A longitudinal study of implicit motor learning - CiteSeerX

Jour nal of Sports Sciences, 2000 , 18, 111± 120

From novice to no know-how: A longitudinal study of implicit motor learning J.P. M AXW ELL,* R.S.W. M ASTERS and F.F. EVES School of Sport and E xercise Sciences, University of B ir m ingham, E dgbaston, B ir m ingham B 15 2TT, U K

Accepted 10 O ctober 1999

The aim of this study was to ascertain whether the performances of implicit and explicit learners would converge over an extended period of learning. Participants practised a complex motor skill ± golf putting ± for 3000 trials, either with a concurrent secondary, tone-counting task (implicit learning) or without such a task (explicit learning). The cognitive dem ands of the secondary task were predicted to prevent the accumulation of verbalizable rules about the motor task. The implicit group reported signi® cantly fewer rules than the explicit group on subsequent verbal protocols. The perform ance of the implicit group rem ained below that of the explicit group throughout the learning phase. However, no signi® cant diþ erences were found between groups during a delayed retention test. Additionally, for the participants in the explicit group only, a Reinvestm ent Scale score correlated positively with the number of rules accrued and negatively with overall putting performance during the learning phase. We use the results to argue against the excessive use of verbal instruction during skill acquisition, which m ight be unnecessary and ultimately might ham per perform ance under stressful conditions. K eyw ords : explicit, reinvestment, putting.

Introduction O ver the past decade, the phenom ena of im plicit and explicit learning have received increasing attention. Typically, studies of im plicit learning have required research participants to interact with com plex, rule-governed stim uli, such as arti® cial gram m ars (Reber, 1967; Reber and Allen, 1978), com plex com puter system s (Broadbent and Aston, 1978; Berry and Broadbent, 1984, 1988) or serial reaction tim e tasks (Nissen and Bullem er, 1987). Participants are generally unable to discover, and subsequently report, the com plex rules that govern the stim ulus environm ents in these tasks; however, their perform ance im proves over trials. Several criteria have been identi® ed w hich dissociate im plicit from exp licit processes. Lack of explicit know ledge in the face of im proved perform ance has been taken as the prim ary indication of im plicitly held knowledge (Berry and Broadbent, 1984). Also, im plicit processes are m ore resistant to the eþ ects of psychological stress, disorders and dysfunctions * Author to whom all correspondence should be addressed. e-m ail: j.p.m axwell@ bham .ac.uk Jour nal of Sports Sciences ISSN 0264-041 4 print/ISSN 1466-447 X online Ó http://ww w.tandf.co.uk/journals/tf/00268976 .html

(Schacter, 1987; Abram s and Reber, 1988; Reber, 1993). T hey m ay be m ore durable, or less prone to forgetting, over tim e than explicit processes (Allen and Reber, 1980), and be relatively independent of both age and IQ ; explicit learning and m em ory, however, degrade with age and are closely correlated w ith IQ (Light and Singh, 1987; Reber et al., 1991; Reber, 1993), and are non-attentionally dem anding (Berr y and Broadbent, 1988; H ayes and Broadbent, 1988; Berry and D ienes, 1993; C urran and Keele, 1993). (For detailed discussion of the cognitive literature involving im plicit processes, see Berry and D ienes, 1993; Reber, 1993; Seger, 1994; Shanks and St. John, 1994.) D espite the increasing interest in im plicit learning, the literature on im plicit m otor learning is sparse. M otor skills m ay be acquired im plicitly (M asters, 1992) or becom e im plicit through release from explicit control or autom ization (Fitts and Posner, 1967; Schneider and Schiþ rin, 1977; Anderson, 1983). Im plicit m otor learning refers to the acquisition of a m otor skill without the concurrent acquisition of explicit know ledge about the perform ance of that skill. Skills that are initially learnt through exp licit processes, and subsequently becom e autom ated, m ay be referred to as im plicit but do not fall into the categorization used 2000 Taylor & Francis Ltd

