This article was downloaded by: [University of Otago] On: 07 January 2015, At: 22:34 Publisher: Routledge Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK

Journal of Sports Sciences Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/rjsp20

Haptic perception accuracy depending on selfproduced movement a

Chulwook Park & Seonjin Kim a

b

Laboratory of Human Motor Behavior, Seoul National University, Seoul, Korea

b

Department of Physical Education, Seoul National University, Seoul, Korea Published online: 30 Jan 2014.

Click for updates To cite this article: Chulwook Park & Seonjin Kim (2014) Haptic perception accuracy depending on self-produced movement, Journal of Sports Sciences, 32:10, 974-985, DOI: 10.1080/02640414.2013.873138 To link to this article: http://dx.doi.org/10.1080/02640414.2013.873138

PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http:// www.tandfonline.com/page/terms-and-conditions

Journal of Sports Sciences, 2014 Vol. 32, No. 10, 974–985, http://dx.doi.org/10.1080/02640414.2013.873138

Haptic perception accuracy depending on self-produced movement

CHULWOOK PARK1 & SEONJIN KIM2 1

Laboratory of Human Motor Behavior, Seoul National University, Seoul, Korea and 2Department of Physical Education, Seoul National University, Seoul, Korea

Downloaded by [University of Otago] at 22:34 07 January 2015

(Accepted 29 November 2013)

Abstract This study measured whether self-produced movement influences haptic perception ability (experiment 1) as well as the factors associated with levels of influence (experiment 2) in racket sports. For experiment 1, the haptic perception accuracy levels of five male table tennis experts and five male novices were examined under two different conditions (no movement vs. movement). For experiment 2, the haptic afferent subsystems of five male table tennis experts and five male novices were investigated in only the self-produced movement-coupled condition. Inferential statistics (ANOVA, t-test) and custommade devices (shock & vibration sensor, Qualisys Track Manager) of the data were used to determine the haptic perception accuracy (experiment 1, experiment 2) and its association with expertise. The results of this research show that expert-level players acquire higher accuracy with less variability (racket vibration and angle) than novice-level players, especially in their self-produced movement coupled performances. The important finding from this result is that, in terms of accuracy, the skill-associated differences were enlarged during self-produced movement. To explain the origin of this difference between experts and novices, the functional variability of haptic afferent subsystems can serve as a reference. These two factors (selfproduced accuracy and the variability of haptic features) as investigated in this study would be useful criteria for educators in racket sports and suggest a broader hypothesis for further research into the effects of the haptic accuracy related to variability. Keywords: haptic, self-produced movement, variability

Introduction Vision plays an essential role in movement. A considerable amount of research on the identification of action boundaries has emphasised the role of vision and visual information (Abernethy & Zawi, 2007; Warren & Whang, 1987). In fact, performers place more weight on visual input among various perception systems during complex movements (Gray, 2009), but in some circumstances, movement psychology claims that percepts are clearly affected by other modalities of the performance (Ernst & Banks, 2002). Haptic factors provide the motor system with additional cues to help enhance movement planning (Patton & Mussa-Ivaldi, 2004). Especially during contact with a target, haptic information is crucial when finalising the grasp and exploring or manipulating an object (De Bruin, Sacrey, Brown, Doan, & Whishaw, 2008). Many sports involve aligning a hitting implement with a ball trajectory such that a successful strike is made by haptic perception rather than by visible distinct information (Carello, Thuot,

Anderson, & Turvey, 1999). Thus, haptic perception is important in racket sports because it plays a leading role in the proper accomplishment of making a hitting implement meet a ball trajectory. Many researchers have investigated haptic perception as it is related to accuracy, positing two different conditions of active touch versus passive touch. Haptic accuracy is significantly better using active as compared with passive touch (Gibson, 1962; Heller, Rogers, & Perry, 1990). Others, however, find little difference between active and passive touch. In certain tasks involving, for instance, texture (Katz & Krueger, 1989) and form discrimination (Vega-Bermudez, Johnson, & Hsiao, 1991) passive touch was reported to be better than active touch (Helminen, Mansikka, & Pertovaara, 1994; Richardson, Wuillemin, & MacKintosh, 1981). The perceptual difference between active touching and passively being touched needs to be delineated further in terms of how the haptic system and the motor system engage in sensing an ever-changing environment (Chapman, 1994).

Correspondence: Chulwook Park, Department of Physical Education, Laboratory of Human Motor Behavior, Seoul National University, Seoul, Korea. E-mail: [email protected] © 2014 Taylor & Francis

Haptic perception accuracy In the present study, we measured whether selfproduced movement (what is termed here “self-produced movement” touch is defined as individuals touching themselves) influences haptic perception ability (experiment 1) and investigated the factors associated with the levels of influence (experiment 2) in racket sports. This study is intended to provide useful criteria to educators and posits the opportunity to expand further haptic research horizons in different ways to serve various haptic functions.

