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Magnetoencephalography decoding reveals structural differences within integrative decision processes

Nature Human Behaviour volume 2, pages 670–681 (2018)Cite this article

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Abstract

When confronted with complex inputs consisting of multiple elements, humans use various strategies to integrate the elements quickly and accurately. For instance, accuracy may be improved by processing elements one at a time1,2,3,4 or over extended periods5,6,7,8; speed can increase if the internal representation of elements is accelerated9,10. However, little is known about how humans actually approach these challenges because behavioural findings can be accounted for by multiple alternative process models11 and neuroimaging investigations typically rely on haemodynamic signals that change too slowly. Consequently, to uncover the fast neural dynamics that support information integration, we decoded magnetoencephalographic signals that were recorded as human subjects performed a complex decision task. Our findings reveal three sources of individual differences in the temporal structure of the integration process—sequential representation, partial reinstatement and early computation—each having a dissociable effect on how subjects handled problem complexity and temporal constraints. Our findings shed new light on the structure and influence of self-determined neural integration processes.

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Fig. 1: Task design, performance and model.
Fig. 2: Cortical representations of the players composing a team.
Fig. 3: Temporal components of the neural response.
Fig. 4: Cortical representation of players and goals in 2-s trials.

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References

  1. Rumelhart, D. E. & McClelland, J. L. Parallel Distributed Processing: Explorations in the Microstructure of Cognition (MIT Press, Cambridge, MA, 1986).

    Google Scholar 

  2. Treisman, A. M. Strategies and models of selective attention. Psychol. Rev. 76, 282–299 (1969).

    CAS  PubMed  Google Scholar 

  3. Bergen, J. R. & Julesz, B. Parallel versus serial processing in rapid pattern discrimination. Nature 303, 696–698 (1983).

    CAS  PubMed  Google Scholar 

  4. Feng, S. F., Schwemmer, M., Gershman, S. J. & Cohen, J. D. Multitasking versus multiplexing: toward a normative account of limitations in the simultaneous execution of control-demanding behaviors. Cogn. Affect. Behav. Neurosci. 14, 129–146 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Gold, J. I. & Shadlen, M. N. The neural basis of decision making. Annu. Rev. Neurosci. 30, 535–574 (2007).

    CAS  PubMed  Google Scholar 

  6. Heitz, R. P. & Schall, J. D. Neural mechanisms of speed–accuracy tradeoff. Neuron 76, 616–628 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Hanks, T., Kiani, R. & Shadlen, M. N. A neural mechanism of speed–accuracy tradeoff in macaque area LIP. eLife 3, e02260 (2014).

    PubMed Central  Google Scholar 

  8. Hawkins, G. E., Forstmann, B. U., Wagenmakers, E. J., Ratcliff, R. & Brown, S. D. Revisiting the evidence for collapsing boundaries and urgency signals in perceptual decision-making. J. Neurosci. 35, 2476–2484 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Marti, S., King, J. R. & Dehaene, S. Time-resolved decoding of two processing chains during dual-task interference. Neuron 88, 1297–1307 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Chalk, M., Marre, O. & Tkačik, G. Toward a unified theory of efficient, predictive, and sparse coding. Proc. Natl Acad. Sci. USA 115, 186–191 (2018).

    CAS  PubMed  Google Scholar 

  11. Williams, P., Eidels, A. & Townsend, J. T. The resurrection of Tweedledum and Tweedledee: bimodality cannot distinguish serial and parallel processes. Psychon. Bull. Rev. 21, 1165–1173 (2014).

    PubMed  Google Scholar 

  12. Carlson, T., Tovar, D. A., Alink, A. & Kriegeskorte, N. Representational dynamics of object vision: the first 1000 ms. J. Vis. 13, 1 (2013).

    PubMed  Google Scholar 

  13. Isik, L., Meyers, E. M., Leibo, J. Z. & Poggio, T. The dynamics of invariant object recognition in the human visual system. J. Neurophysiol. 111, 91–102 (2014).

    PubMed  Google Scholar 

  14. Cichy, R. M., Pantazis, D. & Oliva, A. Resolving human object recognition in space and time. Nat. Neurosci. 17, 455–462 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Kurth-Nelson, Z., Barnes, G., Sejdinovic, D., Dolan, R. & Dayan, P. Temporal structure in associative retrieval. eLife 4, e04919 (2015).

