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Performance and learning during voluntary control of breath patterns
Biological Psychology 37 (1994) 147-159 0 1994 Elsevier Science B.V. All rights reserved
Performance and learning of breath patterns Nathalie Blanc-Gras a,*, Fraqois and Jorge Gallego ’
147 0301-051 l/94/$07.00
during voluntary Esteve
‘, Gila Benchetrit
control a,
a Luboratoire de Physiologic, Faculte’ de M&de&e de Grenoble, 38700 La Tronche, France ’ Laboratoire de Physiologic, Faculte’ de Midecine de Paris-Sud, 94276 Le Kremlin-Bic&re, France
Fourteen subjects learned to adjust their breath pattern to two target breaths displayed on a video screen, by using visual feedback, during two sessions 24 h apart. These two targets were respectively the smallest and the largest breaths of a ten-breath sample previously recorded from each subject’s resting spontaneous breathing. Performances were significantly better for the large than for the small target breath. This cannot be directly inferred from current knowledge related to the control of movement time and amplitude, but rather it may be inferred from the periodic character of breathing, to the higher mental load during the small breath task, or to the presumably different frequencies of target breaths in the whole span of spontaneous breathing. In the second session, performance on the two targets levelled out as a result of learning. Keywords: respiration;
breathing
exercises;
learning;
biofeedback
1. Introduction Automatic breathing is a periodic activity controlled by a central pattern generator located in the pons and the medulla. This generator adjusts the period and amplitude of breathing as a function of the variable metabolic needs for oxygen and the variable production of carbon dioxide. These ventilatory adjustments are achieved by combinations of period and amplitude which present large individual variability. Besides its reflex control, breathing can also be controlled voluntarily for many non-homeostatic purposes, such as voice production and speech. The latter functions of the respiratory system are often categorized as the “behavioral component” of breathing, a concept further extended to other activities of the respiratory system (posture, expulsive motor acts), whether they are voluntary or automatic. In normal conditions, the metabolic and behavioral components of breathing influence each other, but the neurological substrate of this interaction remains unclear. The behavioral component is mainly controlled by forebrain * Corresponding
author.
S.SDI 0301-0511(93)00925-A
14x
N. Blanc-Gras et al. /Biological Psychology 37 (1994) 147-159
descending influences, and is thought to excite both the respiratory motoneurones and the output stage of the respiratory bulbar oscillator, while inhibiting the latter’s oscillatory activity (Hugelin, 1986). Recent animal studies show that some of the metabolic and behavioral influences on respiratory motoneurones are integrated within brain stem areas that house the pattern generator (Orem, 1989). This highlights the fact that breathing must be construed as a complex function governed by a set of hierarchically structured control systems, in accordance with a variety of biological and psychological demands (Van Euler, 1983). The interaction between metabolic and behavioral components is illustrated by numerous aspects of normal or pathological breathing. Speech breathing is a compromise between metabolic requirements and the purposeful manipulations of ventilatory flow for voice production. This ability, fully automatized in normal individuals, originates in purposeful manipulation of flow that infants exhibit at 2-4 months (McKenna, 1987). During this period, there is a major shift in the extent to which metabolic and behavioural components of breathing become functionally integrated. Abnormalities in this process have been considered as a possible cause of sudden infant death syndrome (McKenna, 1987). Even those ventilatory acts which are often considered as a pure reflection of metabolic processes are actually a compromise between the metabolic and behavioral components. For instance, the ventilator-y response to exercise in normal adults is influenced by changes which occur prior to the onset of exercise, a phenomenon which has been interpreted as Pavlovian conditioning (Tobin, Perez, Guenther, D’Alonzo & Dantzker, 1986). This hypothesis has been supported by recent findings on conditioned ventilatory responses during exercise (Moosavi, Adams & Guz, 1993). Conditioned changes in breathing have also been observed after concomitant exposures to hypoxic and auditory stimuli (Gallego & Perruchet, 1991a). A critical role for the behavioral control of breathing is postulated in children with congenital central hypoventilation syndrome (Ondine’s curse>. These children display an ineffective chemoreceptor function during sleep, making necessary mechanical ventilation, but they may display a normal ventilation when awake. This ability is attributed to a cortical control of breathing, presumably automatized through practice. These few examples show that the study of ventilatory function-whether normal or pathological -must necessarily incorporate the psychological processes which contribute to the patterning of ventilatory behavior, mainly voluntary control and learning. Learning to change breathing pattern is a current practice in many clinical contexts. Patients with chronic obstructive pulmonary diseases are trained to lower ventilatory frequency and to increase amplitude in order to increase alveolar ventilation (Donner & Howard, 1992; Singh, Wisniewski, Britton & Tattersfield, 1990). Feedback-assisted control of tidal volume and mean
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inspiratory flow helps to reduce weaning time on mechanical ventilation in hard-to-wean patients (Holliday & Hyers, 19901. Alongside traditional techniques of voluntary control of breathing pattern in respiratory obstructive disorders, learning to decrease ventilatory frequency has been shown to improve hyperventilation syndrome, panic attack, to reduce psychophysiological arousal (Cappo & Holmes, 1984; Clark, Salkovskis & Chalkley, 1985; Grossman & Wientjes, 19891, and idiopathic epileptic seizures (Fried, 1992). The underlying processes of these therapies are difficult to elucidate, one difficulty being to discriminate between the specific physiological effects of ventilatory changes (e.g. PCO,) and indirect psychological effects on arousal and anxiety. Investigating the way breathing may be influenced by learning is therefore of both theoretical and practical interest. The way subjects learn to modify their breathing pattern has rarely been addressed in experimental studies, and so empirical approaches prevail in respiratory physiotherapy or rehabilitation. At least two potentially confounding factors have been largely underestimated in previous reports. Firstly, subjects asked to change one breathing variable (for instance frequency) tend to change their whole breathing pattern (Gallego et al., 1986; Gallego & Perruchet, 1993). Secondly, ventilatory patterns display large inter-individual differences, even under conditions of relaxed wakefulness (Shea, Walter, Murphy & Guz, 1987; Benchetrit et al., 1989), and arguably, training a subjects to adopt a pattern which exaggeratedly departs from his/ her individual spontaneous pattern may be detrimental to learning. The purpose of the present experiment was to analyse how subjects may learn to adjust the whole shape of each breath, i.e. the plot of amplitude as a function of time from the beginning of inspiration to the end of expiration. Each subject was trained with feedback on ventilatory movement. As a rule, feedback is a determinant variable in motor skill learning (Schmidt, 19881, including breathing pattern learning (Gallego et al., 1986, Gallego & Camus, 1989). Visual feedback was provided to achieve efficient practice conditions. In contrast with previous experiments in which the subjects were trained to control for only one breathing variable (breath duration, mean flow etc.), our subjects were trained on target breaths. Secondly, we analysed how the accuracy in controlling breathing is influenced by the size of the target breaths. Thirdly, in order to homogenize practice conditions, target breaths were drawn from each subject’s ventilatory recordings. In keeping with current theories in motor learning (Schmidt, 1988; Salmoni, Schmidt & Walter, 19841, we distinguished between the short term variations in performance which are influenced by variations in mood, motivation, attention or fatigue, and the relatively permanent effects of training which characterise learning. Performance was indexed by measures of the errors between the target breaths and the breaths actually performed, and learning was assessed by comparing performance over two sessions 24 h apart.
