Network constraints on learnability of probabilistic motor sequences

Abstract

Human learners are adept at grasping the complex relationships underlying incoming sequential input1. In the present work, we formalize complex relationships as graph structures2 derived from temporal associations3,4 in motor sequences. Next, we explore the extent to which learners are sensitive to key variations in the topological properties5 inherent to those graph structures. Participants performed a probabilistic motor sequence task in which the order of button presses was determined by the traversal of graphs with modular, lattice-like or random organization. Graph nodes each represented a unique button press, and edges represented a transition between button presses. The results indicate that learning, indexed here by participants’ response times, was strongly mediated by the graph’s mesoscale organization, with modular graphs being associated with shorter response times than random and lattice graphs. Moreover, variations in a node’s number of connections (degree) and a node’s role in mediating long-distance communication (betweenness centrality) impacted graph learning, even after accounting for the level of practice on that node. These results demonstrate that the graph architecture underlying temporal sequences of stimuli fundamentally constrains learning, and moreover that tools from network science provide a valuable framework for assessing how learners encode complex, temporally structured information.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Experimental setup.
Fig. 2: Modular graph learning effects.
Fig. 3: Learning rate and edge surprisal: impact of new edges on reaction time.
Fig. 4: Relationship between small- and large-scale graph statistics and reaction time.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. 1.

    Aslin, R. N. & Newport, E. L. Statistical learning: from acquiring specific items to forming general rules. Curr. Dir. Psychol. Sci. 21, 170–176 (2012).

    Article  Google Scholar 

  2. 2.

    Newman, M. E. J. Networks: An Introduction (Oxford Univ. Press, Oxford, 2010).

  3. 3.

    Schapiro, A. C., Rogers, T. T., Cordova, N. I., Turk-Browne, N. B. & Botvinick, M. M. Neural representations of events arise from temporal community structure. Nat. Neurosci. 16, 486–492 (2013).

    CAS  Article  Google Scholar 

  4. 4.

    Karuza, E. A., Kahn, A. E., Thompson-Schill, S. L. & Bassett, D. S. Process reveals structure: how a network is traversed mediates expectations about its architecture. Sci. Rep. 7, 12733 (2017).

    Article  Google Scholar 

  5. 5.

    Newman, M. E. J. Complex systems: a survey. Am. J. Phys. 79, 800–810 (2011).

    Article  Google Scholar 

  6. 6.

    Saffran, J. R., Aslin, R. N. & Newport, E. L. Statistical learning by 8-month-old infants. Science 274, 1926–1928 (1996).

    CAS  Article  Google Scholar 

  7. 7.

    Nissen, M. J. & Bullemer, P. Attentional requirements of learning: evidence from performance measures. Cognit. Psychol. 19, 1–32 (1987).

    Article  Google Scholar 

  8. 8.

    Hunt, R. H. & Aslin, R. N. Statistical learning in a serial reaction time task: access to separable statistical cues by individual learners. J. Exp. Psychol. Gen. 130, 658–680 (2001).

    CAS  Article  Google Scholar 

  9. 9.

    Fiser, J. & Aslin, R. N. Statistical learning of higher-order temporal structure from visual shape sequences. J. Exp. Psychol. Learn. Mem. Cogn. 28, 458–467 (2002).

    Article  Google Scholar 

  10. 10.

    Turk-Browne, N. B., Jungé, J. A. & Scholl, B. J. The automaticity of visual statistical learning. J. Exp. Psychol. Gen. 134, 552–564 (2005).

    Article  Google Scholar 

  11. 11.

    Cleeremans, A. & McClelland, J. L. Learning the structure of event sequences. J. Exp. Psychol. Gen. 120, 235–253 (1991).

    CAS  Article  Google Scholar 

  12. 12.

    Furl, N. et al. Neural prediction of higher-order auditory sequence statistics. NeuroImage 54, 2267–2277 (2011).

    Article  Google Scholar 

  13. 13.

    Newport, E. L. & Aslin, R. N. Learning at a distance I. Statistical learning of non-adjacent dependencies. Cogn. Psychol. 48, 127–162 (2004).

    Article  Google Scholar 

  14. 14.

