Running Head: THE UTILITY OF VISION 1 The Utility ...

Running Head: THE UTILITY OF VISION

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The Utility of Vision During Action: Multiple Visual-‐Motor Processes? Luc Tremblay1, Steve Hansen2, Andrew Kennedy1, & Darian Cheng1 1Faculty of Kinesiology and Physical Education, University of Toronto 2Schulich School of Education, Nipissing University

AUTHOR NOTES This research was supported by the Natural Sciences and Engineering Research Council of Canada as well as the Canada Foundation for Innovation and the Ontario Research Fund. Darian Cheng is now with the University of British Columbia, Okanagan Campus. Correspondence for this article should be sent to Luc Tremblay, 55 Harbord St., Toronto, ON, M5S 2W6, CANADA. Email: [email protected]. Phone: 416-‐946-‐0200. Fax: 416-‐946-‐5310. KEY WORDS: vision, online control, goal-‐directed, pointing

THE UTILITY OF VISION

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Recently, Elliott et al. (2010) asserted that the current control phase of a movement could be segregated in multiple processes, including impulse and limb-‐target regulation processes. This study aimed to provide further empirical evidence and determine some of the constraints that govern these visual-‐motor processes. In two experiments, vision was presented or withdrawn when limb velocity was above or below selected velocity criteria. We observed that vision provided between 0.8 and 0.9 m/s significantly improved impulse regulation processes while vision provided up to 1.1 m/s significantly increased limb-‐target regulation processes. These results lend support to Elliott et al. (2010) and provide evidence that impulse regulation and limb-‐target regulation can take place at different velocities during a movement.


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The Utility of Vision During Action: Multiple Visual-‐Motor Processes? Visual feedback is an extremely valuable source of sensory information for the control of goal-‐directed actions. Goal-‐directed movements can be adjusted in order to improve endpoint accuracy and consistency when vision is available (Woodworth, 1899). These online corrections are believed to occur during the latter stages of movement (i.e., during the current control phase) that follows an initial pre-‐programmed phase of the movement (i.e., the initial impulse; Woodworth, 1899; see Elliott, Helsen, & Chua, 2001 for a review). However, the particular mechanisms and underlying processes of visual feedback utilization remain unclear. Beggs and Howarth (1970; 1972) forwarded one of the first theoretical positions about visual information usage during a goal-‐directed movement. Some of the chronometric approaches in psychology enticed Beggs and Howarth (1970; 1972) to suggest that a single amendment could be made during a rapid aiming movement within a delay equivalent to one reaction time. Due to the limits of visual-‐motor processing, humans could only use the visual information that was available at one reaction time prior to the observed correction. It was suggested that a single correction could be implemented before the end of a rapid movement because the investigated movements were relatively short (e.g., 400 ms) and that reaction times were thought to be not much shorter (i.e., then estimated at approximately 250 ms). Since Beggs & Howarth initial investigations (1970; 1972), other


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researchers have shown that more than one correction can occur during a rapid aiming movement (e.g., Elliott et al., 2004; Meyer et al., 1988). Meyer and colleagues (1988) employed elaborate kinematic analyses to assess the efficiency of goal-‐directed movements and their underlying limb trajectory amendments. Using a wrist rotation movement that controlled the displacement of a cursor towards a target on the screen, Meyer et al. (1988) computed the number of amendments that participants executed. For instance, a correction was identified if the cursor’s acceleration passed from negative to positive after reaching its maximum velocity, indicating that a secondary impulse towards the target had been implemented. In contrast with the predictions made by Beggs and Howarth (1970; 1972), Meyer and colleagues (1988) often observed two or more online limb trajectory amendments during a single movement. Their observations indicated that visual information might be used on multiple occasions during a rapid movement. At the other end of the spectrum of online control models, Elliott et al. (1991) proposed that visual information is used continuously during visually guided actions. They observed that discrete trajectory amendments occur in the absence of vision (see also Woodworth, 1899), but that these amendments do not contribute to endpoint error reduction to the same extent as when vision is available throughout the movement. Based on this observation, Elliott et al., (1991) went on to propose that the visual system is constantly updating the motor system in a pseudo-‐continuous fashion. Therefore, there are many overlapping trajectory amendments that occur during each movement that help to