150
2. Method 2.1. Subjects Eighteen healthy subjects, aged 20-38 years, volunteered for this experiment after giving their informed consent to the procedure. All were unaware of the purpose of the experiment, and none had previous exposure to the task. Three could not complete normal sessions and were discarded: one displayed a high hyperventilation reaction to the task, one did not follow instructions for unexplained reasons, and one displayed an extremely irregular breathing pattern during baseline recording, which made it impossible to select the target breaths according to the standard procedure. The remaining subjects (13 males and one female) completed two experimental sessions. 2.2. Apparatus Thoracic and abdominal displacements were collected using a respiratory inductive plethysmograph (Visuresp System, RBI, France) described elsewhere (Esteve, Blanc-Gras, Baconnier & Benchetrit, 1992; Es&e, BlancGras, Benchetrit & Baconnier, 1992). The abdominal and the thoracic coils of this device were fixed to a jacket (different sizes were available) made of an anisotropic material, the distensibility of which was horizontal, not vertical. This ensured a fixed and reproducible position of the two coils. The abdominal and thoracic signals were digitized and processed by a microcomputer (Amstrad PC 8086) programmed in Pascal. Ventilatory amplitude was obtained by the summation of the thoracic and abdominal signals. Amplitude was continuously sampled (at a frequency higher than 20 Hz). This provided about 100 sampling points per breath. The fraction of CO, in the expired flow (F,CO,) was continuously monitored (Elisa Engstrom Analyzer). All relevant data were stored on the computer hard disk. 2.3. Visual feedback During the training trials, the current breath (i.e. the amplitude signal) and the target breath were displayed on-line on the video screen (23.5 X 18 cm), on a breath-by-breath basis. The screen resolution allowed 280 X 200 pixels (one pixel = 0.9 mm). To facilitate perception, the target breath was surrounded by two equidistant lines, 1 cm above and below the target line, respectively. The small and large targets were represented on the video screen with identical sizes to homogenize the practice conditions. This avoided a bias of providing the subjects a larger and clearer visual feedback this introduced a change in feedback with the large breath. However, sensitivity between the two targets, the more sensitive feedback correspond-
N. Blanc-Gras et al. /Biological Psychology 37 (3994) 147-159
ing to the small target. During of the current breath remained of the following breath. The trials.
151
training, the tracings of the target breath and displayed on the video screen until the onset visual feedback disappeared during the test
2.4. Procedure Once instrumented, each subject was seated in an armchair in a standard position with arms resting on the thighs, opposite the computer display. Each session began with a 10 min period of quiet breathing. After this familiarization period, ten breaths were recorded. The experimenter examined these breaths on the video screen, and selected the smallest and the largest breaths of this sample. This selection lasted about 3 min. Because of the correlation between amplitude and duration of resting breaths during spontaneous breathing, the smallest breath in amplitude was generally the shortest one. Breaths with short duration and large amplitude (or the reverse>, which may occur during spontaneous breathing, were not selected as targets. Then, the task was explained. The subjects were instructed to control their breathing carefully so that the visual plot of their breaths appeared as close as possible to the target breath. Learning was tested by no-feedback trials, during which the subjects had to maintain the same ventilatory pattern as during feedback training trials. The onset of each breath was not imposed. Once a breath was completed, the subjects were free to start the following breath as convenient. The duration of the breaths was imposed by the task, but their sequencing was free, which allowed the self-adjustment of minute ventilation and avoided any respiratory discomfort. All the subjects participated in two 35 min sessions, 24 h apart. Each session was divided into two periods, one for each target. The order of the two targets was randomized across the subjects in the first session and reversed in the second session. During each period, seven training runs of ten breaths with feedback were each followed by a test run of five breaths without feedback. 2.5. Data reduction
and analysis
Performance was analysed on no-feedback trials. Two conventional performance indices were computed for each breath: Absolute Error, denoted AE, and Absolute Constant Error, denoted ACE (Schmidt, 1988). The corresponding formulae are: AE = C ) xi -x * I/N and ACE = 1_Z(x; -x*)/N 1, where X, denotes the ith sampling point of the breath, and x* the corresponding point of the target breath. AE expresses the amount of inaccuracy in controlling the shape of the breaths. ACE represents directional biases in responding. These indices, expressed in arbitrary units, actually corresponded
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to a number of screen pixels. The improvement in performance for the same target over the two sessions reflected retention of the ability to perform the task, which characterizes learning. The data were analysed separately for AE and ACE. Analyses of variance with session (two levels: first versus second day), period (two levels: small versus large target), run (l-71, and breaths (ten feedback trials, or five no-feedback trials) as repeated measures factors were used to analyse performance (Superanova software, Abacus Concepts). The order in which the two targets were practised was a between-subject factor. In order to take the heterogeneous correlations among the repeated measurements into account, within-subject main effects and interaction are presented with p values adjusted using the Huynh-Feldt epsilon when appropriate (Crowder & Hand, 1991).
3. Results As a rule, the subjects adjusted their breathing pattern accurately, as shown by mean values for AE and ACE (Figs. 1 and 2). They remained normocapnic throughout the experimental sessions, which suggested that practising the small and the large targets yielded nearly equal alveolar ventilation. Accordjngly, respiratory discomfort due to hyper- or hypoventilation during the practice of the target breaths was discarded as a possible influential factor. The performance of the two groups of subjects, which only differed in the order of the two targets, was not significantly different, as shown by the fact that the main effect for group factor and the interactions
Absolute Constant Error
Absolute Error 19 18 17 16
12.
T
-
4
\-‘-\r .L
5
15 Session 1
Small breath
Large breath
Session 2
11. lo98Session 1
Session 2
Fig. 1. Between-day changes in performance on the small and large targets during feedback trials. Values are means (*SEM) over the seven runs, ten training breaths, and fourteen subjects (N = 980).
