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Distinct and flexible rates of online control

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Abstract

Elliott et al. (Hum Mov Sci 10:393–418, 1991) proposed a pseudocontinuous model of online control whereby overlapping corrections lead to the appearance of smooth kinematic profiles in the presence of online feedback. More recently, it was also proposed that online control is not a singular process [see Elliott et al. (Psychol Bull 136(6):1023–1044, 2010)]. However, support for contemporary models of online control were based on methodologies that were not designed to be sensitive to different online control sub-processes. The current study sought to evaluate the possibility of multiple distinct (i.e., visual and non-visual) mechanisms contributing to the control of reaching movements completed in either a full-vision, a no-vision, or a no-vision memory-guided condition. Frequency domain analysis was applied to the acceleration traces of reaching movements. In an attempt to elicit a modulation in the online control mechanisms, these movements were completed at two levels of spatio-temporal constraint, namely with 10 and 30 cm target distances. One finding was that performance in the full-vision relative to both no-vision conditions could be distinguished via two distinct frequency peaks. Increases in the peak magnitude at the lower frequencies were associated with visuomotor mechanisms and increases in the peak magnitude at the higher frequencies were associated with non-visual mechanisms. In addition, performance to the 30-cm target led to a lower peak at a lower frequency relative to the 10 cm target, indicating that the iterative rates of visuomotor control mechanisms are flexible and sensitive to the spatio-temporal constraints of the associated movement.

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Notes

  1. It has been previously proposed that online control may not necessarily act at regular intervals (e.g., Cruse, Dean, Heuer, & Schmidt, 1990; Navas & Stark, 1968: see discussion).

  2. Some temporal information could theoretically be ascertained with more advanced frequency domain techniques. However, such temporal information would be inherently limited by the short duration of the movements employed in the current study.

  3. In addition to the alpha range of frequencies (i.e., 8–12 Hz), the theta (4–7.5 Hz) range of frequencies common to neurophysiological research (e.g., Michel, Lehmann, Henggelerm & Brandeis, 1992) was also included in our α peak range. However, alpha was chosen as a parsimonious label for this peak because the peak was observed at frequencies into this higher range (i.e., 9.5 Hz).

  4. For reader interest only, the spectra associated with the non-normalized acceleration values have been provided in Fig. 7. Importantly, regarding both the frequency and amplitude analyses, no significant effects including Vision Condition were observed that either peak, F(2,34)s < 0.901, ps > 0.414, η 2G  < 0.010. Within and between subject, trial-to-trial variability warranted the normalization process that yielded the proportional spectra found in Fig. 6.

  5. Importantly, given the absence of an apparent β peak in the FV condition, the comparison between the FV condition and the two NV conditions could be considered as rather artificial. However, it was deemed important to statistically evaluate the relative power at a comparable frequency to ensure that the presence of the peak lead to a statistically significant increase in pPower.

References

  • Archambault, P. S., Ferrari-Toniolo, S., Caminiti, R., & Battaglia-Mayer, A. (2015). Visually-guided correction of hand reaching movements: The neurophysiological bases in the cerebral cortex. Vision Research, 110, 244–256. doi:10.1016/j.visres.2014.09.009.

    Article  PubMed  Google Scholar 

  • Bagesteiro, L. B., Sarlegna, F. R., & Sainburg, R. L. (2006). Differential influence of vision and proprioception on control of movement distance. Experimental Brain Research, 171(3), 358–370. doi:10.1007/s00221-005-0272-y.

    Article  PubMed  Google Scholar 

  • Bernier, P.-M., Chua, R., Franks, I. M., & Khan, M. A. (2006). Determinants of offline processing of visual information for the control of reaching movements. Journal of Motor Behavior, 38(5), 331–338. doi:10.3200/JMBR.38.5.331-338.

    Article  PubMed  Google Scholar 

  • Bracewell, R. N. (1986). The Fourier transform and its applications. New York: McGraw-Hill.

    Google Scholar 

  • Brenner, E., & Smeets, J. B. J. (1997). Fast responses of the human hand to changes in target position. Journal of Motor Behavior, 29(4), 297–310. doi:10.1080/00222899709600017.

    Article  PubMed  Google Scholar 

  • Carlton, L. (1992). Visual processing time and the control of movement. In L. Proteau & D. Elliott (Eds.), Advances in psychology: Vision and motor control (pp. 3–32). Amsterdam: North-Holland.

