Manipulating peripheral visual information in manual aiming: Exploring the notion of specificity of...

Manipulating peripheral visual information in manualaiming: Exploring the notion of speci®city of learning

Simon Bennett *, Keith Davids

Department of Exercise and Sport Science, Manchester Metropolitan University, Hassall Road, Alsager,

Stoke-on-Trent ST7 2HL, UK

Abstract

The purpose of this study was to determine the e�ects of the addition and removal of pe-

ripheral vision in a simple manual aiming task. In Experiment 1, subjects (n� 5) practised a

manual aiming task that emphasised the central visual information available during the ®nal

homing-in phase, for a moderate number of trials in either a target-only or normal-vision con-

dition. Following the practice phase, subjects were transferred to the other condition. Spatial

accuracy data indicated that the subjects' error increased following the removal and addition

of the relevant central visual information. In Experiment 2, the task constraints were manip-

ulated such that the relevant visual information was available through the peripheral retina

only. Subjects (n� 5) practised the task for a moderate and extensive number of trials in either

a target-only or normal-vision condition. Following each practice phase, subjects were trans-

ferred to the other condition. Spatial accuracy data indicated that while subjects' error in-

creased following the removal of peripheral vision, there were no detrimental e�ects

following its addition. Spatial error was reduced when transferring to the normal-vision con-

dition. Kinematic data indicated that, on transferring to the normal-vision condition, subjects

adapted their response to exploit the added visual information, as evidenced by an increased

number of movement corrections in the deceleration phase. The positive e�ects of the addition

of peripheral vision are not consistent with the notion of increasing speci®city to the informa-

tion available during practice. Ó 1998 Elsevier Science B.V. All rights reserved.

Human Movement Science 17 (1998) 261±287

* Corresponding author. Tel.: +44 161 247 5533; e-mail: [email protected].

0167-9457/98/$19.00 Ó 1998 Elsevier Science B.V. All rights reserved.

PII: S 0 1 6 7 - 9 4 5 7 ( 9 7 ) 0 0 0 3 3 - X

PsycINFO classi®cation: 2300; 2320; 2323

Keywords: Speci®city of learning; Manual aiming; Peripheral vision; Constraints

1. Introduction

The question of how the individual learns to control various motor skillshas received a substantial amount of empirical attention (e.g., Jeannerod,1984; Sporns and Edelman, 1993; Zelaznik et al., 1983). Consequently,how the individual's use of perceptual information to support such skillschanges as a function of learning has been the subject of numerous studies(Beaubaton and Hay, 1986; Adams, 1972). Due to the possible implicationsfor areas such as ergonomics, neuroscience, physical education, and humanmovement science, there have been a variety of approaches to this issue. Still,although examined from di�erent theoretical backgrounds, a reoccurringquestion has been whether learning is speci®c to the conditions of practice,or whether it is a more general process in which particular abilities arelearned (for a review see Bennett, 1996).

While early research on this issue tended to be driven by physical educa-tors interested in whether an individual possessed general or speci®c motorabilities (e.g., Henry, 1968), Proteau and colleagues' have recently introduceda revised speci®city of learning hypothesis that deals with the issue of howlearning strengthens the relationship between the available sensory informa-tion and a speci®c motor response (e.g., Proteau et al., 1987). Using a transferparadigm in which visual information and KR was removed or added after aperiod of practice in its presence or absence respectively, they provided awealth of evidence for the speci®city of learning proposition in manual aim-ing (e.g., Proteau, 1992; Proteau et al., 1987). 1 Further, they highlighted twodi�erent mechanisms to account for the disruption in manual aiming perfor-mance following either the removal or addition of visual information (Pro-teau, 1992; Ivens and Marteniuk, 1995; Proteau et al., 1992; Temprado etal., 1996). When vision was removed, it was suggested that subjects were leftwithout a source of information, that through practise, had been tightly cou-

1 It should be noted that Proteau and Marteniuk (1993) failed to support the speci®city of learning

hypothesis, and suggested that the predictions should be treated with some caution.

262 S. Bennett, K. Davids / Human Movement Science 17 (1998) 261±287

pled with other sources of information. Since individuals were left with anincomplete coupling, their aiming accuracy was predicted to deteriorate (Pro-teau et al., 1987). When vision was added, it was argued that the negativee�ects were the result of the con¯ict caused by the dominant central visualinformation (Proteau et al., 1992). That is, due to con¯ict, they neglectedthe previously attuned information in favour of the newly added visual infor-mation, and the accuracy of their manual aiming performance deterioratedaccordingly.

In the manual aiming task studied by Proteau and colleagues the con-straints demanded the need to accurately control both the direction andamplitude of the movement in order to decelerate the stylus in the ®nal hom-ing-in phase, and accurately position it on the ®nal target. Consequently,there was a high demand upon the need for information regarding the posi-tion of the stylus relative to the target provided through central vision (Carl-ton, 1981; Paillard and Amblard, 1985; Sivak and McKenzie, 1992). Bennettand Davids (1997) reported that the negative e�ects of the addition of visualinformation were not found when accurate performance in the manual aim-ing task studied did not depend on central visual information. In their man-ual aiming task the reliance on central visual information was reduced byshifting the demands of the ®nal homing-in phase such that subjects were re-quired to pass by the ®nal target as accurately as possible rather producing acontrolled contact with the target.

Recently, the crucial role that the interaction between environmental, taskand organismic constraints play on theory formulation has been highlightedby Newell (1986). 2 Having reviewed various models of learning relating toslow and fast movements, oscillatory and discrete movements, simple andcomplex movements, Newell (1989) pointed out that there tends to be a bi-directional relationship between task and theory speci®city. That is, in an at-tempt to provide internal validity, the manipulation of a narrow boundary ofa speci®c task's constraints has led to something more akin to a task descrip-tion rather than a generalisable theory of learning. This persistence in study-ing single versions of experimental tasks was also blamed by Newell as thereason why, ``...much of the current theorising is also task speci®c and unableto accommodate, for example, the adaptive exploratory actions of both chil-

2 For a more detailed account of constraints, the reader is directed to the works of Newell (1986, 1991),

Kugler and Turvey (1987), and MacKenzie (1992).

