Advertisement

Immediate versus delayed control demands elicit distinct mechanisms for instantiating proactive control

  • Jacqueline R. Janowich
  • James F. Cavanagh
Article
  • 49 Downloads

Abstract

Cognitive control is critical for dynamically guiding goal-directed behavior, particularly when applying preparatory, or proactive, control processes. However, it is unknown how proactive control is modulated by timing demands. This study investigated how timing demands may instantiate distinct neural processes and contribute to the use of different types of proactive control. In two experiments, healthy young adults performed the AX-Continuous Performance Task (AX-CPT) or Dot Pattern Expectancy (DPX) task. The delay between informative cue and test probe was manipulated by block to be short (1s) or long (~3s). We hypothesized that short cue-probe delays would rely more on a rapid goal updating process (akin to task-switching), whereas long cue-probe delays would utilize more of an active maintenance process (akin to working memory). Short delay lengths were associated with specific impairments in rare probe accuracy. EEG responses to control-demanding cues revealed delay-specific neural signatures, which replicated across studies. In the short delay condition, EEG activities associated with task-switching were specifically enhanced, including increased early anterior positivity ERP amplitude (accompanying greater mid-frontal theta power) and a larger late differential switch positivity. In the long delay condition, we observed study-specific sustained increases in ERP amplitude following control-demanding cues, which may be suggestive of active maintenance. Collectively, these findings suggest that timing demands may instantiate distinct proactive control processes. These findings suggest a reevaluation of AX-CPT and DPX as pure assessments of working memory and highlight the need to understand how presumably benign task parameters, such as cue-probe delay length, significantly alter cognitive control.

Keywords

Cognitive control Working memory Task-switching AX-Continuous Performance Task EEG, Dot Probe Expectancy 

Notes

Acknowledgements

The authors thank the research assistants of the Cognitive Rhythms and Computation Lab for help with data collection, D.R. Brown, V. Clark, and A. Mayer for discussions and helpful comments. JFC is supported by NIGMS 1P20GM109089-01A1.

Supplementary material

13415_2018_684_MOESM1_ESM.docx (107 kb)
ESM 1 (DOCX 107 kb)
13415_2018_684_MOESM2_ESM.pdf (51 kb)
Supplemental Figure 1 Brain-behavior correlations between Behavioral Shift Index (BSI) for accuracy and neural measures for AX-CPT (left) and DPX (right). Pearson’s r for each condition in inset boxes. A. Differential switch positivity at Cz (400-600 ms) correlations with BSI (Acc). These correlations show marginally significant positive correlations between differential switch positivity to rare “B” cues and BSI, which is greater for short versus long delay. B. (left) AX-CPT sustained posterior-parietal activity (PO3, PO4, PO7, PO8) and (right) DPX left frontal (AF3, AF7, F3, F5, F7) correlations with BSI (Acc). AX-CPT correlations show a trend of negative correlation between long B cue-locked and BSI accuracy. DPX correlations show an unexpected (negative) relationship between short B cue-locked activity and BSI accuracy. (PDF 51 kb)

