Academia.eduAcademia.edu

Outline

Age Differences in Learning-Related Neurophysiological Changes

2023, Journal of Psychophysiology

https://doi.org/10.1027/0269-8803/A000317

Abstract

Research in young adults has demonstrated that neurophysiological measures are able to provide insight into learning processes. However, to date, it remains unclear whether neurophysiological changes during learning in older adults are comparable to those in younger adults. The current study addressed this issue by exploring age differences in changes over time in a range of neurophysiological outcome measures collected during visuomotor sequence learning. Specifically, measures of electroencephalography (EEG), skin conductance, heart rate, heart rate variability, respiration rate, and eye-related measures, in addition to behavioral performance measures, were collected in younger (M age = 27.24 years) and older adults (M age = 58.06 years) during learning. Behavioral responses became more accurate over time in both age groups during visuomotor sequence learning. Yet, older adults needed more time in each trial to enhance the precision of their movement. Changes in EEG during learning demonstrated a stronger increase in theta power in older compared to younger adults and a decrease in gamma power in older adults while increasing slightly in younger adults. No such differences between the two age groups were found on other neurophysiological outcome measures, suggesting changes in brain activity during learning to be more sensitive to age differences than changes in peripheral physiology. Additionally, differences in which neurophysiological outcomes were associated with behavioral performance on the learning task were found between younger and older adults. This indicates that the neurophysiological underpinnings of learning may differ between younger and older adults. Therefore, the current findings highlight the importance of taking age into account when aiming to gain insight into behavioral performance through neurophysiology during learning.

References (50)