Downloaded by [University of Otago] at 22:34 07 January 2015

Experiment 1 Perception contributes to action, but action also contributes to perception (Gibson, 1979). Attempting to study one aspect while neglecting the other may fail to reveal both aspects (Turvey, 1977). Although a number of studies have confirmed this haptic ability in various conditions, most experimental designs are based on the premise that only perception facilitates action. They mainly consider the aspect of perception as an independent variable and the interpretation of the pattern of action as a dependent variable. Although few studies have addressed how action facilitates perception (Oudejans, Michaels, Bakker, & Dolne, 1996; Ranganathan & Carlton, 2007), those measuring the contribution of action may simply be measuring movement or action rather than the self-produced movement influence considering the main dependent variable. Therefore, we investigated non-visual haptic perception accuracy, that is, whether self-produced movement influences haptic perception ability in two different conditions (unproduced and produced). This is done while controlling movement as an independent variable and then interpreting the pattern of perception as a dependent variable. We expected that there is a strong correlation between self-produced movement and expertise as regards the factor determining the skill level. In addition, this may be identified as a new developmental investigation of the action task, providing a template with which perception accuracy ability and haptic function can be assessed.

975

basis of self-reported acute or chronic physical and psychiatric disorders. In addition, all participants had normal mobility of their right arms.

Apparatus and procedures A participant was seated at a desk with the right forearm supported out to the elbow. During each trial, a table tennis racket was placed on the right hand side, with the racket divided into 16 compartments arranged into four columns and four rows. This type of division allowed the most comparable size comparing the ball diameter (40 mm). Participants were instructed to hold the racket on the side (racket hand) occluded by an opaque curtain. In front of the participant (3 m), there was a screen displaying the same image such that the participant was aware of the regional number of each racket (Figure 1a). The task of the experiment was equal in both cases: judging one hit position of the racket with a ball among the 16 compartments and reporting the position number verbally, differing only in terms of self-produced movement being coupled or uncoupled (Figure 1b).

Method Participants The performances of five highly skilled table tennis experts were measured in comparison with those of five novice participants (ages 19–23 years old). All experts played in the highest Korean national league and are considered as “top” players. The novices were five male university students. None of the novices had ever had any regular table tennis technical guidance. Individuals were excluded on the

Figure 1. Schematic drawing of the task conditions (a) and task (b).

Downloaded by [University of Otago] at 22:34 07 January 2015

976

C. Park & S. Kim

When a participant was ready, each trial was initiated by the experimenter. In the uncoupled condition, a ping-pong ball was dropped from a height of 30 cm onto the racket by an assistant. In the coupled condition, a ping-pong ball was suspended by a thread and the participant wielded his/her racket following the beat of a metronome. In order to control the contact location between the ball and the racket, the experimental assistant managed the area randomly at only 50% of the compartment (50%: around the centre, 50%: around the outside). All wielding motions typically occurred in all three spatial dimensions and matched the same contact speed with the uncoupled condition according to the law of acceleration (F = ma). In order to control (in advance) and compare (during the trial) the size of the impulse at the moment of contact between the hitting implement and the ball, the coefficient of restitution (see equation 1), a custom-made device [piezoelectric accelerometer (shock & vibration sensor: vso300A1, T-nest, Korea), and a software program (LabView 8.5, NI, Korea)] were used. sffiffiffiffiffiffiffiffiffiffiffi hup e¼ hdown

(1)

Here, e denotes coefficient of restitution, hup denotes the rebound height of the ball, and hdown represents the falling height of the ball. According to the formula for the related impulse and momentum (F = mvf − mvi), the impulse is increased by a faster velocity, this can be represented by the coefficient of restitution in the circumstance of a collision between two objects. All participants were given a similar impulse according to the formula with the device [stimulus range (uncoupled .03 V–.26 V, coupled condition .02 V–.26 V), significant during the tasks r(160) = −.03, P = .55]. We did not use any device to prevent auditory feedback because while performing the pilot trials, the auditory stimulus did not make any difference due to the controlled impulse procedure. Each participant received eight different consecutive trials in each condition block, with trials separated by approximately a 20-s pause and with each condition block separated by approximately a twomin pause. For each condition block, the participants were instructed to report the compartment number on the screen as quickly and correctly as they could. Prior to each series of critical trials, they were allowed to practice during two trials per task. When they were ready and able to follow instructions, they completed the eight trials with two condition blocks.

Statistical analysis We analysed the data to determine the uncoupled and coupled self-produced movement in terms of haptic perception precision and to determine the level of expertise. A two-way analysis of variance (ANOVA) of the data was used to determine the uncoupled and coupled haptic perception accuracy associated with expertise. The alpha level for all statistical tests was .05.

Results The critical dependent measures were the accuracy of the test depending on the two independent variables (skill levels: 2, conditions: 2); only the relevant and significant findings are presented. Figure 2 shows the non-visual haptic accuracy percentages depending on the self-produced movement, including the skill levels. As shown in the figure, the main effect in the conditions was not significant, F(1, 160) = .12, η2 = .001. The skill level main effect was significant, F(1, 160) = 22.78, η2 = .127 and the significant skill levels by condition on haptic accuracy was significant, F(1, 160) = 4.19, η2 = .026. These results indicate that although the experts exhibited significantly greater levels of performance accuracy in both conditions compared to the novices, the skill-associated differences were intensified during self-produced movement.

Discussion The group of players with expert-level skills showed higher performance levels than the novice players

Figure 2. Mean level of performance accuracy by task condition and skill level in experiment 1. The average frequencies of matching the contact point by experts were 20.8 (wrong: 19.2) in the uncoupled condition (M = 52%) and 28 (wrong: 12) in the coupled condition (M = 70%), while for the novices they were 12.8 (wrong: 28.2) in the uncoupled condition (M = 32%) and 8 (wrong: 32) in the coupled condition (M = 20%).

