The dual role of vision in sequential aiming movements
Research highlights
► Variability at the first target was greater for two than single segment movements. ► Variability increased from the first to second segment when vision was occluded. ► First segment distance was negatively correlated with second segment distance. ► Results support both movement constraint and movement integration hypotheses.
Introduction
In many everyday tasks, actions comprise several components that are executed in sequence (e.g., dialling a telephone, catching and throwing a ball, and grasping and drinking a glass of water). Researchers have adopted numerous approaches to understanding how multiple segment movements are prepared and executed. While an initial surge of research was devoted to understanding the relation between reaction time (RT) and the number of response segments (or elements) (e.g., Henry and Rogers, 1960, Klapp et al., 1974, Sternberg et al., 1978), more recent efforts have been focused on the time it takes to execute movements (e.g., Adam et al., 2000). With regard to the latter, the typical finding is that movement times (MT) to the first target in two target movements are longer than in single target movements. This one-target advantage in movement time (OTA) emerges regardless of participants' hand preference and hand used (Helsen et al., 2001, Lavrysen et al., 2003), and is resistant to practice (Lavrysen et al., 2003) and the occlusion of vision (Lavrysen, Helsen, Elliott, & Adam, 2002).
Different interpretations have been put forward in an attempt to explain the one-target advantage in movement time. These vary in the extent to which the lengthening of MT can be attributed to planning versus on-line control processes. According to the movement constraint hypothesis, movement to the first target is planned more precisely to reduce the variability of endpoints at the first target (Fischman & Reeve, 1992). Based on the assumption that variability increases as the action sequence progresses, the reduction in variability at the first target would ensure that the accuracy demands at the second target are met. Other researchers have proposed an online programming explanation for the one-target advantage (e.g., Chamberlin & Magill, 1989). According to the online programming hypothesis, movement sequences are not prepared entirely prior to response initiation. When responses are relatively complex, participants may program the initial segments during RT but then delay the programming of later segments until after the RT interval, provided there is sufficient time during movement execution. Hence, MT increases due to the additional processing requirements of programming the second movement during the execution of the first movement. By contrast, according to the movement integration hypothesis (Adam et al., 2000), program construction of the entire response is performed prior to response initiation. However, in order to facilitate a smooth and efficient transition between segments, the implementation of the second segment is performed online concurrent with the execution of the first segment. The increased cognitive control associated with the implementation of the second segment during the production of the first segment in two target responses leads to interference and hence the lengthening of MT to the first target.
An issue that is central to understanding how multiple target movements are prepared and executed is the extent to which individual segments are organized together or separately. One factor that influences the degree of overlap between movement segments is the accuracy requirement of the task. It has been shown that reducing the size of the second target lengthens not only the movement times from the first to the second target but also the duration of the first movement segment (Rand and Stelmach, 2000, Ricker et al., 1999). This implies that sequential aiming movements are not controlled separately and that the control characteristics of one segment influences that of the other segment. However, the interdependency between segments is significantly reduced when the accuracy demands at the first target are high. When the size of the first target is relatively small, pause times are lengthened thereby disrupting the transition between the two movements (Adam and Paas, 1996, Adam et al., 1995, Adam et al., 2000, Rand and Stelmach, 2000). In such cases, movements to targets are organized separately and independently, and hence there is little overlap of control processes.
A second factor that influences the organisation of multiple target aiming movements is the availability of visual feedback. When vision is occluded over the first movement segment, participants take longer to initiate their movement and movement times to the first target are increased (Lavrysen et al., 2002). It seems that when vision is not available, participants prepare more precise motor programs (i.e., more constrained) so that there is less reliance on online correction processes. Removing vision from the first movement segment has also been shown to increase pause times at the first target as well as movement times from the first to the second target (Ricker et al., 1999). These findings imply that the transition between the first and second elements was mediated by vision. When vision was removed from the first segment, there was less overlap between control processes and hence the implementation of the second element occurred during the dwell time at the first target rather than during the execution of the first segment. Interestingly, removing vision over the second movement segment has also been shown to increase movement times to the first target (Lavrysen et al., 2002). One implication of this finding is that producing a more constrained first movement would reduce the variability of endpoints at the first target. Hence, there would be a less need to adjust the parameters of the second segment since the initiation point of the second segment is more consistent. Also, increased movement times to the first target could reflect more precise planning of the second movement during the execution of the first segment. This would reduce the need to modify limb trajectories to the second segment when vision is occluded.