  1. Alain, C., & Snyder, J. S. (2008). Age-related differences in auditory evoked responses during rapid perceptual learning. Clinical Neurophysiology, 119(2), 356-366. https://doi.org/ 10.1016/j.clinph.2007.10.024
  2. Althouse, A. D. (2016). Adjust for multiple comparisons? It's not that simple. The Annals of Thoracic Surgery, 101(5), 1644-1645. https://doi.org/10.1016/j.athoracsur.2015.11.024
  3. Antonenko, P., Paas, F., Grabner, R., & Van Gog, T. (2010). Using electroencephalography to measure cognitive load. Educational Psychology Review, 22(4), 425-438. https://doi.org/10.1007/ s10648-010-9130-y Arnal, L. H., & Kleinschmidt, A. K. (2017). Entrained delta oscil- lations reflect the subjective tracking of time. Communicative & Integrative Biology, 10(5-6), Article e1349583. https://doi.org/ 10.1080/19420889.2017.1349583
  4. Berntson, G. G., Cacioppo, J. & Quigley, K. S. (1991). Autonomic determinism: The modes of autonomic control, the doctrine of autonomic space, and the laws of autonomic constraint. Psychological Review, 98(4), Article 459. https://doi.org/ 10.1037/0033-295x.98.4.459
  5. Borghini, G., Astolfi, L., Vecchiato, G., Mattia, D., & Babiloni, F. (2014). Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness. Neuroscience & Biobehavioral Reviews, 44, 58-75. https://doi.org/10.1016/j.neubiorev.2012.10.003
  6. Brouwer, A. M., Hogervorst, M. A., van Erp, J. B., Heffelaar, T., Zimmerman, P. H., & Oostenveld, R. (2012). Estimating work- load using EEG spectral power and ERPs in the n-back task. Journal of Neural Engineering, 9(4), Article 045008. https://doi. org/10.1088/1741-2560/9/4/045008
  7. Charles, R. L., & Nixon, J. (2019). Measuring mental workload using physiological measures: A systematic review. Applied Ergonomics, 74, 221-232. https://doi.org/10.1016/j.apergo. 2018.08.028
  8. Curran, T., & Keele, S. W. (1993). Attentional and nonattentional forms of sequence learning. Journal of Experimental Psychol- ogy: Learning, Memory, and Cognition, 19(1), 189-202. https:// doi.org/10.1037/0278-7393.19.1.189
  9. Eppinger, B., & Kray, J. (2011). To choose or to avoid: Age differences in learning from positive and negative feedback. Journal of Cognitive Neuroscience, 23(1), 41-52. https://doi. org/10.1162/jocn.2009.21364
  10. Eppinger, B., Kray, J., Mock, B., & Mecklinger, A. (2008). Better or worse than expected? Aging, learning, and the ERN. Neuropsychologia, 46 (2), 521-539. https://doi.org/10.1016/j.neuropsychologia. 2007. 09.001
  11. Fairclough, S. H., & Roberts, J. S. (2011). Effects of performance feedback on cardiovascular reactivity and frontal EEG asym- metry. International Journal of Psychophysiology, 81(3), 291- 298. https://doi.org/10.1016/j.ijpsycho.2011.07.012
  12. Fairclough, S. H., Venables, L., & Tattersall, A. (2005). The influence of task demand and learning on the psychophysio- logical response. International Journal of Psychophysiology, 56(2), 171-184. https://doi.org/10.1016/j.ijpsycho.2004.11.003
  13. Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Meth- ods, 39(2), 175-191. https://doi.org/10.3758/bf03193146
  14. Figner, B., Algermissen, J., Burghoorn, F., Held, L., Khalid, A., Klaassen, F., Mosannenzadeh, F., & Quandt, J. (2022, Novem- ber 25). Standard operating procedures for using mixed-effects models. Decision development and psychopathology (D2P2) lab. https://decision-lab.org/resources
  15. Fitzroy, A. B., Kainec, K. A., Seo, J., & Spencer, R. M. (2021). Encoding and consolidation of motor sequence learning in young and older adults. Neurobiology of Learning and Memory, 185, Article 107508. https://doi.org/10.1016/j.nlm.2021.107508
  16. Gevins, A., & Smith, M. E. (2003). Neurophysiological measures of cognitive workload during human-computer interaction. Theo- retical Issues in Ergonomics Science, 4(1-2), 113-131. https:// doi.org/10.1080/14639220210159717
  17. Grady, C. L., & Craik, F. I. (2000). Changes in memory processing with age. Current Opinion in Neurobiology, 10(2), 224-231. https://doi.org/10.1016/s0959-4388(00)00073-8
  18. Hanslmayr, S., Axmacher, N., & Inman, C. S. (2019). Modulating human memory via entrainment of brain oscillations. Trends in Neurosciences, 42(7), 485-499. https://doi.org/10.1016/j. tins.2019.04.004
  19. Hogervorst, M. A., Brouwer, A. M., & Van Erp, J. B. (2014). Combining and comparing EEG, peripheral physiology and eye-related measures for the assessment of mental workload. Frontiers in Neuroscience, 8, Article 322. https://doi.org/ 10.3389/fnins.2014.00322
  20. Jabès, A., Klencklen, G., Ruggeri, P., Antonietti, J. P., Banta Lavenex, P., & Lavenex, P. (2021). Age-related differences in resting-state EEG and allocentric spatial working memory performance. Frontiers in Aging Neuroscience, 13, Article 704362. https://doi.org/10.3389/fnagi.2021.704362
  21. King, B. R., Fogel, S. M., Albouy, G., & Doyon, J. (2013). Neural correlates of the age-related changes in motor sequence learning and motor adaptation in older adults. Frontiers in Human Neuroscience, 7, Article 142. https://doi.org/10.3389/ fnhum.2013.00142
  22. Krigolson, O. E., Cheng, D. R., & Binsted, G. (2015). The role of visual processing in motor learning and control: Insights from electroencephalography. Vision Research, 110, 277-285. https://doi.org/10.1016/j.visres.2014.12.024
  23. Leff, D. R., Orihuela-Espina, F., Elwell, C. E., Athanasiou, T., Delpy, D. T., Darzi, A. W., & Yang, G. Z. (2011). Assessment of the cerebral cortex during motor task behaviours in adults: A systematic review of functional near infrared spectroscopy (fNIRS) studies. NeuroImage, 54(4), 2922-2936. https://doi.org/ 10.1016/j.neuroimage.2010.10.058
  24. Lopez-Loeza, E., Rangel-Argueta, A. R., Lopez-Vazquez, M. A., Cervantes, M., & Olvera-Cortes, M. E. (2016). Differences in EEG power in young and mature healthy adults during an incidental/ spatial learning task are related to age and execution effi- ciency. Age, 38(2), Article 37. https://doi.org/10.1007/s11357- 016-9896-z