112 in this paper. O ne of the ® rst studies to explore im plicit m otor learning directly had participants perform a pursuit tracking task for 14 days, 24 trials on each day (Pew, 1974). T he participants used a joystick to follow a waveform presented on an oscilloscope. T he waveform consisted of three sections of w hich the m iddle section was invariant, w hereas the ® rst and last sections were random ly generated on each trial. Pew found that individuals im proved their perform ance on the repeated section w ith practice; however, when questioned, they were unaware of the repeating section. M ag ill and Hall (1989) replicated Pew ’ s ® ndings and dem onstrated that the position of the repeated segm ent did not alter previous ® ndings (M ag ill et al., 1990). Wulf and Schm idt (1997) found better transfer to novel speed scalings of the repeated pattern after variable practice. T heir participants practised the tracking task designed by Pew for 4 days. T he waveform could be m anipulated to produce variable practice by altering the tim e or am plitude scaling while relative tim ing and am plitude rem ained constant. Participants w ho had variable practice during acquisition showed better transfer to novel scalings of the repeating segm ent than participants who experienced only one variation of the waveform (Wulf and Schm idt, 1997; M agill, 1998). Incidental m otor learning was examined by D ickinson (1977, 1978) using a serial positioning task. He found that perform ers who were unaware of a subsequent recall test perform ed as well as those who were aware of the test. D ickinson claim ed that intention to learn was not a necessar y prerequisite for skill acquisition. This ® nding closely resem bles that of Reber (1967) using his arti® cial gram m ar paradigm . U nfortunately, D ickinson’ s studies did not exclude the use of an explicit strategy by the incidental learners, a point highlighted in a later study demonstrating no diþ erence in perform ance between incidental and intentional learners under norm al and distractor task learning conditions (C rocker and D ickinson, 1984). G reen and F lowers (1991) used a com puterized catching task to identify diþ erences in perform ance brought about by providing or withholding task instructions. T heir participants used a joystick to m anipulate a cursor on a com puter screen with w hich they tried to catch a descending single-pixel `ball’ . A `glitch’ in the descent of the ball predicted a 75% probability of a fade (or sharp break) to the right in the ® nal 300 m s of its descent. Participants w ho were not inform ed of this relationship perform ed the task m ore accurately ± and in a qualitatively diþ erent m anner ± than those w ho were inform ed. G reen and F lowers proposed that their uninform ed participants had im plicitly learned the glitch± fade relationship. H owever, they did not report whether the uninform ed group had becom e conscious of the relationship during learning.

M axwell et al. In recent studies by M asters (1992) and Hardy et al. (1996), individuals were required to learn a com plex m otor skill ± golf putting ± under exp licit or im plicit conditions. To facilitate im plicit learning, M asters had par ticipants in the im plicit condition perform a secondar y task w hile putting. T he secondary task ± random letter generation (Baddeley, 1966) ± was hypothesized to prevent the acquisition of explicit knowledge. Im plicit learners failed to accrue verbalizable, explicit knowledge of the putting skill com pared with explicit learners. F urtherm ore, the putting perform ance of the im plicit group im proved over trials, show ing that learning had occurred. By dem onstrating learning in the absence of exp licit knowledge, M asters concluded that the m otor skill had been acquired im plicitly. M asters (1992) also demonstrated that the perform ance of the im plicit group im proved under psychological stress, whereas the perform ance of the explicit group was degraded. T hese ® ndings were later replicated by H ardy et al. (1996), w ho initially claim ed that the better perform ance of the im plicit group during the stress phase was due to release from the secondary task load. To test this claim , they had two im plicit groups learn the golf putting task while perform ing the letter generation task. However, during the stress phase, one im plicit group continued to perform the secondary task whereas the other perform ed the putting task only. Hardy et al. argued that the putting-only group would show an increase in perform ance during the stress phase, but that the secondary-task group would not, indicating that release from the secondary task load was responsible for the increased perform ance of M asters’ putting-only group during the stress phase. D espite their predictions, the perform ance of both groups im proved during the stress phase, lending support to M asters’ im plicit learning hypothesis. Although the results of Bright and F reedm an (1998) supported the increased perform ance of im plicit learners w hen released from a secondary task load during a stressed transfer test, they refuted the im plicit m otor learning hypothesis. Bright and Freedm an replicated the exp erim ent of H ardy et al. (1996), although they did not explicitly m ention that study. They found that the perform ance of the im plicit group that continued to perform the secondary task did not im prove during the stress phase, but that the perform ance of the im plicit group released from the secondary task load during the stress phase did im prove. Bright and F reedm an interpreted these results as indicating that the increased perform ance of the latter group was sim ply due to release from the secondary task load. To add further support to their argum ent, Bright and Freedm an (1998) carried out a second study. Two groups perform ed the golf putting task under secondar y