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reduce endpoint errors. Moreover, these overlapping limb trajectory corrections may go undetected at the kinematic level. More recently, Elliott et al. (2010) reviewed the existing literature and forwarded a multiple-‐process model of online control of discrete goal-‐directed actions. One set of online control processes relates to comparing the expected and actual sensory consequences of the primary movement impulse. Specifically, as the initial limb impulse ends, comparisons between the expected and actual position of the limb may be made to regulate the initial limb impulse early in the movement. The secondary set of online control processes occurs later in the movement and compares the relative positions of the limb and target. Specifically, a comparison of limb and target position after the initial impulse can be made to home in onto the target and perhaps correct errors arising from the previous impulse regulation processes. These processes can be referred to as impulse and limb-‐target regulation processes, respectively. In line with the pseudo-‐continuous control principle (Elliott et al., 1991), these processes are thought to overlap significantly during a limb trajectory (Elliott et al., 2010). It is also understood that online trajectory amendments take place more often when required (see Khan et al., 2006). The purpose of the current study was to gain a better understanding of when impulse regulation and limb-‐target regulation processes take place during the trajectory of a reaching movement. According to the model forwarded by Elliott et al. (2010), impulse regulation starts early in the trajectory (i.e., as early as movement onset), while limb-‐target regulation starts after peak limb acceleration (see Figure 3 of Elliott et al., 2010). In order to determine when vision is used for impulse regulation and limb-‐target regulation, we


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manipulated the presence of vision of the entire visual field based on the real-‐time characteristics of the limb while participants made goal-‐directed reaches with their index finger (see also Hansen et al., 2008). Assuming that impulse and limb-‐target regulation are made based on some kinematic characteristics of the trajectory (see Elliott et al., 2010), we anticipated that using real-‐time kinematics to implement sensory manipulations would allow us to parse out the influence of the separate online regulation processes. Specifically, if impulse regulation takes place as planned in the presence of vision, individuals should alter their limb trajectory based on early visual feedback (i.e., possibly before peak acceleration) thereby influencing the variability in the movement endpoint distribution (i.e., consistency or variable error). Because the impulse-‐regulation processes are made based on early limb position (cf. target position), early trajectory amendments associated with impulse-‐regulation could yield greater influences on the precision (i.e., variable error) than the bias or accuracy (i.e., constant error) of endpoint distributions. That is, impulse regulation could have a greater influence on the variance across endpoints than on the bias between endpoint and target because these amendments are solely based on limb position. Conversely, any limb-‐target regulation that occurs after peak acceleration should facilitate the implementation of online trajectory corrections that minimize endpoint bias (i.e., accuracy or constant error). Because these adjustments are made based on the position of the limb relative to the target, it was anticipated that trajectory amendments associated with limb-‐target regulation processes should have a greater influence on the accuracy of endpoint distributions (i.e., constant error). In a nutshell, if varying real-‐time movement-‐ dependent manipulation criteria can induce separate influences on variable and constant


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errors at movement endpoint, then we would find empirical support for the concept of separate impulse and limb-‐target regulation processes. Experiment 1 In this experiment, visual feedback was provided while the finger travelled above or below 0.8 m/s (i.e., VHigh and VLow, respectively). This criterion was selected following multiple pilot experiments because: 1) 0.8 m/s is typically first reached between peak acceleration and peak velocity and 2) using this criterion throughout the trajectory yields approximately the same amount of time with vision above and below the criterion. More importantly, a limb velocity criterion was employed instead of position or time manipulations (e.g., Carlton, 1981; Chua & Elliott, 1993) in order to minimize alterations of normal reaching movements. Two control conditions were also used (i.e., normal vision [V] and no-‐vision [NV]). If the visual feedback gathered in the earliest stages of the trajectory is primarily used for impulse control processes then withdrawing visual feedback below 0.8 m/s (VHigh) should significantly increase variable error as compared to normal vision conditions (V). As well, if the visual feedback gathered closer to peak limb velocity is primarily used for limb-‐target regulation, then withdrawing visual feedback above 0.8 m/s (VLow) should significantly increase constant error compared to trials with vision (V). Likewise, the limb-‐target regulation processes, as evidence through online trajectory amendment measures, should also be significantly decreased in the VLow compared to the V condition. Such limb-‐target regulation processes were assessed using correlational methods.


