Skilful behavior, as in music, or when typing, often unfolds with little attention to individual movements, or even little explicit knowledge about them. Despite this high degree of automaticity, motor skills can retain some flexibility, e.g., when musicians transfer the same rhythm to different melodies. We examine how the neural encoding of skill entails automaticity while retaining some degree of flexibility in behavior, and how attention, metacognition, and explicit knowledge influence automaticity and transfer across human motor learning.
Dr. Elena Azañón
Elena Azañón studied Psychology at the University of Barcelona. After her doctoral thesis with Salvador Soto-Faraco, she was granted a Marie Curie Fellowship to work at the Institute of Cognitive Neuroscience (University College London) in the group of Patrick Haggard (2012-2014). After completing her first postdoctoral position, she continued her scientific work at Birkbeck University London as a senior postdoc in the working group of Matthew Longo (2014-2018). In 2018, she joined the Faculty of Natural Sciences at the Otto von Guericke University Magdeburg, with a Dorothea Erxleben Visiting Professorship. She is currently leading the Somatosensory and Body Perception Lab at the Leibniz Institute for Neurobiology and at the Otto von Guericke University Magdeburg as a Junior Research leader.
Dr. med. Max-Philipp Stenner
Max-Philipp Stenner is head of the Motor Learning Lab at the Leibniz Institute for Neurobiology and at Otto-von-Guericke University, and deputy head of the LIN Department of Behavioral Neurology. His research, funded by a Freigeist Fellowship of the Volkswagen Foundation, investigates how human motor control and perception interact for motor learning, and how our subjective experience of control emerges from this interaction. As a clinician scientist, he is particularly interested in the subjective experience of control in neurological and neuropsychiatric disorders, including Tourette’s syndrome, ADHD, and obsessive-compulsive disorder. His major expertise is a combination of psychophysics with human non-invasive and invasive neurophysiology, including magneto- and electroencephalography as well as intracranial and spinal recordings of local field potentials in humans.
I am a doctoral researcher working in the groups of Elena Azañón and Max-Philipp Stenner, at Leibniz Institute for Neurobiology, Magdeburg. Prior starting my PhD in September 2021, I pursued my master’s in neuroscience from National Brain Research Centre, in India. At SFB1436, I am a part of the sub-project C03 and we are investigating the cognitive resources associated with motor skill learning in healthy humans. We use behavioural measurements and magnetoencephalography (MEG) data for answering our research questions.magnetoencephalography (MEG) data for answering our research questions.
Born in Thessaloniki, Greece, he started his career in the path of music by studying Musicology at the Aristotle University of Thessaloniki. His profound interest in both the sciences and the fundamentals of music perception, cognition, and motor performance, led him to neuroscience. He worked as a research assistant at the Medical Physics Laboratory AUTH, conducting EEG research on multisensory perception. As a PhD student in the project C03 of the CRC1436, he investigates interactions of attention and motor-skill learning.
Cognitive resource through automaticity
When movements become automatic, attention can be allocated to other concurrent tasks, at little or no cost. Automaticity thus liberates cognitive resources. On the other hand, automaticity also entails a degree of stereotypy and autonomy that can be detrimental to performance, e.g., when there is a substantial change in task requirements. However, despite a high degree of automaticity, motor skills often retain some degree of flexibility. For example, having learnt a musical rhythm, musicians can transfer that rhythm to new melodies. How can highly automatized behavior remain thus flexible?
A modular hierarchy of skill encoding?
We follow the idea that automaticity and flexibility of motor skills derive from a neural organisation of skill encoding that is both hierarchical and modular in nature (Diedrichsen and Kornysheva 2015). In a modular hierarchy, components of skilful behavior, such as individual movements of a motor skill, are thought to be merged in representational units, coding, e.g. for independent spatial and temporal patterns of behavior. Instead of selecting each movement individually in an effortful, time-consuming and resource-absorbing process, entire patterns can be selected for automatized execution as a unit. When different features of skilful behavior, such as temporal and spatial patterns of muscle activations (which muscle to activate, and when), are merged in representational units that are independent of one another, they are available for flexible recombination, allowing behavioral transfer (Kornysheva et al. 2013; Ullén et al. 2003). In theory, a modular hierarchy has the potential to liberate cognitive resources, enhance behavioral efficiency, and enable transfer through flexible recombination of behavioral patterns.
Evidence for a modular hierarchy
Behavioral and neuroimaging research has provided evidence that the cortical motor system in humans may indeed encode skills in a hierarchical and modular fashion (Kornysheva and Diedrichsen 2014; Yokoi and Diedrichsen 2019). However, key behavioral and neurophysiological predictions of the idea of a modular hierarchy have remained untested, and its potential and regulation as a resource is poorly understood. For example, previous studies point to learning and performance costs when attending to motoric details of one’s own behavior (Beilock et al. 2002; Wulf 2013). A modular hierarchy of skill encoding may explain this effect. Cognitive inquiry into motoric details during skill learning may hamper delegation of control over these details to merged representational units, and thus disrupt automaticity and impair performance and learning.
If the same principle of skill encoding entails both automaticity and flexibility, they should be coupled across learning. Interventions that influence automaticity, for example, attending to motor details, should therefore also influence flexibility. Understanding their coupling could thus guide cognitive learning strategies with a potential to accelerate skill learning.