  25. Luu, P., Poulsen, C., & Tucker, D. M. (2009). Neurophysiological measures of brain activity: Going from the scalp to the brain. Foundations of augmented cognition. In D. D. Schmorrow, I. V. Estabrooke, & M. Grootjen (Eds.), Foundations of augmented cognition: Neuroergonomics and operational neuroscience. FAC 2009. Lecture notes in computer science (Vol. 5638, pp. 488-494). Springer. https://doi.org/10.1007/978-3-642-02812-0_57
  26. Moisello, C., Crupi, D., Tunik, E., Quartarone, A., Bove, M., Tononi, G., & Ghilardi, M. F. (2009). The serial reaction time task revisited: A study on motor sequence learning with an arm- reaching task. Experimental Brain Research, 194(1), 143-155. https://doi.org/10.1007/s00221-008-1681-5
  27. Neider, M. B., Boot, W. R., & Kramer, A. F. (2010). Visual search for real world targets under conditions of high target-background similarity: Exploring training and transfer in younger and older adults. Acta Psychologica, 134(1), 29-39. https://doi.org/ 10.1016/j.actpsy.2009.12.001
  28. Nissen, M. J., & Bullemer, P. (1987). Attentional requirements of learning: Evidence from performance measures. Cognitive Psychology, 19(1), 1-32. https://doi.org/10.1016/0010-0285 (87)90002-8
  29. Oostenveld, R., Fries, P., Maris, E., & Schoffelen, J. M. (2011). FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Computational Intelligence and Neuroscience, 2011, Article 156869. https:// doi.org/10.1155/2011/156869
  30. Perneger, T. V. (1998). What's wrong with Bonferroni adjustments. British Medical Journal, 316(7139), 1236-1238. https://doi.org/ 10.1136/bmj.316.7139.1236
  31. Pietschmann, M., Endrass, T., & Kathmann, N. (2011). Age-related alterations in performance monitoring during and after learn- ing. Neurobiology of Aging, 32(7), 1320-1330. https://doi.org/ 10.1016/j.neurobiolaging.2009.07.016
  32. Pietschmann, M., Simon, K., Endrass, T., & Kathmann, N. (2008). Changes of performance monitoring with learning in older and younger adults. Psychophysiology, 45(4), 559-568. https://doi. org/10.1111/j.1469-8986.2008.00651.x
  33. R Core Team. (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https:// www.r-project.org/
  34. Reuter-Lorenz, P. (2002). New visions of the aging mind and brain. Trends in Cognitive Sciences, 6(9), 394-400. https://doi.org/ 10.1016/s1364-6613(02)01957-5
  35. Rothman, K. J. (1990). No adjustments are needed for multiple comparisons. Epidemiology, 1(1), 43-46. https://doi.org/ 10.1097/00001648-199001000-00010
  36. Sato, N., & Yamaguchi, Y. (2007). Theta synchronization networks emerge during human object-place memory encoding. Neuroreport, 18(5), 419-424. https://doi.org/10.1097/WNR.0b013e3280586760
  37. Schneider, W., & Chein, J. M. (2003). Controlled & automatic processing: Behavior, theory, and biological mechanisms. Cognitive Science, 27(3), 525-559.
  38. Takeuchi, T., Puntous, T., Tuladhar, A., Yoshimoto, S., & Shirama, A. (2011). Estimation of mental effort in learning visual search by measuring pupil response. PLoS One, 6(7), Article 5. https:// doi.org/10.1371/journal.pone.0021973
  39. Tesche, C. D., & Karhu, J. (2000). Theta oscillations index human hippocampal activation during a working memory task. Pro- ceedings of the National Academy of Sciences, 97(2), 919-924. https://doi.org/10.1073/pnas.97.2.919
  40. Tinga, A. M., de Back, T. T., & Louwerse, M. M. (2019). Non- invasive neurophysiological measures of learning: A meta- analysis. Neuroscience & Biobehavioral Reviews, 99, 59-89. https://doi.org/10.1016/j.neubiorev.2019.02.001
  41. Tinga, A. M., de Back, T. T., & Louwerse, M. M. (2020a). Neuro- physiological changes in visuomotor sequence learning provide insight in general learning processes: Measures of brain activity, skin conductance, heart rate and respiration. Interna- tional Journal of Psychophysiology, 151, 40-48. https://doi.org/ 10.1016/j.ijpsycho.2020.02.015
  42. Tinga, A. M., de Back, T. T., & Louwerse, M. M. (2020b). Non- invasive neurophysiology in learning and training: Mechanisms and a SWOT analysis. Frontiers in Neuroscience, 14, Article 589. https://doi.org/10.3389/fnins.2020.00589
  43. Tinga, A. M., Clim, M. A., de Back, T. T., & Louwerse, M. M. (2021). Measures of prefrontal functional near-infrared spectroscopy in visuomotor learning. Experimental Brain Research, 239(4), 1061-1072. https://doi.org/10.1007/s00221-021-06039-2
  44. Verhaeghen, P. (2016). Age-related slowing in response times, causes and consequences. In N. Pachana (Ed.), Encyclopedia of geropsychology. Springer. https://doi.org/10.1007/978-981- 287-080-3_211-2
  45. Webb, E. J., Campbell, D. T., Schwartz, R. D., & Sechrest, L. (1966). Unobtrusive measures: Nonreactive research in the social sciences. Rand McNally.
  46. Willingham, D. B., Nissen, M. J., & Bullemer, P. (1989). On the development of procedural knowledge. Journal of Experimental Psychology: Learning, Memory and Cognition, 15(6), 1047-1060. https://doi.org/10.1037/0278-7393.15.6.1047
  47. Winn, B., Whitaker, D., Elliott, D. B., & Phillips, N. J. (1994). Factors affecting light-adapted pupil size in normal human-subjects. Investigative Ophthalmology and Visual Science, 35(3), 1132- 1137.
  48. Woods, D. L., Wyma, J. M., Yund, E. W., Herron, T. J., & Reed, B. (2015). Age-related slowing of response selection and produc- tion in a visual choice reaction time task. Frontiers in Human Neuroscience, 9, Article 193. https://doi.org/10.3389/ fnhum.2015.00193
  49. Wrobel, A. (2000). Beta activity: A carrier for visual attention. Acta Neurobiologiae Experimentalis, 60(2), 247-260. History Received September 1, 2021 Revision received December 2, 2022
  50. Accepted December 5, 2022 Published online January 19, 2023