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In order to identify online corrections within the context of the pseudo-‐continuous model (Elliott et al., 1991), researchers have used correlation analyses contrasting the limb position at different proportions of the total movement time and the limb position at movement end (Heath, 2005; Elliott et al., 1991; Messier & Kalaska, 1999: see also Khan et al., 2002; 2006 for an alternate method). The premise of this analysis is that the limb’s position at various proportions of the overall movement duration can predict the limb position at movement end if the movements are relatively stereotyped (i.e., the actions are pre-‐planned). In the absence of online corrections or deviations in the trajectory, a movement should unfold as planned. Therefore, the limb positions reached at various movement time proportions should predict movement endpoint. Alternatively, lower correlation coefficients mean that movements are less stereotyped for one trial to the next (i.e., online trajectory amendments occurred). When independently manipulating the vision of the target and vision of the limb, Heath (2005) observed that variations in the correlation coefficients are significant only when vision of the limb was manipulated and suggested that vision of the limb is the key visual information for online control of limb trajectories. Thus, in addition to the above predictions about constant and variable error, we also expected our visual feedback manipulations to influence evidence of online trajectory amendments. As in Heath (2005), we expected that R2 values calculated using limb position at various movement time proportions and movement end would increase across movement time proportions and be higher in NV compared to V. As mentioned above, considering that limb-‐target regulation processes take place later in the trajectory than


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impulse-‐regulation processes, we anticipated that the VLow, but not the VHigh, condition would yield higher R2 values (i.e., fewer online trajectory amendments) compared to V. Methods Eight (8) self-‐declared right-‐handed individuals (20-‐36 years old: 3 female, 5 male) from the University of Toronto community participated. This research was conducted according to the 1964 Declaration of Helsinki. The participants sat on an adjustable seat at a 73.5 cm high desk. On the edge of the desk was a 25 cm by 50 cm custom aiming board equipped with a translucent polymer surface. The target was marked by a green light emitting diode (LED) 7 mm in diameter, which was located under the aiming surface. Thus, the target could only be seen when lit and could not generate tactile terminal feedback. The home position was a 1 cm black piece of tape located 15 cm from the edge of the aiming board. The home to target distance was 30 cm and both locations were aligned with the midline of the participant. Each participant completed 100 trials in total. First, they completed 20 familiarization trials with vision to get accustomed to the movement time bandwidth (350-‐ 450 ms). Specifically, participants were asked to be as accurate as possible while maintaining their movement time within the prescribed bandwidth. These instructions were also re-‐iterated for the 80 experimental trials that included 20 trials from each of four experimental conditions. In two control conditions, vision was available (vision: V) or occluded (no-‐vision: NV) throughout the trajectory. In two other conditions, vision was either provided when the limb was traveling above (VHigh) or below the 0.8 m/s velocity criterion (VLow; see Figure 1). Presentation order of the trials was pseudo-‐random and the


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same condition was not presented for more than 3 consecutive trials. No feedback about movement endpoint accuracy was provided during either phase of the experiment and participants were instructed to prioritize endpoint accuracy while maintaining their movement time within the prescribed bandwidth. Vision was available prior to movement initiation in each condition. At the beginning of each trial, the experimenter provided a “ready” signal. Shortly after, the target illuminated, indicating to the participants that they should start their movement. Vision was always removed at the end of the movement (i.e., when the limb velocity fell below 0.03 m/s for 2 subsequent samples) in order to minimize learning effects associated with terminal feedback. An 800 Hz tone lasting 500 ms indicated the end of the trial and acted as a signal for participants to return to the home position. A minimum delay of 5 s was used between the trials in order to reduce the influence of the preceding trial (see Cheng et al., 2008). An Optotrak Certus (Northern Digital Inc.) recorded the location of an infrared light emitting diode (IRED) for 1 s at 500 Hz. The IRED was located on the distal end of the right index finger of the participant. A pair of liquid crystal goggles was employed to manipulate visual feedback (Milgram, 1987). A custom MatLab program (The Mathworks Inc.) gathered the Optotrak data, calculated limb velocity, and triggered the liquid-‐crystal goggles through a National Instruments data acquisition board (PCI-‐6024E). Movement onset and offset was detected when the limb velocity rose above or fell below 0.03 m/s for 2 subsequent samples. In the VHigh and VLow conditions, vision was manipulated when the limb velocity rose above or fell below 0.8 m/s for two subsequent samples. The delay between the first sample


