Introduction

Humans are adept at reaching to targets in cluttered environments. This suggests the existence of a sophisticated goal-directed reaching system that can rapidly select targets and plan trajectories toward them while avoiding potential obstacles. Experiments examining reaching and grasping movements in the presence of non-target objects have indeed shown that obstacles, distracting stimuli and other potential goal objects all effect the kinematics of the performed action (e.g. Castiello, 1996; Deubel & Schneider, 2004; Jackson, Jackson, & Rosicky, 1995; Jax & Rosenbaum, 2007; Mon-Williams, Tresilian, Coppard, & Carson, 2001; Tipper, Howard, & Jackson, 1997; Tresilian, 1998; van der Wel, Fleckenstein, Jax, & Rosenbaum, 2007). Those studies specifically quantifying obstacle avoidance behaviour have shown that objects interfering with the transport or grip phase of a movement result in automatic deviations away from the obstacle and a slowing of the reach (Chapman & Goodale, 2008; Mon-Williams et al., 2001; Tresilian, 1998). Our own work (Chapman & Goodale, 2008) exploring this behaviour was motivated by the finding from the patient literature that the dorsal visual stream plays a key role in obstacle avoidance. These patient studies demonstrate both preserved avoidance in patients with preserved dorsal stream function (i.e. neglect (McIntosh, McClements, Dijkerman, Birchall, & Milner, 2004; Milner & McIntosh, 2004),

extinction (McIntosh, McClements, Schindler et al., 2004; Milner & McIntosh, 2004) and visual form agnosia (Rice et al., 2006)), and impaired avoidance in patients with damage to the dorsal stream (i.e. optic ataxia (Rice et al., 2008; Schindler et al., 2004)). In

1 _{A version of this chapter has been published. Chapman, C. S., & Goodale, M. A. (2010). Seeing all the}

obstacles in your way: the effect of visual feedback and visual feedback schedule on obstacle avoidance while reaching. Exp Brain Res, 202(2), 363-375.

extending the patient work, we adopted a similar paradigm, a reaching task where participants made reaches to a target strip (i.e. specified in depth but not direction) without visual feedback of the hand. Removing visual feedback at movement onset is an important aspect of the studies with patients as it ensures that one can isolate deficits in movement planning as opposed to deficits in online control. In the real world, however, vision of your hand is rarely occluded at the precise moment you begin to move; nor do you typically perform reaches to a target whose location is not specified. It is possible that denying participants‘ vision during the reaching movement affected their avoidance behaviour. For example, knowing that they would not be able to see their hand, the obstacle or the goal during the reach, participants may have reacted cautiously and given more room for error in their reach, leading to increased avoidance. To test this

possibility, the current study examines obstacle avoidance behaviour during reaches to a specific target location when vision of the hand is available in flight (V) with the same reaches when vision is not available (NV).

However, the possible effects of visual feedback during obstacle avoidance may not be as simple as an effect of allowing or removing vision. Instead, effects could be driven by the expectation of having or not having vision during an upcoming reaching. Indeed, when manipulating visual feedback in reach tasks, it has been shown that not only are there differences between V and NV trials (Elliott, Binsted, & Heath, 1999; Elliott, Carson, Goodman, & Chua, 1991; Elliott, Chua, Pollock, & Lyons, 1995; Heath, 2005; Heath, Westwood, & Binsted, 2004), but also that the order of the V and NV trials within an experimental block plays an important role (Chua & Elliott, 1993; Elliott & Allard, 1985; Elliott, Helsen, & Chua, 2001; Heath, Rival, & Neely, 2006; Jakobson & Goodale, 1991; Khan, Elliott, Coull, Chua, & Lyons, 2002; Neely, Tessmer, Binsted, & Heath, 2008; Whitwell & Goodale, 2009; Whitwell, Lambert, & Goodale, 2008; Zelaznik, Hawkins, & Kisselburgh, 1983). It should be noted that the majority of the

aforementioned studies manipulated vision by occluding both the target and the limb while two occluded only the limb (Chua & Elliott, 1993; Elliott et al., 1995). One study provided a direct test of the effects of target and limb occlusion separately and together (Heath, 2005), and demonstrated that occlusion of both the limb and target yields the most robust NV versus V differences. In one early study investigating the role of

