Hostname: page-component-586b7cd67f-dsjbd Total loading time: 0 Render date: 2024-11-24T02:54:01.374Z Has data issue: false hasContentIssue false
  • English
  • Français

Critical Motor Involvement in Prediction of Human and Non-biological Motion Trajectories

Published online by Cambridge University Press:  16 February 2017

Matthieu M. de Wit  and
Laurel J. Buxbaum
Matthieu M. de Wit*
Affiliation:
Moss Rehabilitation Research Institute, Elkins Park, Pennsylvania
Laurel J. Buxbaum
Affiliation:
Moss Rehabilitation Research Institute, Elkins Park, Pennsylvania
*
Correspondence and reprint requests to: Matthieu M. de Wit, Moss Rehabilitation Research Institute, 50 Township Line Road, Elkins Park, PA 19027. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Objectives: Adaptive interaction with the environment requires the ability to predict both human and non-biological motion trajectories. Prior accounts of the neurocognitive basis for prediction of these two motion classes may generally be divided into those that posit that non-biological motion trajectories are predicted using the same motor planning and/or simulation mechanisms used for human actions, and those that posit distinct mechanisms for each. Using brain lesion patients and healthy controls, this study examined critical neural substrates and behavioral correlates of human and non-biological motion prediction. Methods: Twenty-seven left hemisphere stroke patients and 13 neurologically intact controls performed a visual occlusion task requiring prediction of pantomimed tool use, real tool use, and non-biological motion videos. Patients were also assessed with measures of motor strength and speed, praxis, and action recognition. Results: Prediction impairment for both human and non-biological motion was associated with limb apraxia and, weakly, with the severity of motor production deficits, but not with action recognition ability. Furthermore, impairment for human and non-biological motion prediction was equivalently associated with lesions in the left inferior parietal cortex, left dorsal frontal cortex, and the left insula. Conclusions: These data suggest that motor planning mechanisms associated with specific loci in the sensorimotor network are critical for prediction of spatiotemporal trajectory information characteristic of both human and non-biological motions. (JINS, 2017, 23, 171–184)

Keywords

SimulationMotor planningLimb apraxiaHemiparesisForward modelPremotorParietalInsulaDomain-specificDomain-generalBiological motion
Type
Research Articles
Information
Journal of the International Neuropsychological Society , Volume 23 , Special Issue 2: Special Issue: Motor Cognition , February 2017 , pp. 171 - 184
Copyright
Copyright © The International Neuropsychological Society 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aglioti, S.M., Cesari, P., Romani, M., & Urgesi, C. (2008). Action anticipation and motor resonance in elite basketball players. Nature Neuroscience, 11(9), 1109–1116.Google Scholar
Avants, B.B., Schoenemann, P.T., & Gee, J.C. (2006). Lagrangian frame diffeomorphic image registration: Morphometric comparison of human and chimpanzee cortex. Medical Image Analysis, 10(3 SPEC. ISS.), 397–412.Google Scholar
Balser, N., Lorey, B., Pilgramm, S., Stark, R., Bischoff, M., Zentgraf, K., & Munzert, J. (2014). Prediction of human actions: Expertise and task-related effects on neural activation of the action observation network. Human Brain Mapping, 35(8), 4016–4034.Google Scholar
Barsalou, L.W. (2009). Simulation, situated conceptualization, and prediction. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 364(1521), 1281–1289.CrossRefGoogle ScholarPubMed