Downloaded by [University of Otago] at 22:34 07 January 2015

Haptic perception accuracy during both coupled and uncoupled performances. This result is partially consistent with earlier findings that suggested that action also facilitates perception. Participants who produced their own movements were better at identifying tactual shapes than participants who did not (Gibson, 1962). The self-production of active exploratory movement results from the integration of the associated motor and sensory information. This role of integration in self-produced touch would discover the exterospecific component in the flux of complex stimulation (Drewing et al., 2003). However, Figure 2 also indicates that our result is somewhat inconsistent with the earlier suggestion in that the coupled self-produced movement did not improve haptic accuracy in the novices’ cases. According to training effect comparison studies, based on participant baseline performance levels, a significant difference in learning was noted between the two training conditions. The participants with higher baselines benefited significantly more from challenging training such as an error-amplification method, whereas participants with lower baselines were helped relatively more from less challenging training such as haptic guidance (Milot, MarchalCrespo, Green, Cramer, & Reinkensmeyer, 2010). In accordance with previous studies, it is possible that the self-produced movement condition was more challenging compared to the uncoupled condition. For novices, self-produced movement tasks would be detrimental to performance due to the excessive amount of demand; their motor system given a great quantity of new information to process would be overwhelmed. On the other hand, the difficulty of the task during self-produced movement allows the experts to reach an optimal challenge point, which they have enough ability to deal with. This provides a new perspective in that the contribution of action was not identical depending on the skill level. In addition, this result shows that more studies need to be conducted to ascertain which mechanisms are related to this interaction effect. The haptic perceptual subsystem quite relevant to experiment 1 is commonly known to be dynamic touch (Gibson, 1966). People can perceive a number of spatial and other properties of objects without the benefit of vision simply by wielding and hefting the objects (Turvey, 1996). Dynamic touch is implicated whenever mechanical contact affects the tensile states of muscles, tendons, and fascia, and in turn patterns the ensemble activity of receptors (Fonseca & Turvey, 2006). Although our touch in this experiment is consistent with dynamic touch in that the primary deformations of body tissues take place in the muscles and tendons when the racket is grasped and then wielded, our form of touch is somewhat

977

different compared to dynamic touch because this is not simply referring to an impression of an object’s length, orient, and position. The most important variable in our experimental design of touch is the capability impression of hitting the ball with the racket accompanied by a stinging feeling in the hands. These types of perceptions are tied to the two haptic afferent subsystems because this afferent consequence is closely connected not only to kinesthetic (muscle, tendon, joint deformation) input but also to cutaneous (skin vibration deformation) input (Clark & Horch, 1986; Klatzky & Lederman, 2003). Thus, our result is not consistent with earlier findings about dynamic touch in that the findings pertaining to the implement length and sweet spot of a racket are unmediated by experiences between experts and novices (Carello et al., 1999). Our challenge is the lack of the identification of the location on a particular object accurately by dynamic welding. We measured the locations on the racket where the ball was in contact through the stinging feeling in a participant’s hand. The consequences of the different contact positions during the trials would be the basis for the learning or experience in our experiments. This may be analogous to a difference in percepts which is so large that players give a special name to the area on the racket where contact with the ball produces a strong feeling in the hands (Gray, 2009). The chief issue raised by but unresolved in this study was the type of source that created this difference, especially in the self-produced movement coupled condition, between the expert performance and the novice performance. Therefore, we investigated reference sources pertaining to haptic perception ability.

Experiment 2 Haptic information is composed of two different subsystems (cutaneous input and kinesthetic input). When both systems operate together, they constitute what is termed the haptic system (Lederman & Klatzky, 2009). Behavioural experiments embracing a number of tasks have clearly demonstrated the critical role of tactile sensory input (Johansson, 2007). However, the design of most studies is such that the definitive separation of cutaneous input from kinesthetic input is not possible. Similarly, in most experiments in the large field of haptic perception, multisensory information is present (Klatzky & Lederman, 2008). Even in those experiments in which vision is occluded, it is usually not possible to determine whether the sensory information arises from cutaneous sources, kinesthetic sources, or a combination of the two. When this is addressed, it will be possible fully to appreciate the nature of

Downloaded by [University of Otago] at 22:34 07 January 2015

978

C. Park & S. Kim

haptic mechanisms and to make meaningful comparisons between the two afferent haptic subsystems. The purpose of experiment 2, therefore, was to investigate the fundamental composition of the substances between the two afferent haptic subsystems depending on the skill level in only the selfproduced movement coupled condition. We used variability to examine this particular characteristic (Latash, 2008; Sternad & Abe, 2010), as variability is considered a useful and reliable assessment tool to determine the cardinal features of various groups or skill levels in fields connected to motor control and learning (Newell, 1993; Torres, 2011). For the measure of cutaneous variability, we used a vibration sensor considering that vibration detection is the most important primary function during tactile feature sensitivity (Gray, 2009; Wolfe et al., 2008). For the measure of kinesthetic variability, we used a motion-capture device because the kinesthetic inputs from mechanoreceptors in muscles, tendons, and joints contribute to humans’ perception of limb positions and limb movements in space (Gandevia, 1996). Method Participants Five male undergraduates constituted the novice group. None of the novices had had any regular table-tennis technical guidance. The experts were five male varsity table-tennis players and were considered as players at the highest level in the Korean university league. Individuals were excluded on the basis of self-reported acute or chronic disorders. In addition, all participants (ages 19–23 years old) had normal mobility in their right arms and none had participated in experiment 1.