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with a limb velocity above or below 0.8 m/s and the change in the state of the goggles was less than 10 ms and therefore the manipulations were considered to occur in real-‐time. The main dependent variables were movement endpoint accuracy (i.e., constant error: CE) and consistency (i.e., variable error: VE) in the primary and secondary movement axes. Note that positive CE’s indicate an overshoot and a rightward bias in the primary and secondary movement axes, respectively. We also calculated movement time (MT), the time taken to reach peak limb velocity (TTPV), and the time between peak velocity and movement end (i.e., time after peak velocity; TAPV). All of these variables were submitted to separate one-‐way ANOVAs contrasting the 4 Vision Conditions (V, NV, VHigh, VLow). Alpha was set at .05 for all analyses and Tukey HSD post-‐hoc analyses were used to decompose the significant effects. In addition, correlation coefficients between the finger’s position at consecutive 25% of the movement time and the finger’s position at the movement endpoint (i.e., 100%) were calculated. As stated above, lower correlation coefficients are associated with online trajectory amendments and higher coefficients are associated with pre-‐planning (e.g., Heath, 2005). Coefficients were then squared to obtain a normal distribution. The associated ANOVA contrasted the R2 for the 4 Vision Conditions (V, NV, VHigh, and VLow) across 3 Movement Time Proportion (25%, 50%, and 75%). Alpha was set at .05 for these analyses. A Tukey HSD procedure was used where necessary. Results The analysis of MT revealed a significant main effect, F (3, 21) = 5.81, p < .01. Longer MTs were observed in the VHigh condition (425 ms, SD 24) than in all other conditions, ps


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< .05 (NV: 404 ms, SD 24; VLow: 405 ms, SD 26; V: 406 ms, SD 28). Notably, this difference was reflected in TAPV, F (3, 21) = 4.26, p < .05. Specifically, there was a longer TAPV in the VHigh (256 ms, SD 25) than in the V condition (235 ms, SD 22), which was not different from the other conditions (NV: 237 ms, SD 22; VLow: 238 ms, SD 22). In contrast, the analysis of TTPV did not reach significance (p > .2). Analyses of the primary movement axis endpoint accuracy (CE), F(3, 21) = 3.47, p< .01, and consistency (VE), F (3, 21) = 9.66, p < .001, revealed significant main effects for Vision. Participants executed shorter reaching amplitudes (i.e., smaller CE or less overshoot) in the VLow and NV conditions as compared to the VHigh condition. The comparison between V and NV yielded a p-‐value of 0.055 (Figure 2A). The analysis of VE revealed comparable associations for the NV-‐VLow and V-‐VHigh conditions, ps < .05 (Figure 2B). In other words, there were no differences between V and VHigh in terms of consistency and accuracy. Also, the V and VHigh conditions resulted in more consistent movement endpoint distributions than in both the NV and VLow conditions, which were not different from each other. In terms of the secondary axis, the CE analysis did not yield significant differences, F (3, 21) = 1.80, p < .18, although the pattern of differences was qualitatively comparable to the primary axis results (see Figure 2C). The VE analyses on the secondary axis yielded a significant effect for Vision Conditions, F (3, 21) = 30.52, p < .001, as was the case for the CE and VE results in the primary axis. Specifically, VE in the secondary axis was larger in both the NV and VLow conditions than in both the V and VHigh conditions (ps < .05). Contrasts