N. Blanc-Gras
et al. /Biological
Absolute Constant Error
Absolute Error 36
T
30
-
34
- e
28
-
32
-
26
-
24
-
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20
-
18
-
30 28
;;
\
:
j-------j
I
Small
breath
Large breath
Small
I I
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Session 2
153
Psychology 37 (1994) 147-159
Session 1
i
breath
Large breath ,
Session 2
Fig. 2. Between-day changes in performance on the small and large targets during no-feedback trials. Values are means (k SEM) over the seven runs, five test breaths, and fourteen subjects (N = 490).
between this and other factors were not significant. Accordingly, the order factor was no longer considered and the subjects were pooled together for further analyses. The mean duration of the individual targets, averaged over the 14 subjects, were 4.14 s (SD = 1.02) for the small target, and 5.38 s (SD = 1.47) for the large target. The corresponding amplitudes were 661 (arbitrary units, SD = 221), and 846 (arbitrary units, SD = 262). The Amplitude/ Duration ratios were nearly the same for the small and the large targets. As a mean, the large targets were 30% longer, and 31% larger in amplitude than the small targets. These differences were small, if compared with the range of spontaneous variations met at different levels of ventilation. Despite the similarity of the two targets, the corresponding pattern of performance was very different. During training trials (with feedback), AE and ACE were lower (i.e. better) with the large target, (F,, r3 = 5.37, p = 0.038 and F,, 13= 4.46, p = 0.054, respectively). This was mainly due to the significant difference between the two targets in the first session (F,, 13= 5.32, p = 0.038 for AE, and F, ,3 = 5.48, p = 0.035 for ACE). Main effects for runs were significant (Fe, 78= ‘4.24, p = 0.001 for AE and Fe, 78= 3.46, p = 0.004 for ACE). This effect was due to the fact that performance stabilised only after the two first runs. Similarly, the poor performance in the two first breaths of each run led to significant breath-by-breath changes in AE and = 14.09, p < 0.0001, and F9, 117 = 8.08, p < 0.0001). AE and ACE (&I, 117 ACE were lower in the second session than in the first one, but this improvement did not reach significance. As a rule, performance with or without feedback displayed similar trends (Figs. 1 and 21, but the subjects performed less accurately without feedback.
154
Error
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20 Large breath
T
_)
18 -
Large breath
16 14 -
/
/
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/ 3
4
1
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1
2
Trial Fig. 3. Breath-by-breath changes in performance (+ SEM) over the seven trial runs, two sessions,
/
1
/
3
4
5
Trial during no-feedback and fourteen subjects
trials. Values (N = 196).
are means
During no-feedback test trials, AE and ACE were significantly lower (i.e. better) with the large target, (F,, ,3 = 13.004, p = 0.003 and F,. ,3 = 14.492, p = 0.002, respectively). Within sessions changes in performance were not signifi~nt (main effects for runs were non-significant in the two sessions). However, AE and ACE were lower in the second session than in the first one. This improvement was confirmed by the significant main effect for session (F,, ,3 = 7.618, p = 0.016 and F,, ,3 = 9.433, p = 0.009, respectively). This main effect revealed a retention effect over sessions for both AE and ACE which provided evidence for learning. Learning did not affect the two targets in a similar fashion and specially concerned the small target, as shown by the interaction between target and session factors, (I;,, ,3 = 4.067, p = 0.065 and F,, ,3 = 7.598, p = 0.016, respectively; note, however, that P-level for AE failed to reach the 0.05 level). The between session improvement in performance for the large target did not reach significance. In the second session, performance for the large target was better than for the small target, but this difference did not reach significance (for either AE or ACE). In summa~, performance was better for the Iarge target, but a learning effect on the small target levelled out performance in the second session. Breath-by-breath changes during no-feedback tests are shown in Fig. 3. The general trend over these five breaths was an increase in AE and ACE, confirmed by a significant main effect for these two indices (F3, s2 = 3.526, However, changes in p = 0.046, and F4. s2 = 3.170, p = 0.061, respectively). performance were not consistent over the five breaths. AE and ACE decreased in the second breath. From the third to the fifth breath, the subjects
tended to drift away from the target, and AE and ACE increased accordingly. Although performance was globally better in the second session, as already mentioned, this specific pattern of breath-by-breath changes in performance was similar in the two sessions (no interaction with session factor was observed). This pattern did not display significant changes within each session either (main effects and interactions involving the run factor were not significant).
4. lliscussion We investigated how subjects may learn to adjust the shape of their breaths to two different target breaths by using a visual ventilatory feedback. These two targets displayed rather small differences in terms of duration and amplitude. The relative differences in these variables between the two targets were lower than 30%. However, the pattern of performance corresponding to these two target breaths displayed significant differences in the first session. Performance was better with the large breath than with the small one, especially in the first session. Performance levelled out in the second session, as a result of a strong learning effect on the small target. Performance on the large target did not improve, possibfy because of the high level of accuracy already achieved in the first session (ceiling effect). Better performance was observed with the large target, which was practised with a less sensitive feedback, suggesting that feedback sensitivity was not a determining factor. However, it is unclear whether our visual feedback was optimally designed. Concurrent (i.e. on-line) feedback, such as ours, is often considered as a temporary crutch to performance, and supposedly less efficient for learning than terminal feedback. It has been reported that subjects receiving concurrent feedback perform better than subjects receiving terminal feedback, but if a retention test is performed after a delay, the reverse is observed: subjects trained on terminal feedback display better performance than subjects trained on concurrent feedback (Patrick & ~ontlusoy, 1982; Salmoni et al., 1984). Possibly, when the subjects pracrise on concurrent feedback, they tend to rely principally on this source of information, and to neglect proprio~~ptive cues generated by the movement. To avoid interference between the processing of internal and external information, feedback must be delivered after the movement has been completed. This was not possible here, because the feedback for a given breath would have been delivered during the following breath, a very confusing experimental situation. This difficulty has been encountered in previous ventilatory feedback studies (Gallego, Perez de la Sota, Vardon & Jaeger-Denavit, 1991). In the present experiment, the alternation of feedback trials and no-feedback trials has certainly prompted the subjects to focus on afferent