References

  1. Astle, D. E., Jackson, G. M., & Swainson, R. (2006). Dissociating neural indices of dynamic cognitive control in advance task-set preparation: an ERP study of task switching. Brain Research, 1125(1), 94–103.  https://doi.org/10.1016/j.brainres.2006.09.092 Google Scholar
  2. Astle, D. E., Jackson, G. M., & Swainson, R. (2008). Fractionating the cognitive control required to bring about a change in task: a dense-sensor event-related potential study. Journal of Cognitive Neuroscience, 20(2), 255–267.  https://doi.org/10.1162/jocn.2008.20015 Google Scholar
  3. Barak, O., & Tsodyks, M. (2014). Working models of working memory. Current Opinion in Neurobiology, 25, 20–24.  https://doi.org/10.1016/j.conb.2013.10.008 Google Scholar
  4. Barcelo, F., Escera, C., Corral, M. J., & Periáñez, J. a. (2006). Task switching and novelty processing activate a common neural network for cognitive control. Journal of Cognitive Neuroscience, 18, 1734–1748.  https://doi.org/10.1162/jocn.2006.18.10.1734 Google Scholar
  5. Barch, D. M., Berman, M. G., Engle, R., Jones, J. H., Jonides, J., MacDonald, A., … Sponheim, S. R. (2009). CNTRICS final task selection: Working memory. Schizophrenia Bulletin, 35(1), 136–152.  https://doi.org/10.1093/schbul/sbn153 Google Scholar
  6. Barch, D. M., Braver, T. S., Nystrom, L. E., Forman, S. D., Noll, D. C., & Cohen, J. D. (1997). Dissociating working memory from task difficulty in human prefrontal cortex. Neuropsychologia, 35(10), 1373–80.Google Scholar
  7. Bertelson, P. & Boons, J.P. (1960). Time uncertainty and choice reaction time. Nature, 187, 531-532.Google Scholar
  8. Blackman, R. K., Crowe, D. A., DeNicola, A. L., Sakellaridi, S., MacDonald, A. W., & Chafee, M. V. (2016). Monkey Prefrontal Neurons Reflect Logical Operations for Cognitive Control in a Variant of the AX Continuous Performance Task (AX-CPT). Journal of Neuroscience, 36(14), 4067–4079.  https://doi.org/10.1523/JNEUROSCI.3578-15.2016 Google Scholar
  9. Blackman, R. K., Macdonald, A. W., & Chafee, M. V. (2013). Effects of ketamine on context-processing performance in monkeys: a new animal model of cognitive deficits in schizophrenia. Neuropsychopharmacology : Official Publication of the American College of Neuropsychopharmacology, 38(11), 2090–100.  https://doi.org/10.1038/npp.2013.118 Google Scholar
  10. Brainard, D. H. (1997). The Psychophysics Toolbox. Spatial Vision, 10(4), 433–436.  https://doi.org/10.1163/156856897X00357 Google Scholar
  11. Braver, T. S. (2012). The variable nature of cognitive control: A dual mechanisms framework. Trends in Cognitive Sciences, 16(2), 106–113.  https://doi.org/10.1016/j.tics.2011.12.010 Google Scholar
  12. Braver, T. S., Barch, D. M., & Cohen, J. D. (1999). Mechanisms of cognitive control: Active memory, inhibition, and the prefrontal cortex. Pittsburgh (PA): Carnegie Mellon University. Retrieved from ftp://46.13.242.59/IMATION_HDD/Documents/Work/Archives/2008/CIANS2008/2nd set/literatura/var/COGNITIVNI_FUNKCE.PDFGoogle Scholar
  13. Braver, T. S., Paxton, J. L., Locke, H. S., & Barch, D. M. (2009). Flexible neural mechanisms of cognitive control within human prefrontal cortex. Proceedings of the National Academy of Sciences of the United States of America, 106(15), 7351–7356.  https://doi.org/10.1073/pnas.0808187106 Google Scholar
  14. Braver, T. S., Satpute, A. B., Rush, B. K., Racine, C. A., & Barch, D. M. (2005). Context processing and context maintenance in healthy aging and early stage dementia of the Alzheimer’s type. Psychology and Aging, 20(1), 33–46.  https://doi.org/10.1037/0882-7974.20.1.33 Google Scholar