A longitudinal study of im plicit m otor lear ning task loads. A hard dual-task group perform ed the random letter generation task every second, w hereas an easy dual-task group perform ed the random letter generation task every 3 s. Bright and Freedm an argued that, w hen released from the secondary task load during the stress phase, the hard dual-task group would show a greater increase in perform ance than the easy dual-task group because of the diþ ering cognitive dem ands of the secondary task loads. T he results of their study supported their hypothesis: the hard dual-task group showed a greater increase in perform ance than the easy dual-task group when released from the dem ands of the secondary task load. However, changes to M asters’ (1992) protocol were m ade by Bright and Freedm an (1998) that seriously underm ine their ® ndings. Bright and Freedm an claim ed that the participants in their study were novice golfers; however, their lax criteria allowed individuals with som e experience to participate. Individuals with prior gol® ng experience are likely to bring explicit knowledge to the experim ent, confounding the verbal protocols. C onsistent with this proposition, the num ber of rules reported by both secondar y task groups in the ® rst experim ent was high (m ean = 3.5 rules) com pared w ith the m atching groups in the study by M asters (m ean = 1 rule), despite com pletion of only 160 trials during the learning phase com pared w ith 400 in the studies by M asters (1992) and H ardy et al. (1996). In addition, based on their rebuttal of the im plicit learning hypothesis, they chose not to present verbal report scores for their second experim ent. T herefore, our contention that the participants of Bright and Freedm an (1998) m ay have brought explicit know ledge to the study cannot be refuted. C learly, individuals w ho already have explicit know ledge pertaining to the prim ary m otor task cannot be considered im plicit learners. We contend that the ® ndings of experim ents in w hich novices acquire a skill im plicitly cannot be com pared with those from experim ents using non-novices, for whom critical inform ation on type of learning are confounded or absent. Closely linked w ith the phenom ena of im plicit and explicit processes is the concept of reinvestm ent. M asters et al. (1993) proposed that the breakdow n of skilled perform ance under psychological stress m ay be due to `reinvestm ent of controlled processing . . . a tendency to introduce conscious control of a m ovem ent by isolating and focusing on speci® c com ponents of it’ (p. 664). T hat is, under pressure, perform ers try consciously to control norm ally autom atic, im plicit processing by exp licit rule utilization. A golfer attem pting a short putt to win a m atch m ight tr y to recall and use rules that describe the action about to be perform ed, such as `move the club head back 10 cm, then sm oothly and gently push through the ball and stop 10 cm after hitting the ball, ensuring contact is m ade with the centre