    Chapter  Google Scholar 

  • Carson, R. G., Chua, R., Elliott, D., & Goodman, D. (1990). The contribution of vision to asymmetries in manual aiming. Neuropsychologia, 28(11), 1215–1220. doi:10.1016/0028-3932(90)90056-T.

    Article  PubMed  Google Scholar 

  • Churchland, M. M., Santhanam, G., & Shenoy, K. V. (2006). Preparatory activity in premotor and motor cortex reflects the speed of the upcoming reach. Journal of Neurophysiology, 96(6), 3130–3146. doi:10.1152/jn.00307.2006.

    Article  PubMed  Google Scholar 

  • Cressman, E. K., Cameron, B. D., Lam, M. Y., Franks, I. M., & Chua, R. (2010). Movement duration does not affect automatic online control. Human Movement Science, 29(6), 871–881. doi:10.1016/j.humov.2010.07.001.

    Article  PubMed  Google Scholar 

  • Crossman, E. R. F. W., & Goodeve, P. J. (1983). Feedback control of hand-movement and Fitts’ law. The Quarterly Journal of Experimental Psychology Section A : Human Experimental Psychology, 35A(2), 251–278.

    Article  Google Scholar 

  • Cruse, H., Dean, J., Heuer, H., & Schmidt, R. A. (1990). Utilization of sensory information for motor control. In O. Neumann & W. Prinz (Eds.), Relationships between perception and action: Current approaches (pp. 43–74). Berlin: Springer.

    Chapter  Google Scholar 

  • de Grosbois, J., & Tremblay, L. (2015). Quantifying online visuomotor feedback in the frequency domain. Behavior Research Methods, 48, 1653–1666. doi:10.3758/s13428-015-0682-0.

    Article  Google Scholar 

  • Desmurget, M., & Grafton, S. (2000). Forward modeling allows feedback control for fast reaching movements. Trends in Cognitive Sciences, 4(11), 423–431. doi:10.1016/S1364-6613(00)01537-0.

    Article  PubMed  Google Scholar 

  • Dimitriou, M., Wolpert, D. M., & Franklin, D. W. (2013). The temporal evolution of feedback gains rapidly update to task demands. The Journal of Neuroscience, 33(26), 10898–10909. doi:10.1523/JNEUROSCI.5669-12.2013.

    Article  PubMed  PubMed Central  Google Scholar 

  • Elble, R. J., & Koller, W. C. (1990). Tremor. Baltimore: Johns Hopkins University Press.

    Google Scholar 

  • Elliott, D., Carson, R., Goodman, D., & Chua, R. (1991). Discrete vs. continuous visual control of manual aiming. Human Movement Science, 10, 393–418. doi:10.1016/0167-9457(91)90013-N.

    Article  Google Scholar 

  • Elliott, D., Hansen, S., Grierson, L. E. M., Lyons, J., Bennett, S. J., & Hayes, S. J. (2010). Goal-directed aiming: Two components but multiple processes. Psychological Bulletin, 136(6), 1023–1044. doi:10.1037/a0020958.

    Article  PubMed  Google Scholar 

  • Elliott, D., Hansen, S., Mendoza, J., & Tremblay, L. (2004). Learning to optimize speed, accuracy, and energy expenditure: A framework for understanding speed-accuracy relations in goal-directed aiming. Journal of Motor Behavior, 36(3), 339–351. doi:10.3200/JMBR.36.3.339-351.

    Article  PubMed  Google Scholar 

  • Elliott, D., Lyons, J., Hayes, S. J., Burkitt, J. J., Roberts, J. W., Grierson, L. E. M., & Bennett, S. J. (2017). Neuroscience and biobehavioral reviews the multiple process model of goal-directed reaching revisited. Neuroscience and Biobehavioral Reviews, 72, 95–110. doi:10.1016/j.neubiorev.2016.11.016.

    Article  PubMed  Google Scholar 

  • Elliott, D., & Madalena, J. (1987). The influence of premovement visual information on manual aiming. The Quarterly Journal of Experimental Psychology Section A, 39, 541–559. doi:10.1080/14640748708401802.