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information during test trials, thus compensating for the drawbacks of concurrent feedback. In this study, we focused on performance without feedback because this is the aim of many concrete applications of breathing pattern learning. In most clinical contexts, the patients are expected to adopt the new breathing pattern outside the clinical environment, without any external support. Interestingly, in the present study, performance in the no-feedback trials significantly improved over the two sessions, whereas the corresponding improvements in feedback trials did not reach significance. This may be because, during feedback trials, the subjects explored new strategies to perform the task, as occurs in many motor learning situations. This exploration may be detrimental to immediate performance, but the benefits appear when the motor skill is actually tested. In our experiment, the control of breath patterns entailed a large number of strategies in terms of thoraco-abdominal co-ordination, thoracic gas volume etc., all corresponding to different levels of difficulty, accuracy and respiratory comfort. Our direct observation of the subjects suggested that performances during training trials were strongly influenced by the subject’s exploration of these strategies. This source of variation did not affect no-feedback test trials, which may explain why only these trials displayed significant improvements over the two sessions. The fact that large breaths were easier to perform than small breaths cannot be directly inferred from current knowledge on motor learning. Although direct comparison is difficult, the observed pattern of performance can be contrasted with traditional ideas stemming from motor learning studies, concerning specifically, performance in time production, and the influence of the amplitude of positioning movement on the accuracy in pointing. It has long been observed that short intervals of time are easier to produce than long intervals of time (Schmidt, 1988). Similarly, it is a long-held view in motor learning experiments that movement error is linearly related to movement amplitude (Schmidt, 1988). This contrasts with our observation that long and large breaths were easier to perform. Arguably, the voluntary control of the shape of breaths cannot be reduced to the separate control of their amplitudes and durations. Furthermore, breathing is a continuous and periodic movement, not a sequence of single breaths. Despite the fact that our task was designed as a sequence of independent trials, it is unclear whether the subjects’ strategy followed this breath-by-breath sequencing. Probably, the subjects may have adopted a more natural and spontaneous way of controlling breathing which is to adjust the global breathing rhythmicity to the imposed breath pattern. Since the large targets corresponded to a slower-and presumably easier-rhythm, this may partly explain the better performance achieved with this target. As a rule, velocity is a critical determinant of the movement difficulty in motor learning situations. In the present experiment, “velocity” would corre-
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1.57
spond to the derivative of amplitude as a function of time, i.e. the instantaneous flow. We did not analyse this function, which is difficult to deal with for methodological and mathematical reasons. We recall, however, that the two target breaths had similar ratios of amplitude by duration (i.e. the mean flow). In other words, the mean flow of each breath was the same. Therefore, the differences in performance were not a consequence of the differences in mean velocity. However, the number of breaths per time unit (the ventilatory frequency) was higher with small target breaths. It is therefore possible that this induced a higher mental load with this small target, compared with the less frequent large target. This might also explain the better performance achieved with the large target. A further interpretation of the subjects’ higher level of accuracy in the large target might be linked to the different degrees of “familiarity” of the two targets. The difficulty in completing a given breath might be related to the frequency of this breath in the whole span of ventilator-y activity, the more familiar breaths being the most easily performed. Here, the fact that the two targets were the extreme values of a sample of resting breaths does not imply that both were equally frequent in the complete ventilatory repertoire. This hypothesis would imply that the accuracy during the voluntary control of breathing is influenced by the way breathing is organised during automatic breathing, thus revealing another unexpected interaction between the metabolic and behavioral component of breathing. However, this interpretation will remain speculative until the performances are related to an objective estimation of the relative frequency of the target breaths. Achieving a learned modification of breathing pattern requires subtle changes in the activation and co-ordination of numerous respiratory muscles (mainly the diaphragm, the intercostal and the abdominal muscles). It is implicitly assumed in most clinical contexts that a sufficient amount of practice would entail the automatization of the new breathing pattern, an hypothesis which until now has not received experimental support (Gallego & Perruchet, 1991b). Automatization would imply that the learned movement becomes less and less dependent on external cues (Schmidt, 1987). This process has not been observed in the present experiment, as shown by the changes in performance across the five no-feedback trials. The poor performance in the first breath is easily explained by the sudden withdrawal of visual feedback after the training period. The improvement in the second breath revealed some self-correcting abilities, but performance then consistently decreased in the three remaining breaths. This suggests that the memory trace of the learned breaths was weak. Our data, however, show a significant global improvement in performance from the first to the second session. Although learned modification of spontaneous breathing patterns may only result from extended practice in good practice conditions, our data show more limited learning effects after relatively short practice periods.
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Further investigations on short-term learning might shed light on the processes by which the automatization of a learned breathing pattern may occur.
Acknowledgements
The authors express their gratitude to Michelle Delaire for her technical assistance, and to Pierre Perruchet for his helpful comments on this manuscript.
References Benchetrit, G., Shea. T., Pham Dinh, S., Bodocco, P., Baconnier, P., & Guz, A. (1989). Individuality of breathing patterns in adults assessed over time. Respiration Physiology, 75, 199-210. Cappo, B.M., & Holmes, D.S. (1984). The utility of prolonged respiratory exhalation for reducing physiological and psychological arousal in non-threatening and threatening situations. Journul of Psychosomatic Research, 28, X5-273. Clark. D.M., Salkovskis, P.M., & Chalkley, A.J. (1985). Respiratory control as a treatment of panic attack. Journal of Therapy and Experimental Psychiatry, 16, 23-30. Crowder, M.J., & Hand, D.J. (1991). Analysis of repeated measures. New York: Chapman & Hall. Donner, C.F., & Howard, P. (1992). Pulmonary rehabilitation in chronic obstructive pulmonary disease (COPD) with recommendations for its use. European Respiratory Journal, 5, X6--275. Es&e, F., Blanc-Gras, N., Baconnier, P., &r Benchetrit, G. 11992). A new device for pulmonary rehabilitation method based on visual feedback: Principle and methods. ~~~~e~~~~~~ of the 14th Atznrtul l~telnutiojlul Conference ofthe IEEE in Medicine and Biology Society. fp. 16121. Est&ve, F., Blanc-Gras, N., Benchetrit, G., & Baconnier, P. (1992). A new device for pulmonary rehabilitation method based on visual feedback: Computer programs. Proceedings of the 14th Annual International Conference of the IEEE in Medicine and Biology Society. (p. 1654). Fried, R. (1992). The psychophysiology and physiology of breathing in behavioral medicine, clinical psychology, and psychiatry. New York: Plenum Press. Gallego, J., Ankaoua, J., Lethielleux, M., Chambille, B., Vardon, G., & Jacquemin, C. (1986). Retention of ventilatory pattern learning in normal subjects. Journal of Applied Physio/o,qy, 61. 1-6. Gallego, J., 6r Camus, J.F. (1989). Optimization of informative feedback in ventilatory pattern learning. European Bulletin of Cognitice Psychology. 9, 505-520. Gallego, J., Perez de la Sota, A., Vardon, G., Jaeger-Denavit, 0. (19911. Electromyographic feedback for learning to activate thoracic inspirato~ muscles. Arnericun Journal of Physical Medicine and Re~liIb~lit~t~o~, 70, 1X6-190. Gallego, J.. & Perruchet, P. (1991a). Classical conditioning of ventilatory responses in humans.Journal of Applied Physiology, 70, 676482. Gallego, J., & Perruchet, P. (1991b). Effect of practice on the voluntary control of a learned breathing pattern. Physiology and Behavior, 49, 315-319. Gallego, J., & Perruchet, P. (1993). The influence of voluntary breathing on reaction time. Journal of Psychosomatic Research, 37, 63-70. Grossman P., & Wientjes, C.J.E. (1989). Respiratory disorders: Asthma and hyperventilation syndrome. In: G. Turpin (Ed), Handbook of Clinical Psychophysiology (pp. 519-554). New York: Wiley.