A high degree of automaticity in behavior typically requires extensive training. However, learning itself may be partly automatic. Recently, initial learning of a motor skill has been associated with a rapid form of consolidation (memory stabilisation) that may occur autonomously, without explicit deliberation or awareness, i.e., displaying properties of automatic processes (Logan 1997). Specifically, learning of sequential finger movements benefits from interleaved rest periods (Bönstrup et al. 2019), during which neural replay of the required sequence has been detected using magnetoencephalography, at a rate that is too rapid to reflect explicit rehearsal (Buch et al. 2021). Indeed, these studies explicitly asked participants to refrain from rehearsing or imagining the required sequence during rest periods. While this may point to autonomy of the observed consolidation process, the role of explicit knowledge and voluntary learning strategies, such as mental rehearsal and motor imagery (imagining a movement), for rapid consolidation has not yet been tested. Again, understanding this role may guide learning strategies that accelerate skill learning.
Our goals are twofold
We aim to test key behavioral and neurophysiological predictions of the idea that automaticity and flexibility of motor skills derive from a neural organisation of skill encoding that is hierarchical and modular. The idea of a modular hierarchy predicts that not only spatial, but also temporal patterns of skilled movements should display a high degree of automaticity. This prediction has received little attention, and, to date, no support. Furthermore, a modular hierarchy predicts tight coupling of automaticity and transfer across learning. Hampering automaticity, e.g., via close monitoring of motoric details of performance, should therefore also hamper the emergence of transfer, e.g., transfer of a temporal pattern to new spatial patterns. Conversely, enforcing highly automatic performance, e.g., by imposing a secondary, concurrent task, should facilitate transfer. Finally, if individual elements of skilful behavior are bound into coherent representations that are selected for execution as units, a neurophysiological signature of an entire, forthcoming sequence of sub-movements should be decodable around sequence initiation (Tanji and Shima 1994). Crucially, this pre-movement signature should be specific to conditions under which behavior unfolds in an automatized way. We test these predictions in young, healthy humans using a combination of behavioral testing and magnetoencephalography.
Second, we aim to understand to what extent rapid consolidation of a motor skill during interleaved rest periods is really autonomous, and to what extent it can be harnessed via explicit knowledge and voluntary cognitive strategies, such as motor imagery. To this end, we decode neural replay, recorded via magnetoencephalography in young, healthy individuals, during periods of rest interleaved with active practice under varying instructions and explicit information about the task structure.
Beilock SL, Carr TH, MacMahon C, Starkes JL. When paying attention becomes counterproductive: Impact of divided versus skill-focused attention on novice and experienced performance of sensorimotor skills. J Exp Psychol Appl 8: 6–16, 2002.
Bönstrup M, Iturrate I, Thompson R, Cruciani G, Censor N, Cohen LG. A Rapid Form of Offline Consolidation in Skill Learning. Curr Biol 29: 1346-1351.e4, 2019.
Buch ER, Claudino L, Quentin R, Bönstrup M, Cohen LG. Consolidation of human skill linked to waking hippocampo-neocortical replay. Cell Rep 35, 2021.
Diedrichsen J, Kornysheva K. Motor skill learning between selection and execution. Trends Cogn Sci 19: 227–233, 2015.
Kornysheva K, Diedrichsen J. Human premotor areas parse sequences into their spatial and temporal features. Elife 3: e03043, 2014.
Kornysheva K, Sierk A, Diedrichsen J. Interaction of temporal and ordinal representations in movement sequences. J Neurophysiol 109: 1416–1424, 2013.
Logan GD. Automaticity and reading: Perspectives from the instance theory of automatization. Read Writ Q 13: 123–146, 1997.
Tanji J, Shima K. Role for supplementary motor area cells in planning several movements ahead. Nature 371: 413–416, 1994.
Ullén F, Bengtsson SL, Ull F. Independent Processing of the Temporal and Ordinal Structure of Movement Sequences Independent Processing of the Temporal and Ordinal Structure of Movement Sequences. J Neurophysiol 90: 3725–3735, 2003.
Wulf G. Attentional focus and motor learning: A review of 15 years. Int Rev Sport Exerc Psychol 6: 77–104, 2013.
Yokoi A, Diedrichsen J. Neural Organization of Hierarchical Motor Sequence Representations in the Human Neocortex. Neuron 103: 1178-1190.e7, 2019.
A glimpse into the future
Understanding principles of neural encoding of automatized, yet flexible, skilful behavior may have strong implications both for learning strategies across the lifespan, and for understanding and treating neuropsychiatric disorders. Disorders that likely involve altered attention to motoric details of action, and thereby likely inefficient use of cognitive resources, include obsessive-compulsive disorder and functional movement disorders. Investigating neural replay, thought to be hippocampus-dependent (Buch et al. 2021), across the lifespan, on the other hand, can reveal how the functional integrity of medial temporal lobe influences sensorimotor function.
Publications of the project C03
Jahangir Esfandiari, Seyedsina Razavizadeh, Max-Philipp Stenner Journal of Neurophysiology (2022)
Discriminating Free Hand Movements Using Support Vector Machine and Recurrent Neural Network Algorithms
Christoph Reichert, Lisa Klemm, Raghava Vinaykanth Mushunuri, Avinash Kalyani, Stefanie Schreiber, Esther Kühn, Elena Azañón Sensors (2022)
Bankim Subhash Chander, Matthias Deliano, Elena Azañón, Lars Büntjen, Max-Philipp Stenner Neuroimage (2021)