113 of the club face and that the arm s are . . . ’ . This use of explicit processes is ineý cient, attention-demanding and slow, ultim ately leading to a breakdown in perform ance. To explore this concept, the Reinvestm ent Scale was devised. T his 20-item questionnaire is purported to m easure an individual’ s predisposition to focus attention inwards on the m echanics of the m ovem ents. T he questionnaire consists of one item taken from the C ognitive Failures Q uestionnaire (Broadbent et al., 1982), seven item s from the `Rehearsal’ factor of the Em otional Control Q uestionnaire (Roger and N esshoever, 1987) and 12 item s from the `Private’ and `Public’ factors of the Self-C onsciousness Scale (Fenigstein et al., 1975). A high internal reliability (coeý cient alpha = 0.80) and test± retest correlation (r = 0.74) have been reported. High reinvesters, as m easured by the Reinvestm ent Scale, were found to be signi® cantly m ore likely to suþ er skill failure under pressure than low reinvesters. An attem pt at conscious control of the m ovem ent repertoire when executing a norm ally autom atic m otor skill was hypothesized by M asters et al. (1993) to interfere with the norm al processing of the m otor schema, leading to a breakdown in the natural coordination of the m ovem ent. That is, the use of conscious, explicit, rule-based knowledge m ay interfere with the execution of im plicit m otor processes w hen perform ing a m otor skill. Breakdown in perform ance under pressure or `choking’ was exam ined in a series of ® ve experim ents by Baum eister (1984). H e claim ed that choking was a result of self-focused attention or of attending to one’ s actions rather than perform ance of the task. D ecreasing attention to the task results in im portant inform ation being m issed, and thus perform ance breakdown. Support for this contention was provided in both laboratory and ® eld settings. Baum eister also found that individuals high in self-consciousness, as m easured by the Self-Consciousness Scale (Fenigstein et al., 1975), perform ed worse than individuals low in self-consciousness during non-pressure practice trials. U nder pressure, however, this relationship was reversed: individuals high in self-consciousness perform ed better than those who were low in self-consciousness. Baum eister proposed that highly self-conscious individuals perform under self-scrutiny during practice, whereas those low in selfconsciousness do not. U nder pressure, therefore, highly self-conscious individuals are m ore able to cope w ith the associated self-scrutiny, because they are used to perform ing in this way, than individuals low in selfconsciousness. Lewis and Linder (1997) found further support for Baum eister’ s (1994) self-focus hypothesis. T hey found that breakdown in perform ance under stressful conditions was attenuated if participants were acclim atized to self-awareness during earlier practice sessions.

114 Acclim atization was achieved by increasing self-awareness during practice trials in the presence of a video cam era. Individuals that were acclim atized experienced less of a breakdow n in perform ance than those not acclim atized during a stressful transfer test. Lewis and Linder (1997) also addressed the possibility that breakdow n in perform ance under stress m ay be due to distraction from task-relevant inform ation (Kahneman, 1973; Eysenck, 1979). T hey found that adding a distractor task during the stressed transfer test did not have an additive eþ ect on breakdown in perform ance. They concluded that paradoxical breakdown in perform ance under pressure is m ediated by self-focused attention rather than distraction. C learly, breakdown in perform ance under pressure is a m ajor problem for som e athletes, who would bene® t from a training regim e that m inim izes the risk of `choking’ . M inim izing the num ber of explicit rules, and the additional bene® ts of robustness and durability associated with learning a skill im plicitly, suggest that these m ethods m ay be a viable defence against reinvestm ent. The possibility that im plicit learning m ay im m unize the perform er against the often debilitating in¯ uence of psychological stress on m otor output should not go unheeded. H owever, in the studies of M asters (1992) and H ardy et al. (1996), im plicit learners tended to exhibit poorer perform ance than explicit learners during the 400-trial learning phase. D espite the poorer perform ance of the im plicit learners, there was a suggestion in both studies that the perform ance of the im plicit learning group was converging with that of the explicit group. T he m ain aim of this exp erim ent was to determ ine whether the perform ances of im plicit and explicit groups would converge over an extended period of practice. All participants practised a golf putting task for 5 days, over which they com pleted a total of 3000 trials. Im plicit learners perform ed a secondar y tonecounting task w hile perform ing the putting task. This task was hypothesized to prom ote im plicit learning by preventing acquisition of exp licit knowledge. Learning was assessed by perform ance on a delayed retention test. T he process of acquiring new m otor skills requires the learner to advance through stages from novice to expert. The early stages of learning are characterized by conscious, explicit control of m ovem ents. D uring the later stages of learning, skills are perform ed autom atically with little conscious control (Fitts and Posner, 1967; Schneider and Schiþ rin, 1977; Anderson, 1983). We proposed that the Reinvestm ent Scale will predict perform ance during the earlier stages of learning, w hen participants try to control their actions explicitly. High reinvesters m ay accum ulate m ore explicit rules than low reinvesters during learning because of their propensity

M axwell et al. to explicitly analyse and focus on aspects of their perform ance. T herefore, all participants com pleted the Reinvestm ent Scale (M asters et al., 1993) to ascertain whether this scale predicts perform ance and the propensity for rule acquisition during learning.