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between the NV and VLow as well as between the V and VHigh conditions did not reveal reliable differences (Figure 2D). For the trajectory analyses, there was only a main effect for the movement proportion, F (2, 14) = 67.19, p < .0011. Decomposing the main effect revealed increases in R2 values across all movement time proportions (25%: 0.09, SD 0.09; 50%: 0.22, SD 0.08; 75%: 0.63, SD 0.04). Discussion Unexpectedly, the movement endpoint accuracy and consistency were comparable in the V and VHigh conditions. While the consistency (i.e., VE in the primary and secondary movement axes) was significantly better in both the V and VHigh condition than in both the NV and VLow conditions, a larger target overshoot was observed in both the V and VHigh than in both the NV and VLow conditions (i.e., CE in the primary axis). This pattern of results can be associated with the emphasis that participants placed on endpoint accuracy. Indeed, humans tend to overshoot the target in V compared to NV conditions when asked to focus more on accuracy than on speed (e.g., Elliott et al., 1991: Experiment 1; Westwood, Heath, & Roy, 2003). Nevertheless, endpoint accuracy and consistency were comparable between the V and VHigh conditions. These conditions were significantly different from both the NV and VLow conditions. The comparable levels of consistency and accuracy between the V and Vhigh conditions and also between the NV and VLow conditions might

1 Note that a simple t-‐test conducted between VHigh and VLow R2 values at 75% of MT did yield a significant difference (p < .05).


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indicate that both impulse regulation and limb-‐target regulation take place at limb velocities above 0.8 m/s. Support for the utility of vision when the limb travelled above 0.8 m/s also comes from the overall movement time analysis. Specifically, participants took an extra 20 ms in the VHigh condition compared to all other conditions. This movement time difference was reflected in the time taken to reach peak limb velocity. Thus, even in the absence of vision early during the limb trajectories, participants were able to engage in both impulse and limb-‐target regulation processes in the VHigh condition. We purport that the utility of vision was most relevant between 0.8 m/s and peak limb velocity (i.e., in the VHigh condition). As for evidence of online trajectory amendments, R2 values did not significantly differ across conditions but increased across movement proportions as expected. We did not observe the expected significant differences between the vision conditions. It is possible that not knowing the visual feedback condition before each trial, as was the case in Heath (2005), influenced movement planning and online control strategies (see Elliott et al., 2004; Hansen, 2010; Zelaznik et al., 1983). In other words, the equal levels of uncertainty regarding the visual condition may have led to similar levels of trajectory amendments. In this experiment, visual feedback was introduced or withdrawn at high limb velocities. When vision was introduced at 0.8 m/s (i.e., VHigh), visual-‐motor processes required more time to reach peak limb velocity (i.e., 20 ms) and yielded comparable endpoint consistency and accuracy as a normal vision condition (i.e., V). While visual perturbation protocols can elicit online trajectory amendments throughout most of the


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trajectory (e.g., Bard et al., 1985; Bootsma & Van Wieringen, 1990; Brenner & Smeets, 2003; Carlton, 1992; Proteau & Masson, 1997; Proteau et al., 2009; Sarlegna et al., 2004; Saunders & Knill, 2005), our results suggest that visual samples provided above 0.8 m/s can be used to complete both impulse and limb-‐target regulation processes. Also, a significant proportion of these processes are likely to take place before peak limb velocity. However, it is still unclear how much overlap exists between these impulse and limb-‐target regulation processes. Experiment 2 In the second experiment, we sought to determine to extent of overlap for the use of vision for impulse and limb-‐target regulation when visual feedback is available at higher limb velocities than the 0.8 m/s threshold tested in Experiment 1. Specifically, we aimed to assess if the central nervous system can use visual information for both visual-‐motor processes when vision is provided closer to peak limb velocity. To answer this research question, we manipulated the criteria at which vision was provided. The participants completed trials in twelve different vision conditions. As in Experiment 1, manipulating vision as a function of limb velocity was employed for the methodological advantages it offers over temporal or spatial manipulations (see above). Vision was either provided when the limb travelled above (VHigh) or below (VLow) one of the six velocity criteria (0.03 m/s [i.e., the usual vision (V) and no-‐vision (NV) conditions), 0.8 m/s, 0.9 m/s, 1 m/s, 1.1 m/s, & 1.2 m/s). If the impulse regulation processes take place prior to limb-‐target regulation processes, then increasing the velocity cutoff for the VHigh conditions should first have an