tracking software Visual 3D (C-motion, Inc., USA). This system allows the user to perform 2D and 3D motion capturing and is adaptable to different movement characteristics (Figure 3). The same shock and vibration sensor in experiment 1 were used here as well. The signals were sampled at 1k Hz and were converted by an A/D bolt device (NI usb-6009, NI) and a software program (LabView 8.5, NI, Korea). Statistical analyses First, we analysed the data to determine only the precision of coupled haptic perception. Simple regression and Chi-square testes were used to determine the coupled haptic perception accuracy associated with expertise. Second, we used a t-test to determine the dependent variables as the standard deviation (SD) of the racket features (vibration, angle, trajectory, speed), while the independent variables were the levels of skill of the participants (novices and experts). The alpha level for all statistical tests was .05. The amount of variation that occurs in several measures of a variable from the same participant is termed the within-participant variability. To determine this individual difference, the average distance of each person’s score from the mean (M) of the group was calculated and described in terms of the SD. This denotes the degree of variability from the average. Finally, we established which measures of variability were or not predictive of the different haptic accuracy scores to determine which has the strongest effect on task performance. To determine this, the variability difference between the two groups was compared for each feature. The relative ratio of the variability differences between the two groups (VDB) and the variability ratio of each group [f(e) = expert group variability, f(n) = novice group variability] were calculated using the following equations:

Apparatus and behavioural task The second experimental setup was similar to the first experimental setup, but only the self-produced movement coupled condition was used. Additionally, in the other setup, to measure the absolute error size at each trial, participants had to indicate the perceived hit position on the racket using a thin pencil (after reporting the position number verbally, i.e., recording one hit position from among the 16 compartments, as in experiment 1). Four electrode markers and a vibration sensor were attached to the holding racket. We used a motion-capture system with eight Oqus 322 5series cameras (Qualisys, Sweden), denoted here as the Qualisys Track Manager (QTM). Movements were monitored in real time and were captured at 150 Hz using four electromagnetic sensors and the motion-

SDðeÞ  100 SDðeÞ þ SDðnÞ SDðnÞ f ðnÞ ¼  100 SDðnÞ þ SDðeÞ

f ðeÞ ¼

VDB ¼

SDðeÞ  SDðnÞ  100 SDðeÞ SDðnÞ

(2)

(3)

Results 2 Behavioural accuracy and cutaneous variability (racket vibration) In the first part of the analysis, we focused on the accuracy of the performance. On average, the nonvisual haptic accuracy percentages for only self-

979

Figure 3. Method-movement trajectory decomposition and profiles in experiment 2. The Left trajectory shows a sequenced routine with the event (initial, contact of the racket with the ball, and maximum) in a racket trajectory. Each dot indicates a marker mounted on the racket (light circle 4); the front side (1), the back side (1), the right side (1), and the left side (1). A vibration sensor is attached to the reverse side of the racket centre (black circle 1). The Right upper graph shows one of participant’s event profile of the racket movement angles (top: X axis, middle: Y axis, bottom: Z axis), and the bottom graph shows the racket vibration at the moment of contact between the racket and the ball.

b

a 8.0 7.0 6.0 5.0 4.0 3.0 2.0 1.0 0.0

Experts Novices

Amplitude (V)

produced movement were significantly different between the two groups [correct match number: experts = right: 28, wrong: 12; novices = right: 10, wrong: 30 (absolute error_size: experts = M: 2. 13 cm; novices = M: 4. 19 cm); P < .001]. As shown in experiment 1, the main effect of the skill level was significant, χ2 [(1, N = 80) = 16.24, P > .001], (R2 = .62). These results once more demonstrate that while the experts exhibited significantly greater performance accuracy compared to the novices, the skill-associated differences were enhanced during self-produced movement (Figure 4a). As in the result of experiment 1, we also compared the degree of racket vibration using a piezoelectric accelerometer (a shock & vibration sensor: vso300A1, Tnest, Korea) for the same reasons. Figure 4 shows that skill-level-associated differences in performance accuracy were not due to strength differences in the size of the racket impulse (experts, M = .16 V vs. novices, M = .15 V). In addition, the experimental protocol did not induce any significantly different results during the tasks. We compared the racket vibration variability of the experts and the novices with the SD. As shown in Figure 4b, the vibration of the SD difference

Performance accuracy (M)

Downloaded by [University of Otago] at 22:34 07 January 2015

Haptic perception accuracy

CM (f)

AE (cm)

0.18 0.16 0.14 0.12 0.10 0.08 0.06 0.04 0.02 0.00

Experts Novices

Mean

SD

Figure 4. Comparison of the performance accuracy in experiment 2 (a) shows participants’ frequency (f) of a correct match (CM: black bar) (+SE), size (cm) of the absolute error (AE: white P bar)ðAE ¼ j χ i  Tj=nÞ between an actual hit position and the perceived designated position of the racket (+SE). The average frequency of matching the contact point by experts was 5.6 (70%) with a pointed error size of 1.9 cm, while for novices the matching frequency was 2 (25%) and the pointed error size was 4.3 cm. (b) shows the racket vibration (+SE); also shown are the voltage mean (left bar graph) and standard deviation (right bar graph) at the moment of a strike of the ball with the racket between the two groups.

