Observing Action Sequences Elicits Sequence-Specific Neural Representations in Frontoparietal Brain Regions

Learning new skills by watching others is important for social and motor development throughout the lifespan. Prior research has suggested that observational learning shares common substrates with physical practice at both cognitive and brain levels. In addition, neuroimaging studies have used multivariate analysis techniques to understand neural representations in a variety of domains, including vision, audition, memory, and action, but few studies have investigated neural plasticity in representational space. Therefore, although movement sequences can be learned by observing other people's actions, a largely unanswered question in neuroscience is how experience shapes the representational space of neural systems. Here, across a sample of male and female participants, we combined pretraining and posttraining fMRI sessions with 6 d of observational practice to determine whether the observation of action sequences elicits sequence-specific representations in human frontoparietal brain regions and the extent to which these representations become more distinct with observational practice. Our results showed that observed action sequences are modeled by distinct patterns of activity in frontoparietal cortex and that such representations largely generalize to very similar, but untrained, sequences. These findings advance our understanding of what is modeled during observational learning (sequence-specific information), as well as how it is modeled (reorganization of frontoparietal cortex is similar to that previously shown following physical practice). Therefore, on a more fine-grained neural level than demonstrated previously, our findings reveal how the representational structure of frontoparietal cortex maps visual information onto motor circuits in order to enhance motor performance. SIGNIFICANCE STATEMENT Learning by watching others is a cornerstone in the development of expertise and skilled behavior. However, it remains unclear how visual signals are mapped onto motor circuits for such learning to occur. Here, we show that observed action sequences are modeled by distinct patterns of activity in frontoparietal cortex and that such representations largely generalize to very similar, but untrained, sequences. These findings advance our understanding of what is modeled during observational learning (sequence-specific information), as well as how it is modeled (reorganization of frontoparietal cortex is similar to that previously shown following physical practice). More generally, these findings demonstrate how motor circuit involvement in the perception of action sequences shows high fidelity to prior work, which focused on physical performance of action sequences.

[1]  V. Penhune,et al.  Specific Increases within Global Decreases: A Functional Magnetic Resonance Imaging Investigation of Five Days of Motor Sequence Learning , 2010, The Journal of Neuroscience.

[2]  Scott T. Grafton,et al.  Evidence for a distributed hierarchy of action representation in the brain. , 2007, Human movement science.

[3]  Martine Turgeon,et al.  Cognitive Control Structures in the Imitation Learning of Spatial Sequences and Rhythms—An fMRI Study , 2018, Cerebral cortex.

[4]  L. Cohen,et al.  Neuroplasticity Subserving Motor Skill Learning , 2011, Neuron.

[5]  Jörn Diedrichsen,et al.  Effector-Independent Motor Sequence Representations Exist in Extrinsic and Intrinsic Reference Frames , 2014, The Journal of Neuroscience.

[6]  Denis Cousineau,et al.  Confidence intervals in within-subject designs: A simpler solution to Loftus and Masson's method , 2005 .

[7]  Paul L. Gribble,et al.  Repetitive Transcranial Magnetic Stimulation to the Primary Motor Cortex Interferes with Motor Learning by Observing , 2009, Journal of Cognitive Neuroscience.

[8]  Scott T. Grafton,et al.  Differential Recruitment of the Sensorimotor Putamen and Frontoparietal Cortex during Motor Chunking in Humans , 2012, Neuron.

[9]  Elger L. Abrahamse,et al.  Control of automated behavior: insights from the discrete sequence production task , 2013, Front. Hum. Neurosci..

[10]  G. Rizzolatti,et al.  Neural and Computational Mechanisms of Action Processing: Interaction between Visual and Motor Representations , 2015, Neuron.

[11]  Simon B. Eickhoff,et al.  A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data , 2005, NeuroImage.

[12]  Jörn Diedrichsen,et al.  Reliability of dissimilarity measures for multi-voxel pattern analysis , 2016, NeuroImage.

[13]  Paul L. Gribble,et al.  Functional Plasticity in Somatosensory Cortex Supports Motor Learning by Observing , 2016, Current Biology.

[14]  Emily S. Cross,et al.  Sensitivity of the action observation network to physical and observational learning. , 2008, Cerebral cortex.

[15]  Hein T. van Schie,et al.  Observational Learning of New Movement Sequences Is Reflected in Fronto-Parietal Coherence , 2010, PloS one.

[16]  Dov Sagi,et al.  Common mechanisms of human perceptual and motor learning , 2012, Nature Reviews Neuroscience.

[17]  S. Schütz-Bosbach,et al.  Towards a common framework of grounded action cognition: Relating motor control, perception and cognition , 2016, Cognition.

[18]  Joshua Carp,et al.  The secret lives of experiments: Methods reporting in the fMRI literature , 2012, NeuroImage.

[19]  Emily S. Cross,et al.  Additive Routes to Action Learning: Layering Experience Shapes Engagement of the Action Observation Network , 2015, Cerebral cortex.

[20]  Andrew A G Mattar,et al.  Motor Learning by Observing , 2005, Neuron.