S. Bennett, K. Davids / Human Movement Science 17 (1998) 261±287 263

dren and adults or even elementary task manipulations'' (p. 95). Such aposition is reminiscent of the comments made by Battig (1978) when referringto the study typical of motor behaviour, ``...our thinking about scienti®c the-ory ± perhaps in the tradition of the physical sciences ± have been far too nar-row to cope with the complexities of human behavior'' (as cited in Schmidt,1980, p. 123).

Newell (1989, 1991) also suggested that an approach to developing amore generalisable theory should be based on a principled analysis of thee�ects of di�erent task constraints. That is not to say that one should sim-ply adopt a shotgun approach to examining tasks without a particular ra-tionale. Rather, the aim should be to seek to understand what it is aboutthe task constraints that causes certain e�ects. Only then may it be possibleto predict an outcome based on the knowledge of the tasks' particular con-straints. Recently, while there has been a resurgence of interest on the issueof speci®city of learning, there has also been some equivocality with dis-crepancies noted in data from tasks such as power-lifting (Bennett and Da-vids, 1995), beam walking (Robertson et al., 1994; Robertson and Elliott,1996), manual aiming (e.g., Bennett and Davids, 1997; Elliott et al.,1995), and one-handed catching (Savelsbergh and Whiting, 1992; Whitinget al., 1995). To date, it is still unclear why these di�erences have occurred.By following a line of investigation, in which the task constraints are ma-nipulated in an attempt to understand what it is about the task being stud-ied that causes an e�ect following a change of environment, it may bepossible to reconcile these data.

There are a variety of manual aiming tasks that provide di�erent con-straints on performance (Marteniuk et al., 1987). One such constraint iswhether the hand and stylus have to be moved through the air, or across asurface in order to make contact with a stationary target. Under the formertask constraints, the movement is not completely self-terminating since partof the deceleration phase occurs when the stylus makes contact with the sur-face. When the stylus is moved across a surface an active deceleration is re-quired since there is very little passive deceleration due to contact. In suchtasks it has been found that subjects reach peak acceleration earlier in themovement in order to spend more time decelerating in the homing-in phase(e.g., Adam, 1992; Milner and Ijaz, 1990). Also, during this homing-in phaseit has been found that subjects make more visually based movement correc-tions, as evidenced by a larger number of zero crossings and signi®cant devi-ations in the acceleration pro®le (Chua and Elliott, 1993). This type ofmovement organisation has been suggested to be a characteristic of manual


aiming tasks that emphasise the visual information available during the hom-ing-in phase. 3

Another task constraint on accurate manual aiming performance is thetype of information that is available during the two phases of the movement. 4

e the target to be reached (Abrams, 1992). In the present study, since the eyeposition is kept constant it is assumed that extraretinal information will alsobe constant. Only retinal information will be discussed. 4 For example, if thesubject maintains a central ®xation point whilst moving to a peripherally lo-cated target, the necessary visual information can only be provided throughthe peripheral retina. The hand and stylus are still transported through theperipheral visual ®eld, as in the typical manual aiming task. However, whenthe hand and stylus are in close proximity to the ®nal target position, the vi-sual information relating the position to the ®nal target can only be providedthrough peripheral vision. Since it has been suggested that the detrimental ef-fects following the addition of vision occur as a result of the con¯ict causedby the dominant central visual information available during the ®nal homing-in phase (Proteau et al., 1992), an interesting question is whether the samecon¯ict, and hence performance decrement result from the addition of pe-ripheral visual information? Such a ®nding would provide further under-standing of the speci®city of learning e�ects.

In order to cause con¯ict, the added information would presumably haveto be perceived to be important by the learner for reducing aiming error. It isa general ®nding that the availability of peripheral vision during the trans-port phase only does not lead to improvements in aiming accuracy comparedto a no-vision condition (e.g., see Beaubaton and Hay, 1986; Carlton, 1981;Prablanc et al., 1986; Temprado et al., 1996). The proposed reason for this isthat visual information available beyond approximately 40° in the periphery,does not a�ord information which bene®ts movement control (Paillard,1980). However, when peripheral vision of both the transport phase andhoming-in phase is available, subjects can use this information to signi®cant-

3 Whether an increased number of movement corrections occurs independently of the availability of

visual information, or if the number of modi®cations correlates with ®nal target accuracy has been the

subject of some debate in the literature (e.g. Elliott et al., 1991; Meyer et al., 1988).4 Since the work of Woodworth (as cited in Carlton, 1994), the generic manual aiming task has been

classi®ed in two distinct limb movement phases: transport and homing-in (Paillard, 1980; Todor and

Cisneros, 1985). Other researchers have also suggested that a movement preparation phase precedes the

transport phase (Prablanc et al., 1979). Here, both retinal and extraretinal information may be used to

locate the target to be reached (Abrams, 1992). In the present study, since the eye position is kept constant

it is assumed that extraretinal information will also be constant. Only retinal information will be discussed.


ly reduce directional aiming error (Bard et al., 1985), and to maintain accu-rate reaching and grasping performance (Sivak and McKenzie, 1992). 5 Wheninformation regarding the location of the moving hand and stylus in relationto the stationary target (i.e., the ®nal homing-in phase) is provided throughperipheral vision, aiming error is found to be signi®cantly less than that ex-hibited in a target-only condition. Thus, it would be expected that subjectspractising in a normal-vision condition, in which peripheral vision of boththe homing-in and transport phase was available, would perform better thanthose who practised in a similar target-only condition (see Bard et al., 1985;Sivak and McKenzie, 1992). Furthermore, with the removal of peripheral vi-sion of the moving hand and stylus and the structured environment duringtransfer, it would be predicted that subjects would experience a decrementin performance. Whether the detrimental e�ect on performance should in-crease as a function of the number of practice trials, because subjects becomeincreasingly dependent on this speci®c source of visual information, remainsunclear.