between the two groups was greater compared to the average (M) vibration (experts, SD = .05 V vs. novices, SD = .08 V). These results indicate two facts; first, our impulse magnitudes were successful in that skill-level-associated differences in

980

C. Park & S. Kim compared to the novices (experts, SD sagittal plane = .04, SD of frontal plane = .04 deg; novices, SD of sagittal plane = .04, SD of frontal plane = .03 deg). These results indicated that while the experts exhibited a significantly greater angle size than the novices, the angle variability did not vary as much as that of the novices. This finding needs to be considered with some caution, as it may contain the origin of reference of the haptic perception ability.

performance accuracy were not due to strength differences in the size of the racket impulse. Second, although the skill-level-associated performance accuracy was not due to strength differences (gap between two groups’ M values = .006) of the magnitude of the racket vibration, the skill-associated variability (gap between two groups’ SD values = .03) may be the origin of reference pertaining to the difference in the haptic perception ability levels between the performance of the experts and that of the novices.

Total variability (all value types) For a finer delineation of the variability in the skilllevel changes, the relative variability ratio difference between the two groups was compared for each feature. The relative ratio of the variability differences between the two groups (VDB) and the variability ratio for each group [f(e), f(n)] were calculated, as shown in Table II and Figure 7. The variability difference between the novices and experts increased for the vibration and angle features as opposed to the other features. In conclusion, as shown in Figure 7, variability may be a valuable indicator of haptic changes. Not only does the skill level exhibit different rates of accuracy but also different rates of variability. Both of these contribute to the increased heterogeneity in the haptic system functions of novices.

Downloaded by [University of Otago] at 22:34 07 January 2015

Kinesthetic variability (values of three types of movement) We measured the racket movements of the trajectory, velocity, and angle. These data are reserved for different reports. During their performances, participants were not provided with accurate performance feedback. The average (M) and variability (SD) for a number of movement parameters are depicted in Table I. These data were analysed using SPSS v. 18 in independent t tests for a comparison of the two groups of participants. A P-value of .05 was used to determine statistical significance. As presented in Table I, the analysis of the kinematic data confirmed that the skill-level difference was not significant, except for the anteroposterior axis angle; t(78) = 4. 52, P = .001. In Figure 5, the means and standard deviations for a number of movement parameters are illustrated. The within-group standard deviation was taken as the respective measure of the variability in the different kinematic variables. As shown in Table I and Figure 5, the SD of the racket angle increased for the novices (experts, SD lateral axis = 8.54, SD of anteroposterior axis = 6.55 deg; novices, SD of lateral axis = 16.02, SD of anteroposterior axis = 9.66 deg), while the M-value of the racket angle decreased (experts, M of lateral axis = 25.61, M of anteroposterior axis = 14.14 deg; novices, M of lateral axis = 24.76, M of anteroposterior axis = 5.80 deg). In contrast to the racket trajectory, the SD was greater for the experts as

Discussion It is well known that the accuracy of movement can be recorded in different planes of motion, lending to different estimates of system variability. This variability in system parameters can be examined at more micro levels of analysis, as in the various mechanisms of motor control (Newell, 1993; Torres, 2011). In addition, there has been considerable research on the inter-sensory interactions between haptic perception and other sensory modalities (Ernst & Banks, 2002; Helbig & Ernst, 2007). Research on intrasensory interactions between cutaneous inputs and kinesthetic inputs has never focused on which plays a more crucial role in various situations. Considering

Table I. Three types of racket movements variables. Racket trajectory (m) Sagittal plane Statistics M SD t (78)

Racket velocity (m/s)

Frontal plane

Max

Racket angle (deg)

Mean

Lateral axis

Anteroposterior axis

E

N

E

N

E

N

E

N

E

N

E

N

0.047 0.041

0.046 0.037

0.012 0.04

0.005 0.027

2.048 0.379

1.919 0.456

0.984 0.183

0.903 0.213

25.609 8.535

24.760 16.016

14.135 6.554

5.796 9.655

0.903

0.382

0.072

Note: **P < .01. M = mean; SD = standard deviation; t = t-value; E = experts; N = novices.

0.173

0.768

0.001**

Haptic perception accuracy

0.04

SD of trajectory (m)

Novice

0.03 0.02 0.01 0.00

Sagittal plane

Mean of speed (m/s)

Novice

0.03 0.02 0.01 Sagittal plane

Frontal plane

0.7 Expert

2.0

Novice

1.5 1.0 0.5

0.6

Expert

0.5

Novice

0.4 0.3 0.2 0.1

0.0

Max

0

Mean

30

Expert

25

Max

Mean Expert

20

Novice

SD of angle (deg)

Mean of angle (deg)

Expert

0.04

0.00

Frontal planel

2.5

Downloaded by [University of Otago] at 22:34 07 January 2015

0.05

Expert

SD of speed (m/s)

Mean of trajectory (m)

0.05

981

20 15 10 5 0

12 8 4 0

Lateral axis Anteroposterior axis

Novice

16

Lateral axis Anteroposterior axis

Figure 5. Movement variables in experiment 2. The upper shows the mean (left bar graph) and standard deviation (right bar graph) of the racket movement trajectory (+SE). The Middle shows the mean (left bar graph) and standard deviation (right bar graph) of the racket velocity (+SE). The Bottom shows the mean (left bar graph) and standard deviation (right bar graph) of the racket angle (+SE).