Bates, D.M., Kliegl, R., Vasishth, S., & Baayen, H. (2015). Parsimonious mixed models. arXiv Preprint arXiv:1506.04967, 1–27.Google Scholar
Bernardi, G., Ricciardi, E., Sani, L., Gaglianese, A., Papasogli, A., Ceccarelli, R., & Pietrini, P. (2013). How skill expertise shapes the brain functional architecture: An fMRI study of visuo-spatial and motor processing in professional racing-car and naive drivers. PLoS One, 8(10), e77764.Google Scholar
Brattan, V.C., Baker, D.H., & Tipper, S.P. (2015). Spatiotemporal judgments of observed actions: Contrasts between first- and third-person perspectives after motor priming. Journal of Experimental Psychology: Human Perception and Performance, 41(5), 1236–1246.Google Scholar
Bubic, A., von Cramon, D.Y., & Schubotz, R.I. (2010). Prediction, cognition and the brain. Frontiers in Human Neuroscience, 4, 25.Google ScholarPubMed
Buxbaum, L.J., Johnson-Frey, S.H., & Bartlett-Williams, M. (2005). Deficient internal models for planning hand-object interactions in apraxia. Neuropsychologia, 43(6), 917–929.CrossRefGoogle ScholarPubMed
Buxbaum, L.J., Kyle, K.M., & Menon, R. (2005). On beyond mirror neurons: Internal representations subserving imitation and recognition of skilled object-related actions in humans. Cognitive Brain Research, 25(1), 226–239.Google Scholar
Calvo-Merino, B., Glaser, D.E., Grèzes, J., Passingham, R.E., & Haggard, P. (2005). Action observation and acquired motor skills: An fMRI study with expert dancers. Cerebral Cortex, 15(8), 1243–1249.Google Scholar
Caramazza, A., Anzellotti, S., Strnad, L., & Lingnau, A. (2014). Embodied cognition and mirror neurons: A critical assessment. Annual Review of Neuroscience, 37, 1–15.Google Scholar
Clark, A. (2015). Surfing uncertainty: Prediction, action, and the embodied mind. Oxford: Oxford University Press.Google Scholar
Colling, L.J., Thompson, W.F., & Sutton, J. (2014). The effect of movement kinematics on predicting the timing of observed actions. Experimental Brain Research, 232(4), 1193–1206.Google Scholar
Coslett, H.B., Buxbaum, L.J., & Schwoebel, J. (2008). Accurate reaching after active but not passive movements of the hand: Evidence for forward modeling. Behavioural Neurology, 19(3), 117–125.Google Scholar
Coull, J.T., Vidal, F., Goulon, C., Nazarian, B., & Craig, C. (2008). Using time-to-contact information to assess potential collision modulates both visual and temporal prediction networks. Frontiers in Human Neuroscience, 2, 10.Google Scholar
Cross, E.S., Stadler, W., Parkinson, J., Schütz-Bosbach, S., & Prinz, W. (2013). The influence of visual training on predicting complex action sequences. Human Brain Mapping, 34(2), 467–486.Google Scholar
Csibra, G. (2008). Action mirroring and action understanding: An alternative account. In P. Haggard, Y. Rosetti & M. Kawato (Eds.), Sensorymotor foundations of higher cognition. (Attention and Performance XXII) (pp. 435–459). Oxford: Oxford University Press.Google Scholar
Dawson, A.M., Buxbaum, L.J., & Duff, S.V. (2010). The impact of left hemisphere stroke on force control with familiar and novel objects: Neuroanatomic substrates and relationship to apraxia. Brain Research, 1317, 124–136.Google Scholar
Di Russo, F., Lucci, G., Sulpizio, V., Berchicci, M., Spinelli, D., Pitzalis, S., & Galati, G. (2016). Spatiotemporal brain mapping during preparation, perception, and action. Neuroimage, 126, 1–14.Google Scholar
Eidenmuller, S., Randerath, J., Goldenberg, G., Li, Y., & Hermsdorfer, J. (2014). The impact of unilateral brain damage on anticipatory grip force scaling when lifting everyday objects. Neuropsychologia, 61(1), 222–234.CrossRefGoogle ScholarPubMed