  15. Brookes, M. J., Wood, J. R., Stevenson, C. M., Zumer, J. M., White, T. P., Liddle, P. F., & Morris, P. G. (2011). Changes in brain network activity during working memory tasks: a magnetoencephalography study. NeuroImage, 55(4), 1804–15.  https://doi.org/10.1016/j.neuroimage.2010.10.074 Google Scholar
  16. Buhusi, C. V., & Meck, W. H. (2005). What makes us tick? Functional and neural mechanisms of interval timing. Nature Reviews Neuroscience, 6(10), 755–765.  https://doi.org/10.1038/nrn1764 Google Scholar
  17. Cacioppo, J. T., & Tassinary, L. G. (1990). Inferring psychological significance from physiological signals. The American Psychologist, 45(1), 16–28.  https://doi.org/10.1037/0003-066X.45.1.16 Google Scholar
  18. Capizzi, M., Feher, K., Penolazzi, B., & Vallesi, A. (2015). Task-switching preparation across semantic and spatial domains: An event-related potential study. Biological Psychology, 110, 148–158.  https://doi.org/10.1016/j.biopsycho.2015.06.011 Google Scholar
  19. Carter, C. S., Braver, T. S., Barch, D. M., Botvinick, M. M., Noll, D., & Cohen, J. D. (1998). Anterior cingulate cortex, error detection, and the online monitoring of performance. Science (New York, N.Y.), 280(May), 747–749.  https://doi.org/10.1126/science.280.5364.747 Google Scholar
  20. Cavanagh, J. F., & Castellanos, J. (2016). Identification of canonical neural events during continuous gameplay of an 8-bit style video game. NeuroImage.  https://doi.org/10.1016/j.neuroimage.2016.02.075
  21. Cavanagh, J. F., Cohen, M. X., & Allen, J. J. B. (2009). Prelude to and resolution of an error: EEG phase synchrony reveals cognitive control dynamics during action monitoring. The Journal of Neuroscience : The Official Journal of the Society for Neuroscience, 29(1), 98–105.  https://doi.org/10.1523/JNEUROSCI.4137-08.2009 Google Scholar
  22. Cavanagh, J. F., & Frank, M. J. (2014). Frontal theta as a mechanism for cognitive control. Trends in Cognitive Sciences, 18(8), 414–421.  https://doi.org/10.1016/j.tics.2014.04.012 Google Scholar
  23. Chiew, K. S., & Braver, T. S. (2013). Temporal dynamics of motivation-cognitive control interactions revealed by high-resolution pupillometry. Frontiers in Psychology, 4(January), 15.  https://doi.org/10.3389/fpsyg.2013.00015 Google Scholar
  24. Christophel, T. B., Klink, P. C., Spitzer, B., Roelfsema, P. R., & Haynes, J.-D. (2017). The Distributed Nature of Working Memory. Trends in Cognitive Sciences, xx, 1–14.  https://doi.org/10.1016/j.tics.2016.12.007
  25. Cohen, J. D., Barch, D. M., Carter, C., & Servan-Schreiber, D. (1999). Context-processing deficits in schizophrenia: converging evidence from three theoretically motivated cognitive tasks. Journal of Abnormal Psychology, 108(1), 120–133.  https://doi.org/10.1037/0021-843X.108.1.120 Google Scholar
  26. Cohen, J. D., Peristein, W. M., Braver, T. S., Nystrom, L. E., Noll, D. C., Jonides, J., & Smith, E. E. (1997). Temporal dynamics of brain activation during a working memory task. Nature Publishing Group.Google Scholar
  27. Cohen, M. X. (2014). Analyzing Neural Time Series Data: Theory and Practice. MIT Press. Retrieved from https://books.google.com/books?id=jTSkAgAAQBAJ
  28. Collins, A. G. E., Cavanagh, J. F., & Frank, M. J. (2014). Human EEG Uncovers Latent Generalizable Rule Structure during Learning. Journal of Neuroscience, 34(13), 4677–4685.  https://doi.org/10.1523/JNEUROSCI.3900-13.2014 Google Scholar
  29. Cooper, P. S., Darriba, Á., Karayanidis, F., & Barceló, F. (2016). Contextually sensitive power changes across multiple frequency bands underpin cognitive control. NeuroImage, 132, 499–511.  https://doi.org/10.1016/j.neuroimage.2016.03.010 Google Scholar