The primary purpose of the present study was to test the assumption that vision plays a dual role in the control of sequential aiming movements. Similar to single target movements, vision may be used within each movement segment to correct errors in the limb trajectory as the limb approaches the respective targets. In addition to its role in modifying limb trajectories within each segment, vision may be playing a critical role in the transition between segments. This could take the form of visually monitoring the endpoint location at the first target in order to adjust the parameters for the second movement (Khan, Lawrence, Buckolz, & Franks, 2006). For instance, in order to compensate for a longer distance travelled on the first movement, the amplitude of the second movement would have to be shortened, and vice versa. It is also plausible that visually based error corrections during the first segment would lower variability at the first target thereby making the implementation of the second segment more efficient because the need for modifying its parameters is minimized.
We investigated the role of visual feedback in sequential aiming movements from a slightly different angle to that of past research. In previous investigations, movements have been performed to targets of a finite size with the instruction to move as fast as possible (i.e., time minimization). In the current study, point targets were used and movement time to the first target was constrained (i.e., 450 ms). Hence, accuracy rather than movement time became our primary measure of interest. Many investigators have used time constrained movements to minimize tradeoffs between speed and accuracy (Khan et al., 2003, Proteau et al., 1992, Schmidt et al., 1979; see Carlton, 1994 for a discussion of time minimization versus time constrained movements). Also, constraining movement times would minimize the possibility of strategically redistributing planning and control processes when movement times are free to vary under manipulations of visual feedback. Hence, examining movement accuracy and limb trajectory kinematics with and without vision under time constrained conditions would offer a direct test of the role of visual feedback in sequential aiming and the underlying assumptions of both the movement constraint and movement integration hypotheses.
The use of visual feedback in correcting errors in the limb trajectory within each movement segment was investigated by analysing the variability in limb position at peak velocity and at the end of each movement segment for vision and no vision conditions (Khan et al., 2006; also see Messier and Kalaska, 1997, Messier and Kalaska, 1999). Variability at peak velocity would give a reasonable indicator of the extent to which errors arise from programming processes (Elliott, Helsen, & Chua, 2001). Evidence for visually based movement modifications would be revealed from a greater reduction in limb trajectory variability from peak velocity to the end of the movement segment in the vision compared to no vision condition.
In order to assess the role of vision in the transition between movement segments, we adapted the correlation analysis used by Elliott, Binsted, and Heath (1999) for single target movements. In their study, the distance travelled at peak velocity was correlated with the distance travelled from peak velocity to the end of the movement. Negative correlations would imply that adjustments to the movement occurred during movement execution. For example, a large distance travelled at peak velocity would be compensated for by a shorter distance between peak velocity and the end of the movement. Elliott et al. showed that negative correlations existed between the distance at peak velocity and the distance between peak velocity and the end of the movement in both vision and no-vision conditions. However, stronger correlations were evident when visual feedback was available, thereby providing evidence for the important role of vision during movement execution. We modified the analysis of Elliott et al. (1999) for two segment movements by correlating the distance travelled on the first segment and the distance travelled on the second segment. If vision is being used to adjust and implement the parameters for the second movement, we would expect a negative correlation between the distances travelled on both elements.
The underlying assumption of the movement constraint hypothesis is that variability increases as movement progresses and that variability at the first target is contingent on the accuracy demands of the second target (Sidaway, Sekiya, & Fairweather, 1995). When participants are instructed to minimize movement time while being accurate, movement times to the first target are said to be lengthened in order to reduce variability at the first target so that the accuracy demands at the second target are met. Based on this explanation, we expected that when movement times to the first target are constrained and not allowed to vary, variability at the second target would be greater than at the first target when vision is not available. This is because participants would not have the flexibility to constrain the first segment through more precise movement planning (i.e., longer movement times) and hence variability increases would emerge at the second target. However, while we expected that variability would increase from the first to the second segment when vision is occluded, reductions in variability between peak velocity and the end of the first segment would curtail these increases in variability when vision is available.