A typical characteristic of information available in the peripheral visual®eld is that, unlike information from the central visual ®eld, it has beenshown to bene®t accurate movement positioning when it is picked-up sub-consciously (Prablanc et al., 1986). Such a ®nding leads to the propositionthat peripheral vision may not be as dominant during manual aiming as cen-tral vision. On the basis of previous work (see Bennett and Davids, 1997), itwould be predicted that following the addition of relevant peripheral visualinformation, subjects would not experience con¯ict with the previously at-tuned information. If the added peripheral visual information did not causethe same con¯ict, then subjects would not experience the same problemsusing the newly added visual information. Therefore, the addition of visualinformation in a manual aiming task with constraints that removed the de-pendence on central vision, would not be expected to cause a decrement inperformance. Such a ®nding would have important implications for the de-velopment of knowledge regarding the speci®city of learning, and conse-quently a generalisable theory of motor learning.

In Experiment 2, a form of the manual aiming task in which the hand-heldstylus is moved across a surface is used. This is quite di�erent to the under-swinging manual aiming task used by Proteau and colleagues. For example,

5 Still, aiming is generally more accurate when the target to be reached is ®xated in central vision and

the hand is transported through the periphery (Prablanc et al., 1979).


previous research has suggested that more emphasis is placed on the need toactively decelerate the movement in order to make contact with a ®nal targetin the former task. While the main thrust of Experiment 2 was to examine thein¯uence of the type of information manipulated, the di�erences between thetwo tasks could act as a further mediating variable. Therefore, before any ex-perimental manipulation of the type of information available in this task, itwould ®rst be necessary to replicate the ®nding that both the addition andremoval of visual information caused decrements in performance of the aim-ing task in which the stylus is moved across a surface. This was the purposeof Experiment 1.

2. Experiment 1

2.1. Method

Subjects. Ten male and female volunteers (Age, Mean 19.5 y, SD 2.7)served as subjects. All reported to be right-hand dominant, and had normalor corrected-to-normal vision. Subjects were informed of the requirements ofthe experiment in both verbal and written form. They informed the experi-menter they understood the instructions and then gave their written consentto participate.

Task and apparatus. Subjects were required to perform 240 manual aimingtrials with a hand-held stylus on a digitising tablet (TDS LC series) with thenon-dominant left hand. They were informed only about the type of environ-ment in which they would be practising the task. No details were given re-garding the experimental design, and the use of a transfer condition. Twotargets (¯at surface LEDs powered by a 6V mains power pack) were placedin a piece of black-painted hardwood, covered with clear plexiglass, andmounted on top of the digitising tablet. Subjects were seated facing the tabletwhich was located on a 1 m high table. The output signal from the digitisingtablet was sent to a computer (Commodore 486DX) located 2 m to the sub-jects' right. Here the signal was converted from space±time co-ordinates inthe tablet reference frame, to real world space±time co-ordinates. The col-our-coded targets (diameter � 2 mm) were arranged in a triangular forma-tion which resulted in a movement of 250 mm on the y-axis and 90 mm onthe x-axis (see Fig. 1). A red ®ltered LED was used for the ®nal target inorder to reduce the ambient level of illumination at this critical region. Anorange ®ltered LED was used for the start target.


Design and procedure. Subjects were randomly assigned to one of two prac-tice conditions: N (practising in a normal-vision environment) and TO (prac-tising with an illuminated target-only in an otherwise dark room). Subjectsreceived a moderate number of trials (n� 200) in the practice phase. Thiswas partitioned into ®ve blocks of forty trials with a 2-min intra-block delay.After a further 5-min post-practice delay, they received a further 40 transfertrials. The experimental phases were organised in a cross-over transfer design(see Bamford and Marteniuk, 1988; Elliott et al., 1995; Fumoto, 1981). Onegroup transferred to the normal-vision condition after practice in the darkwith the target-only visible, while the conditions were reversed for the othergroup. The number of trials and the order of conditions (practice phase fol-lowed by transfer phase) were as follows for each group:

Group (1) TO (1-200) - N (201-240);Group (2) N (1-200) - TO(201-240).

In typical motor learning studies using a transfer paradigm, the mostcommonly used design is where all groups of subjects transfer to a commonlevel of an independent variable (Schmidt, 1988). However, this is not theonly type of transfer design (see Bennett and Davids, 1997). By the natureof the theoretical questions addressed here, the main focus of interest was

Fig. 1. Schematic representation of the arrangements of LEDs in the experimental set-up. The orange

LED represents the initial start point and the red LED represents the ®nal target to be aimed at.


on the within-group analyses of the practice and transfer interaction. Thesecomparisons were of importance to the theoretical issues of the experimentsince they indicate whether or not the manipulation of the available infor-mation a�ected the subjects' performance. A potential with the interactionbetween these comparisons could be the in¯uence of asymmetry introducedby the di�erent visual conditions. That is, there is a possibility the removaland addition of the visual information in transfer may have asymmetricale�ects, with one group being more a�ected by the manipulation. By break-ing down any interaction into its simple e�ects, and treating the within-group comparisons individually, it is possible to examine any asymmetry ef-fects (see also Elliott et al., 1995). Following similar reasoning, between-groups comparisons of practice performance, which are informative ofthe relevance of the available information, may be treated as simple com-parisons of group means. Therefore, they can also be made where theoret-ically appropriate (Kirk, 1968).