feedback and feedback schedule in a simple reach-to-point task, researchers showed that lateral errors were larger for NV trials than V trials, and importantly, that the difference in error between NV and V trials was larger when trials were blocked as opposed to randomized (Zelaznik et al., 1983). That the blocked schedule, where all the trials of one type of feedback are received consecutively, shows larger differences than a randomized schedule, where the visual feedback on any given trial is randomly determined, has at least two possible explanations. It could be the case that the unpredictability of feedback associated with the randomized schedule caused participants to adopt a different strategy from the one they used in the blocked schedules where feedback was perfectly

predictable. In other words, in the blocked trials, they could use their knowledge of what was going to happen next to plan the action on the upcoming trial. Alternatively, it could be the case that the repetitive nature of the blocked trials causes the motor system to simply repeat what it did on the last trial(s) and to use knowledge about the nature of the schedule to predict what will happen next. To test between these alternatives, Zelaznik and colleagues (1983) ran an experiment where the availability of visual feedback alternated from trial to trial. With an alternating schedule, the predictability of the type of feedback is high, just as high as it is in the blocked trials. But in terms of making use of trial to trial consistency, the alternating schedule is no different from the randomized schedule. In fact, given that there are likely to be small runs of consecutive trials with the same feedback type in the random schedule, the alternating schedule has minimal

consistency. Therefore, if reaching behaviour with the alternating schedule resembles reaching behaviour with the blocked schedules, and has large NV versus V differences, then this would suggest that participants can use their knowledge about the nature of the schedule to predict what to do on an upcoming trial. However, if the alternating schedule is more like the random schedule with small differences between NV and V, then this would suggest that the motor system controlling reaching in these situations is simply relying on trial history. Under the alternating schedule, Zelaznik et al. found significant differences in errors between NV and V trials. Unfortunately, as the alternating condition was run in a separate experiment in this study, the magnitude of this difference between conditions could not be compared to the blocked or random schedules. Neverthless, the

authors concluded that accuracy on V trials is superior to NV trials and that this

difference is independent of the type of feedback that was experienced on earlier trials. Since the Zelaznik et al. study (1983), there have been several studies targeting the differences in reaching and grasping kinematics between blocked and random visual feedback schedules. In reach to grasp studies (Fukui & Inui, 2006; Heath et al., 2006; Jakobson & Goodale, 1991; Whitwell & Goodale, 2009; Whitwell et al., 2008), the difference between V and NV trials in blocked schedules is characterized by a larger opening of the hand and more time spent in the later phase of the movement for NV as compared to V trials, presumably reflecting the need to build in a margin of error when visual feedback is not available. In one grasping study, when trials were presented with a random feedback schedule (Jakobson & Goodale, 1991), the hand now opened wider on both V and NV trials as compared to the blocked-V trials. These results suggest that when performing grasping during a random feedback schedule, the uncertainty causes V trials to be treated like NV trials. More recent studies both support (Heath et al., 2006) and challenge (Fukui & Inui, 2006; Whitwell & Goodale, 2009; Whitwell et al., 2008) this view. Those that challenge the view show that with longer reaches, V trials, even during random schedules, show smaller hand openings (albeit the opening remains larger than the blocked-V trials). This suggests that with longer movements, there is enough time for participants to use visual information, despite its unpredictability (Fukui & Inui, 2006; Whitwell & Goodale, 2009; Whitwell et al., 2008). Other research has supported the original view that in random schedules with high uncertainty, participants adopt a strategy that matches the blocked-NV trials – that is they prepare themselves as though they will not receive, and therefore cannot maximally use, vision (Heath et al., 2006). Only one of the above grasping studies directly tested the effects of predictability by introducing an alternating feedback schedule (Whitwell et al., 2008). Here, the results from the alternating and random schedules showed identical differences between NV and V trials. This gives strong support for the idea that differences between blocked and random schedules have more to do with the motor system using recent trial history than with the employment of a cognitive strategy based on predictability. Recently, this view has received more support in a grasping study showing that the effect of having vision

(i.e. smaller hand openings) accumulates over trials, such that consecutive V trials show progressively smaller hand openings, while consecutive NV trials show progressively larger hand openings (Whitwell & Goodale, 2009).