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Holliday, J.E., & Hyers, T.M. (1990). The reduction of weaning time from mechanical ventilation using tidal volume and relaxation biofeedback. American Rel,iew of Respiratory Disease, 141, 1214-1220. Hugelin, A. (1986). Forebrain and midbrain influences on respiration. In: N.S. Cherniack, & J.G. Widdicombe (Eds.), Handbook of Physiology (pp. 69-91). Baltimore: Williams & Wilkins. McKenna, J.J. (1987). An anthropological perspective on the Sudden Infant Death Syndrome (SIDS): The role of parental breathing cues and speech breathing adaptations. Medical Anthropology, 10, 9-92. Moosavi, S.H., Adams, L., & Guz, A. (1993). Conditioning the ventilatory response to exercise in humans. XIth International Symposium on Respiratory Psychophysiology. Biological Psychology (to appear). Orem, J. (19891. Behavioral inspiratory inhibition: Inactivated and activated cells. Journal of Neurophysiology, 62, 1069-1078. Patrick, J., & Montlusoy, F. (1982). The relationship between types of feedback gain on a display and feedback precision in acquisition of a simple motor task. Quarterly Journal of Experimental Psychology, 34, 171-182. Salmoni, A.W., Schmidt, R.A., & Walter, C.B. (1984). Knowledge of results and motor learning: A review and critical reappraisal. Psychological Bulletin, 9.5, 355-386. Schmidt, R.A. (1987). The acquisition of skill: Some modifications to the perception-action relationship through practice. In H. Heuer & A.F. Sanders (Eds) Perspectil,es on perception and action. (pp. 77-103). Hillsdale: Lawrence Erlbaum Associates. Schmidt, R.A. (1988). Motor Control and Learning. Champaign, IL: Human Kinetics Publ. Shea, S.A., Walter, J., Murphy, K., & Guz, A. (1987). Evidence for individuality of breathing patterns in resting healthy man. Respiration Physiology, 68, 331-344. Singh, V., Wisniewski, A., Britton, J., & Tattersfield, A. (1990). Effect of yoga breathing exercises fpranayama) on airway reactivity in subjects with asthma. The Lancet, 335, 13811383. Tobin, M.J., Perez, W., Guenther, S.M., D’Alonzo, G., & Dantzker, D.R. (1986). Breathing pattern and metabolic behavior during anticipation of exercise. Journal ofApplied PhysioroRy, 60, 1306-1312. Von Euler, C. (1983). On the central pattern generator for the basic breathing rhythmicity. Journal of Applied Physiology, 55, 1647-1659.
Performance and learning of breath patterns Nathalie Blanc-Gras a,*, Fraqois and Jorge Gallego ’
147 0301-051 l/94/$07.00
during voluntary Esteve
‘, Gila Benchetrit
control a,
a Luboratoire de Physiologic, Faculte’ de M&de&e de Grenoble, 38700 La Tronche, France ’ Laboratoire de Physiologic, Faculte’ de Midecine de Paris-Sud, 94276 Le Kremlin-Bic&re, France
Fourteen subjects learned to adjust their breath pattern to two target breaths displayed on a video screen, by using visual feedback, during two sessions 24 h apart. These two targets were respectively the smallest and the largest breaths of a ten-breath sample previously recorded from each subject’s resting spontaneous breathing. Performances were significantly better for the large than for the small target breath. This cannot be directly inferred from current knowledge related to the control of movement time and amplitude, but rather it may be inferred from the periodic character of breathing, to the higher mental load during the small breath task, or to the presumably different frequencies of target breaths in the whole span of spontaneous breathing. In the second session, performance on the two targets levelled out as a result of learning. Keywords: respiration;
breathing
exercises;
learning;
biofeedback
1. Introduction Automatic breathing is a periodic activity controlled by a central pattern generator located in the pons and the medulla. This generator adjusts the period and amplitude of breathing as a function of the variable metabolic needs for oxygen and the variable production of carbon dioxide. These ventilatory adjustments are achieved by combinations of period and amplitude which present large individual variability. Besides its reflex control, breathing can also be controlled voluntarily for many non-homeostatic purposes, such as voice production and speech. The latter functions of the respiratory system are often categorized as the “behavioral component” of breathing, a concept further extended to other activities of the respiratory system (posture, expulsive motor acts), whether they are voluntary or automatic. In normal conditions, the metabolic and behavioral components of breathing influence each other, but the neurological substrate of this interaction remains unclear. The behavioral component is mainly controlled by forebrain * Corresponding
author.