The subjects' task was to move a hand-held stylus from the initial startpoint to the ®nal target point in a movement time (MT) of 500 � 50 ms. Sub-jects were instructed to depress the end of the stylus when they reached the®nal target. No emphasis was placed on reaction time. Rather, the subjectswere required to perform the aiming movement in their own time in responseto the auditory tone that signalled the start of each trial. Subjects were in-formed that there was a threshold around the start point of 5 mm diameterin which the temporal data would not be collected. Therefore, if they stayedwithin this threshold upon hearing the tone, only their actual movement timewould be recorded. A second threshold of 50 mm diameter around the ®naltarget was included to remove trials in which subjects accidentally depressedthe stylus when moving across the tablet. In the event of such an error an``INVALID'' message was returned on the computer monitor and the trialwas run again. KR regarding error on the x-axis and y-axis in mm, andMT in ms, was provided on the computer monitor after every successful trial.

Data reduction. The co-ordinate and temporal data were recorded on-lineat a sampling frequency of 125 Hz and a relative measurement accuracy of0.11% on the x-axis, and 0.08% on the y-axis. After the testing session, theroot mean square error (RMSE) was calculated from the unsmoothed tempo-ral and spatial data for the ®nal 40 trials in each block. The RMSE was sug-gested by Henry (1975) to represent the best overall measure of performanceaccuracy. The RMSE represents a combination of both the variability (vari-able error) and bias (constant error) in subjects performance. Variable errorand constant error are both measures of aiming performance that change


with practice. Therefore, it was deemed that RMSE would provide the mostappropriate measure.

A high intercorrelation between dependent variables has been argued as ajusti®cation for using a multivariate technique. The dependent variables ofthe present study could be interpreted as being intercorrelated. For example,the well known speed-accuracy trade-o� e�ects are evidence of the intercor-relation between the temporal and spatial variables. However, to permit com-parison with the previous manual aiming work in which the dependentvariables were analysed individually (e.g. Proteau et al., 1987; Elliott et al.,1995), separate univariate analyses were deemed more appropriate (for a dis-cussion of this rationale see Huberty and Morris, 1989; Thomas, 1977). Todetermine the degree of change in performance following the removal and ad-dition of vision e�ect size data were also calculated (Cohen's d). As no con-trol group was included in the design the pooled standard deviation of thetwo groups being compared was used as the denominator in the e�ect sizecalculations (Cohen, 1988).

2.2. Results

Practice. The practice data for the three dependent variables was submit-ted to separate two-way ANOVA (2 groups ´ 5 trials blocks), with repeatedmeasures on the trial block factor. No signi®cant e�ects were noted for MT.Subjects exhibited a similar MT in the ®ve blocks of the practice. As instruct-ed, they performed the task in the pre-determined temporal bandwidth of500 � 50 ms. 6

No signi®cant e�ects were noted for the x-axis data. However, subjects'performance in the normal-vision condition did improve, although not signif-icantly as measured by the statistical procedure, over the ®ve blocks of prac-tice (see Fig. 2). The largest e�ect was noted between the ®rst and last blocksof practice (d� 0.88).

A main e�ect of trial block for the y-axis data approached conventionallevels of signi®cance, F(4,32) � 2.46, p<0.07, (see Fig. 3). A t-test indicatedthat subjects in the normal-vision condition were signi®cantly more accuratein the last block, compared to the ®rst block of practice trials, t(4) � 2.734,

6 Although trials outside of this temporal bandwidth were classi®ed as being invalid, and performed

again, it would theoretically be possible for subjects to exhibit a 100 ms di�erence in their MT. As such, the

inclusion of this cut-o� point would not necessarily mask any practice e�ect.


p<0.03. 7 Subjects aiming performance in the normal-vision condition im-proved steadily over the practice phase. This e�ect was most pronounced be-tween the ®rst and last blocks of practice (d� 1.04). There was no suchimprovement for subjects in the target-only condition.

Practice vs. transfer. The RMSE data for the last block of 40 trials in prac-tice and the 40 trials in transfer for each dependent variable were submittedto separate two-way ANOVAs (2 groups ´ 2 experimental phases), with re-peated measures on the experimental phase factors. A signi®cant main e�ect

7 When the ANOVA fails to reach signi®cance, it is still possible to examine, post hoc, the di�erence

between comparison means with a t-test (see Howell, 1992).

Fig. 2. RMSE on the x-axis as a function of trial block and practice group.

Fig. 3. RMSE on the y-axis as a function of trial block and practice group.


of phase was found for the y-axis data only, F(1,8) � 27.4, p<0.001, (seeFig. 4). Analysis of the simple e�ects indicated that the removal and additionof visual information caused a signi®cant decrement in aiming performance(d� 2.21 and d� 0.57, respectively). A signi®cant group di�erence in thepractice data was also found (p<0.05). Subjects in the normal-vision practicegroup were signi®cantly more accurate than those in the target-only practicegroup at the end of the practice trials (d� 0.83).

2.3. Discussion

Analysis of the RMSE data on the y-axis indicates that subjects in the nor-mal-vision condition exhibited better performance by the end of practice thanthose in the target-only condition. The dynamic visual information in thismanual aiming task, available in the normal-vision condition, permittedmore accurate performance than when vision of the target only was available.The ®nding that subjects' performance in the target-only condition did notimprove over practice was somewhat surprising given that previous manualaiming work has found performance improvements when performing a sim-ilar amount of practice trials in a target-only condition (Proteau et al., 1987).Two explanations may be possible for this ®nding. Firstly, it is feasible thatthe task was too di�cult to enable improvements after only a moderateamount of practice. However, a comparison of the mean for the target-onlypractice condition to that of a similar condition in the study of Proteau et al.(1987) would suggest that this is not the case (e.g. y-axis RMSE of approxi-

Fig. 4. RMSE on the y-axis as a function of experimental phase and group. The abbreviation PR repre-

sents practice, and TR represents transfer. The numbers between parentheses represents the trials from

which the RMSE was calculated.


mately 12 mm and 24 mm, respectively). The second explanation could relateto the use of a threshold around the ®nal target of 50 mm to remove trials inwhich excessive accidental errors were made. In the target-only practice con-dition this may have resulted in the initial trials, in which large errors are typ-ically found, being ignored. Since these data were discarded, unfortunately itwas not possible to check this second explanation. While the use of a 50 mmcut-o� threshold was a limitation of the present experiment, a change in thethreshold value could overcome any future problems.