  30. Cooper, P. S., Wong, A. S. W., Fulham, W. R., Thienel, R., Mansfield, E., Michie, P. T., & Karayanidis, F. (2015). Theta frontoparietal connectivity associated with proactive and reactive cognitive control processes. NeuroImage, 108(September 2015), 354–363.  https://doi.org/10.1016/j.neuroimage.2014.12.028 Google Scholar
  31. Coull, J.T. (2009). Neural substrates of mounting temporal expectation. PLOS Biology 7(8): e1000166. doi: https://doi.org/10.1371/journal.pbio.1000166.Google Scholar
  32. Cunillera, T., Fuentemilla, L., Periañez, J., Marco-Pallarès, J., Krämer, U. M., Càmara, E., … Rodríguez-Fornells, A. (2012). Brain oscillatory activity associated with task switching and feedback processing. Cognitive, Affective, & Behavioral Neuroscience, 12(1), 16–33.  https://doi.org/10.3758/s13415-011-0075-5 Google Scholar
  33. Delorme, A., & Makeig, S. (2004). EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134(1), 9–21.  https://doi.org/10.1016/j.jneumeth.2003.10.009 Google Scholar
  34. 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.  https://doi.org/10.1016/j.neuroimage.2015.11.036 Google Scholar
  35. Edwards, B. G., Barch, D. M., & Braver, T. S. (2010). Improving prefrontal cortex function in schizophrenia through focused training of cognitive control. Frontiers in Human Neuroscience, 4(April), 32.  https://doi.org/10.3389/fnhum.2010.00032 Google Scholar
  36. Eriksson, J., Vogel, E. K., Lansner, A., Bergström, F., & Nyberg, L. (2015). Neurocognitive Architecture of Working Memory. Neuron, 88(1), 33–46.  https://doi.org/10.1016/j.neuron.2015.09.020 Google Scholar
  37. Folstein, J. R., & Van Petten, C. (2008). Influence of cognitive control and mismatch on the N2 component of the ERP: A review. Psychophysiology, 45(1), 152–170.  https://doi.org/10.1111/j.1469-8986.2007.00602.x
  38. Freunberger, R., Werkle-Bergner, M., Griesmayr, B., Lindenberger, U., & Klimesch, W. (2011). Brain oscillatory correlates of working memory constraints. Brain Research, 1375, 93–102.  https://doi.org/10.1016/j.brainres.2010.12.048 Google Scholar
  39. Frohlich, F., Bazhenov, M., Timofeev, I., Steriade, M., & Sejnowski, T. J. (2006). Slow State Transitions of Sustained Neural Oscillations by Activity-Dependent Modulation of Intrinsic Excitability. Journal of Neuroscience, 26(23), 6153–6162.  https://doi.org/10.1523/JNEUROSCI.5509-05.2006 Google Scholar
  40. Gajewski, P. D., & Falkenstein, M. (2011). Diversity of the P3 in the task-switching paradigm. Brain Research, 1411, 87–97.  https://doi.org/10.1016/j.brainres.2011.07.010 Google Scholar
  41. Gajewski, P. D., Kleinsorge, T., & Falkenstein, M. (2010). Electrophysiological correlates of residual switch costs. Cortex, 46(9), 1138–1148.  https://doi.org/10.1016/j.cortex.2009.07.014 Google Scholar
  42. Gulbinaite, R., van Rijn, H., Cohen, M.X. (2014). Fronto-parietal network oscillations reveal relationship between working memory capacity and cognitive control. Frontiers in Human Neuroscience, 8(761), 1-13.  https://doi.org/10.3389/fnhum.2014.00761 Google Scholar
  43. Harper, J., Malone, S. M., Bachman, M. D., & Bernat, E. M. (2016). Stimulus sequence context differentially modulates inhibition-related theta and delta band activity in a go/no-go task. Psychophysiology, 53,  https://doi.org/10.1111/psyp.12604
  44. Harper, J., Malone, S. M., & Bernat, E. M. (2014). Theta and delta band activity explain N2 and P3 ERP component activity in a go/no-go task. Clinical Neurophysiology, 125(1), 124–132.  https://doi.org/10.1007/978-1-62703-673-3 Google Scholar