    Gómez, R. L. Variability and detection of invariant structure. Psychol. Sci. 13, 431–436 (2002).

    Article  Google Scholar 

  15. 15.

    Bollobas, B. Random Graphs (Cambridge Univ. Press, Cambridge, 2001).

  16. 16.

    Jarvis, J. P. & Shier, D. R. in Applied Mathematical Modeling: A Multidisciplinary Approach (eds Shier, D. R. & Wallenius, K. T.) Ch. 13 (Chapman and Hall/CRC Press, Boca Raton, 1999).

  17. 17.

    Goldstein, R. & Vitevitch, M. S. The influence of clustering coefficient on word-learning: how groups of similar sounding words facilitate acquisition. Front. Psychol. 5, 2009–2014 (2014).

    Article  Google Scholar 

  18. 18.

    Bales, M. E. & Johnson, S. B. Graph theoretic modeling of large-scale semantic networks. J. Biomed. Inform. 39, 451–464 (2006).

    Article  Google Scholar 

  19. 19.

    Vitevitch, M. S. What can graph theory tell us about word learning and lexical retrieval? J. Speech. Lang. Hear. Res. 51, 408–422 (2008).

    Article  Google Scholar 

  20. 20.

    Palla, G., Barabasi, A. L. & Vicsek, T. Quantifying social group evolution. Nature 446, 664–667 (2007).

    CAS  Article  Google Scholar 

  21. 21.

    Girvan, M. & Newman, M. E. J. Community structure in social and biological networks. Proc. Natl Acad. Sci. USA 99, 7821–7826 (2002).

    CAS  Article  Google Scholar 

  22. 22.

    Karuza, E. A., Thompson-Schill, S. L. & Bassett, D. S. Local patterns to global architectures: influences of network topology on human learning. Trends Cogn. Sci. 20, 629–640 (2016).

    Article  Google Scholar 

  23. 23.

    Steyvers, M. & Tenenbaum, J. B. The large-scale structure of semantic networks: statistical analyses and a model of semantic growth. Cogn. Sci. 29, 41–78 (2005).

    Article  Google Scholar 

  24. 24.

    Mengistu, H., Huizinga, J., Mouret, J. B. & Clune, J. The evolutionary origins of hierarchy. PLoS Comput. Biol. 12, e1004829 (2016).

    Article  Google Scholar 

  25. 25.

    Hermundstad, A. M. et al. Variance predicts salience in central sensory processing. eLife 3, e03722 (2014).

    Article  Google Scholar 

  26. 26.

    Heathcote, A., Brown, S. & Mewhort, D. J. K. The power law repealed: the case for an exponential law of practice. Psychon. Bull. Rev. 7, 185–207 (2000).

    CAS  Article  Google Scholar 

  27. 27.

    Karuza, E. A., Farmer, T. A., Fine, A. B., Smith, F. X. & Jaeger, T. F. On-line measures of prediction in a self-paced statistical learning task. In Proc. 36th Annual Meeting of the Cognitive Science Society (2014).

  28. 28.

    Cleeremans, A., Destrebecqz, A. & Boyer, M. Implicit learning: news from the front. Trends Cogn. Sci. 2, 406–416 (1998).

    CAS  Article  Google Scholar 

  29. 29.

    Robertson, E. M. The serial reaction time task: implicit motor skill learning? J. Neurosci. 27, 10073–10075 (2007).

    CAS  Article  Google Scholar 

  30. 30.

    Reber, A. S. Implicit learning of artificial grammars. J. Verbal Learning Verbal Behav. 6, 855–863 (1967).

    Article  Google Scholar 

  31. 31.

    Verwey, W. B., Abrahamse, E. L. & de Kleine, E. Cognitive processing in new and practiced discrete keying sequences. Front. Psychol. 1, 1–13 (2010).

    Google Scholar 

  32. 32.

    Kiesel, A. et al. Control and interference in task switching—a review. Psychol. Bull. 136, 849–874 (2010).

    Article  Google Scholar 

  33. 33.

    Koch, I. Automatic and intentional activation of task sets. J. Exp. Psychol. Learn. Mem. Cogn. 27, 1474–1486 (2001).

    CAS  Article  Google Scholar 

  34. 34.