Table II. Each type of variability and relative differences variability. Racket vibration (V)

Racket position (m) Sagittal plane

Racket velocity (m/s)

Frontal plane

Max

Racket angle (deg)

Mean

Lateral axis

Anteroposterior axis

Statistics

E

N

E

N

E

N

E

N

E

N

E

N

E

N

SD f(x) (%)

0.04 36.28

0.07 63.72

0.04 52.56

0.03 47.44

0.04 59.70

0.02 40.30

0.38 45.39

0.46 54.61

0.18 46.21

0.21 53.79

8.54 34.76

16.02 65.24

6.55 40.43

9.66 59.57

VDB (%)

27.43

5.13

19.40

9.22

7.58

30.47

19.13

Note: SD = standard deviation; f(x) = variability ratio or each group; VDB = relative variability differences between two groups. E = experts; N = novices. [formula: f(e) = experts SD/(experts SD + novices SD) × 100, f (n) = novices SD/(experts SD + novices SD) × 100), VDB = (experts SD−novices SD)/(experts SD + novices SD) × 100].

this, the study examines accuracy in which cutaneous and kinesthetic inputs provide pivotal information at a finer level. It has functional significance, as many instances of the haptic accuracy of hitting implements with a ball trajectory are guided by cutaneous vibration input variability rather than kinesthetic input variability. The results of passive-touch studies demonstrate that cutaneous inputs alone are sufficient to induce

subjective sensations. However, many studies fail to confirm the important role of cutaneous sensing when active exploration is permitted (Gibson, 1962). We were clearly able to reconfirm Gibson’s view in our first experiment. From our second experiment, we had to review the role of the cutaneous system because, as shown in Figure 7, vibration has a higher value. Recent observations have shown that cutaneous senses play an indispensable

Downloaded by [University of Otago] at 22:34 07 January 2015

982

C. Park & S. Kim

Figure 6. Kinesthetic features profile in experiment 2. The upper graphs show the racket movement trajectory’s sagittal plane profile for the experts (left) and novices (right). The bottom graphs show a participant’s racket movement angles’ lateral axis profile for an expert (left) and a novice (right).

Figure 7. The relative difference in the variability between the groups (VDB) in experiment 2. The black bars indicate that the experts’ variability values are greater (+SE). The white bars indicate that the novices’ values are larger (+SE). The bar lengths represent the size difference between the groups. V: velocity, T_X: trajectory_X axis, T_Y: trajectory _Y axis, V_Mx: velocity max, V_Mn: velocity mean, A_X: angle_X axis, A_Y: angle_Y axis.

role in the action of the receptors located in the skin adjacent to the finger joints. This applies when moving the forearm and hand using tendons that cross more than one joint. The kinesthetic afferent inputs are potentially equivocal in such a case. The nearness of cutaneous subsystems adjacent to each joint offers specific inputs (Sturnieks, Wright, & Fitzpatrick, 2007). The present view is that the contribution at most kinesthetic receptors is likely to be less than that of the cutaneous receptors (Proske & Gandevia, 2009). However, we suggest that cutaneous vibration is not complete in a limited impulse condition because cutaneous variability (vibration) requires precise control of the variability of movement (angle). The difference in the variability levels between the novices and the experts increased not only in terms of the vibration feature but also in terms of the angle feature. This means that the difference in variability related to performance accuracy between the groups

Downloaded by [University of Otago] at 22:34 07 January 2015

Haptic perception accuracy may be caused by a hitting implement with a ball contact function (Bootsma & Van Wieringen, 1990). The values of the racket’s vibration and angle across trials varied more in the novice participants, while the values of the racket’s trajectory across trials varied more in the expert participants. In accordance with these observations, we refer to previous findings of experts’ forward motions of a bat, stick, or racket and a slight variability in the moment of the ball contact over many trials (Tyldesley & Whiting, 1975). According to Bootsma and Van Wieringen (1990), expert table tennis players’ spatial variability is more reduced at the ball and racket hit contact point than at initiation. In order to produce standard, unaltered, low-variability performance, the racket hand would be adapted to meet the requirements of specific conditions, allowing them to make contact with their racket accurately enough. This higher accuracy at the moment of ball and bat contact is caused by functional variation, i.e., a reduction in the variability of the contact accompanied with an increase in the variability of the individual limb joint motions (Arutyunyan, Gurfinkel, & Mirskii, 1968). These previous results are in good agreement with our demonstrated haptic-accuracy differences in expertise; they were not due to the strength of the kinematic information and the cutaneous information but instead were due to the variability especially related to contact-moment stable inputs. Moreover, this reduced contact-moment variability is accomplished by the cutaneous vibration constancy related to the kinesthetic racket angle along with an increase in the compensatory actions of the movement feature. Multiple degrees of freedom in motor systems provide optimal solutions with various references. These solution sources include the variability acquired from given motor task performances. Some variability does not have any influence on goal-oriented performance, while other types of variability have a negative or positive influence (Latash, 2008). During every trial, more than one type of variability existed, combining and acting together. Moreover, a relatively large form may serve as a crucial reference of goal-oriented motor performance. This is interpreted by the index of difficulty (ID), as shown below. ID ¼

NV AV

AV ¼ Pi  Ni

(4)

(5)

Here, ID denotes the index of difficulty of the goal-oriented motor performance, NV denotes the non-affective component of variability, and AV