  45. Henderson, D., Poppe, A. B., Barch, D. M., Carter, C. S., Gold, J. M., Ragland, J. D., … MacDonald, A. W. (2012). Optimization of a goal maintenance task for use in clinical applications. Schizophrenia Bulletin, 38(1), 104–13.  https://doi.org/10.1093/schbul/sbr172 Google Scholar
  46. Herbst, S.K., Fielder, L., & Obleser, J. (2018). Tracking temporal hazard in the human electroencephalogram using a forward encoding model. eNeuro 5(2), ENEURO.0017-18.2018. 10.1523/ENEURO.0017-18.2018Google Scholar
  47. Herbst, S.K. & Obleser, J. (2017). Implicit variations of temporal predictability: Shaping the neural oscillatory and behavioral response. Neuropsychologia 101, 141-152.  https://doi.org/10.1016/j.neuropsychologia.2017.05.019 Google Scholar
  48. Hutzler, F. (2014). Reverse inference is not a fallacy per se: Cognitive processes can be inferred from functional imaging data. NeuroImage, 84, 1061–1069.  https://doi.org/10.1016/j.neuroimage.2012.12.075 Google Scholar
  49. Jacoby, L. L. (1999). Ironic effects of repetition: Measuring age-related differences in memory. Journal of Experimental Psychology: Learning, Memory, and Cognition, 25(1), 3–22.Google Scholar
  50. Jamadar, S., Hughes, M., Fulham, W. R., Michie, P. T., & Karayanidis, F. (2010). The spatial and temporal dynamics of anticipatory preparation and response inhibition in task-switching. NeuroImage, 51(1), 432–449.  https://doi.org/10.1016/j.neuroimage.2010.01.090 Google Scholar
  51. Janowich, J. R., & Cavanagh, J. F. (2018). Delay knowledge and trial set count modulate use of proactive versus reactive control : A meta-analytic review. Psychonomic Bulletin and Review.  https://doi.org/10.3758/s13423-018-1502-1
  52. Jensen, O., & Lisman, J. E. (2005). Hippocampal sequence-encoding driven by a cortical multi-item working memory buffer. Trends in Neurosciences, 28(2), 67–72.  https://doi.org/10.1016/j.tins.2004.12.001 Google Scholar
  53. Jensen, O., & Tesche, C. D. (2002). Frontal theta activity in humans increases with memory load in a working memory task. Neuroscience, 15(8), 1395–1399.  https://doi.org/10.1046/j.1460-9568.2002.01975.x Google Scholar
  54. de Jong, R. (2000). An intention–activation account of residual switch costs. In Control of Cognitive Processes: Attention and Performance XVIII (pp. 357–376).Google Scholar
  55. Karayanidis, F., Jamadar, S., Ruge, H., Phillips, N., Heathcote, A., & Forstmann, B. U. (2010). Advance preparation in task-switching: converging evidence from behavioral, brain activation, and model-based approaches. Frontiers in Psychology, 1(July), 25.  https://doi.org/10.3389/fpsyg.2010.00025 Google Scholar
  56. Karayanidis, F., Mansfield, E. L., Galloway, K. L., Smith, J. L., Provost, A., & Heathcote, A. (2009). Anticipatory reconfiguration elicited by fully and partially informative cues that validly predict a switch in task. Cognitive, Affective & Behavioral Neuroscience, 9(2), 202–15.  https://doi.org/10.3758/CABN.9.2.202 Google Scholar
  57. Karayanidis, F., Provost, A., Brown, S., Paton, B., & Heathcote, A. (2011). Switch-specific and general preparation map onto different ERP components in a task-switching paradigm. Psychophysiology, 48(4), 559–568.  https://doi.org/10.1111/j.1469-8986.2010.01115.x Google Scholar
  58. Kayser, J., & Tenke, C. E. (2006). Principal components analysis of Laplacian waveforms as a generic method for identifying ERP generator patterns: II. Adequacy of low-density estimates. Clinical Neurophysiology, 117(2), 369–380.  https://doi.org/10.1016/j.clinph.2005.08.033 Google Scholar