M ethods Participants Twenty-seven volunteers from the University of Birm ingh am were assigned at random to one of three groups: im plicit learning, im plicit learning control and explicit learning. Each group com prised nine participants, w hose ages ranged from 20 to 29 years (22.81 ± 2.17; m ean ± s). All participants were novice golfers, received paym ent for their participation (£ 20 sterling) and provided inform ed consent. Apparatus A standard golf putter (length 89 cm ) and standard white golf balls were used by all participants. Putts were m ade to a hole 11.5 cm in diam eter from a distance of 3 m . T he surface used was arti® cial grass (C ounty Turf, En-Tout-Cas), raised 14 cm above ground level to allow a collecting duct to be ® tted below the hole. T he surface was even and level. Tones were generated by a D ell 486P/33 com puter for the secondary task condition. All participants com pleted the Reinvestm ent Scale (M asters et al., 1993). Procedure The experim ent was divided into two phases, learning and retention, perform ed over 8 days. T he learning phase consisted of ® ve sessions perform ed over ® ve consecutive days. D uring each session, 12 blocks of 50 trials (3000 in total) were com pleted by all participants. E ach block was separated by 1 m in rest, w ith the exception of blocks 4± 5 and blocks 9± 10, which were each separated by 5 m in rest to relieve tiredness and m aintain m otivation. N o instructions were given to the participants regarding putting technique. T hey were sim ply asked to putt as m any balls as possible using any techniques they found to be successful. Since, by design, im plicit groups cannot receive instructions and are, therefore, `selftaugh t’ , a non-instructed com parison group was used rather than an instructed group. T he explicit learning group, therefore, learned by discovery, which has previously been show n to result in a large pool of explicit knowledge (M asters, 1992; H ardy et al., 1996). For exam ple, the control group in the study of H ardy et al. (1996) received no technical instructions but reported

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A longitudinal study of im plicit m otor lear ning m ore rules than the instructed explicit group. The im plicit learning and im plicit learning control groups were required to perform a concurrent secondary task w hile putting in the learning phase. H owever, the random letter generation task used in previous studies was considered too diý cult to m onitor and control over an extended learning period. A tone-counting task sim ilar to those used in serial reaction tim e studies (e.g. N issen and Bullem er, 1987; C ohen et al., 1990; Reed and Johnson, 1994) was adopted as the secondar y task. The tone-counting task required participants to m onitor and subsequently report the num ber of high- and low -pitched tones presented during each block of 50 trials. O n the ® rst day, tones were presented at intervals of 2 s. T he inter-tone inter val was reduced by 250 m s for each subsequent day of the learning phase to m aintain diý culty and prevent the accum ulation of verbalizable (explicit) rules about the prim ary m otor task. T hus, on the ® nal day of the learning phase, tones were presented at a rate of one per second. T he im portance of m aintaining an accurate count during each block was explained to all participants. Participants were required to return to the laboratory after 72 h for a retention test. Retention was assessed over one block of 50 trials, before w hich all participants were allowed to take 20 warm -up shots. Both im plicit groups perform ed the secondary task during the warm up. D uring the retention test, the explicit and im plicit learning groups perform ed the putting task only; the im plicit learning control group, however, continued to count tones presented at a rate of one per second w hile putting to determ ine whether the im plicit learning group acquired rules during this phase. Perform ance during learning and retention was based on the num ber of successful putts m ade during each block. Tone-counting perform ance was calculated as the absolute percent accuracy. At the end of the learning and test phases, the par ticipants com pleted verbal protocols, describing any `rules, knowledge, m ethods or techniques’ they were aware of using to com plete the task successfully. In addition, the participants com pleted the Reinvestm ent Scale questionnaire before beginning the study.