    Article  Google Scholar 

  • Fautrelle, L., Prablanc, C., Berret, B., Ballay, Y., & Bonnetblanc, F. (2010). Pointing to double-step visual stimuli from a standing position: very short latency (express) corrections are observed in upper and lower limbs and may not require cortical involvement. Neuroscience, 169(2), 697–705. doi:10.1016/j.neuroscience.2010.05.014.

    Article  PubMed  Google Scholar 

  • Fitts, P. (1954). The information capacity of the human motor system in controlling the amplitude of movement. Journal of Experimental Psychology, 47(6), 381–391.

    Article  PubMed  Google Scholar 

  • Flash, T., & Henis, E. (1991). Trajectory modifications during reaching towards visual targets. Journal of Cognitive Neuroscience, 3(3), 220–230. doi:10.1162/jocn.1991.3.3.220.

    Article  PubMed  Google Scholar 

  • Flash, T., & Hogan, N. (1985). The coordination of arm movements: An experimentally confirmed mathematical model. The Journal of Neuroscience, 5(7), 1688–1703.

    Article  PubMed  Google Scholar 

  • Franklin, D. W., Reichenbach, A., Franklin, S., & Diedrichsen, J. (2016). Temporal evolution of spatial computations for visuomotor control. The Journal of Neuroscience, 36(8), 2329–2341. doi:10.1523/JNEUROSCI.0052-15.2016.

    Article  PubMed  PubMed Central  Google Scholar 

  • Franklin, D. W., So, U., Osu, R., & Kawato, M. (2008). Conflicting visual and proprioceptive reflex responses during reaching movements. In M. Ishikawa, K. Doya, H. Miyamoto, & T. Yamakawa (Eds.), Neural information processing (pp. 1002–1011). Heidelberg: Springer.

    Chapter  Google Scholar 

  • Georgopoulos, A. P., Kalaska, J. F., Caminiti, R., & Massey, J. T. (1983). Interruption of motor cortical discharge subserving aimed arm movements. Experimental Brain Research, 49, 327–340. doi:10.1007/BF00238775.

    Article  PubMed  Google Scholar 

  • Gordon, J., & Ghez, C. (1987a). Trajectory control in targeted force impulses II. Pulse height control. Experimental Brain Research, 67, 241–252. doi:10.1007/BF00248546.

    Article  PubMed  Google Scholar 

  • Gordon, J., & Ghez, C. (1987b). Trajectory control in targeted force impulses III. Compensatory adjustments for initial errors. Experimental Brain Research, 67, 253–269. doi:10.1007/BF00248547.

    Article  PubMed  Google Scholar 

  • Hansen, S., Glazebrook, C. M., Anson, J. G., Weeks, D. J., & Elliott, D. (2006). The influence of advance information about target location and visual feedback on movement planning and execution. Canadian Journal of Experimental Psychology, 60(3), 200–208. doi:10.1037/cjep2006019.

    Article  PubMed  Google Scholar 

  • Heath, M. (2005). Role of limb and target vision in the online control of memory-guided reaches. Motor Control, 9(3), 281–311.

    Article  PubMed  Google Scholar 

  • Heath, M., Westwood, D. A., & Binsted, G. (2004). The control of memory-guided reaching movements in peripersonal space. Motor Control, 8, 76–106.

    Article  PubMed  Google Scholar 

  • Howarth, C. I., Beggs, W. D. A., & Bowden, J. (1971). The relationship between speed and accuracy of movement aimed at a target. Acta Psychologica, 35, 207–218.

    Article  Google Scholar 

  • Jeannerod, M. (1986). Are corrections in accurate arm movements corrective? In H. J. Freund, U. Buttner, B. Cohen, & J. Noth (Eds.), Progress in brain research (Vol. 64, pp. 353–360). Amsterdam: Elsevier Science Publishers.

    Google Scholar 

  • Khan, M. A., Elliott, D., Coull, J., Chua, R., & Lyons, J. L. (2002). Feedback schedules: Kinematic evidence. Journal of Motor Behavior, 34(1), 45–57. doi:10.1080/00222890209601930.

    Article  PubMed  Google Scholar 

  • Khan, M. A., Franks, I. M., Elliott, D., Lawrence, G. P., Chua, R., Bernier, P., & Weeks, D. J. (2006). Inferring online and offline processing of visual feedback in target-directed movements from kinematic data. Neuroscience and Biobehavioral Reviews, 30, 1106–1121. doi:10.1016/j.neubiorev.2006.05.002.