983

represents the affective component of variability. Given this equation, the AV is then partitioned into two components: the component of positive variance (Pi) and the component of negative variance (Ni). The calculation shows that the ID increased with a decrease in the AV and that the AV also decreased with an increase in the Ni. In contrast, the ID decreased with an increase in the AV and the AV increased with an increase in the Pi as well. Based on the above interpretation and our investigation, the cutaneous vibration related to the kinesthetic angle at the moment of contact appears as negative variability (Ni ); however, the kinesthetic position trajectory during the movement appears as positive variability (Pi ). More importantly, this relationship within the AV was not the same in participants with different skill levels. Given the result shown in Figure 7, the values of the racket’s vibration and angle across trials had more variance in the novice participants, while the values of the racket’s trajectory across trials showed more variance in the expert participants. This indicates that the variability derived from the same component can contribute differently depending on the user’s capability. Our question from experiment 2 was whether gradual differences also appear in the movement variability across repetitions of the same movements which map onto different levels of functionality. The stable levels of variability across measures and within a task across occasions, as observed in this study, support the hypothesis that both accuracyrelated declines in performance and increases in the differences in the two haptic afferent subsystems are a function of the increasing variability in the haptic perception mechanism. It is possible of course that there is a limit in how these two features interact with each other; moreover, the methodological design restricts some interpretations in that the cutaneous inputs through the racket are not fully defined by the determined facts. However, this functional mechanism may be emphasised by the importance of sources as indicators of skill-related changes in haptic accuracy performance. This is determined by the index of difficulty of the goal-oriented motor performance. Conclusions and practical applications Under general circumstances, visual information plays a crucial role in movements. This has been studied in a number of areas, and these many excellent behavioural works stand on their own research in neuroscience, mechanical engineering, medicine, and learning. These findings are important in that performance can be facilitated by attempting to match haptic features with participants’ skill levels. In a related study, we measured haptic perception

Downloaded by [University of Otago] at 22:34 07 January 2015

984

C. Park & S. Kim

accuracy depending on self-produced movement in racket sports. From the results of these two experiments, we found that expert-level players acquire higher accuracy than novice-level players, especially in performances involving self-produced movement. The implication of these results was that the skill-associated differences were exacerbated during self-produced movement. This haptic accuracy was predicted slightly more accurately by cutaneous input (vibration) as derived from the kinesthetic (angle) variability. Furthermore, these features stemming from the same component can contribute differently depending on the user’s capability. Throughout the earlier discussions of accuracy and skill levels, many references were made to observations that accuracy is lower in novices. However, most of the data reported are average performance scores over many individuals at different skill levels rather than reflections of change within individuals over a series of trials. In addition to the general responses, performance variability is another indicator of haptic perception integrity, and from these concepts, it is indeed clear that the self-produced accuracy and variability levels of haptic features differ depending on the skill level. Thus, based on the active condition, participants become less heterogeneous as regards the key features of a skill with an increase in the skill level. These factors as investigated in this study would be useful criteria to educators and suggest a broader hypothesis for further research into the effects of the haptic accuracy related to variability in racket sports.

Acknowledgements We thank the experimental participants for their patience, and especially wish to thank Dr. Hyeongsaeng Park (Dept. Psychology, Seoul National University) for his valuable comments during the development of this article.

References Abernethy, B., & Zawi, K. (2007). Pickup of essential kinematics underpins expert perception of movement patterns. Journal of Motor Behavior, 39(5), 353–367. Arutyunyan, G., Gurfinkel, V., & Mirskii, M. (1968). Investigation of aiming at a target. Biophysics, 13, 536–538. Bootsma, R. J., & Van Wieringen, P. C. (1990). Timing an attacking forehand drive in table tennis. Journal of Experimental Psychology: Human Perception and Performance, 16(1), 21–29. Carello, C., Thuot, S., Anderson, K. L., & Turvey, M. T. (1999). Perceiving the sweet spot. Perception, 28(3), 307–320. Chapman, C. E. (1994). Active versus passive touch: Factors influencing the transmission of somatosensory signals to