S.SDI 0301-0511(93)00925-A
14x
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descending influences, and is thought to excite both the respiratory motoneurones and the output stage of the respiratory bulbar oscillator, while inhibiting the latter’s oscillatory activity (Hugelin, 1986). Recent animal studies show that some of the metabolic and behavioral influences on respiratory motoneurones are integrated within brain stem areas that house the pattern generator (Orem, 1989). This highlights the fact that breathing must be construed as a complex function governed by a set of hierarchically structured control systems, in accordance with a variety of biological and psychological demands (Van Euler, 1983). The interaction between metabolic and behavioral components is illustrated by numerous aspects of normal or pathological breathing. Speech breathing is a compromise between metabolic requirements and the purposeful manipulations of ventilatory flow for voice production. This ability, fully automatized in normal individuals, originates in purposeful manipulation of flow that infants exhibit at 2-4 months (McKenna, 1987). During this period, there is a major shift in the extent to which metabolic and behavioural components of breathing become functionally integrated. Abnormalities in this process have been considered as a possible cause of sudden infant death syndrome (McKenna, 1987). Even those ventilatory acts which are often considered as a pure reflection of metabolic processes are actually a compromise between the metabolic and behavioral components. For instance, the ventilator-y response to exercise in normal adults is influenced by changes which occur prior to the onset of exercise, a phenomenon which has been interpreted as Pavlovian conditioning (Tobin, Perez, Guenther, D’Alonzo & Dantzker, 1986). This hypothesis has been supported by recent findings on conditioned ventilatory responses during exercise (Moosavi, Adams & Guz, 1993). Conditioned changes in breathing have also been observed after concomitant exposures to hypoxic and auditory stimuli (Gallego & Perruchet, 1991a). A critical role for the behavioral control of breathing is postulated in children with congenital central hypoventilation syndrome (Ondine’s curse>. These children display an ineffective chemoreceptor function during sleep, making necessary mechanical ventilation, but they may display a normal ventilation when awake. This ability is attributed to a cortical control of breathing, presumably automatized through practice. These few examples show that the study of ventilatory function-whether normal or pathological -must necessarily incorporate the psychological processes which contribute to the patterning of ventilatory behavior, mainly voluntary control and learning. Learning to change breathing pattern is a current practice in many clinical contexts. Patients with chronic obstructive pulmonary diseases are trained to lower ventilatory frequency and to increase amplitude in order to increase alveolar ventilation (Donner & Howard, 1992; Singh, Wisniewski, Britton & Tattersfield, 1990). Feedback-assisted control of tidal volume and mean
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inspiratory flow helps to reduce weaning time on mechanical ventilation in hard-to-wean patients (Holliday & Hyers, 19901. Alongside traditional techniques of voluntary control of breathing pattern in respiratory obstructive disorders, learning to decrease ventilatory frequency has been shown to improve hyperventilation syndrome, panic attack, to reduce psychophysiological arousal (Cappo & Holmes, 1984; Clark, Salkovskis & Chalkley, 1985; Grossman & Wientjes, 19891, and idiopathic epileptic seizures (Fried, 1992). The underlying processes of these therapies are difficult to elucidate, one difficulty being to discriminate between the specific physiological effects of ventilatory changes (e.g. PCO,) and indirect psychological effects on arousal and anxiety. Investigating the way breathing may be influenced by learning is therefore of both theoretical and practical interest. The way subjects learn to modify their breathing pattern has rarely been addressed in experimental studies, and so empirical approaches prevail in respiratory physiotherapy or rehabilitation. At least two potentially confounding factors have been largely underestimated in previous reports. Firstly, subjects asked to change one breathing variable (for instance frequency) tend to change their whole breathing pattern (Gallego et al., 1986; Gallego & Perruchet, 1993). Secondly, ventilatory patterns display large inter-individual differences, even under conditions of relaxed wakefulness (Shea, Walter, Murphy & Guz, 1987; Benchetrit et al., 1989), and arguably, training a subjects to adopt a pattern which exaggeratedly departs from his/ her individual spontaneous pattern may be detrimental to learning. The purpose of the present experiment was to analyse how subjects may learn to adjust the whole shape of each breath, i.e. the plot of amplitude as a function of time from the beginning of inspiration to the end of expiration. Each subject was trained with feedback on ventilatory movement. As a rule, feedback is a determinant variable in motor skill learning (Schmidt, 19881, including breathing pattern learning (Gallego et al., 1986, Gallego & Camus, 1989). Visual feedback was provided to achieve efficient practice conditions. In contrast with previous experiments in which the subjects were trained to control for only one breathing variable (breath duration, mean flow etc.), our subjects were trained on target breaths. Secondly, we analysed how the accuracy in controlling breathing is influenced by the size of the target breaths. Thirdly, in order to homogenize practice conditions, target breaths were drawn from each subject’s ventilatory recordings. In keeping with current theories in motor learning (Schmidt, 1988; Salmoni, Schmidt & Walter, 19841, we distinguished between the short term variations in performance which are influenced by variations in mood, motivation, attention or fatigue, and the relatively permanent effects of training which characterise learning. Performance was indexed by measures of the errors between the target breaths and the breaths actually performed, and learning was assessed by comparing performance over two sessions 24 h apart.
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2. Method 2.1. Subjects Eighteen healthy subjects, aged 20-38 years, volunteered for this experiment after giving their informed consent to the procedure. All were unaware of the purpose of the experiment, and none had previous exposure to the task. Three could not complete normal sessions and were discarded: one displayed a high hyperventilation reaction to the task, one did not follow instructions for unexplained reasons, and one displayed an extremely irregular breathing pattern during baseline recording, which made it impossible to select the target breaths according to the standard procedure. The remaining subjects (13 males and one female) completed two experimental sessions. 2.2. Apparatus Thoracic and abdominal displacements were collected using a respiratory inductive plethysmograph (Visuresp System, RBI, France) described elsewhere (Esteve, Blanc-Gras, Baconnier & Benchetrit, 1992; Es&e, BlancGras, Benchetrit & Baconnier, 1992). The abdominal and the thoracic coils of this device were fixed to a jacket (different sizes were available) made of an anisotropic material, the distensibility of which was horizontal, not vertical. This ensured a fixed and reproducible position of the two coils. The abdominal and thoracic signals were digitized and processed by a microcomputer (Amstrad PC 8086) programmed in Pascal. Ventilatory amplitude was obtained by the summation of the thoracic and abdominal signals. Amplitude was continuously sampled (at a frequency higher than 20 Hz). This provided about 100 sampling points per breath. The fraction of CO, in the expired flow (F,CO,) was continuously monitored (Elisa Engstrom Analyzer). All relevant data were stored on the computer hard disk. 2.3. Visual feedback During the training trials, the current breath (i.e. the amplitude signal) and the target breath were displayed on-line on the video screen (23.5 X 18 cm), on a breath-by-breath basis. The screen resolution allowed 280 X 200 pixels (one pixel = 0.9 mm). To facilitate perception, the target breath was surrounded by two equidistant lines, 1 cm above and below the target line, respectively. The small and large targets were represented on the video screen with identical sizes to homogenize the practice conditions. This avoided a bias of providing the subjects a larger and clearer visual feedback this introduced a change in feedback with the large breath. However, sensitivity between the two targets, the more sensitive feedback correspond-
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ing to the small target. During of the current breath remained of the following breath. The trials.