The removal of the dynamic visual information of the moving arm and en-vironment, after having practised for 200 trials in its presence, led to a signi-®cant reduction in aiming accuracy. Further, although there were noimprovements over practice in the target-only condition, its addition in trans-fer also caused a signi®cant disruption to aiming performance. These ®ndingsare consistent with the predictions of the speci®city of learning hypothesis.When the task constraints in manual aiming are such that the hand-held sty-lus is moved across a surface towards a stationary ®nal target, the additionand removal of visual information causes a decrement in aiming perfor-mance. This is not surprising since this version of the manual aiming taskhas also been suggested to place a major emphasis on the relevant informa-tion available during the homing-in phase (e.g., see Chua and Elliott, 1993),which has been linked with the disruption in performance (Proteau et al.,1992; Temprado et al., 1996).

In relation to the proceeding experiment, the data reported here indicatethat the act of sliding across the tablet itself, does not have a mediating e�ecton the manipulation of the relevant information that may confound the predic-tions of the speci®city of learning hypothesis. Therefore, any e�ect of manip-ulating the type of information available during the homing-in phase of thistask, would likely be a result of that particular manipulation and not the taskdi�erences. The e�ect of changing the constraints in this task, so that visual in-formation available in the homing-in phase could only be directly picked-upthrough the peripheral retina only, was next examined in Experiment 2.

3. Experiment 2

3.1. Method

Subjects. Ten male volunteers (Age: Mean 22 y, SD 4.5) served as subjects.All reported to be right-hand dominant and had normal or corrected-to-nor-


mal-vision. Subjects were informed of the requirements of the experiment inboth verbal and written form. They then gave their written consent to partic-ipate.

Task and apparatus. Subjects were instructed that they would be perform-ing 1200 manual aiming trials with a hand-held stylus on the digitising tablet(TDS LC series) with the non-dominant left hand. Again, no information re-garding the experimental design was given. The target set-up was similar tothat used in Experiment 1. In addition, a green ®ltered target located 250mm on the y-axis from the initial target, served as a ®xation point (seeFig. 5).

Design and procedure. Subjects were randomly assigned to one of two prac-tice conditions: N (practising in a normal-vision environment) and TO (prac-tising with an illuminated target-only in an otherwise dark room). Subjectsreceived moderate (200 trials) and extensive (a further 920 trials) practicein two phases interspersed by two blocks of transfer trials (n� 40). Thepractice phases were partitioned into blocks of forty trials with a 2-minintra-block delay. A 5-min intra-phase delay was also given in order to allowsubjects to adapt to the new condition. The experimental phases were again

Fig. 5. Schematic representation of the arrangements of LEDs in the experimental set-up. The orange

LED represents the initial start point, the green LED acted as the ®xation point and the red LED repre-

sents the ®nal target to be aimed at.


organised in a cross-over transfer design. One group transferred to thenormal-vision condition after practice with only the target visible, whilethe conditions were reversed for the other group. The number of trials andthe order of conditions (practice phases followed by transfer phases) wereas follows for each group:

Group (1) TO (1-200) - N (201-240) - TO (241-1160) - N (1161-1200);Group (2) N (1-200) - TO (201-240) - N (241-1160) - TO (1161-1200).

Again, this design is not typical of that reported in the motor learning lit-erature. Still, by the nature of the theoretical questions addressed, we weremainly interested in the within-group analyses of practice and transfer perfor-mance. With two levels of practice, there are further reasons why these with-in-group comparisons were of importance to the theoretical issues of theexperiment. That is, they indicate whether or not the manipulation of theavailable information a�ected the subjects' performance, and also if the e�ectincreases as a function of the number of practice trials. By breaking down theinteraction into its simple e�ects, these within-group comparisons can bemade without a�ecting the symmetry of the practice x transfer interaction.

A problem with the use of the cross-over design with two levels of practicecould be the possibility of carry-over e�ects (Campbell and Stanley, 1963;Poulton, 1981). In the following experiment, there are two di�erent ways inwhich the carry-over e�ects could in¯uence the data. Firstly, if subjects wereinformed that the transfer phase was di�erent to the practice phase, it is fea-sible that they may choose to ignore the di�erent sources of informationavailable in the practice condition (see Smyth, 1977). In e�ect they would at-tempt to keep the practice and transfer conditions as similar as possible. Inthe study reported here, subjects were not informed that they would be per-forming in a di�erent transfer condition. They were, however, given 5 min atthe end of a practice phase to adapt to the new condition. Subjects could nothave anticipated future environmental constraints and re-organised behav-iour accordingly. Secondly, there is also the possibility that if the three-way interaction is not signi®cant due to no change over practice, the two-way interaction will be contaminated by carry-over e�ects. That is, the sec-ond transfer e�ects will be contaminated by the ®rst 40 transfer trials. Thesame could be true of the practice e�ects also. However, by breaking the in-teraction down into its simple e�ects, even if it not signi®cant, this problemcan be quite easily overcome (Howell, 1992).

Subjects were instructed to ®xate a 5 mm target in central vision whilstmoving a hand-held stylus from the initial start point to the ®nal target point(5 mm in diameter and positioned at 20° in the left hemi®eld) in a movement


time (MT) of 500 � 50 ms. Again, no emphasis was placed on the reactiontime to the auditory tone. The threshold around the ®nal target was increasedto 100 mm diameter. It was reasoned that any errors outside this thresholdwould likely be the result of subjects accidentally depressing the stylus whenmoving across the tablet. In the event of such an error, an ``INVALID'' mes-sage was returned on the computer monitor and the trial was performedagain. KR regarding error on the x-axis and y-axis in mm (i.e. spatial) andMT in ms (i.e. temporal) was provided on the computer monitor after everytrial.