Fellows, L.K., Heberlein, A.S., Morales, D.A., Shivde, G., Waller, S., & Wu, D.H. (2005). Method matters: An empirical study of impact in cognitive neuroscience. Journal of Cognitive Neuroscience, 17(6), 850–858.CrossRefGoogle ScholarPubMed
Folstein, M.F., Folstein, S.E., & McHugh, P.R. (1975). “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12(3), 189–198.Google Scholar
Friston, K.J., Mattout, J., & Kilner, J. (2011). Action understanding and active inference. Biological Cybernetics, 104(1–2), 137–160.CrossRefGoogle ScholarPubMed
Fuster, J.M., & Bressler, S.L. (2015). Past makes future: Role of pFC in prediction. Journal of Cognitive Neuroscience, 27(4), 639–654.Google Scholar
Graf, M., Reitzner, B., Corves, C., Casile, A., Giese, M., & Prinz, W. (2007). Predicting point-light actions in real-time. NeuroImage, 36(Suppl. 2), T22–T32.CrossRefGoogle ScholarPubMed
Holmes, C.J., Hoge, R., Collins, L., Woods, R., Toga, A.W., & Evans, A.C. (2015). Enhancement of MR images using registration for signal averaging. Journal of Computer Assisted Tomography, 22(2), 324–333.Google Scholar
Jax, S.A., Buxbaum, L.J., & Moll, A.D. (2006). Deficits in movement planning and intrinsic coordinate control in ideomotor apraxia. Journal of Cognitive Neuroscience, 18(12), 2063–2076.Google Scholar
Jeannerod, M. (1986). The formation of finger grip during prehension. A cortically mediated visuomotor pattern. Behavioural Brain Research, 19(2), 99–116.Google Scholar
Johnson, P.C.D. (2014). Extension of Nakagawa & Schielzeth’s R2GLMM to random slopes models. Methods in Ecology and Evolution, 5(9), 944–946.Google Scholar
Johnson, S.H. (2000). Imagining the impossible: Intact motor representations in hemiplegics. Neuroreport, 11, 729–732.Google Scholar
Johnson, S.H., Sprehn, G., & Saykin, A.J. (2002). Intact motor imagery in chronic upper limb hemiplegics: Evidence for activity-independent action representations. Journal of Cognitive Neuroscience, 14(6), 841–852.Google Scholar
Kalénine, S., Buxbaum, L.J., & Coslett, H.B. (2010). Critical brain regions for action recognition: Lesion symptom mapping in left hemisphere stroke. Brain, 133(11), 3269–3280.Google Scholar
Kemmerer, D., Rudrauf, D., Manzel, K., & Tranel, D. (2012). Behavioral patterns and lesion sites associated with impaired processing of lexical and conceptual knowledge of actions. Cortex, 48(7), 826–848.Google Scholar
Kertesz, A. (1982). The western aphasia battery. New York: Grune & Stratton.Google Scholar
Knoblich, G., & Flach, R. (2001). Predicting the effects of actions: Interactions of perception and action. Psychological Science, 12(6), 467–472.Google Scholar
Li, Y., Randerath, J., Goldenberg, G., & Hermsdörfer, J. (2011). Size-weight illusion and anticipatory grip force scaling following unilateral cortical brain lesion. Neuropsychologia, 49(5), 914–923.Google Scholar
Makris, N., Kennedy, D.N., McInerney, S., Sorensen, A.G., Wang, R., Caviness, V.S., & Pandya, D.N. (2005). Segmentation of subcomponents within the superior longitudinal fascicle in humans: A quantitative, in vivo, DT-MRI study. Cerebral Cortex, 15(6), 854–869.Google Scholar
Makris, S., & Urgesi, C. (2013). Neural underpinnings of superior action prediction abilities in soccer players. Social Cognitive and Affective Neuroscience, 10(3), 342–351.CrossRefGoogle Scholar
Mirman, D., & Graziano, K.M. (2013). The neural basis of inhibitory effects of semantic and phonological neighbors in spoken word production. Journal of Cognitive Neuroscience, 25(9), 1504–1516.CrossRefGoogle ScholarPubMed
Mulligan, D., Lohse, K.R., & Hodges, N.J. (2015). An action-incongruent secondary task modulates prediction accuracy in experienced performers: Evidence for motor simulation. Psychological Research, 1–14.Google Scholar