primary somatosensory cortex. Canadian Journal of Physiology and Pharmacology, 72(5), 558–570. Clark, F. J., & Horch, K. W. (1986). Kinesthesia. In K. R. Boff, L. Kaufman, & J. P. Thomas (Eds.), Handbook of perception and human performance: Vol. 1. Sensory processes and perception (pp. 13–62). New York, NY: John Wiley & Sons. De Bruin, N., Sacrey, L. A. R., Brown, L. A., Doan, J., & Whishaw, I. Q. (2008). Visual guidance for hand advance but not hand withdrawal in a reach-to-eat task in adult humans: Reaching is a composite movement. Journal of Motor Behavior, 40(4), 337–346. Drewing, K., Hartwig, V., Ricciardi, E., Vanello, N., Kheddar, A., Bresciani, J.-P., & Ernst, M. O. (2003). Passive and active touch–preliminary report (pp. 1–36). Touch-Hapsys. Retrieved from http://www.touch-hapsys.org Ernst, M. O., & Banks, M. S. (2002). Humans integrate visual and haptic information in a statistically optimal fashion. Nature, 415(6870), 429–433. Fonseca, S. T., & Turvey, M. T. (2006). Biotensegrity perceptual hypothesis: A medium of haptic perception. Paper presented at the North America Meeting of the International Society for Ecological Psychology, Cincinnati, OH. Gandevia, S. C. (1996). Kinesthesia: Roles for afferent signals and motor commands. In L. B. Rowell & J. T. Shepherd (Eds.), Handbook of physiology (pp. 128–172). New York, NY: American Physiological Society. Gibson, J. J. (1962). Observations on active touch. Psychological Review, 69(6), 477–491. Gibson, J. J. (1966). The senses considered as perceptual systems. Boston, MA: Houghton Mifflin. Gibson, J. J. (1979). The ecological approach to visual perception. Boston, MA: Houghton Mifflin. Gray, R. (2009). How do batters use visual, auditory, and tactile information about the success of a baseball swing? Research Quarterly for Exercise and Sport, 80(3), 491–501. Helbig, H. B., & Ernst, M. O. (2007). Optimal integration of shape information from vision and touch. Experimental Brain Research, 179(4), 595–606. Heller, M. A., Rogers, G. J., & Perry, C. L. (1990). Tactile pattern recognition with the optacon: Superior performance with active touch and the left hand. Neuropsychologia, 28(9), 1003–1006. Helminen, R., Mansikka, H., & Pertovaara, A. (1994). Lowered or increased cutaneous sensitivity during movement depends on stimulus intensity. Perceptual and Motor Skills, 78(3), 721–722. Johansson, R. S. (2007). Sensory input and control of grip. In G. R. Bock & J. A. Goode (Eds.), Sensory Guidance of Movement (pp. 45–63). London: John Wiley & Sons. Katz, D., & Krueger, L. E. (1989). The world of touch. Hillsdale, NJ: L. Erlbaum Associates. Klatzky, R., & Lederman, S. (2008). Perceiving object properties through a rigid link. In M. C. Lin & M. Otaduy (Eds.), Haptic rendering: Foundations, algorithms and applications (pp. 7–19). Natick, MA: A K Peters. Klatzky, R. L., & Lederman, S. J. (2003). Touch. In A. F. Healy & R. W. Proctor (Eds.), Handbook of psychology, Vol. 4 (pp. 147–176). New York, NY: John Wiley & Sons. Latash, M. L. (2008). Neurophysiological basis of movement (2nd ed.). Champaign, IL: Human Kinetics. Lederman, S., & Klatzky, R. (2009). Haptic perception: A tutorial. Attention, Perception, & Psychophysics, 71(7), 1439–1459. Milot, M. H., Marchal Crespo, L., Green, C. S., Cramer, S. C., & Reinkensmeyer, D. J. (2010). Comparison of error-amplification and haptic-guidance training techniques for learning of a timing-based motor task by healthy individuals. Experimental Brain Research, 201(2), 119–131.

Downloaded by [University of Otago] at 22:34 07 January 2015

Haptic perception accuracy Newell, K. M. (1993). Variability and motor control. Champaign, IL: Human Kinetics. Oudejans, R. R. D., Michaels, C. F., Bakker, F. C., & Dolne, M. A. (1996). The relevance of action in perceiving affordances: Perception of catchableness of fly balls. Journal of Experimental Psychology: Human Perception and Performance, 22(4), 879–891. Patton, J. L., & Mussa-Ivaldi, F. A. (2004). Robot-assisted adaptive training: Custom force fields for teaching movement patterns. IEEE Transactions on Biomedical Engineering, 51(4), 636–646. Proske, U., & Gandevia, S. C. (2009). The kinaesthetic senses. The Journal of Physiology, 587(17), 4139–4146. Ranganathan, R., & Carlton, L. G. (2007). Perception-action coupling and anticipatory performance in baseball batting. Journal of Motor Behavior, 39(5), 369–380. Richardson, B. L., Wuillemin, D. B., & MacKintosh, G. J. (1981). Can passive touch be better than active touch? A comparison of active and passive tactile maze learning. British Journal of Psychology, 72(3), 353–362. Sternad, D., & Abe, M. O. (2010). Variability, noise, and sensitivity to error in learning a motor task. In F. Danion & M. Latash (Eds.), Motor control: Theories, experiments, and applications (pp. 267–295). New York, NY: Oxford University Press.

985

Sturnieks, D. L., Wright, J. R., & Fitzpatrick, R. C. (2007). Detection of simultaneous movement at two human arm joints. The Journal of Physiology, 585(3), 833–842. Torres, E. (2011). Two classes of movements in motor control. Experimental Brain Research, 215(3–4), 269–283. Turvey, M. T. (1977). Preliminaries to a theory of action with reference to vision. In R. Shaw & J. Bransford (Eds.), Perceiving, acting, and knowing: Toward an ecological psychology (pp. 211–265). New York, NY: Lawrence Erlbaum. Turvey, M. T. (1996). Dynamic touch. American Psychologist, 51(11), 1134–1152. Tyldesley, D. A., & Whiting, H. T. (1975). Operational timing. Journal of Human Movement Studies, 1(4), 172–177. Vega-Bermudez, F., Johnson, K. O., & Hsiao, S. S. (1991). Human tactile pattern recognition: Active versus passive touch, velocity effects, and patterns of confusion. Journal of Neurophysiology, 65(3), 531–546. Warren, W. H., & Whang, S. (1987). Visual guidance of walking through apertures: Body-scaled information for affordances. Journal of Experimental Psychology: Human Perception and Performance, 13(3), 371. Wolfe, J. M., Kluender, K. R., Levi, D. M., Bartoshuk, L. M., Herz, R. S., Klatzky, R. L., & Lederman, S. J. (2008). Sensation and perception (2nd ed.). Sunderland, MA: Sinauer.