Due to the target arrangement (see Fig. 5), and the pre-determined ®xationpoint, the entire movement was performed in the peripheral visual ®eld. Thismanipulation was required in order to examine the main hypothesis of thestudy. Any eye movements during the trial from the ®xation point to the ®naltarget would invalidate this manipulation. Therefore, eye movement activitywas recorded with an eye movement registration system (ASL 4000) to checkfor changes in ®xation during the task. On-line observation of the eye move-ment data con®rmed that subjects ®xated the centrally located target duringall trials. Involuntary or voluntary ®xations of the peripheral target were nota problem in this study.

Data reduction. Co-ordinate and temporal data were recorded on-line at asampling frequency of 125 Hz. A Fast Fourier Transform indicated that themajority of the signal frequency was less than 5 Hz. A Butterworth ®lter witha cut-o� frequency of 5 Hz was used to smooth the raw x-axis and y-axis da-ta. This procedure was run through twice in order to remove the phase shift(Wood, 1982). The resultant displacement was then calculated from the ®l-tered x-axis and y-axis data. The resultant displacement data were di�erenti-ated using a two-point ®nite di�erence technique to derive instantaneousmeasures of velocity. 8 The velocity data were di�erentiated using the sametechnique to derive instantaneous measures of acceleration. The RMSEwas calculated from the unsmoothed data for the temporal and spatial de-pendent variables from the ®nal 40 trials in each phase. These trials were ta-ken to be representative of the level of performance achieved in each phase.

Movement modi®cations. To analyse subjects' movement kinematics, acomputer program was developed to identify certain critical points in the dis-

8 Because the calculation of the two-dimensional movement of the stylus requires the use of scalar

values rather than vectors, distance and speed would be more appropriate terms than displacement and

velocity. However, in keeping with traditional descriptions of kinematics associated in aiming movements,

the terms displacement and velocity will be used throughout this paper.


placement, velocity and acceleration pro®les (see Carlton, 1994; Chua and El-liott, 1993). These were peak velocity (PV) of the movement, time to peak ve-locity (TPV) as a proportion of the movement time, displacement at the timeof peak velocity (DPV) as a proportion of the target distance, peak acceler-ation (PA) of the movement, time to peak acceleration (TPA) as a proportionof the movement time, displacement at the time of peak acceleration (DPA)as a proportion of the target distance, the number of signi®cant deviations inthe acceleration pro®le prior to peak velocity (SDP), and the number of sig-ni®cant deviations in the acceleration pro®le after peak velocity (SDA).

To identify a signi®cant deviation in the acceleration pro®le prior to peakvelocity, a search for a reversal, other than that occurring at peak accelera-tion was conducted. For a signi®cant deviation to be deemed present, the re-versal in acceleration had to satisfy both a temporal and magnitude criteria(see Chua and Elliott, 1993; van Donkelaar and Franks, 1991). Firstly, theamplitude between a negative-to-positive reversal and the subsequent posi-tive-to-negative reversal had to be larger than 10% of the absolute peak ac-celeration. Secondly, the duration between these two reversals had to equalor exceed a temporal limit of 80 ms (10 ®elds). To identify a signi®cant devi-ation in the acceleration pro®le after peak velocity, a search for the devia-tions in acceleration pro®le that satis®ed the same two criteria wasperformed. Having retrieved these kinematic variables the intra-individualmeans and standard deviations were calculated for the ®nal 40 trials of eachpractice and transfer phase.

3.2. Results

The data for each dependent variable were submitted to separate three-way ANOVA (2 groups ´ 2 experimental phases ´ 2 amounts of practice),with repeated measures on the experimental phase and amount of practicefactors.

Spatial and temporal measures. No signi®cant di�erences were found forMT. Subjects were able to perform the task in the required temporal band-width regardless of the information available. The implication is that the spa-tial data were not confounded by the well known speed-accuracy trade-o�e�ect. This ®nding is consistent with that of Experiment 1, and other manualaiming research in which pre-determined temporal constraints were imposed(e.g. Proteau and Marteniuk, 1993).

A signi®cant group ´ phase interaction was noted for the x-axis data,F(1,8) � 11.78, p<0.01, (see Fig. 6). The ®nding that the three-way interac-


tion (e.g. group ´ phase ´ amount of practice) was not signi®cant, suggestedthat there was no improvement over practice. However, by collapsing the twolevels of practice and transfer, and comparing these data, important e�ectsmay have been masked. For this reason, it was deemed appropriate thatthe three-way interaction should be broken down into its simple e�ects (fora discussion see Howell, 1992). Having been broken down into its simple ef-fects, a signi®cant di�erence was revealed between moderate practice andtransfer performance for the target-only practice group (p<0.025). Althoughno signi®cant e�ects were noted when transferring from the normal-visioncondition to the target-only condition, the e�ect size data indicated that sub-jects exhibited a greater performance decrement in transfer after extensivepractice (i.e., d� 1.63 and d� 3.99).

A signi®cant group ´ phase interaction was noted for the y-axis data,F(1,8) � 27.35, p<0.001, (see Fig. 7). Again, for the reason outlined above,the three-way interaction was broken down into its simple e�ects. A signi®-cant di�erence between moderate practice and transfer (p<0.01), and exten-sive practice and transfer (p<0.05), was noted for the normal-vision practicegroup. A larger e�ect was indicated after extensive practice (i.e., d� 2.27 andd� 8.51, respectively). For subjects in the target-only practice group, a signi-®cant improvement in transfer performance was found after moderate prac-tice only (p<0.025). Still, all subjects showed a general trend towardsimproved performance following the addition of vision at both levels of prac-tice. It is important to note here that ®nding no signi®cant decrements in per-formance contradicts the predictions of the speci®city of learning hypothesis.

