Investigating differential cognitive aging with eye movement

doi:10.3724/SP.J.1042.2024.01408

Abstract

Abstract:

Cognitive decline with aging places significant economic and psychological burden on the elderly, their families, and society. Promoting healthy and successful aging is a critical issue for governments and science. In recent years, an increasing number of studies have highlighted the observation that cognitive changes in the elderly, referred to as cognitive aging, are heterogeneous. This heterogeneity is characterized not only by cognitive decline, but also by the enhancement of top-down processing, which is adaptive or compensatory. Understanding these patterns is crucial for developing strategies to promote successful aging. Our research aims to explore the mechanisms of cognitive enhancement during aging and the patterns of differential aging by examining the enhancement of top-down processing in healthy elderly individuals. By revealing how top-down processing can be enhanced and how the degree of this enhancement varies, our research seeks to provide a theoretical foundation for using cognitive enhancement as a means to promote successful aging.

Our research comprises three studies, each designed to investigate different aspects of cognitive enhancement and aging. Study 1 will use the classic saccade learning paradigm known as the "double-step" paradigm. This paradigm will be employed to separate the manifestations of cognitive enhancement from those of cognitive decline within saccade learning. The goal is to uncover the mechanisms that underpin the enhancement of top-down processing in the elderly. By differentiating between cognitive decline and enhancement, Study 1 will shed light on the specific processes that contribute to maintained or improved cognitive function in the elderly.

Study 2 will focus on the dynamic integration of top-down and bottom-up processing in saccades. This will be achieved by manipulating the influence of top-down processing through rewards and punishments and manipulating the influence of bottom-up processing through the saliency of targets. By examining how top-down processing in saccadic movements can be modulated by value, we will investigate how different processing strategies affect decision-making. Specifically, we will look at the dependency of decision-making on reaction time, providing insights into the general conditions under which cognitive enhancement occurs. This sub-study aims to reveal how elderly individuals adapt their cognitive strategies in response to different incentives and task demands.

Study 3 will use natural scene images as experimental stimuli to investigate how different types of processing and their interactions change with age. This will involve manipulating top-down processing (such as the consistency between the target and the scene) and bottom-up processing (such as the saliency of the target stimuli). By examining the interplay between these types of processing across different age groups, this study will reveal how cognitive enhancement varies with the ecological validity of scenes. It will provide a comprehensive understanding of how aging affects cognitive processing in more complex and realistic settings.

The foundation of our research is based on the observation that elderly individuals perform comparably to younger individuals in most saccadic tasks. By using saccadic movements, which are closely related to cognitive functions, as a technical means, our research aims to investigate the mechanisms underlying differential cognitive aging. In particular, we focus on the mechanisms of cognitive enhancement in aging. Our research will differentiate between enhanced and declined cognitive functions in aging and examine how enhanced cognition varies with different tasks and scenarios. By elucidating the mechanisms of cognitive enhancement and the conditions under which it occurs, our research aims to provide valuable insights for promoting successful aging. Understanding these mechanisms will enable the development of targeted interventions that can leverage cognitive enhancement to mitigate the effects of cognitive decline. This, in turn, can reduce the economic and psychological burden associated with aging, thereby contributing to a healthier and more successful aging processes.

In summary, this study seeks to advance the understanding of cognitive aging by focusing on the enhancement of top-down processing in the elderly. Through three carefully designed studies, we will explore the mechanisms, conditions, and patterns of cognitive enhancement, providing a robust theoretical basis for future research and interventions aimed at promoting successful aging.

Key words: cognitive aging, successful aging, cognitive enhancement, top-down control

CLC Number:

B844

HUANG Jing, LIU Licong, LI Mingyu, LONG Yiming, LI Xiaoli. Investigating differential cognitive aging with eye movement[J]. Advances in Psychological Science, 2024, 32(9): 1408-1415.