rater reliability (r = 0.84, P < 0.001 and r = 0.84, P < 0.001 for the post-learning and post-retention protocols, respectively), in line w ith the results of previous studies. However, correlational analysis m easures the degree to w hich the data are related and is not an appropriate m easure of ag reem ent between raters (Nevill, 1996). Therefore, 95% lim its of ag reem ent were calculated using the m ean diþ erence between the num ber of rules scored by each rater (Atkinson and N evill, 1998). For both the post-learning and postretention protocols, the lim its of ag reem ent were 0.11 ± 2.06 rules, re¯ ecting the sm all num ber of rules accrued during the retention phase. O nly one observation fell outside these lim its (< 5% ), suggesting a high level of ag reem ent between the two raters. A group ´ phase (3 ´ 2) analysis of variance (AN OVA) with repeated m easures on the latter factor showed a signi® cant eþ ect of group (F 2,24 = 7.53, P = 0.003), but no eþ ect of phase (F 1,24 = 1.00, P = 0.33). T here was no group ´ phase interaction (F 2,24 = 1.00, P = 0.38). A N ewm an-Keuls a posteriori test indicated that the explicit learning group reported signi® cantly m ore rules than both im plicit groups, w hich did not diþ er from each other. Few participants recalled additional rules after the retention phase, despite the im plicit learning group having perform ed w ith no secondar y task (Fig. 1). Lear ning phase O ne-way analysis of variance, with num ber of balls successfully putted during the ® rst block of the learning phase as the dependent m easure, indicated a signi® cant

R esults Verbal protocols T he verbal protocols collected after the learning phase and the retention phase were assessed by two independent raters. Correlational analysis has been used in previous studies to test the consistency of independent raters and was used by us to replicate these ® ndings. Pearson’ s correlation coeý cients indicated high inter-

F ig. 1. M ean number of rules acquired during learning and retention. EL = explicit learning, IL = implicit learning, ILC = implicit learning control. h , post-learning phase; j , post-retention phase.

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Fig. 2. M ean number of successful putts during the learning and retention (R) phases. e , explicit learning; d , implicit learning; n , implicit learning control.

diþ erence between groups (F 2,26 = 4.00, P = 0.03). Post-h oc analysis (Newm an-K euls) revealed that the explicit learning group perform ed better than both im plicit groups, which did not diþ er from each other (Fig. 2). T he secondar y task loads appear to have had an im m ediate, detrim ental eþ ect on the perform ance of participants in the im plicit condition. For analysis of the learning phase, a group ´ day ´ block (3 ´ 5 ´ 12) design was used. T his analysis m ay be inappropriate because the day and block factors are not orthogonal. Separate group ´ block and group ´ day analyses of variance were calculated but no diþ erences to the reported eþ ects were found. T he use of a m ultivariate analysis of variance (M AN OVA) is appropriate for the analysis of these data, since assum ptions concerning the sphericity of the covariance m atrix can be avoided (H owell, 1997). However, because of the relatively sm all num ber of participants and large num ber of blocks inherent in this design, the use of M AN OVA is prohibited in certain cases, ow ing to a shortage of residual degrees of freedom . For this reason, AN OVA with repeated m easures on the day and block factors is reported for all m ain eþ ects and their interactions. Greenhouse-G eisser’ s epsilon adjusted probabilities are reported for all repeated m easures and their interactions. W here use of M AN OVA was possible, no diþ erences from the reported results were found. F igure 2 show s learning curves for all three groups over the learning phase and the m ean num ber of balls holed during the retention phase. H ighly signi® cant m ain eþ ects of group (F 2,24 = 5.12, P = 0.01), block

(F 11,264 = 17.6, P < 0.001) and day (F 4,96 = 59.9, P < 0.001) were evident. Post-hoc tests (Newm an-K euls) showed that both im plicit groups perform ed worse than the explicit group. N o signi® cant interactions were found. All three groups had sim ilar-shaped learning curves as m easured by polynom ial trend analysis over `day’ . A signi® cant linear com ponent (F 1,24 = 130.6, P < 0.001), accounting for 90.2% of the variance, and quadratic com ponent (F 1,24 = 16.7, P < 0.001), accounting for 8.88% of the variance, were found, indicating negatively accelerated learning in all three groups. T he increase in perform ance and rate of skill acquisition of all groups appeared to be sim ilar, despite the two im plicit groups’ overall worse perform ance. Retention phase A one-way analysis of variance, with num ber of successful putts as the dependent factor, revealed no signi® cant eþ ect of group (F 2,26 = 1.65, P = 0.21), despite the perform ance of the two im plicit groups being worse than that of the explicit group (Fig. 2). Secondar y task: Tone counting M ean tone-counting accuracy (calculated as the absolute percent concordance/ag reem ent between participants’ reports and num ber of tones presented during each block) was 92.2 ± 3.21 and 90.9 ± 3.62 for the im plicit learning and im plicit learning control groups respectively. It is possible that tone-counting accuracy