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influence on movement consistency (i.e., variable error associated with impulse regulation) before influencing movement accuracy (i.e., constant error associated with limb-‐target regulation). Likewise, online trajectory amendments, as evidenced by the correlation analyses, should decrease with vision available with the higher velocity cutoffs. Methods Eleven (11) self-‐declared right-‐handed persons (8 male and 3 female) from the University of Toronto Community participated in the study. Their mean age was 22.8 +/-‐ 2.4 years. This research was conducted according to the 1964 Declaration of Helsinki. The experimental setup, data collection and typical trial procedures were the same as in Experiment 1 with only one exception. The movement time bandwidth was increased from 350-‐450 ms to 400-‐500 ms. The experimental session included 20 familiarization trials and 160 experimental trials, for a total of 180 trials. The familiarization trials were completed with vision and enabled participants to get accustomed to the movement time bandwidth. Subsequently, the participants completed 20 trials in V and NV and 12 trials in each of the other ten experimental conditions in the experimental phase. The trial order was pseudo-‐randomized with the limitation of not running the same condition more than 3 times in a row. As in Experiment 1, no endpoint feedback was provided and participants were asked to be as accurate as possible while not exceeding the movement time bandwidth. The main dependent variables were the same as in Experiment 1 (i.e., MT, CE, VE, TTPV, TAPV, and R2 values). All dependent variables were submitted to a 2 Vision (VHigh, VLow) by 6 Velocity Criteria (0.03 m/s, 0.8 m/s, 0.9 m/s, 1.0 m/s, 1.1 m/s, 1.2 m/s) analysis


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of variance (ANOVA) with repeated measures on both factors. R2 values were calculated between the limb position for consecutive 25% of the movement time and the limb position at the movement endpoint (i.e., 100%). The R2 values were submitted to a 2 Vision (VHigh, VLow) by 6 Velocity Criteria (0.03 m/s, 0.8 m/s, 0.9 m/s, 1.0 m/s, 1.1 m/s, 1.2 m/s) by 3 Movement Time Proportions (25%, 50%, 75%) ANOVA with repeated measures on all factors. Alpha was set at .05 for all analyses and a Tukey HSD post-‐hoc analysis was used where necessary. Results Analyses of MT revealed significant main effects for Vision, F (1, 10) = 16.16, p < .01, Velocity Criteria, F (5, 50) = 4.52, p < .01, and a significant interaction between Vision and Velocity Criteria, F (5, 50) = 6.88, p < .01. For the 1.0 and 1.1 m/s Velocity Criteria, movement durations were longer in the VHigh compared to the VLow conditions (see Table 1). These MT differences were reflected in the time taken after peak limb velocity (TAPV), which also revealed main effects for Vision, F (1, 10) = 31.10, p < .001 and Velocity Criteria, F (5, 50) = 3.05, p < .05, as well as a significant interaction between Vision and Velocity Criteria, F (5, 50) = 4.43, p < .01. Post-‐hoc analyses revealed that TAPV was longer in the VHigh than VLow condition at the 0.9 m/s, 1.0 m/s, and 1.1 m/s velocity criterion (see Table 1). A main effect for Vision, F (1, 10) = 13.33, p < .01, was observed for time taken to reach peak velocity (TTPV). TTPV was significantly longer in the VLow than the in VHigh conditions (see Table 1).