[21]  G. Cumming Understanding the New Statistics: Effect Sizes, Confidence Intervals, and Meta-Analysis , 2011 .

[22]  L. Proteau,et al.  Cognitive Processes Underlying Observational Learning of Motor Skills , 1999 .

[23]  Angela R. Laird,et al.  ALE meta-analysis of action observation and imitation in the human brain , 2010, NeuroImage.

[24]  Li Su,et al.  A Toolbox for Representational Similarity Analysis , 2014, PLoS Comput. Biol..

[25]  J. Diedrichsen,et al.  On the distribution of cross-validated Mahalanobis distances , 2016, 1607.01371.

[26]  R. Adolphs,et al.  A new look at domain specificity: insights from social neuroscience , 2017, Nature Reviews Neuroscience.

[27]  Anders M. Dale,et al.  Cortical Surface-Based Analysis I. Segmentation and Surface Reconstruction , 1999, NeuroImage.

[28]  Emily S. Cross,et al.  Using guitar learning to probe the Action Observation Network's response to visuomotor familiarity , 2017, NeuroImage.

[29]  N. Kriegeskorte,et al.  Author ' s personal copy Representational geometry : integrating cognition , computation , and the brain , 2013 .

[30]  Sean M. Polyn,et al.  Beyond mind-reading: multi-voxel pattern analysis of fMRI data , 2006, Trends in Cognitive Sciences.

[31]  Simon B. Eickhoff,et al.  A quantitative meta-analysis and review of motor learning in the human brain , 2013, NeuroImage.

[32]  W. K. Simmons,et al.  Circular analysis in systems neuroscience: the dangers of double dipping , 2009, Nature Neuroscience.

[33]  Karl J. Friston,et al.  Assessing the significance of focal activations using their spatial extent , 1994, Human brain mapping.

[34]  Simon B. Eickhoff,et al.  Imitation and observational learning of hand actions: Prefrontal involvement and connectivity , 2012, NeuroImage.

[35]  Paul E. Downing,et al.  A comparison of volume-based and surface-based multi-voxel pattern analysis , 2011, NeuroImage.

[36]  Stefan Panzer,et al.  Role of action observation and action in sequence learning and coding. , 2010, Acta psychologica.

[37]  J. Diedrichsen,et al.  Motor skill learning between selection and execution , 2015, Trends in Cognitive Sciences.

[38]  Rainer Goebel,et al.  Information-based functional brain mapping. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[39]  Emily S. Cross,et al.  Fluid intelligence and working memory support dissociable aspects of learning by physical but not observational practice , 2019, Cognition.

[40]  A. Kelly,et al.  Human functional neuroimaging of brain changes associated with practice. , 2005, Cerebral cortex.

[41]  Alexander Borst,et al.  How does Nature Program Neuron Types? , 2008, Front. Neurosci..

[42]  D H Brainard,et al.  The Psychophysics Toolbox. , 1997, Spatial vision.

[43]  Jörn Diedrichsen,et al.  A multivariate method to determine the dimensionality of neural representation from population activity , 2013, NeuroImage.

[44]  W. Prinz Perception and Action Planning , 1997 .

[45]  A. Williams,et al.  What is modelled during observational learning? , 2007, Journal of sports sciences.

[46]  William A. Cunningham,et al.  Type I and Type II error concerns in fMRI research: re-balancing the scale. , 2009, Social cognitive and affective neuroscience.

[47]  Daniël Lakens,et al.  Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs , 2013, Front. Psychol..

[48]  P. Haggard,et al.  Action observation and execution: What is shared? , 2008, Social neuroscience.

[49]  Ravi S. Menon,et al.  Intrinsic signal changes accompanying sensory stimulation: functional brain mapping with magnetic resonance imaging. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[50]  Jacob Cohen,et al.  A power primer. , 1992, Psychological bulletin.

[51]  Jörn Diedrichsen,et al.  Skill learning strengthens cortical representations of motor sequences , 2013, eLife.

[52]  Nikolaus Kriegeskorte,et al.  Frontiers in Systems Neuroscience Systems Neuroscience , 2022 .

[53]  C. Heyes,et al.  Sequence learning by action, observation and action observation. , 2005, British journal of psychology.

[54]  J. Culham,et al.  The role of parietal cortex in visuomotor control: What have we learned from neuroimaging? , 2006, Neuropsychologia.

[55]  J. Mattingley,et al.  Brain regions with mirror properties: A meta-analysis of 125 human fMRI studies , 2012, Neuroscience & Biobehavioral Reviews.

[56]  V. Penhune,et al.  Author's Personal Copy Behavioural Brain Research Parallel Contributions of Cerebellar, Striatal and M1 Mechanisms to Motor Sequence Learning , 2022 .

[57]  D. Ostry,et al.  Sensory Plasticity in Human Motor Learning , 2016, Trends in Neurosciences.

[58]  S. Vogt,et al.  From visuo-motor interactions to imitation learning: Behavioural and brain imaging studies , 2007, Journal of sports sciences.