Dynamic Cognitive States Explain Individual Variability in Behavior and Modulate with EEG Functional Connectivity During Working Memory

Computational Brain & Behavior - 2022
Christine Beauchene1, Thomas Hinault2, Sridevi V. Sarma1, Susan M. Courtney3,4
1Institute for Computational Medicine, Biomedical Engineering Department, Johns Hopkins University, Baltimore, USA
2U1077 INSERM-EPHE-UNICAEN, Caen, France
3Psychological and Brain Sciences Department, Johns Hopkins University, Baltimore, USA
4Neuroscience Department, Johns Hopkins University, Baltimore, USA

Tóm tắt

Fluctuations in strategy, attention, or motivation can cause large variability in performance across task trials. Typically, this variability is treated as noise, and assumed to cancel out, leaving supposedly stable relationships among behavior, neural activity, and experimental task conditions. Those relationships, however, could change with a participant’s internal cognitive states, and variability in performance may carry important information regarding those states, which cannot be directly measured. Therefore, we used a mathematical, state-space modeling framework to fit internal cognitive states to measured behavioral data, quantifying each participant’s sensitivity to factors such as past errors or distractions, to characterize their underlying fluctuations in reaction time. We show how integrating the states into the modeling framework could help explain trial-by-trial variability in behavior. Further, we identify EEG functional connectivity features that modulate with each state. These results illustrate the potential of this approach and how it could enable quantification of intra- and inter-individual differences and provide insight into their neural bases.

Từ khóa


Tài liệu tham khảo

Anderson, B. A. (2013). A value-driven mechanism of attentional selection. Journal of Vision, 13(3), 7–7.

Anderson, B. A. (2016). The attention habit: How reward learning shapes attentional selection. Annals of the New York Academy of Sciences, 1369(1), 24–39.

Baddeley, A. (2010). Working memory. Current Biology, 20(4), R136–R140.

Baddeley, A. D. & Hitch, G. (1974). Working memory. Psychology of Learning and Motivation (Vol. 8, pp. 47–89). Elsevier.

Bell, A. J., & Sejnowski, T. J. (1995). An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 7(6), 1129–1159.

Breault, M. S., González-Martínez, J. A., Gale, J. T. & Sarma, S. V. (2019). Neural correlates of internal states that capture movement variability. 2019 41st Annual International Conference of the IEEE Engineering In Medicine And Biology Society (EMBC) (pp. 534–537).

Castellanos, F. X., Sonuga-Barke, E. J., Milham, M. P., & Tannock, R. (2006). Executive function: is there a central executive? Trends in Cognitive Sciences, 3(10), 117–123.

Chang, S., Cunningham, C. A., & Egeth, H. E. (2019). The power of negative thinking: Paradoxical but effective ignoring of salient-but-irrelevant stimuli with a spatial cue. Visual Cognition, 27(3–4), 199–213.

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.

D’Esposito, M., & Postle, B. R. (2015). The cognitive neuroscience of working memory. Annual Review of Psychology, 66, 115–142.

Dinstein, I., Heeger, D. J., & Behrmann, M. (2015). Neural variability: friend or foe? Trends in Cognitive Sciences, 19(6), 322–328.

ElShafei, H. A., Fornoni, L., Masson, R., Bertrand, O., & Bidet-Caulet, A. (2020). What’s in your gamma? activation of the ventral fronto-parietal attentional network in response to distracting sounds. Cerebral Cortex, 30(2), 696–707.

Forster, S., & Lavie, N. (2016). Establishing the attention-distractibility trait. Psychological Science, 27(2), 203–212.

Goris, R. L., Movshon, J. A., & Simoncelli, E. P. (2014). Partitioning neuronal variability. Nature Neuroscience, 17(6), 858–865.

Hahn, T., Heinzel, S., Dresler, T., Plichta, M. M., Renner, T. J., Markulin, F., & Fallgatter, A. J. (2011). Association between reward-related activation in the ventral striatum and trait reward sensitivity is moderated by dopamine transporter genotype. Human Brain Mapping, 32(10), 1557–1565.

Harrell, F. E., Jr., Lee, K. L., Califf, R. M., Pryor, D. B., & Rosati, R. A. (1984). Regression modelling strategies for improved prognostic prediction. Statistics in Medicine, 3(2), 143–152.

Harrell, F. E., Jr., Lee, K. L., & Mark, D. B. (1996). Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Statistics in Medicine, 15(4), 361–387.

Hedge, C., Powell, G., & Sumner, P. (2018). The reliability paradox: Why robust cognitive tasks do not produce reliable individual differences. Behavior Research Methods, 50(3), 1166–1186.