Fig. 6. RMSE on the x-axis as a function of experimental phase and group.


A signi®cant di�erence in moderate practice performance was noted betweenthe two groups, p<0.01. As hypothesised, subjects practising in the normal-vision condition were more accurate than those in the target-only condition.However, this initial di�erence had diminished by the end of the extensivepractice phase.

Movement kinematics. Signi®cant group ´ phase interactions were notedfor the means of peak acceleration, F(1,8) � 6.85, the number of signi®cantdeviations in the acceleration pro®le prior to peak velocity, F(1,8) � 13.39,the number of signi®cant deviations in the acceleration pro®le after peak ve-locity, F(1,8) � 5.73, and time to peak acceleration as a proportion of themovement time, F(1,8) � 5.77, all p<0.04. Having been broken down intotheir simple e�ects, di�erences were found between moderate practice andtransfer for the target-only practice group in peak acceleration, the numberof signi®cant deviations in the acceleration pro®le prior to peak velocity, timeto peak acceleration as a proportion of the movement time (all p<0.01), andthe number of signi®cant deviations in the acceleration pro®le after peak ve-locity (p<0.025) (see Table 1).

Signi®cant group ´ phase interactions were also noted for the mean intra-individual standard deviations of peak acceleration, F(1,8) � 7.15, time topeak acceleration as a proportion of the movement time, F(1,8) � 6.37, peakvelocity, F(1,8) � 5.65, displacement at the time of peak velocity as a propor-tion of the target distance, F(1,8) � 5.65, and time to peak velocity as a pro-portion of the movement time, F(1,8) � 9.42, all p<0.05. Analysis of thesimple e�ects revealed signi®cant di�erences between the mean intra-individ-

Fig. 7. RMSE on the y-axis as a function of experimental phase and group.


ual standard deviations exhibited at both moderate and extensive practiceand transfer for the target-only practice group (see Table 1 and Table 2).No other signi®cant di�erences were found.

3.3. Discussion

Analysis of the RMSE data on the y-axis indicates that subjects in the nor-mal-vision practice group exhibited signi®cantly better performance after 200trials than those in the target-only practice group. Peripheral vision of themoving arm to the target at 20° in the left hemi®eld proved to be a highlyrelevant source of information that subjects exploited to improve their man-

Table 2

Mean intra-individual means and standard deviations of the kinematic variables for the normal-vision

practice group

PR (161±200) TR (201±240) PR (1121±1160) TR (1161±1200)

M SD M SD M SD M SD

PA (m/s2) 23.6 11.5 25.0 11.2 16.1 5.3 15.4 8.7

DPA (%) 34 21 34 27 22 13 27 26

TPA (%) 35 14 36 18 25 10 27 22

PV (m/s) 1.3 0.3 1.4 0.3 1.1 0.1 1.0 0.1

DPV (%) 60 12 61 13 60 7 60 11

TPV (%) 46 8 46 10 47 7 48 10

SDP 0.3 0.5 0.5 0.6 0.3 0.6 0.4 0.9

SDA 1.0 0.6 1.0 0.7 1.7 0.7 1.8 0.9

Table 1

Mean intra-individual means and standard deviations of the kinematic variables for the target-only prac-

tice group

PR (161±200) TR (201±240) PR (1121±1160) TR (1161±1200)

M SD M SD M SD M SD

PA (m/s2) 26.0 12.1 9.6 6.6 17.8 12.8 9.2 6.3

DPA (%) 40 24 26 27 29 26 22 28

TPA (%) 49 22 29 24 35 25 23 24

PV (m/s) 1.1 0.2 0.9 0.2 1.0 0.2 0.9 0.1

DPV (%) 70 14 56 9 63 13 57 9

TPV (%) 60 12 51 9 55 11 49 9

SDP 0.7 0.7 0.4 0.9 0.5 1.1 0.3 1.0

SDA 0.7 0.8 1.4 1.0 1.5 1.2 2.0 1.1


ual aiming performance (see also Bard et al., 1985; Sivak and McKenzie,1992). The ®nding that subjects' aiming accuracy did not improve signi®cant-ly with a further 920 practice trials, indicates that they were able to take ad-vantage of this information after only 200 practice trials. This is consistentwith other manual aiming work also ®nding that subjects reach asymptoticlevels of performance after moderate levels of practice (e.g., Elliott et al.,1995; Proteau and Cournoyer, 1990; Proteau and Marteniuk, 1993).

The removal of peripheral visual information of the moving arm caused asigni®cant reduction in aiming accuracy. Although the decrement did not in-crease signi®cantly as a function of practice, the e�ect size data suggest thatthere was a greater reduction in performance on the x-axis after extensivepractice compared to moderate practice (i.e., d� 1.63 and d� 3.99). Thesame trend in the e�ect size data of the y-axis was noted (i.e., d� 2.27 andd� 8.51). The implication is that the use of peripheral visual informationto maintain accurate manual aiming performance did not decrease as a func-tion of practice. Therefore, this ®nding may be interpreted as being partiallysupportive of the speci®city of learning hypothesis. The addition of peripher-al visual information in transfer did not have any deleterious e�ects on thesubjects' aiming performance. Rather, they exhibited a signi®cant improve-ment in accuracy on both axes after a moderate amount of practice. Again,the performance change following the manipulation of the available informa-tion did not increase signi®cantly as a function of practice. This would seemto be the result of the improvement shown between the two practice phases.Still, since the data indicate that subjects' aiming performance was improvedby the addition of vision in both practice phases, the prediction of the speci-®city of learning hypothesis concerning the addition of relevant visual infor-mation was not supported.