    Article  PubMed  Google Scholar 

  • Khan, M. A., Lawrence, G., Fourkas, A., Franks, I. M., Elliott, D., & Pembroke, S. (2003). Online versus offline processing of visual feedback in the control of movement amplitude. Acta Psychologica, 113(1), 83–97. doi:10.1016/S0001-6918(02)00156-7.

    Article  PubMed  Google Scholar 

  • Klapp, S. T., & Erwin, C. I. (1976). Relation between programming time and duration of the response being programmed. Journal of Experimental Psychology: Human Perception and Performance, 2(4), 591–598. doi:10.1037/0096-1523.2.4.591.

    PubMed  Google Scholar 

  • Knill, D. C., Bondada, A., & Chhabra, M. (2011). Flexible, task-dependent use of sensory feedback to control hand movements. The Journal of Neuroscience, 31(4), 1219–1237. doi:10.1523/JNEUROSCI.3522-09.2011.

    Article  PubMed  PubMed Central  Google Scholar 

  • Krigolson, O., Clark, N., Heath, M., & Binsted, G. (2007). The proximity of visual landmarks impacts reaching performance. Spatial Vision, 20(4), 317–336. doi:10.1163/156856807780919028.

    Article  PubMed  Google Scholar 

  • Liu, D., & Todorov, E. (2007). Evidence for the flexible sensorimotor strategies predicted by optimal feedback control. The Journal of Neuroscience, 27(35), 9354–9368. doi:10.1523/JNEUROSCI.1110-06.2007.

    Article  PubMed  Google Scholar 

  • Manson, G. A., Alekhina, M., Srubiski, S. L., Williams, C. K., Bhattacharjee, A., & Tremblay, L. (2014). Effects of robotic guidance on sensorimotor control: planning vs. online control? NeuroRehabilitation, 35(4), 689–700. doi:10.3233/NRE-141168.

    PubMed  Google Scholar 

  • Meunier, S., & Pierrot-Deseilligny, E. (1989). Gating of the afferent volley of the monosynaptc stretch reflex during movement in man. Journal of Physiology, 419, 753–763. doi:10.1113/jphysiol.1989.sp017896.

    Article  PubMed  Google Scholar 

  • Meyer, D. E., Abrams, R. A., Kornblum, S., Wright, C. E., & Smith, J. E. (1988). Optimality in human motor performance: Ideal control of rapid aimed movements. Psychological Review, 95(3), 340–370. doi:10.1037/0033-295X.95.3.340.

    Article  PubMed  Google Scholar 

  • Miall, R. C. (1996). Task-dependent changes in visual feedback control: A frequency analysis of human manual tracking. Journal of Motor Behavior, 28(2), 125–135. doi:10.1080/00222895.1996.9941739.

    Article  PubMed  Google Scholar 

  • Miall, R. C., & Jackson, J. K. (2006). Adaptation to visual feedback delays in manual tracking: Evidence against the Smith Predictor model of human visually guided action. Experimental Brain Research, 172(1), 77–84. doi:10.1007/s00221-005-0306-5.

    Article  PubMed  Google Scholar 

  • Michel, C. M., Lehmann, D., Henggeler, B., & Brandeis, D. (1992). Localization of the sources of EEG delta, theta, alpha and beta frequency bands using the FFT dipole approximation. Electroencephalography and Clinical Neurophysiology, 82, 38–44. doi:10.1016/0013-4694(92)90180-P.

    Article  PubMed  Google Scholar 

  • Milgram, P. (1987). A spectacle-mounted liquid-crystal tachistoscope. Behavior Research Methods, Instruments, & Computers, 19(5), 449–456. doi:10.3758/BF03205613.

    Article  Google Scholar 

  • Navas, F., & Stark, L. (1968). Sampling or intermittency in hand control system dynamics. Biophysical Journal, 8(2), 252–302. doi:10.1016/S0006-3495(68)86488-4.

    Article  PubMed  PubMed Central  Google Scholar 

  • Noy, L., Alon, U., & Friedman, J. (2015). Corrective jitter motion shows similar individual frequencies for the arm and the finger. Experimental Brain Research, 233(4), 1307–1320. doi:10.1007/s00221-015-4204-1.