    PubMed Central  Google Scholar 

  16. Kurth-Nelson, Z., Economides, M., Dolan, R. J. & Dayan, P. Fast sequences of non-spatial state representations in humans. Neuron 91, 194–204 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Hunt, L. T. et al. Mechanisms underlying cortical activity during value-guided choice. Nat. Neurosci. 15, 470–476 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Hotelling, H. Relations between two sets of variates. Biometrika 28, 321–377 (1936).

    Google Scholar 

  19. Simon, H. A. Models of Bounded Rationality: Empirically Grounded Economic Reason (MIT Press, Cambridge, MA, 1982).

  20. Howes, A., Vera, A., Lewis, R. L. & McCurdy, M. Cognitive constraint modeling: a formal approach to supporting reasoning about behavior. In Proc. 26th Annual Meeting of the Cognitive Science Society (eds Forbus, K., Gentner, D. & Regier, T.) 595–600 (Lawrence Erlbaum, 2004).

  21. Wickelgren, W. A. Speed–accuracy tradeoff and information processing dynamics. Acta Psychol. 41, 67–85 (1977).

    Google Scholar 

  22. Krajbich, I., Armel, C. & Rangel, A. Visual fixations and the computation and comparison of value in simple choice. Nat. Neurosci. 13, 1292–1298 (2010).

    CAS  PubMed  Google Scholar 

  23. Dai, J. & Busemeyer, J. R. A probabilistic, dynamic, and attribute-wise model of intertemporal choice. J. Exp. Psychol. Gen. 143, 1489–1514 (2014).

    PubMed  PubMed Central  Google Scholar 

  24. Rich, E. L. & Wallis, J. D. Decoding subjective decisions from orbitofrontal cortex. Nat. Neurosci. 19, 973–980 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Townsend, J. T. & Wenger, M. J. The serial-parallel dilemma: a case study in a linkage of theory and method. Psychon. Bull. Rev. 11, 391–418 (2004).

    PubMed  Google Scholar 

  26. Sternberg, R. J. & Grigorenko, E. L. Are cognitive styles still in style? Am. Psychol. 52, 700–712 (1997).

    Google Scholar 

  27. Rayner, S. & Riding, R. Towards a categorisation of cognitive styles and learning styles. Educ. Psychol. 17, 5–27 (1997).

    Google Scholar 

  28. Nisbett, R. E., Peng, K., Choi, I. & Norenzayan, A. Culture and systems of thought: holistic versus analytic cognition. Psychol. Rev. 108, 291–310 (2001).

    CAS  PubMed  Google Scholar 

  29. Felder, R. M. & Spurlin, J. Applications, reliability and validity of the index of learning styles. Int. J. Eng. Educ. 21, 103–112 (2005).

    Google Scholar 

  30. Choi, I., Koo, M. & Choi, J. A. Individual differences in analytic versus holistic thinking. Pers. Soc. Psychol. Bull. 33, 691–705 (2007).

    PubMed  Google Scholar 

  31. Kozhevnikov, M. Cognitive styles in the context of modern psychology: toward an integrated framework of cognitive style. Psychol. Bull. 133, 464–481 (2007).

    PubMed  Google Scholar 

  32. Eldar, E., Cohen, J. D. & Niv, Y. The effects of neural gain on attention and learning. Nat. Neurosci. 16, 1146–1153 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Montague, P. R., Dolan, R. J., Friston, K. J. & Dayan, P. Computational psychiatry. Trends Cogn. Sci. 16, 72–80 (2012).

    PubMed  Google Scholar 

  34. Wang, X. J. & Krystal, J. H. Computational psychiatry. Neuron 84, 638–654 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Eldar, E. & Niv, Y. Interaction between emotional state and learning underlies mood instability. Nat. Commun. 6, 6149 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Eldar, E., Hauser, T. U., Dayan, P. & Dolan, R. J. Striatal structure and function predict individual biases in learning to avoid pain. Proc. Natl Acad. Sci. USA 113, 4812–4817 (2016).

    CAS  PubMed  Google Scholar 

  37. Schwarz, G. Estimating the dimension of a model. Ann. Stat. 6, 461–464 (1978).

    Google Scholar 

  38. Bishop, C. M. Pattern Recognition and Machine Learning (Springer, Heidelberg, 2006)..

  39. Kass, R. E. & Raftery, A. E. Bayes factors. J. Am. Stat. Assoc. 90, 773–795 (1995).

    Google Scholar 

  40. Huys, Q. J. et al. Bonsai trees in your head: how the Pavlovian system sculpts goal-directed choices by pruning decision trees. PLoS Comp. Biol. 8, e1002410 (2012).