The di�erences in the kinematic variable means of peak acceleration, timeto peak acceleration as a proportion of movement time, the number of signi-®cant deviations in the acceleration pro®le prior to peak velocity, and thenumber of signi®cant deviations in the acceleration pro®le after peak velocityfor the target-only practice group are consistent with the RMSE data. In re-sponse to the addition of the important visual information, subjects signi®-cantly adapted their movement kinematics. On transferring to the normal-vision condition after moderate practice, subjects achieved a reduced meanpeak acceleration proportionally earlier in the movement pro®le. This wasaccompanied by a reduced number of signi®cant deviations prior to peak ve-locity and an increased number of signi®cant deviations after peak velocity.In turn, these movement adaptations positively a�ected the ®nal performance


accuracy. The changes in these kinematic data with the addition of peripheralvisual information, point to a change towards a visually based strategy of re-ducing aiming error. That is, by exhibiting both a reduced peak accelerationand number of signi®cant deviations prior to peak velocity, subjects organ-ised a strategy whereby they allocated more time towards deceleration duringthe most important homing-in phase of the movement. Such a strategy wouldseem consistent with that exhibited by subjects attempting to achieve optimalaccuracy (see Adam, 1992). The ®nding of a larger number of signi®cant de-viations after peak velocity, would also support the notion of an increaseddegree of on-line visual regulation (Chua and Elliott, 1993).

The lack of a signi®cant e�ect on the kinematics of the normal-vision prac-tice group following the removal of vision, despite the signi®cant decrementin aiming accuracy, could be taken to indicate that the kinematic analysis em-ployed in the present study was not sensitive enough to measure the contin-uous on-line corrections. Such an explanation has previously been invoked inother manual aiming studies (see Elliott et al., 1995). However, since kine-matic di�erences were found following the addition of peripheral visual infor-mation, it is quite feasible that subjects merely exhibited less of an adaptivee�ect following the removal of this source of information.

The data concerning the positive e�ects on aiming performance followingthe addition of peripheral vision are consistent with the predictions made pre-viously by Bennett and Davids (1997). These ®ndings also add further sup-port to the suggestion that movements are not subserved by rigid andspeci®c central representations. In this experiment, the spatial error and kine-matic data, point to an adaptive and ¯exible system, that is organised to bestexploit the changing availability of information (see Edelman, 1992; Weeksand Proctor, 1992). That is, on transferring between conditions, subjects or-ganised a functionally e�ective task solution based on the information pres-ent (see Bingham, 1988, Marteniuk, 1992; Reed, 1988). Bingham (1988)suggests that such a task-speci®c solution would be temporarily organisedinto a functional and e�ective form of ``machinery'' for achieving a particulartask goal. Such organisation would necessarily take advantage of the``smart'' design of the human system.

Given the complexity of the dynamically changing interaction of humanswithin their environment, the construction of rigid and speci®c representa-tions would be di�cult to uphold (see Bootsma and van Wieringen, 1992).Therefore, the more e�ective solution would be to temporarily adapt an ex-isting response (i.e., that used in the normal-vision condition). Current repre-sentational and dynamical systems theories of motor behaviour o�er ways of


achieving this adaption (e.g., Jordan, 1990; SchoÈner et al., 1992). Common toboth viewpoints is the suggestion that subjects are able to adapt an existingresponse on the basis of the constraints at a particular instant to still achievethe task goal. It is therefore the interaction between the task, environmentand organism that constrains the emergent functional response. 9

A fundamental ®nding in the motor learning literature is that practisingone task, which includes some of the elements that are of importance to a sec-ond task, can be both bene®cial or detrimental to performance on the secondtask (Bamford and Marteniuk, 1988; Elliott et al., 1995; Fumoto, 1981;Holding, 1962). That standard research and design textbooks (Howell,1992) warn of the potential carry-over e�ects when using repeated measuresdesigns, gives a further indication of the residual e�ects of practising in sim-ilar conditions. Current theorising concerning the concept of speci®city inmotor learning has started to take account of the fact that interspersed prac-tice under di�erent constraints can have residual e�ects on performance. Forexample, Elliott et al. (1995) have suggested that there is a speci®city to mo-tor learning in that subjects learn speci®c procedures that enable them to ex-ploit the available information. These procedures may then be transferredbetween conditions if there is a degree of similarity. Such a view is consistentwith the concept of transfer of appropriate processing (Lee, 1988).

If behaviour emerges from the task, environmental and organismic con-straints, it will be necessary to understand how manipulating these con-straints results in a particular adaption. Further, if there are residual e�ectsof practising in conditions of di�erent constraints, it will also be importantto take account of what it is about practising one task that co-operates orcompetes with the performance of another task (Elliott et al., 1997; Walteret al., 1993). In the present study (see also Bennett and Davids, 1997), ithas been shown that when the task constraints do not emphasise the centralvisual information available during the ®nal homing-in phase, speci®city oflearning e�ects are no longer found. By continuing with this principled ma-nipulation of the constraints it may be possible to make more robust and gen-eralisable predictions regarding the speci®city of learning. The use of akinematic analysis to support global measures of performance (e.g., RMSE),is assisting in the interpretation of the data.

9 Clearly, there are some similarities between these theoretical viewpoints. This is not surprising when

the data call for such an explanation of behaviour. However, it is at the philosophical level that there is the

greatest disparity (Churchland, 1984; Edelman, 1992). It was not the intention, and would not be fruitful

to pursue these di�erences within the scope of present paper.


To conclude, it appears that when the task constraints in manual aimingare such that the addition of vision of the moving arm and target occur at20° in the left hemi®eld, there does not seem to be a performance decrementregardless of the level of practice. Rather, the implication is that individualsare able to exploit the addition of various sources of information, in order toimprove their manual aiming performance. Such a ®nding is consistent withthe predictions of Bennett and Davids (1997) regarding the speci®city oflearning. Rather than controlling actions on the basis of speci®c central rep-resentations, the spatial error and kinematic data of the present experimentpoint to an adaptive and ¯exible system that emerges from the con¯uenceof constraints.

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