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training, the tracings of the target breath and displayed on the video screen until the onset visual feedback disappeared during the test
2.4. Procedure Once instrumented, each subject was seated in an armchair in a standard position with arms resting on the thighs, opposite the computer display. Each session began with a 10 min period of quiet breathing. After this familiarization period, ten breaths were recorded. The experimenter examined these breaths on the video screen, and selected the smallest and the largest breaths of this sample. This selection lasted about 3 min. Because of the correlation between amplitude and duration of resting breaths during spontaneous breathing, the smallest breath in amplitude was generally the shortest one. Breaths with short duration and large amplitude (or the reverse>, which may occur during spontaneous breathing, were not selected as targets. Then, the task was explained. The subjects were instructed to control their breathing carefully so that the visual plot of their breaths appeared as close as possible to the target breath. Learning was tested by no-feedback trials, during which the subjects had to maintain the same ventilatory pattern as during feedback training trials. The onset of each breath was not imposed. Once a breath was completed, the subjects were free to start the following breath as convenient. The duration of the breaths was imposed by the task, but their sequencing was free, which allowed the self-adjustment of minute ventilation and avoided any respiratory discomfort. All the subjects participated in two 35 min sessions, 24 h apart. Each session was divided into two periods, one for each target. The order of the two targets was randomized across the subjects in the first session and reversed in the second session. During each period, seven training runs of ten breaths with feedback were each followed by a test run of five breaths without feedback. 2.5. Data reduction
and analysis
Performance was analysed on no-feedback trials. Two conventional performance indices were computed for each breath: Absolute Error, denoted AE, and Absolute Constant Error, denoted ACE (Schmidt, 1988). The corresponding formulae are: AE = C ) xi -x * I/N and ACE = 1_Z(x; -x*)/N 1, where X, denotes the ith sampling point of the breath, and x* the corresponding point of the target breath. AE expresses the amount of inaccuracy in controlling the shape of the breaths. ACE represents directional biases in responding. These indices, expressed in arbitrary units, actually corresponded
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to a number of screen pixels. The improvement in performance for the same target over the two sessions reflected retention of the ability to perform the task, which characterizes learning. The data were analysed separately for AE and ACE. Analyses of variance with session (two levels: first versus second day), period (two levels: small versus large target), run (l-71, and breaths (ten feedback trials, or five no-feedback trials) as repeated measures factors were used to analyse performance (Superanova software, Abacus Concepts). The order in which the two targets were practised was a between-subject factor. In order to take the heterogeneous correlations among the repeated measurements into account, within-subject main effects and interaction are presented with p values adjusted using the Huynh-Feldt epsilon when appropriate (Crowder & Hand, 1991).
3. Results As a rule, the subjects adjusted their breathing pattern accurately, as shown by mean values for AE and ACE (Figs. 1 and 2). They remained normocapnic throughout the experimental sessions, which suggested that practising the small and the large targets yielded nearly equal alveolar ventilation. Accordjngly, respiratory discomfort due to hyper- or hypoventilation during the practice of the target breaths was discarded as a possible influential factor. The performance of the two groups of subjects, which only differed in the order of the two targets, was not significantly different, as shown by the fact that the main effect for group factor and the interactions
Absolute Constant Error
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Fig. 1. Between-day changes in performance on the small and large targets during feedback trials. Values are means (*SEM) over the seven runs, ten training breaths, and fourteen subjects (N = 980).
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Absolute Constant Error
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Psychology 37 (1994) 147-159
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Fig. 2. Between-day changes in performance on the small and large targets during no-feedback trials. Values are means (k SEM) over the seven runs, five test breaths, and fourteen subjects (N = 490).
between this and other factors were not significant. Accordingly, the order factor was no longer considered and the subjects were pooled together for further analyses. The mean duration of the individual targets, averaged over the 14 subjects, were 4.14 s (SD = 1.02) for the small target, and 5.38 s (SD = 1.47) for the large target. The corresponding amplitudes were 661 (arbitrary units, SD = 221), and 846 (arbitrary units, SD = 262). The Amplitude/ Duration ratios were nearly the same for the small and the large targets. As a mean, the large targets were 30% longer, and 31% larger in amplitude than the small targets. These differences were small, if compared with the range of spontaneous variations met at different levels of ventilation. Despite the similarity of the two targets, the corresponding pattern of performance was very different. During training trials (with feedback), AE and ACE were lower (i.e. better) with the large target, (F,, r3 = 5.37, p = 0.038 and F,, 13= 4.46, p = 0.054, respectively). This was mainly due to the significant difference between the two targets in the first session (F,, 13= 5.32, p = 0.038 for AE, and F, ,3 = 5.48, p = 0.035 for ACE). Main effects for runs were significant (Fe, 78= ‘4.24, p = 0.001 for AE and Fe, 78= 3.46, p = 0.004 for ACE). This effect was due to the fact that performance stabilised only after the two first runs. Similarly, the poor performance in the two first breaths of each run led to significant breath-by-breath changes in AE and = 14.09, p < 0.0001, and F9, 117 = 8.08, p < 0.0001). AE and ACE (&I, 117 ACE were lower in the second session than in the first one, but this improvement did not reach significance. As a rule, performance with or without feedback displayed similar trends (Figs. 1 and 21, but the subjects performed less accurately without feedback.
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trials. Values (N = 196).
are means
During no-feedback test trials, AE and ACE were significantly lower (i.e. better) with the large target, (F,, ,3 = 13.004, p = 0.003 and F,. ,3 = 14.492, p = 0.002, respectively). Within sessions changes in performance were not signifi~nt (main effects for runs were non-significant in the two sessions). However, AE and ACE were lower in the second session than in the first one. This improvement was confirmed by the significant main effect for session (F,, ,3 = 7.618, p = 0.016 and F,, ,3 = 9.433, p = 0.009, respectively). This main effect revealed a retention effect over sessions for both AE and ACE which provided evidence for learning. Learning did not affect the two targets in a similar fashion and specially concerned the small target, as shown by the interaction between target and session factors, (I;,, ,3 = 4.067, p = 0.065 and F,, ,3 = 7.598, p = 0.016, respectively; note, however, that P-level for AE failed to reach the 0.05 level). The between session improvement in performance for the large target did not reach significance. In the second session, performance for the large target was better than for the small target, but this difference did not reach significance (for either AE or ACE). In summa~, performance was better for the Iarge target, but a learning effect on the small target levelled out performance in the second session. Breath-by-breath changes during no-feedback tests are shown in Fig. 3. The general trend over these five breaths was an increase in AE and ACE, confirmed by a significant main effect for these two indices (F3, s2 = 3.526, However, changes in p = 0.046, and F4. s2 = 3.170, p = 0.061, respectively). performance were not consistent over the five breaths. AE and ACE decreased in the second breath. From the third to the fifth breath, the subjects
tended to drift away from the target, and AE and ACE increased accordingly. Although performance was globally better in the second session, as already mentioned, this specific pattern of breath-by-breath changes in performance was similar in the two sessions (no interaction with session factor was observed). This pattern did not display significant changes within each session either (main effects and interactions involving the run factor were not significant).