    Article  PubMed  Google Scholar 

  • Olejnik, S., & Algina, J. (2003). Generalized eta and omega squared statistics: Measures of effect size for some common research designs. Psychological Methods, 8(4), 434–447. doi:10.1037/1082-989X.8.4.434.

    Article  PubMed  Google Scholar 

  • Pélisson, D., Prablanc, C., Goodale, A., & Jeannerod, M. (1986). Visual control of reaching movements without vision of the limb: II. Evidence of fast unconscious processes correcting the trajectory of the hand to the final position of a double-step stimulus. Experimental Brain Research, 62, 303–311. doi:10.1007/BF00238849.

    Article  PubMed  Google Scholar 

  • Plamondon, R., & Alimi, A. M. (1997). Speed/accuracy trade-offs in target-directed movements. The Behavioral and Brain Sciences, 20(2), 279–303. doi:10.1017/S0140525X97001441.

    PubMed  Google Scholar 

  • Poston, B., Van Gemmert, A. W. A., Sharma, S., Chakrabarti, S., Zavaremi, S. H., & Stelmach, G. (2013). Movement trajectory smoothness is not associated with the endpoint accuracy of rapid multi-joint arm movements in young and older adults. Acta Psychologica, 143(2), 157–167. doi:10.1016/j.actpsy.2013.02.011.

    Article  PubMed  PubMed Central  Google Scholar 

  • Proteau, L., Roujoula, A., & Messier, J. (2009). Evidence for continuous processing of visual information in a manual video-aiming task. Journal of Motor Behavior, 41(3), 219–231. doi:10.3200/JMBR.41.3.219-231.

    Article  PubMed  Google Scholar 

  • Randall, R. B. (2008). Spectral analysis and correlation. In D. Havelock, S. Kuwano, & M. Vorlander (Eds.), Handbook of signal processing in acoustics (pp. 33–52). New York: Springer.

    Chapter  Google Scholar 

  • Rossetti, Y., Stelmach, G., Desmurget, M., Prablanc, C., & Jeannerod, M. (1994). The effect of viewing the static hand prior to movement onset on pointing kinematics and variability. Experimental Brain Research, 101(2), 323–330. doi:10.1007/BF00228753.

    Article  PubMed  Google Scholar 

  • Salmoni, A. W., Schmidt, R. A., & Walter, C. B. (1984). Knowledge of results and motor learning: A review and critical reappraisal. Psychological Bulletin, 95(3), 355–386. doi:10.1037/0033-2909.95.3.355.

    Article  PubMed  Google Scholar 

  • Sarlegna, F. R. (2006). Impairment of online control of reaching movements with aging: A double-step study. Neuroscience Letters, 403(3), 309–314. doi:10.1016/j.neulet.2006.05.003.

    Article  PubMed  Google Scholar 

  • Sarlegna, F., Blouin, J., Bresciani, J.-P., Bourdin, C., Vercher, J.-L., & Gauthier, G. M. (2003). Target and hand position information in the online control of goal-directed arm movements. Experimental Brain Research, 151(4), 524–535. doi:10.1007/s00221-003-1504-7.

    Article  PubMed  Google Scholar 

  • Sarlegna, F. R., & Mutha, P. K. (2015). The influence of visual target information on the online control of movements. Vision Research, 110, 144–154. doi:10.1016/j.visres.2014.07.001.

    Article  PubMed  Google Scholar 

  • Saunders, J. A., & Knill, D. C. (2003). Humans use continuous visual feedback from the hand to control fast reaching movements. Experimental Brain Research, 152(3), 341–352. doi:10.1007/s00221-003-1525-2.

    Article  PubMed  Google Scholar 

  • Saunders, J. A., & Knill, D. C. (2005). Humans use continuous visual feedback from the hand to control both the direction and distance of pointing movements. Experimental Brain Research, 162, 458–473. doi:10.1007/s00221-004-2064-1.

    Article  PubMed  Google Scholar 

  • Scott, S. H., Cluff, T., Lowrey, C. R., & Takei, T. (2015). Feedback control during voluntary motor actions. Current Opinion in Neurobiology, 33, 85–94. doi:10.1016/j.conb.2015.03.006.

    Article  PubMed  Google Scholar 

  • Soechting, J. F. (1984). Effect of target size on spatial and temporal characteristics of a pointing movement in man. Experimental Brain Research, 54, 121–132. doi:10.1007/BF00235824.