    Gotler, A., Meiran, N. & Tzelgov, J. Nonintentional task set activation: evidence from implicit task sequence learning. Psychon. Bull. Rev. 10, 890–896 (2003).

    Article  Google Scholar 

  35. 35.

    Schneider, D. W. & Logan, G. D. Hierarchical control of cognitive processes: switching tasks in sequences. J. Exp. Psychol. 135, 623–640 (2006).

    Article  Google Scholar 

  36. 36.

    Gleiser, P. & Danon, L. Community structure in jazz. Adv. Complex. Syst. 6, 565–573 (2003).

    Article  Google Scholar 

  37. 37.

    Tompson, S. H., Kahn, A. E., Falk, E. B., Vettel, J. M. & Bassett, D. S. Individual differences in learning social and nonsocial network structures. J. Exp. Psychol. Learn. Mem. Cogn. https://doi.org/10.1037/xlm0000580 (2018).

  38. 38.

    Gebhart, A. L., Aslin, R. N. & Newport, E. L. Changing structures in midstream: learning along the statistical garden path. Cogn. Sci. 33, 1087–1116 (2009).

    Article  Google Scholar 

  39. 39.

    Deroost, N. & Soetens, E. Perceptual or motor learning in SRT tasks with complex sequence structures. Psychol. Res. 70, 88–102 (2006).

    Article  Google Scholar 

  40. 40.

    Messinger, A., Squire, L. R., Zola, S. M. & Albright, T. D. Neuronal representations of stimulus associations develop in the temporal lobe during learning. Proc. Natl Acad. Sci. USA 98, 12239–12244 (2001).

    CAS  Article  Google Scholar 

  41. 41.

    Li, N. & DiCarlo, J. J. Unsupervised natural experience rapidly alters invariant object representation in visual cortex. Science 321, 1502–1507 (2008).

    CAS  Article  Google Scholar 

  42. 42.

    Garvert, M. M., Dolan, R. J. & Behrens, T. E. A map of abstract relational knowledge in the human hippocampal-entorhinal cortex. eLife 6, e17086 (2017).

    Article  Google Scholar 

  43. 43.

    Newman, M. E. Modularity and community structure in networks. Proc. Natl Acad. Sci. USA 103, 8577–8582 (2006).

    CAS  Article  Google Scholar 

Download references

Acknowledgements

We thank D. M. Lydon-Staley and S. H. Tompson for feedback on earlier versions of this manuscript. This work was supported by the National Science Foundation CAREER award (PHY-1554488 to D.S.B.), Army Research Laboratory through contract number W911NF-10-2-0022 and Army Research Office through contract number Grafton-W911NF-16-1-0474 and contract number DCIST- W911NF-17-2-0181. We also acknowledge additional support from the John D. and Catherine T. MacArthur Foundation, Alfred P. Sloan Foundation, ISI Foundation, Paul G. Allen Family Foundation, Army Research Office (Bassett-W911NF-14-1-0679), Office of Naval Research, National Institute of Mental Health (2-R01-DC-009209-11, R01 MH-112847, R01 MH-107235, R21 MH-106799 and R01 MH-113550), National Institute of Child Health and Human Development (1R01HD086888-01), National Institute of Neurological Disorders and Stroke (R01 NS099348) and National Science Foundation (BCS-1441502, BCS-1631550 and CNS-1626008). The content is solely the responsibility of the authors and does not necessarily represent the official views of any of the funding agencies. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Affiliations

Authors

Contributions

A.E.K., E.A.K., J.M.V. and D.S.B. conceived the project and planned the experiments and analyses. A.E.K. performed the experiments and analyses. A.E.K., E.A.K. and D.S.B. wrote the manuscript and Supplementary Information. E.A.K., J.M.V. and D.S.B. edited the manuscript and Supplementary Information.

Corresponding author

Correspondence to Danielle S. Bassett.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Methods, Supplementary Figures 1–10, Supplementary Tables 1–6

Reporting Summary

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Kahn, A.E., Karuza, E.A., Vettel, J.M. et al. Network constraints on learnability of probabilistic motor sequences. Nat Hum Behav 2, 936–947 (2018). https://doi.org/10.1038/s41562-018-0463-8

Download citation

Further reading