    CAS  Google Scholar 

  41. Oostenveld, R., Fries, P., Maris, E. & Schoffelen, J. M. FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput. Intell. Neurosci. 2011, 156869 (2011).

    PubMed  Google Scholar 

  42. Sun, L., Ji, S. & Ye, J. Canonical correlation analysis for multilabel classification: a least-squares formulation, extensions, and analysis. IEEE Trans. Pattern Anal. Mach. Intell. 33, 194–200 (2011).

    PubMed  Google Scholar 

  43. Hoffman, K. L. & McNaughton, B. L. Coordinated reactivation of distributed memory traces in primate neocortex. Science 297, 2070–2073 (2002).

    CAS  PubMed  Google Scholar 

  44. Busch, N. & VanRullen, R. in Subjective Time: The Philosophy, Psychology, and Neuroscience of Temporality (eds Arstila, V. & Lloyd. D.) 161–178 (MIT Press, Cambridge, MA, 2014).

  45. Maris, E. & Oostenveld, R. Nonparametric statistical testing of EEG- and MEG-data. J. Neurosci. Methods 164, 177–190 (2007).

    PubMed  Google Scholar 

  46. Kessels, R. P., Van Zandvoort, M. J., Postma, A., Kappelle, L. J. & De Haan, E. H. The Corsi block-tapping task: standardization and normative data. Appl. Neuropsychol. 7, 252–258 (2000).

    CAS  PubMed  Google Scholar 

  47. Wechsler, D. & Hsiao-pin, C. WASI-II: Wechsler Abbreviated Scale of Intelligence (Pearson, San Antonio, TX, 2011).

  48. Hoekstra, R. A. et al. The construction and validation of an abridged version of the autism-spectrum quotient (AQ-Short). J. Autism Dev. Disord. 41, 589–596 (2011).

    PubMed  Google Scholar 

  49. Kessler, R. C. et al. The World Health Organization adult ADHD self-report scale (ASRS): a short screening scale for use in the general population. Psychol. Med. 35, 245–256 (2005).

    PubMed  Google Scholar 

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Acknowledgements

This work was funded by the Wellcome Trust’s Cambridge–University College London Mental Health and Neurosciences Network grant 095844/Z/11/Z (E.E. and R.J.D.), the Wellcome Trust Investigator Award 098362/Z/12/Z (R.J.D.), the Gatsby Charitable Foundation (P.D.) and the Max Planck Society (Z.K.-N.). The Max Planck University College London Centre is a joint initiative supported by University College London and the Max Planck Society. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank T. E. Behrens and R. Moran for helpful feedback on previous versions of this manuscript.

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Author notes

  1. These authors contributed equally: Peter Dayan, Raymond J. Dolan.

Authors and Affiliations

  1. Wellcome Trust Centre for Neuroimaging, University College London, London, UK

    Eran Eldar, Gyung Jin Bae, Zeb Kurth-Nelson & Raymond J. Dolan

  2. Max Planck University College London Centre for Computational Psychiatry and Ageing Research, London, UK

    Eran Eldar, Gyung Jin Bae, Zeb Kurth-Nelson, Peter Dayan & Raymond J. Dolan

  3. Gatsby Computational Neuroscience Unit, University College London, London, UK

    Peter Dayan

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  1. Eran Eldar
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Contributions

E.E. conceptualized the study. E.E., G.J.B., Z.K.-N. and P.D. conceptualized the methodology. E.E. and G.J.B. undertook the investigation. E.E. wrote the original draft of the paper. E.E., Z.K.-N., P.D. and R.J.D. reviewed and edited the paper. R.J.D. acquired funding. P.D. and R.J.D. supervised the study.

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Correspondence to Eran Eldar.

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Supplementary Figures 1–12, Supplementary Table 1, Supplementary References

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Eldar, E., Bae, G.J., Kurth-Nelson, Z. et al. Magnetoencephalography decoding reveals structural differences within integrative decision processes. Nat Hum Behav 2, 670–681 (2018). https://doi.org/10.1038/s41562-018-0423-3

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