Mutha, P.K., Sainburg, R.L., & Haaland, K.Y. (2010). Coordination deficits in ideomotor apraxia during visually targeted reaching reflect impaired visuomotor transformations. Neuropsychologia, 48(13), 3855–3867.Google Scholar
Nakagawa, S., & Schielzeth, H. (2013). A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods in Ecology and Evolution, 4(2), 133–142.Google Scholar
Negri, G.A.L., Rumiati, R.I., Zadini, A., Ukmar, M., Mahon, B.Z., & Caramazza, A. (2007). What is the role of motor simulation in action and object recognition? Evidence from apraxia. Cognitive Neuropsychology, 24(8), 795–816.Google Scholar
Nieuwenhuis, R., Te Grotenhuis, M., & Pelzer, B. (2012). Influence.ME: Tools for detecting influential data in mixed effects models. R Journal, 4(2), 38–47.Google Scholar
Ochipa, C., Rapcsak, S.Z., Maher, L.M., Rothi, L.J., Bowers, D., & Heilman, K.M. (1997). Selective deficit of praxis imagery in ideomotor apraxia. Neurology, 49(2), 474–480.Google Scholar
Pickering, M.J., & Garrod, S. (2013). An integrated theory of language production and comprehension. The Behavioral and Brain Sciences, 36(4), 329–347.Google Scholar
R Core Team. (2015). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Retrieved from https://www.R-project.org/.Google Scholar
Rizzolatti, G., Fogassi, L., & Gallese, V. (2001). Neurophysiological mechanisms underlying the understanding and imitation of action. Nature Reviews Neuroscience, 2, 661–670.Google Scholar
Roth, M.J., Synofzik, M., & Lindner, A. (2013). The cerebellum optimizes perceptual predictions about external sensory events. Current Biology, 23(10), 930–935.Google Scholar
Schmahmann, J.D., Pandya, D.N., Wang, R., Dai, G., D’Arceuil, H.E., De Crespigny, A.J., & Wedeen, V.J. (2007). Association fibre pathways of the brain: Parallel observations from diffusion spectrum imaging and autoradiography. Brain, 130(3), 630–653.Google Scholar
Schnur, T.T., Schwartz, M.F., Kimberg, D.Y., Hirshorn, E., Coslett, H.B., & Thompson-Schill, S.L. (2009). Localizing interference during naming: Convergent neuroimaging and neuropsychological evidence for the function of Broca’s area. Proceedings of the National Academy of Sciences of the United States of America, 106(1), 322–327.Google Scholar
Schubotz, R.I. (2007). Prediction of external events with our motor system: Towards a new framework. Trends in Cognitive Sciences, 11(5), 211–218.Google Scholar
Schubotz, R.I., Sakreida, K., Tittgemeyer, M., & von Cramon, D.Y. (2004). Motor areas beyond motor performance: Deficits in serial prediction following ventrolateral premotor lesions. Neuropsychology, 18(4), 638–645.Google Scholar
Schubotz, R.I., & von Cramon, D.Y. (2002). Predicting perceptual events activates corresponding motor schemes in lateral premotor cortex: An fMRI study. Neuroimage, 15(4), 787–796.Google Scholar
Schwartz, M.F., Brecher, A.R., Whyte, J., & Klein, M.G. (2005). A patient registry for cognitive rehabilitation research: A strategy for balancing patients’ privacy rights with researchers’ need for access. Archives of Physical Medicine and Rehabilitation, 86(9), 1807–1814.CrossRefGoogle ScholarPubMed
Sirigu, A., Cohen, L., Duhamel, J.R., Pillon, B., Dubois, B., Agid, Y., & Pierrot-Deseilligny, C. (1995). Congruent unilateral impairments for real and imagined hand movements. Neuroreport, 6(7), 997–1001.Google Scholar
Sirigu, A., Duhamel, J.-R., Cohen, L., Pillon, B., Dubois, B., & Agid, Y. (1996). The mental representation of hand movements after parietal cortex damage. Science, 273(5281), 1564–1568.CrossRefGoogle ScholarPubMed