Hinault, T., Blacker, K., Gormley, M., Anderson, B., & Courtney, S. (2019). Value-driven attentional capture is modulated by the contents of working memory: An EEG study. Cognitive, Affective, & Behavioral Neuroscience, 19(2), 253–267.

Hinault, T., Larcher, K., Zazubovits, N., Gotman, J., & Dagher, A. (2019). Spatio-temporal patterns of cognitive control revealed with simultaneous electroencephalography and functional magnetic resonance imaging. Human Brain Mapping, 40(1), 80–97.

Jones, K. T., Johnson, E. L., & Berryhill, M. E. (2020). Frontoparietal theta-gamma interactions track working memory enhancement with training and TDCS. Neuroimage, 211, 116615.

Jones, K. T., Peterson, D. J., Blacker, K. J., & Berryhill, M. E. (2017). Frontoparietal neurostimulation modulates working memory training benefits and oscillatory synchronization. Brain Research, 1667, 28–40.

Lachaux, J. P., Rodriguez, E., Martinerie, J., Varela, F. J., et al. (1999). Measuring phase synchrony in brain signals. Human Brain Mapping, 8(4), 194–208.

Makeig, S., Bell, A. J., Jung, T. P., Sejnowski, T. J. et al. (1996). Independent component analysis of electroencephalographic data. Advances in Neural Information Processing Systems, 145–151.

Montojo, C. A., & Courtney, S. M. (2008). Differential neural activation for updating rule versus stimulus information in working memory. Neuron, 59(1), 173–182.

Peduzzi, P., Concato, J., Kemper, E., Holford, T. R., & Feinstein, A. R. (1996). A simulation study of the number of events per variable in logistic regression analysis. Journal of Clinical Epidemiology, 49(12), 1373–1379.

Sacré, P., Kerr, M. S., Kahn, K., Gonzalez-Martinez, J., Bulacio, J., Park, H. J., et al. (2016). Lucky rhythms in orbitofrontal cortex bias gambling decisions in humans. Scientific Reports, 6(1), 1–10.

Sacré, P., Kerr, M. S., Subramanian, S., Fitzgerald, Z., Kahn, K., Johnson, M. A., et al. (2019). Risk-taking bias in human decision-making is encoded via a right-left brain push-pull system. Proceedings of the National Academy of Sciences, 116(4), 1404–1413.

Sacré, P., Subramanian, S., Kerr, M. S., Kahn, K., Johnson, M. A., Bulacio, J., & Gale, J. T. (2017). The influences and neural correlates of past and present during gambling in humans. Scientific Reports, 7(1), 1–9.

Slobodin, O., Cassuto, H., & Berger, I. (2018). Age-related changes in distractibility: Developmental trajectory of sustained attention in ADHD. Journal of Attention Disorders, 22(14), 1333–1343.

Stepan, M. E., Fenn, K. M., & Altmann, E. M. (2019). Effects of sleep deprivation on procedural errors. Journal of Experimental Psychology: General, 148(10), 1828.

Stoll, F. M., Fontanier, V., & Procyk, E. (2016). Specific frontal neural dynamics contribute to decisions to check. Nature Communications, 7(1), 1–14.

Uddin, L. Q. (2020). Bring the noise: Reconceptualizing spontaneous neural activity. Trends in Cognitive Sciences, 24(9), 734–746.

VanRullen, R., Busch, N. A., Drewes, J., & Dubois, J. (2011). Ongoing EEG phase as a trial-by-trial predictor of perceptual and attentional variability. Frontiers in Psychology, 2, 60.

Voloh, B., Valiante, T. A., Everling, S., & Womelsdorf, T. (2015). Theta-gamma coordination between anterior cingulate and prefrontal cortex indexes correct attention shifts. Proceedings of the National Academy of Sciences, 112(27), 8457–8462.

Wu, S., Hitchman, G., Tan, J., Zhao, Y., Tang, D., Wang, L., & Chen, A. (2015). The neural dynamic mechanisms of asymmetric switch costs in a combined stroop-task-switching paradigm. Scientific Reports, 5(1), 1–11.

Xu, K. Z., Anderson, B. A., Emeric, E. E., Sali, A. W., Stuphorn, V., Yantis, S., & Courtney, S. M. (2017). Neural basis of cognitive control over movement inhibition: Human FMRI and primate electrophysiology evidence. Neuron, 96(6), 1447–1458.

Thông tin
Thông tin xuất bản

Computational Brain & Behavior

Thông tin tác giả