4. lliscussion We investigated how subjects may learn to adjust the shape of their breaths to two different target breaths by using a visual ventilatory feedback. These two targets displayed rather small differences in terms of duration and amplitude. The relative differences in these variables between the two targets were lower than 30%. However, the pattern of performance corresponding to these two target breaths displayed significant differences in the first session. Performance was better with the large breath than with the small one, especially in the first session. Performance levelled out in the second session, as a result of a strong learning effect on the small target. Performance on the large target did not improve, possibfy because of the high level of accuracy already achieved in the first session (ceiling effect). Better performance was observed with the large target, which was practised with a less sensitive feedback, suggesting that feedback sensitivity was not a determining factor. However, it is unclear whether our visual feedback was optimally designed. Concurrent (i.e. on-line) feedback, such as ours, is often considered as a temporary crutch to performance, and supposedly less efficient for learning than terminal feedback. It has been reported that subjects receiving concurrent feedback perform better than subjects receiving terminal feedback, but if a retention test is performed after a delay, the reverse is observed: subjects trained on terminal feedback display better performance than subjects trained on concurrent feedback (Patrick & ~ontlusoy, 1982; Salmoni et al., 1984). Possibly, when the subjects pracrise on concurrent feedback, they tend to rely principally on this source of information, and to neglect proprio~~ptive cues generated by the movement. To avoid interference between the processing of internal and external information, feedback must be delivered after the movement has been completed. This was not possible here, because the feedback for a given breath would have been delivered during the following breath, a very confusing experimental situation. This difficulty has been encountered in previous ventilatory feedback studies (Gallego, Perez de la Sota, Vardon & Jaeger-Denavit, 1991). In the present experiment, the alternation of feedback trials and no-feedback trials has certainly prompted the subjects to focus on afferent
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information during test trials, thus compensating for the drawbacks of concurrent feedback. In this study, we focused on performance without feedback because this is the aim of many concrete applications of breathing pattern learning. In most clinical contexts, the patients are expected to adopt the new breathing pattern outside the clinical environment, without any external support. Interestingly, in the present study, performance in the no-feedback trials significantly improved over the two sessions, whereas the corresponding improvements in feedback trials did not reach significance. This may be because, during feedback trials, the subjects explored new strategies to perform the task, as occurs in many motor learning situations. This exploration may be detrimental to immediate performance, but the benefits appear when the motor skill is actually tested. In our experiment, the control of breath patterns entailed a large number of strategies in terms of thoraco-abdominal co-ordination, thoracic gas volume etc., all corresponding to different levels of difficulty, accuracy and respiratory comfort. Our direct observation of the subjects suggested that performances during training trials were strongly influenced by the subject’s exploration of these strategies. This source of variation did not affect no-feedback test trials, which may explain why only these trials displayed significant improvements over the two sessions. The fact that large breaths were easier to perform than small breaths cannot be directly inferred from current knowledge on motor learning. Although direct comparison is difficult, the observed pattern of performance can be contrasted with traditional ideas stemming from motor learning studies, concerning specifically, performance in time production, and the influence of the amplitude of positioning movement on the accuracy in pointing. It has long been observed that short intervals of time are easier to produce than long intervals of time (Schmidt, 1988). Similarly, it is a long-held view in motor learning experiments that movement error is linearly related to movement amplitude (Schmidt, 1988). This contrasts with our observation that long and large breaths were easier to perform. Arguably, the voluntary control of the shape of breaths cannot be reduced to the separate control of their amplitudes and durations. Furthermore, breathing is a continuous and periodic movement, not a sequence of single breaths. Despite the fact that our task was designed as a sequence of independent trials, it is unclear whether the subjects’ strategy followed this breath-by-breath sequencing. Probably, the subjects may have adopted a more natural and spontaneous way of controlling breathing which is to adjust the global breathing rhythmicity to the imposed breath pattern. Since the large targets corresponded to a slower-and presumably easier-rhythm, this may partly explain the better performance achieved with this target. As a rule, velocity is a critical determinant of the movement difficulty in motor learning situations. In the present experiment, “velocity” would corre-
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spond to the derivative of amplitude as a function of time, i.e. the instantaneous flow. We did not analyse this function, which is difficult to deal with for methodological and mathematical reasons. We recall, however, that the two target breaths had similar ratios of amplitude by duration (i.e. the mean flow). In other words, the mean flow of each breath was the same. Therefore, the differences in performance were not a consequence of the differences in mean velocity. However, the number of breaths per time unit (the ventilatory frequency) was higher with small target breaths. It is therefore possible that this induced a higher mental load with this small target, compared with the less frequent large target. This might also explain the better performance achieved with the large target. A further interpretation of the subjects’ higher level of accuracy in the large target might be linked to the different degrees of “familiarity” of the two targets. The difficulty in completing a given breath might be related to the frequency of this breath in the whole span of ventilator-y activity, the more familiar breaths being the most easily performed. Here, the fact that the two targets were the extreme values of a sample of resting breaths does not imply that both were equally frequent in the complete ventilatory repertoire. This hypothesis would imply that the accuracy during the voluntary control of breathing is influenced by the way breathing is organised during automatic breathing, thus revealing another unexpected interaction between the metabolic and behavioral component of breathing. However, this interpretation will remain speculative until the performances are related to an objective estimation of the relative frequency of the target breaths. Achieving a learned modification of breathing pattern requires subtle changes in the activation and co-ordination of numerous respiratory muscles (mainly the diaphragm, the intercostal and the abdominal muscles). It is implicitly assumed in most clinical contexts that a sufficient amount of practice would entail the automatization of the new breathing pattern, an hypothesis which until now has not received experimental support (Gallego & Perruchet, 1991b). Automatization would imply that the learned movement becomes less and less dependent on external cues (Schmidt, 1987). This process has not been observed in the present experiment, as shown by the changes in performance across the five no-feedback trials. The poor performance in the first breath is easily explained by the sudden withdrawal of visual feedback after the training period. The improvement in the second breath revealed some self-correcting abilities, but performance then consistently decreased in the three remaining breaths. This suggests that the memory trace of the learned breaths was weak. Our data, however, show a significant global improvement in performance from the first to the second session. Although learned modification of spontaneous breathing patterns may only result from extended practice in good practice conditions, our data show more limited learning effects after relatively short practice periods.
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Further investigations on short-term learning might shed light on the processes by which the automatization of a learned breathing pattern may occur.
Acknowledgements
The authors express their gratitude to Michelle Delaire for her technical assistance, and to Pierre Perruchet for his helpful comments on this manuscript.
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