Along the lines of the movement integration hypothesis, the OTA arises due to interference caused from the increased cognitive demands associated with implementing the second element during the execution of the first segment. Hence, if movement times to the first target are constrained, the interference caused by the overlap of control processes during the first segment would result in increased errors and variability at the first target for the two compared to single target movements. In effect this finding would be the accuracy equivalent of the OTA. Again we would expect accuracy to be higher in the vision condition since visually based corrections may compensate for errors caused from overlapping control processes.
Section snippets
Participants
Twenty four volunteers (males = 18 and females = 6; ages 18–32) participated in the study. All participants were self-declared right hand dominant, and had reported normal or corrected to normal vision. They all signed consent forms before taking part in the experiment and the study was carried out according to the ethical guidelines stated by the Ethics Committee of the School of Sport, Health and Exercise Sciences, Bangor University, for research involving human participants.
Apparatus
Participants sat at a
Results
For the first target, MT1, CEy1, and CEx1 were analysed using separate 2 Target (1 Target, 2 Targets) × 2 Vision Condition (vision, no vision) repeated measures ANOVAs. For two target movements, PT, MT2, CEy2 and CEx2 were analysed using repeated measures t-tests with vision as the independent variable. Interactions were broken down using Tukey post hoc tests (p < 0.05). Means and standard deviations are reported in Table 1.
Discussion
Past research has demonstrated that the execution of early segments in a multiple target aiming response is influenced by the presence and properties of later segments (Adam et al., 1995, Rand et al., 1997, Rand and Stelmach, 2000). Based on this evidence, it has been suggested that individual components in an aiming sequence are not prepared and controlled independently. Whereas previous investigations have examined how movement times are influenced by factors such as the number of response
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2018, Human Movement ScienceCitation Excerpt :Vision during these multiple-segment movements ensured a smooth transition between both movements, resulting in the two movements being executed as one. Khan et al. (2011) concluded that vision helps with not only error correction (i.e., movement constraint hypothesis) but with movement integration during the first movement (i.e., movement integration hypothesis). Though the OTA can be explained by the movement constraint hypothesis or the movement integration hypothesis, it seems that when investigating this phenomenon under an array of methodologies, both hypotheses have been supported.
The integration of sequential aiming movements: Switching hand and direction at the first target
2016, Acta PsychologicaCitation Excerpt :Aside from the combined central and peripheral process explanation for the increased PTs in the two-target single-hand tasks, it is possible that the increased PTs are a result of additional central processes associated with the movement constraint hypothesis. That is, in the single hand tasks participants adopt central control strategies that involve both the implementation of the pre-programmed motor commands of movement two (i.e., the movement integration hypothesis) together with applying online control mechanisms to ensure accurate endpoint locations of movement one (i.e., the movement constraint hypothesis) (Khan et al., 2011). The reduced PTs of the two target two hand movements relative to the two target single hand movements, might therefore be due to the removal of the processes associated with constraining the endpoint of the first movement.
Common vs. independent limb control in sequential vertical aiming: The cost of potential errors during extensions and reversals
2016, Acta PsychologicaCitation Excerpt :The use of these z-scores was intended to explore the extent to which participants used online sensory feedback acquired within the first movement component to subsequently update the movement executed in the second component. Typically, strong negative correlations are synonymous with an enhanced use of online sensory feedback, whilst weak, or positive correlations, reflect feedforward limb-control (see Khan, Sarteep, Mottram, Lawrence, & Adam, 2011). For example, if the limb is moved with a higher magnitude of peak acceleration and peak velocity, and thus travels further than the target in the first component, an adjustment should be made to reduce the displacement of the second component in order to reach the second target.