation occurring ~200 ms after movement onset.

Fig 4 .

 4 Fig 4. Average reaching trajectories for each condition in the reaching protocol. Panels (a) and (b) depict perturbations occurring before movement onset. Panels (c) and (d) depict perturbations 100 ms after movement onset. Panels (e) and (f) depict perturbations occurring 200 ms after movement onset. Trajectories were normalized with each point representing 2% of movement duration. Error bars indicate the between-subject standard deviation of spatial position.

  (1,13) = 70.07, p <.001, ηP 2 = 0.84 perturbation time, F(2,26) = 39.81, p <.001, ηP 2 = 0.75 HSD = 0.63 cm, perturbation direction, F(1,13) = 7.71, p <.05, ηP 2 = 0.37 and a target modality by perturbation time interaction, F(2,26) = 10.04, p < .001, ηP 2 = 0.44, HSD = 0.55 cm. Participants exhibited larger corrections in response to targets perturbed toward the body (M = 3.83 cm, SD = 1.47) compared to targets perturbed away from the body (M = 3.42 cm, SD = 1.20).

Fig 5 .

 5 Fig 5. (a) Direction constant error. Participants were more accurate when performing reaches to somatosensory targets perturbed after movement onset compared to when performing reaches to visual targets. When aiming to visual targets participants exhibited a larger under-correction relative to when making movements to somatosensory targets. (b) Correction magnitude.Participants exhibited larger corrections in response to somatosensory target perturbations than in response to visual target perturbations at all perturbation times. Furthermore, for both modalities, participants exhibited smaller corrections in response to perturbations at 200 ms than in response to the before and 100 ms perturbation times.

Fig 6 .

 6 Fig 6. Correction latencies in response to target perturbations. Overall, participants had shorter correction latencies in response to somatosensory target perturbations than in response to visual target perturbations. Furthermore, for visual targets, correction latencies were longer in response to targets perturbed toward the body.

  These findings lend support to the idea that different sensorimotor transformations and perhaps different cortical networks are responsible for the online control of movements to somatosensory and visual targets performed without vision of the limb. Blouin J, Saradjian AH, Lebar N, et al (2014) Opposed optimal strategies of weighting somatosensory inputs for planning reaching movements toward visual and proprioceptive targets. J Neurophysiol 112:2290-2301. doi: 10.1152/jn.00857.2013 Bock O (1986) Contribution of retinal versus extraretinal signals towards visual localization in goal-directed movements. Exp Brain Res 64:476-482. doi: 10.1007/BF00340484 Buneo CA, Andersen RA (2006) The posterior parietal cortex: sensorimotor interface for the planning and online control of visually guided movements. Neuropsychologia 44:2594-2606. doi: 10.1016/j.neuropsychologia.2005.10.011 Burnod Y, Baraduc P, Battaglia-Mayer A, et al (1999) Parieto-frontal coding of reaching: an integrated framework. Exp Brain Res 129:325-346. doi: 10.1007/s002210050902 Carlton LG (1992) Visual processing time and the control of movement. In: Proteau L, Elliott D (eds) Advances in Psychology. North-Holland, Oxford, England, pp 3-31 Dadarlat MC, O'Doherty JE, Sabes PN (2015) A learning-based approach to artificial sensory feedback leads to optimal integration. Nat Neurosci 18:138-144. doi: 10.1038/nn.3883 Day BL, Lyon IN (2000) Voluntary modification of automatic arm movements evoked by motion of a visual target. Exp Brain Res 130:159-168. doi: 10.1007/s002219900218

Table 1 :

 1 Mean and standard deviation for the temporal and kinematic variables of movements to somatosensory (somato) and visual targets.

	Perturbation Conditions

Table 2 :

 2 Mean and standard deviation (in cm) for the accuracy variables of movements to somatosensory (somato) and visual targets.

	)	62 (6)

Acknowledgements

The authors gratefully acknowledge Intishar Kazi for his help during data collection and Stephen Bested for the design of Fig. 1. The authors also acknowledge Damian Manzone and Sadiya Abdulrabba for providing helpful comments on the manuscript. This work was funded by the Natural Sciences and Engineering Research Council of Canada, the Canadian Foundation for Innovation, the Ontario Research Fund, and Campus France.