Springer, A., Brandstädter, S., & Prinz, W. (2013). Dynamic simulation and static matching for action prediction: Evidence from body part priming. Cognitive Science, 37(5), 936–952.Google Scholar
Springer, A., & Prinz, W. (2010). Action semantics modulate action prediction. Quarterly Journal of Experimental Psychology (2006), 63(11), 2141–2158.Google Scholar
Stadler, W., Ott, D.V.M., Springer, A., Schubotz, R.I., Schütz-Bosbach, S., & Prinz, W. (2012). Repetitive TMS suggests a role of the human dorsal premotor cortex in action prediction. Frontiers in Human Neuroscience, 6, 20.CrossRefGoogle ScholarPubMed
Stadler, W., Schubotz, R.I., von Cramon, D.Y., Springer, A., Graf, M., & Prinz, W. (2011). Predicting and memorizing observed action: Differential premotor cortex involvement. Human Brain Mapping, 32(5), 677–687.Google Scholar
Stadler, W., Springer, A., Parkinson, J., & Prinz, W. (2012). Movement kinematics affect action prediction: Comparing human to non-human point-light actions. Psychological Research, 76(4), 395–406.Google Scholar
Stapel, J.C., Hunnius, S., Meyer, M., & Bekkering, H. (2016). Motor system contribution to action prediction: Temporal accuracy depends on motor experience. Cognition, 148, 71–78.Google Scholar
Tarhan, L., Watson, C., & Buxbaum, L. (2015). Shared and distinct neuroanatomic regions critical for tool-related action production and recognition: Evidence from 131 left-hemisphere stroke patients. Journal of Cognitive Neuroscience, 27(12), 2491–2511.Google Scholar
Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., & Joliot, M. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage, 15(1), 273–289.Google Scholar
Urgesi, C., Candidi, M., & Avenanti, A. (2014). Neuroanatomical substrates of action perception and understanding: An anatomic likelihood estimation meta-analysis of lesion-symptom mapping studies in brain injured patients. Frontiers in Human Neuroscience, 8, 344.Google Scholar
van Elk, M. (2014). The left inferior parietal lobe represents stored hand-postures for object use and action prediction. Frontiers in Psychology, 5, 1–12.Google Scholar
van Elk, M., Crajé, C., Beeren, M.E., Steenbergen, B., van Schie, H.T., & Bekkering, H. (2010). Neural evidence for compromised motor imagery in right hemiparetic cerebral palsy. Frontiers in Neurology, 1, 1–7.CrossRefGoogle ScholarPubMed
Vannuscorps, G., & Caramazza, A. (2015). Typical action perception and interpretation without motor simulation. Proceedings of the National Academy of Sciences of the United States of America, 113(1), 86–91.Google Scholar
Watson, C.E., & Buxbaum, L.J. (2015). A distributed network critical for selecting among tool-directed actions. Cortex, 65, 65–82.CrossRefGoogle ScholarPubMed
Wheaton, L., Fridman, E., Bohlhalter, S., Vorbach, S., & Hallett, M. (2009). Left parietal activation related to planning, executing and suppressing praxis hand movements. Clinical Neurophysiology, 120(5), 980–986.Google Scholar
Wiener, M., Turkeltaub, P.E., & Coslett, H.B. (2010). Implicit timing activates the left inferior parietal cortex. Neuropsychologia, 48(13), 3967–3971.Google Scholar
Wolpert, D., Doya, K., & Kawato, M. (2003). A unifying computational framework for motor control and social interaction. Philosophical Transactions of the Royal Society of London, 358(1431), 593–602.Google Scholar
Wolpert, D., Ghahramani, Z., & Jordan, M. (1995). An internal model for sensorimotor integration. Science, 269(5232), 14–16.Google Scholar
Yang, J. (2014). The influence of motor expertise on the brain activity of motor task performance: A meta-analysis of functional magnetic resonance imaging studies. Cognitive, Affective & Behavioral Neuroscience, 15(2), 381–394.Google Scholar