  59. Kessler, Y., Baruchin, L. J., & Bouhsira-Sabag, A. (2015). Working memory updating occurs independently of the need to maintain task-context: accounting for triggering updating in the AX-CPT paradigm. Psychological Research.  https://doi.org/10.1007/s00426-015-0717-2
  60. Kieffaber, P. D., & Hetrick, W. P. (2005). Event-related potential correlates of task switching and switch costs. Psychophysiology, 42(1), 56–71.  https://doi.org/10.1111/j.1469-8986.2005.00262.x Google Scholar
  61. Kikumoto, A., & Mayr, U. (2017). The Nature of Task Set Representations in Working Memory. Journal of Cognitive Neuroscience.  https://doi.org/10.1162/jocn
  62. Kleiner, M., Brainard, D. H., & Pelli, D. (2007). What’s new in Psychtoolbox-3? Retrieved January 2, 2016, from http://www.perceptionweb.com/abstract.cgi?id=v070821
  63. Lamm, C., Pine, D. S., & Fox, N. A. (2013). Impact of negative affectively charged stimuli and response style on cognitive-control-related neural activation: an ERP study. Brain and Cognition, 83(2), 234–43.  https://doi.org/10.1016/j.bandc.2013.07.012 Google Scholar
  64. Lavric, A., Mizon, G. a., & Monsell, S. (2008). Neurophysiological signature of effective anticipatory task-set control: A task-switching investigation. European Journal of Neuroscience, 28(5), 1016–1029.  https://doi.org/10.1111/j.1460-9568.2008.06372.x Google Scholar
  65. Lee, I. A., & Preacher, K. J. (2013, September). Calculation for the test of the difference between two dependent correlations with one variable in common [Computer software]. Available from http://quantpsy.org.
  66. Lenartowicz, A., Escobedo-Quiroz, R., & Cohen, J. D. (2010). Updating of context in working memory: an event-related potential study. Cognitive, Affective & Behavioral Neuroscience, 10(2), 298–315.  https://doi.org/10.3758/CABN.10.2.298 Google Scholar
  67. Lesh, T. A., Westphal, A. J., Niendam, T. A., Yoon, J. H., Minzenberg, M. J., Ragland, J. D., … Carter, C. S. (2013). Proactive and reactive cognitive control and dorsolateral prefrontal cortex dysfunction in first episode schizophrenia. NeuroImage. Clinical, 2, 590–9.  https://doi.org/10.1016/j.nicl.2013.04.010 Google Scholar
  68. Li, L., Wang, M., Zhao, Q.-J., & Fogelson, N. (2012). Neural mechanisms underlying the cost of task switching: an ERP study. PloS One, 7(7), e42233.  https://doi.org/10.1371/journal.pone.0042233 Google Scholar
  69. Lopez-Garcia, P., Lesh, T. A., Salo, T., Barch, D. M., MacDonald, A. W., Gold, J. M., … Carter, C. S. (2015). The neural circuitry supporting goal maintenance during cognitive control: a comparison of expectancy AX-CPT and dot probe expectancy paradigms. Cognitive, Affective, & Behavioral Neuroscience, (December). 10.3758/s13415-015-0384-1Google Scholar
  70. Lucenet, J., & Blaye, A. (2014). Age-related changes in the temporal dynamics of executive control: a study in 5- and 6-year-old children. Frontiers in Psychology, 5(July), 1–11.  https://doi.org/10.3389/fpsyg.2014.00831 Google Scholar
  71. MacDonald, A. W., Goghari, V. M., Hicks, B. M., Flory, J. D., Carter, C. S., & Manuck, S. B. (2005). A convergent-divergent approach to context processing, general intellectual functioning, and the genetic liability to schizophrenia. Neuropsychology, 19(6), 814–821.  https://doi.org/10.1037/0894-4105.19.6.814 Google Scholar
  72. Manzi, A., Nessler, D., Czernochowski, D., & Friedman, D. (2011). The Development of anticipatory cognitive control processes in Task-Switching: An ERP Study in Children, Adolescents and Young Adults. Psychophysiology, 48(9), 1258–1275.  https://doi.org/10.1111/j.1469-8986.2011.01192.x.The Google Scholar