Although some progress has been made in determining whether the motor system uses conscious prediction or trial history to cope with changes in the availability of visual feedback during grasping, there is almost no work that has examined this question in the case of pointing, particularly in the context of a more complicated reaching task like obstacle avoidance. Since the initial Zelaznik study (1983), pointing on blocked trials with visual feedback (V) has been shown to result in faster reaction times (Khan et al., 2002), increased accuracy (Chua & Elliott, 1993; Elliott & Allard, 1985; Heath, 2005; Heath et al., 2004; Khan et al., 2002), and more time spent during the later phases of movement (i.e. decelerating, or time spent after peak velocity) (Chua & Elliott, 1993; Elliott et al., 1991; Khan et al., 2002) than pointing on blocked trials without visual feedback (NV). In a randomized schedule of V and NV trials, the differences between feedback trials tend to become smaller (Chua & Elliott, 1993; Elliott & Allard, 1985; Khan et al., 2002; Neely et al., 2008; Zelaznik et al., 1983). Reach-to-point studies that have included an alternating feedback schedule (Khan et al., 2002; Zelaznik et al., 1983), which theoretically could have addressed whether the motor system makes use of

conscious knowledge or trial history, have not run this schedule in the same experiment alongside blocked and random schedules making direct comparison difficult. In both these studies, however, the alternating schedule still showed a significant difference between the V and NV trials, providing at least some suggestion that pointing movements made with an alternating schedule may be closer to those made with blocked schedules. Despite the ambiguity surrounding the results of the alternating schedule, and the

reduction of the NV/V difference when randomized, it is clear that whenever visual feedback is available, it can be used to increase endpoint accuracy – usually resulting in movements with longer deceleration phases (for review see: Elliott et al., 2001). A sensitive technique that reveals the accuracy benefits of V trials is to compare how well positions throughout the movement predict the variability observed in the movement endpoint (Heath, 2005; Heath et al., 2004; Neely et al., 2008); for extensive review of this and other procedures analyzing variability see: (Khan et al., 2006; Messier & Kalaska,

1999). In general, NV trials show higher correlations between mid-reach or late-reach positions and endpoint than the V trials do. This is likely due to the fact that visual feedback can modulate the late reach response, and thus reduce how well the position of an earlier point predicts endpoint position. This correlation analysis was recently used in a reach-to-point study that examined blocked versus random feedback schedules (Neely et al., 2008). This elegant study replicated the finding that hand position at 50% and 75% of movement during blocked-NV trials showed a much higher correlation with endpoint than the 50% and 75% position of blocked-V trials. Importantly, both NV and V trials in the random schedule showed the higher correlation between earlier position and endpoint that was characteristic of the blocked-NV trials. This provides strong evidence that in this reach-to-point study, a random schedule led to an offline control strategy whereby all randomly ordered trials were treated like NV trials.

While the primary aim of the current study is to investigate how the availability of vision affects obstacle avoidance behaviour, as the above discussion demonstrates it is equally important to consider what effects the expectation of vision (or not) and recent trial history may have in this experiment. It is possible that a more complicated reaching task involving the encoding and avoidance of potential obstacles will reveal different

strategies than have been shown for simpler pointing tasks that have previously investigated the effect of feedback schedule. Thus, in the current experiment, every participant completed both NV (both limb and target occluded) and V trials under the three feedback schedules: blocked, random, and alternating. In addition to the kinematic measures we have previously used to test for the effects of obstacles (Chapman & Goodale, 2008), we included both an endpoint variability analysis and the correlation analysis discussed above in an effort to quantify the feedback and schedule effects in this experiment. We predicted that participants would still show sensitivity to the position of obstacles on V trials and when reaching to a specific target. It is possible, however, that with visual feedback participants would be less conservative executing their movements in the presence of obstacles and as a consequence their deviations away from the

obstacles would decrease; this would be manifest as an interaction between obstacle position and the availability of feedback. With respect to visual feedback and visual feedback schedule, previous results from reach-to-point studies suggest that NV trials,

regardless of schedule, will result in more endpoint variability, higher correlations

between mid-reach positions and endpoint and shorter deceleration phases. In contrast, V trials should show an effect of schedule, with blocked-V trials showing prolonged

deceleration and lower correlations between position and endpoint than the random-V trials. In all cases, we would predict that visual feedback will reduce endpoint variability, but perhaps to less of an extent when trials are randomized as compared to blocked. The critical case of alternating V and NV trials remains an open and interesting question and we were not certain what to expect.