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The analysis of endpoint accuracy in the primary movement axis (i.e., CE) revealed a significant main effect for Vision, F (1, 10) = 7.81, p < .05. Shorter reaching amplitudes (i.e., smaller CE or less overshoot) were observed in the VLow condition compared to the VHigh condition (see Figure 3A and Table 1). The analyses of endpoint consistency (i.e., VE) in the primary movement axis revealed a main effect for Vision, F (1, 10) = 5.53, p < .05, and a significant interaction between Vision and Velocity Criteria, F (5, 50) = 5.77, p < .01. Post-‐ hoc analyses indicated that the VHigh condition yielded a smaller amount of error than the VLow condition with the 0.03 m/s and 0.8 m/s velocity criteria (see Table 1). In addition, the VLow with the 0.03 m/s velocity criteria (i.e., no-‐vision) yielded more endpoint variability than the VHigh conditions with the 0.03, 0.8, and 0.9 m/s velocity criteria (see Figure 3B and Table 1). The analyses for CE and VE on the secondary movement axis did not yield any significant effects or interactions (see Table 1). Note that for VE on the secondary movement axis, the Vision by Velocity Criteria interaction approached conventional levels of significance (p = 0.059). For the trajectory analyses, the comparison of the R2 values at 25%, 50% and 75% of the movement time revealed a significant main effect for Movement Proportion, F (3, 30) = 232.73, p < .001, and significant interactions between Vision and Movement Proportion, F (2, 20) = 7.64, p < .01, and between Vision and Velocity Criteria, F (5, 50) = 5.18, p < .01. As expected, correlation coefficients increased as the movement proportion increased (i.e., from 25% to 50% and from 50% to 75% of MT). The interaction between Vision and Proportion arose because higher R2 values were observed in VHigh compared to VLow


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conditions at 25% of MT. Lastly, post-‐hoc analyses of the Vision by Velocity Criterion interaction only revealed higher correlation coefficients for the VHigh compared to the VLow condition at the 1.2 m/s criterion (see Figure 4).2 Discussion Differences between the VHigh and VLow conditions were found in the accuracy (i.e., CE) and consistency (i.e. VE) of the movement endpoints. Movements were more consistent (i.e., lower VE), but were less accurate (i.e., larger target overshoot) in the VHigh compared to the VLow conditions. The current CE results are consistent with those of Experiment 1. Again, accuracy effects can be explained by the emphasis placed on endpoint accuracy instead of speed (Elliott et al., 1991; Westwood et al., 2003). Further, endpoint consistency was better in the VHigh than in the VLow conditions with the 0.03 and 0.8 m/s criteria. We suggest that vision early in a movement may be providing the most important information for impulse regulation. This result corroborates the findings of Hansen et al. (2005) where individuals choose to see early in the movement when provided the opportunity to self-‐ control the acquisition of vision (see also Hansen 2010). In addition, this finding provides evidence that there is a critical period early in limb trajectory where visual information is most valuable for the efficient engagement of the underlying visual-‐motor processes.

2 More liberal contrasts performed using paired t-‐tests suggest that the Vision and Velocity Criteria interaction can also be explained by lower R2 values in VHigh than VLow at the 0.03 m/s velocity criterion (p = .02) but higher R2 values for VHigh compared to VLow at the 1.1 and 1.2 m/s velocity criteria (ps = 0.05 & 0.003, respectively).


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The analyses also revealed that MT was longer in the VHigh compared to the VLow conditions with the 1.0 and 1.1 m/s criteria. As such, there was a temporal cost when vision was provided above the 1.0 m/s velocity criteria. These longer movement durations were observed along with longer TAPV in the VHigh compared to the VLow conditions with the 0.9 m/s, 1.0 m/s, and 1.1 m/s velocity criteria. These results indicate that providing vision when the limb is travelling at a high limb velocities results in a lengthening of the time to complete the movement and this temporal costs could well be due to the time required for the implementation of sensorimotor processes. In the 1.2 m/s conditions, there was perhaps not enough time between vision onset and peak velocity for visual information to be processed and employed during the trajectory. As proposed by Elliott et al. (2010), impulse regulation processes are initiated earlier in the trajectory compared to limb-‐target regulation processes. In this second experiment, VHigh yielded lower variable error values than VLow for both the 0.03 and 0.8 m/s criteria conditions but not in the 0.9 m/s criterion conditions (see Figure 3B). In contrast, our measures of online trajectory amendments remained comparable across Velocity Criteria conditions, up to 1.1 m/s3. As well, providing vision above 1.2 m/s yielded comparable R2 values than in the NV condition (see Figure 4). Altogether, variable error can be associated with impulse regulation processes and the presence of online trajectory amendments, as revealed through the R2 analyses, can be associated with limb-‐target regulation processes. It is indeed possible that variable error emerges from the early 3 While R2 values were higher in VHigh compared to VLow conditions at 25% of MT, such result should be taken with caution because the associated MTs were also different.