  73. Medalla, M., & Barbas, H. (2009). Synapses with inhibitory neurons differentiate anterior cingulate from dorsolateral prefrontal pathways associated with cognitive control. Neuron, 61(4), 609–20.  https://doi.org/10.1016/j.neuron.2009.01.006 Google Scholar
  74. Meng, X.-L., Rosenthal, R., & Rubin, D. B. (1992). Comparing correlated correlation coefficients. Psychological Bulletin, 111(1), 172–175.  https://doi.org/10.1037/0033-2909.111.1.172 Google Scholar
  75. Miller, E. K., & Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. Annual Review of Neuroscience, 24, 167–202.Google Scholar
  76. Morales, J., Yudes, C., Gómez-Ariza, C. J., & Bajo, M. T. (2014). Bilingualism modulates dual mechanisms of cognitive control: Evidence from ERPs. Neuropsychologia, 66, 157–169.  https://doi.org/10.1016/j.neuropsychologia.2014.11.014 Google Scholar
  77. Morillon, X. B., Schroeder, C. E., Wyart, X. V., & Arnal, X. L. H. (2016). Temporal Prediction in lieu of Periodic Stimulation, 36(8), 2342–2347.  https://doi.org/10.1523/JNEUROSCI.0836-15.2016 Google Scholar
  78. Nicholson, R., Karayanidis, F., Davies, A., & Michie, P. T. (2006). Components of task-set reconfiguration: Differential effects of “switch-to” and “switch-away” cues. Brain Research, 1121(1), 160–176.  https://doi.org/10.1016/j.brainres.2006.08.101 Google Scholar
  79. Nicholson, R., Karayanidis, F., Poboka, D., Heathcote, A., & Michie, P. T. (2005). Electrophysiological correlates of anticipatory task-switching processes. Psychophysiology, 42(5), 540–54.  https://doi.org/10.1111/j.1469-8986.2005.00350.x Google Scholar
  80. Nobre, A.C., Correa, A., & Coull, J.T. (2007). The hazards of time. Current Opinion in Neurobiology, 17(4), 465-470.  https://doi.org/10.1016/j.conb.2007.07.006.Google Scholar
  81. Nolan, H., Whelan, R., & Reilly, R. B. (2010). FASTER: Fully Automated Statistical Thresholding for EEG artifact Rejection. Journal of Neuroscience Methods, 192(1), 152–162.  https://doi.org/10.1016/j.jneumeth.2010.07.015 Google Scholar
  82. Otto, A. R., Skatova, A., Madlon-kay, S., & Daw, N. D. (2015). Cognitive Control Predicts Use of Model-based Reinforcement Learning. Journal of Cognitive Neuroscience, 27(2), 319–333.  https://doi.org/10.1162/jocn Google Scholar
  83. Pelli, D. G. (1997). The VideoToolbox software for visual psychophysics: transforming numbers into movies. Spatial Vision.  https://doi.org/10.1163/156856897X00366
  84. Perrin, F., Pernier, J., Bertrand, O., & Echallier, J. F. (1989). Spherical splines for scalp potential and current density mapping. Electroencephalography and Clinical Neurophysiology, 72(2), 184–187.  https://doi.org/10.1016/0013-4694(89)90180-6 Google Scholar
  85. Polanía, R., Paulus, W., & Nitsche, M. A. (2011). Noninvasively Decoding the Contents of Visual Working Memory in the Human Prefrontal Cortex within High-gamma Oscillatory Patterns. Journal of Cognitive Neuroscience, 24(2), 304–314.  https://doi.org/10.1162/jocn Google Scholar
  86. Poldrack, R. A. (2006). Can cognitive processes be inferred from neuroimaging data? Trends in Cognitive Sciences, 10(2), 59–63.  https://doi.org/10.1016/j.tics.2005.12.004 Google Scholar
  87. Polich, J. (2007). Updating P300: An Integrative Theory of P3a and P3b. Clinical Neurophysiology, 118(10), 2128–2148.  https://doi.org/10.1016/j.clinph.2007.04.019.Updating Google Scholar
  88. Poljac, E., & Yeung, N. (2014). Dissociable neural correlates of intention and action preparation in voluntary task switching. Cerebral Cortex, 24(February), 465–478.  https://doi.org/10.1093/cercor/bhs326 Google Scholar