    Article  PubMed  Google Scholar 

  • Sosnoff, J. J., & Newell, K. M. (2005). Intermittent visual information and the multiple time scales of visual motor control of continuous isometric force production. Perception & Psychophysics, 67(2), 335–344. doi:10.3758/BF03206496.

    Article  Google Scholar 

  • Tremblay, L., Crainic, V. A., de Grosbois, J., Bhattacharjee, A., Kennedy, A., Hansen, S., & Welsh, T. N. (2017). An optimal velocity for limb-target regulation processes? Experimental Brain Research, 235(1), 29–40. doi:10.1007/s00221-016-4770-x.

    Article  PubMed  Google Scholar 

  • Tremblay, L., Hansen, S., Kennedy, A., & Cheng, D. T. (2013). The utility of vision during action: Multiple visuomotor processes? Journal of Motor Behavior, 45(2), 91–99. doi:10.1080/00222895.2012.747483.

    Article  PubMed  Google Scholar 

  • van Beers, R. J., Haggard, P., & Wolpert, D. M. (2004). The role of execution noise in movement variability. Journal of Neurophysiology, 91(2), 1050–1063. doi:10.1152/jn.00652.2003.

    Article  PubMed  Google Scholar 

  • van Donkelaar, P., & Franks, I. M. (1991). Preprogramming vs. on-line control in simple movement sequences. Acta Psychologica, 77(1), 1–19. doi:10.1016/0001-6918(91)90061-4.

    Article  PubMed  Google Scholar 

  • van Galen, G. P., van Doorn, R. R. A., & Schomaker, L. R. B. (1990). Effects of motor programming on the power spectral density function of finger and wrist movements. Journal of Experimental Psychology: Human Perception and Performance, 16(4), 755–765. doi:10.1037/0096-1523.16.4.755.

    PubMed  Google Scholar 

  • Veerman, M. M., Brenner, E., & Smeets, J. B. J. (2008). The latency for correcting a movement depends on the visual attribute that defines the target. Experimental Brain Research, 187(2), 219–228. doi:10.1007/s00221-008-1296-x.

    Article  PubMed  PubMed Central  Google Scholar 

  • Veyrat-Masson, M., Brière, J., & Proteau, L. (2010). Automaticity of online control processes in manual aiming. Journal of Vision, 10(14), 1–14. doi:10.1167/10.14.27.

    Article  Google Scholar 

  • Warner, R. M. (1998). Spectral analysis of time series data. New York: Guilford Press.

    Google Scholar 

  • Westwood, D. A., & Goodale, M. A. (2003). Perceptual illusion and the real-time control of action. Spatial Vision, 16(3–4), 243–254. doi:10.1163/156856803322467518.

    Article  PubMed  Google Scholar 

  • Zelaznik, H. N., Hawkins, B., & Kisselburgh, L. (1983). Rapid visual feedback processing in single-aiming movements. Journal of Motor Behavior, 15(3), 217–236. doi:10.1080/00222895.1983.10735298.

    Article  PubMed  Google Scholar 

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Acknowledgements

This research was supported by the Natural Sciences and Engineering Research Council of Canada, the Canada Foundation for Innovation, the Ontario Research Fund, and a University of Toronto Graduate Student Fellowship. The authors would also like to thank two anonymous reviewers whose constructive input lead to significant improvements in the quality of this manuscript.

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  1. Faculty of Kinesiology and Physical Education, University of Toronto, 55 Harbord St., Toronto, ON, M5S 2W6, Canada

    John de Grosbois & Luc Tremblay

  2. Centre for Motor Control, University of Toronto, Toronto, ON, Canada

    John de Grosbois & Luc Tremblay

  3. Perceptual-Motor Behaviour Laboratory, University of Toronto, Toronto, ON, Canada

    John de Grosbois & Luc Tremblay

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Correspondence to Luc Tremblay.

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The current study was funded by a National Sciences and Engineering Research Council of Canada Grant (RGPIN-2015-05640); a grant via the Ontario Research Fund (10641); a grant from the Canada Foundation for Innovation (10641); and a Graduate student fellowship from the University of Toronto (no number available).

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de Grosbois, J., Tremblay, L. Distinct and flexible rates of online control. Psychological Research 82, 1054–1072 (2018). https://doi.org/10.1007/s00426-017-0888-0

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