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monitoring of the limb impulse leading to optimized endpoint variability (i.e., regardless of target position) while online trajectory amendments, as measured through R2 values, reflect later amendments based on the contrast between actual limb position and desired movement endpoint. It is understood that associating variable error with impulse regulation and R2 values with limb-‐target regulation remain largely speculative at this point. Indeed, because our liquid crystal goggles manipulated the entire visual field, it is not known which type of visual information was actually used at the various limb velocities. It would be most relevant to conduct further experimentations with these velocity criteria while independently manipulating vision of the limb and target (e.g., Heath, 2005; Sarlegna et al., 2003). Nevertheless, we still demonstrate that the visual information necessary for accurate corrections and the initiation of these corrections are primarily gathered at different times, positions, or limb velocities. These findings are consistent with the theoretical underpinnings of the multiple-‐processes model of goal-‐directed action (Elliott et al., 2010). General Discussion & Conclusion Our results demonstrate that vision provided when the limb travels below up to 1.1 m/s (or corresponding times, or position during a trajectory) is crucial for the execution of online correction processes. However, the efficiency of those corrections seems to be determined by whether individuals were also provided with vision when the limb traveled between 0.8 and 0.9 m/s. The implication is that the visual information gathered as the limb travels at moderate velocities (0.8-‐0.9 m/s) is employed to create and optimize movement corrections that are then confirmed, refined, and initiated based on visual information


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gathered at higher limb velocities (i.e., up to 1.1 m/s). At least within the context of the current experiment, we propose that the impulse and limb-‐target regulation processes may be optimized using vision from different limb velocity ranges. While there is ample evidence that the central nervous system can efficiently use visual information throughout most of the limb trajectory (Elliott et al., 1991), sub-‐ processes of online control appear to be optimal at specific times, positions, or limb velocities during a goal-‐directed action. It is important to note that these effects were observed under random visual manipulations (i.e., participants could not anticipate the upcoming vision condition), which is a limitation to this study. Specifically, not knowing which vision condition will be presented can induce fewer online control processes compared to when it is know (e.g., Elliott et al., 2004; Hansen et al., 2006; Zelaznik et al., 1983). In contrast, presenting vision conditions in a blocked fashion would have likely led to altered trajectory profiles between experimental conditions (e.g., blocking VHigh and VLow conditions). As shown by Carlton (1981: Experiment 1), providing visual feedback for the last 25% or 7% of the amplitude induces longer movement times compared to providing vision over the entire trajectory. As such, the randomized condition presentation may have limited the extent to which visual feedback was used but a blocked presentation would have altered the normal reaching patterns. Nevertheless, the present study supports and builds upon the multiple-‐process model of online control (Elliott et al., 2010). At the very least, it appears that the basic concept of pseudo-‐continuous control of goal-‐directed action (Elliott et al., 1991) is less supported than before, while concepts of discrete and iterative online control models (e.g., Begss & Horwarth, 1972; Meyer et al., 1988) present

THE UTILITY OF VISION some fundamental concepts that may need to be revisited.

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Table 1 Means and between-‐participant standard deviations for movement time (MT: ms), time to peak velocity (TtoPV: ms), time after peak velocity (TaPV: ms), constant (CE: mm) and variable (VE: mm) error in the primary (prim) and secondary (sec) movement directions as a function of visual condition, for each velocity criteria


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Figure 1. Average velocity profile for all vision conditions. Dashed line represents the velocity cutoff and the error bars represent the average between-‐ subject standard deviation.


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Figure 2. Constant error (A) and variable error (B) in the primary axis of the movement as well as constant error (C) and variable error (D) in the secondary axis of the movement in Experiment 1. The error bars reflect the standard deviation.


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Figure 3. Constant error (A) and variable error (B) in the primary axis of the movement in Experiment 2. The error bars reflect the standard deviation.


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Figure 4. Square correlation coefficient between limb positions in the trajectory and limb position at movement end as a function of the velocity criteria. The error bars reflect the standard deviation.