  89. Redick, T. S. (2014). Cognitive control in context: Working memory capacity and proactive control. Acta Psychologica, 145(1), 1–9.  https://doi.org/10.1016/j.actpsy.2013.10.010 Google Scholar
  90. Rushworth, M. F. S., Passingham, R. E., & Nobre, A. C. (2005). Components of Attentional Set-switching. Experimental Psychology (Formerly “Zeitschrift Für Experimentelle Psychologie”), 52(2), 83–98.  https://doi.org/10.1027/1618-3169.52.2.83 Google Scholar
  91. Schmitt, H., Ferdinand, N. K., & Kray, J. (2014). Age-differential effects on updating cue information: Evidence from event-related potentials. Cognitive, Affective & Behavioral Neuroscience, 1115–1131. 10.3758/s13415-014-0268-9Google Scholar
  92. Schmitt, H., Ferdinand, N. K., & Kray, J. (2015). The influence of monetary incentives on context processing in younger and older adults: an event-related potential study. Cognitive, Affective, & Behavioral Neuroscience, 15(2), 416–434.  https://doi.org/10.3758/s13415-015-0335-x Google Scholar
  93. Servan-Schreiber, D., Cohen, J. D., & Steingard, S. (1996). Schizophrenic deficits in the processing of context: A test of a theoretical model. Archives of General Psychiatry, 53(12), 1105–1112.  https://doi.org/10.1001/archpsyc.1996.01830120037008
  94. Shenhav, A., Botvinick, M., & Cohen, J. (2013). The expected value of control: An integrative theory of anterior cingulate cortex function. Neuron, 79(2), 217–240.  https://doi.org/10.1016/j.neuron.2013.07.007 Google Scholar
  95. Spaak, E., Watanabe, K., Funahashi, S., & Stokes, M. (2017). Stable and dynamic coding for working memory in primate prefrontal cortex longer time scales stable and dynamic epochs activity returns to baseline. Journal of Neuroscience, 37(27), 6503–6516.  https://doi.org/10.1523/JNEUROSCI.3364-16.2017 Google Scholar
  96. Stanley, D. A., Roy, J. E., Aoi, M. C., Kopell, N. J., & Miller, E. K. (2018). Low-Beta Oscillations Turn Up the Gain During Category Judgments. Cerebral Cortex, 28(1), 116–130.  https://doi.org/10.1093/cercor/bhw356 Google Scholar
  97. Steiger, J. H. (1980). Tests for comparing elements of a correlation matrix. Psychological Bulletin.  https://doi.org/10.1037/0033-2909.87.2.245
  98. Stokes, M. G., Kusunoki, M., Sigala, N., Nili, H., Gaffan, D., & Duncan, J. (2013). Dynamic coding for cognitive control in prefrontal cortex. Neuron, 78(2), 364–75.  https://doi.org/10.1016/j.neuron.2013.01.039 Google Scholar
  99. Unsworth, N., Fukada, K., Awh, E., & Vogel, E. K. (2015). Working memory delay activity predicts individual differences in cognitive abilities. Journal of Cognitive Neuroscience, 27(5), 853–865.  https://doi.org/10.1162/jocn Google Scholar
  100. Vogel, E. K., & Machizawa, M. G. (2004). Neural activity predicts individual differences in visual working memory capacity. Nature, 428(April), 748–751.  https://doi.org/10.1038/nature02447 Google Scholar
  101. Wang, X.-J. (2010). Neurophysiological and Computational Principles of Cortical Rhythms in Cognition. Physiological Reviews, 90(3), 1195–1268.  https://doi.org/10.1152/physrev.00035.2008 Google Scholar
  102. Wasmuht, D., Spaak, E., Buschman, T., Miller, E., & Stokes, M. (2017). Intrinsic neuronal dynamics predict distinct functional roles during working memory.Google Scholar
  103. Yarkoni, T., Poldrack, R. a., Nichols, T. E., Van Essen, D. C., & Wager, T. D. (2011). Large-scale automated synthesis of human functional neuroimaging data. Nature Methods, 8(8), 665–70.  https://doi.org/10.1038/nmeth.1635 Google Scholar

Copyright information

© The Psychonomic Society, Inc. 2019

Authors and Affiliations

  1. 1.Department of PsychologyUniversity of New MexicoAlbuquerqueUSA

Personalised recommendations