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A longitudinal study of im plicit m otor lear ning Table 1. M ean percent accuracy on the tone-counting task on days 1± 5 for the implicit learning and implicit learning control groups

Implicit learning Implicit learning control

Day 1

Day 2

Day 3

Day 4

Day 5

93.5 92.5

92.9 93.2

90.2 89.1

93.6 90.7

90.9 89.2

declined over the days as the task becam e m ore diý cult. To test this possibility, a group ´ day (2 ´ 5) AN OVA w ith repeated m easures on the latter factor indicated a signi® cant eþ ect of day only (F 1,16 = 17.9, P = 0.001). Post-h oc tests (Newm an-K euls) showed that days 3 and 5 were signi® cantly diþ erent from days 1 and 2, but no diþ erences were found between day 4 and any other day, suggesting that there was no consistent trend towards poorer secondary task perform ance over days (Table 1). T hus, poorer secondar y task perform ance does not account for the increase in prim ary task perform ance exhibited by the im plicit groups. Reinvestment C om putation of Pearson’ s product± m om ent correlation coeý cients revealed a signi® cant positive relationship (r = 0.59, P = 0.47) between the num ber of rules accrued by participants in the explicit learning group and their score on the Reinvestment Scale (M asters et al., 1993), as predicted. The reinvestm ent scores for the explicit learners were also found to correlate negatively (r = - 0.73, P = 0.01) w ith overall putting perform ance (calculated as m ean accuracy over 3000 trials). T hese relationships were not evident in the im plicit learning condition, w here accum ulation of rules was arti® cially suppressed by the secondary task (reinvestm ent : rules, r = - 0.27, n = 18, P = 0.29; reinvestm ent : putting perform ance, r = 0.15, n = 18, P = 0.56).

D iscussio n T he m ain aim of this study was to establish whether an extended period of practice under dual-task conditions would enable im plicit learners to perform to the sam e level as individuals w ho learnt under single-task conditions. As predicted, the explicit group repor ted signi® cantly m ore rules pertaining to the putting task than the dual-task groups, suggesting that these groups did learn im plicitly. Sim ilar-shaped learning curves were evident for all three groups, accom panied by sim ilar im provem ents. H owever, as in previous studies that used secondar y task paradigm s, the perform ance of

both the im plicit learning and im plicit learning control groups rem ained below that of the explicit learning group throughout the learning phase. N o statistically reliable diþ erences were found between groups during the delayed retention test, despite the apparent poorer perform ance of the im plicit groups com pared w ith the explicit group. D espite the im plicit learning control group continuing to perform the secondar y task during the retention phase, their perform ance was sim ilar to that of the explicit learning group. D uring the ® rst block of the learning phase, there were signi® cant differences between the perform ance of these two groups, suggesting m oderate perform ance convergence between the groups as a result of practice. It is possible that the num ber of trials used in the present study was still insuý cient to allow the perform ance of the im plicit and explicit groups to converge during learning. In describing unselective and selective m odes of learning, Berry and Broadbent (1988) stated that unselective learning, which is analogous w ith im plicit learning, is slow com pared to selective or explicit learning. T hey proposed that im plicit learning involves the encoding of all action± outcom e contingencies and that im proved perform ance is a result of a gradual build-up in positive outcom es, w hereas explicit learning involves the conscious selection of positive action± outcom e contingencies and avoidance of negative ones. Although our im plicit learners perform ed considerably m ore trials than in previous studies, it would appear that further practice is required to build a suý ciently large pool of positive action± outcom e contingencies to allow them to perform to the sam e standard as exp licit learners. However, no convergence was found in this study. Secondly, the secondary task used in the present study was particularly diý cult to perform , w ith participants rarely perform ing perfectly. An easing of the secondary task load m ay allow im provem ents in perform ance without increasing the num ber of reported rules. T his view re¯ ects that of Gentile (1998), who proposed two processes that m ediate functional skill acquisition. The ® rst describes the functional relationship between a perform er and his or her environm ent, and is explicit. The second process determ ines the functional dynam ics of the m ovem ent in relation to

118 force production and eý ciency, and is im plicit. G entile proposed that these processes act in parallel and that the explicit process is rapid, whereas the im plicit process is slow and requires m uch practice. Gentile proposed m ethods for enhancing both processes during skill acquisition: explicit processing m ight be enhanced by verbal instruction and task-relevant feedback, whereas learning via im plicit processes m ight bene® t from highly constrained learning environm ents. An alternative explanation for the worse perform ance of the im plicit learners was suggested by Baddeley and W ilson (1994). They proposed that im plicit learners are unable to learn from or correct errors and are thus unable to prevent the repetition of errors in subsequent perform ances. T hus, explicit processes function as an error detection and correction m echanism (Jacoby et al., 1989). Baddeley and W ilson (1994) provided evidence for their explanation using am nesic individuals, who learnt word-stem com pletions under errorless or errorful conditions. The participants in the errorful condition perform ed signi® cantly worse than the errorless group on a subsequent stem -com pletion test. It is possible that the im plicit learners in the present study were also unable to prevent the repetition of errors under dual-task conditions, thus accounting for their poor perform ance both during learning and the subsequent retention test. We found a negative correlation between perform ance during learning and the Reinvestment Scale score (M asters et al., 1993) for the exp licit group. This suggests that low reinvesters perform better than high reinvesters during the learning phase. Baum eister (1984) found that highly self-conscious individuals perform ed poorly on a `ball and rod’ task com pared with low self-conscious individuals. Baum eister suggested that highly self-conscious persons tend to rehearse m ore in conditions w here evaluation is likely. The Reinvestment Scale incorporates item s from the Self-C onsciousness Scale (Fenigstein et al., 1975), so high reinvesters m ay sim ply be highly self-conscious and m ore likely to becom e stressed as a result of poor perform ance in conditions open to appraisal. The presence of the experim enter m ay have caused high reinvesters (highly self-conscious) to rehearse the skill m ore frequently, leading to poorer perform ance. Furtherm ore, rehearsal is likely to encourage explicit rule form ation because of increased awareness of the m echanics of the m ovem ent. High reinvesters are likely to accrue m ore explicit rules than low reinvesters because of their propensity to rehearse m otor skills explicitly. T he positive correlation between the Reinvestm ent Scale score and the num ber of reported rules in this experim ent suppor ts this proposal. An additional factor that m ay in¯ uence perform ance in the presence of an experim enter is anxiety. High

M axwell et al. reinvesters, because of their high self-consciousness, m ay have higher trait anxiety than low reinvesters. If this were true, we would expect a high correlation between scores on the Reinvestment Scale and the trait section of Spielberger’ s State± Trait anxiety inventory (Spielberger et al., 1970). Our own unpublished data indicate that the two m easures are signi® cantly correlated (r = 0.55, n = 193, P < 0.001). T hus, high reinvesters are likely to be m ore anxious than low reinvesters, which m ay account for their diþ ering perform ances. Clearly, future research needs to address this issue if a clear understanding of the m echanism s m ediating the stress± rehearsal± perform ance breakdown cycle is to be attained. In conclusion, learning under exp licit and im plicit conditions for an extended period appears to be quantitatively sim ilar but qualitatively dissim ilar. F urtherm ore, it appears that the accum ulation of explicit rules during learning m ay have a negative eþ ect on subsequent perform ance, particularly under stress. T he reduced use of exp licit strategies during learning should be encouraged, especially am ong individuals with a high propensity towards reinvestm ent. A possible strategy to com bat the eþ ects of rehearsal and perform ance breakdow n m ay be im plicit learning. N o relationship between rule acquisition and perform ance was found in the im plicit groups. H owever, the dual-task m ethod used here to induce im plicit learning is clearly unsuited to real-world tasks. F uture research should attem pt to establish alternative m ethods to encourage im plicit skill acquisition and discourage the use of explicit strategies, which m ay be unsuitable for the control of com plex m otor skills.

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