Figures/Tables 3

References 44

[1]	付艳, 王大华. (2009). 认知老化与脑:HAROLD模型之争. 心理科学进展, 17(1), 86-91.
[2]	Billino, J., Bremmer, F., & Gegenfurtner, K. R. (2008). Differential aging of motion processing mechanisms: Evidence against general perceptual decline. Vision Research, 48(10), 1254-1261. doi: 10.1016/j.visres.2008.02.014 pmid: 18396307
[3]	Bowling, A. C., Lindsay, P., Smith, B. G., & Storok, K. (2015). Saccadic eye movements as indicators of cognitive function in older adults. Aging, Neuropsychology and Cognition, 22(2), 201-219.
[4]	Braun, D. I., Schütz, A. C., & Gegenfurtner, K. R. (2021). Age effects on saccadic suppression of luminance and color. Journal of Vision, 21(6), 11.
[5]	Bueno, A., Sato, J. R., & Hornberger, M. (2019). Eye tracking - The overlooked method to measure cognition in neurodegeneration? Neuropsychologia, 133, Article 107191.
[6]	Chen, J., Sperandio, I., Henry, M. J., & Goodale, M. A. (2019). Changing the real viewing distance reveals the temporal evolution of size constancy in visual cortex. Current Biology, 29(13), 2237-2243. doi: S0960-9822(19)30683-9 pmid: 31257140
[7]	Davis, E. E., Chemnitz, E., Collins, T. K., Geerligs, L., & Campbell, K. L. (2021). Looking the same, but remembering differently: Preserved eye-movement synchrony with age during movie watching. Psychology and Aging, 36(5), 604-615.
[8]	Davis, S. W., Dennis, N. A., Daselaar, S. M., Fleck, M. S., & Cabeza, R. (2008). Que pasa? The posterior-anterior shift in aging. Cerebral Cortex, 18(5), 1201-1209.
[9]	de Haas, B., Iakovidis, A. L., Schwarzkopf, D. S., & Gegenfurtner, K. R. (2019). Individual differences in visual salience vary along semantic dimensions. Proceedings of the National Academy of Sciences of the United States of America, 116(24), 11687-11692. doi: 10.1073/pnas.1820553116 pmid: 31138705
[10]	Hardstone, R., Zhu, M., Flinker, A., Melloni, L., Devore, S., Friedman, D.,... He, B. J. (2021). Long-term priors influence visual perception through recruitment of long- range feedback. Nature Communications, 12(1), Article 6288.
[11]	Herzfeld, D. J., Kojima, Y., Soetedjo, R., & Shadmehr, R. (2015). Encoding of action by the Purkinje cells of the cerebellum. Nature, 526(7573), 439-442.
[12]	Herzfeld, D. J., Kojima, Y., Soetedjo, R., & Shadmehr, R. (2018). Encoding of error and learning to correct that error by the Purkinje cells of the cerebellum. Nature Neuroscience, 21(5), 736-743. doi: 10.1038/s41593-018-0136-y pmid: 29662213
[13]	Huang, J., Gegenfurtner, K. R., Schütz, A. C., & Billino, J. (2017). Age effects on saccadic adaptation: Evidence from different paradigms reveals specific vulnerabilities. Journal of Vision, 17(6), 9.
[14]	Huang, J., Hegele, M., & Billino, J. (2018). Motivational modulation of age-related effects on reaching adaptation. Frontiers in Psychology, 9, Article 2285.
[15]	Idrees, S., Baumann, M. P., Franke, F., Münch, T. A., & Hafed, Z. M. (2020). Perceptual saccadic suppression starts in the retina. Nature Communications, 11(1), 1977.
[16]	Jia, L., Du, Y., Chu, L., Zhang, Z., Li, F., Lyu, D., …Jia, J. (2020). Prevalence, risk factors, and management of dementia and mild cognitive impairment in adults aged 60 years or older in China: A cross-sectional study. The Lancet Public Health, 5(12), e661-e671.
[17]	Kim, S., Ogawa, K., Lv, J., Schweighofer, N., & Imamizu, H. (2015). Neural substrates related to motor memory with multiple timescales in sensorimotor adaptation. PLoS Biology, 13(12), Article e1002312.
[18]	Li, T., Wang, L., Huang, W., Zhen, Y., Zhong, C., Qu, Z., & Ding, Y. (2020). Onset time of inhibition of return is a promising index for assessing cognitive functions in older adults. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 75(4), 753-761.
[19]	Li, X., Wang, Y., Wang, W., Huang, W., Chen, K., Xu, K.,... Zhang, Z. (2020). Age-related decline in the topological efficiency of the brain structural connectome and cognitive aging. Cerebral Cortex, 30(8), 4651-4661.
[20]	Long, N. M., & Kuhl, B. A. (2018). Bottom-up and top-down factors differentially influence stimulus representations across large-scale attentional networks. The Journal of Neuroscience, 38(10), 2495-2504.
[21]	Markowitz, D. A., Shewcraft, R. A., Wong, Y. T., & Pesaran, B. (2011). Competition for visual selection in the oculomotor system. The Journal of Neuroscience, 31(25), 9298-9306.
[22]	Najemnik, J., & Geisler, W. S. (2005). Optimal eye movement strategies in visual search. Nature, 434(7031), 387-391.
[23]	Navalpakkam, V., Koch, C., Rangel, A., & Perona, P. (2010). Optimal reward harvesting in complex perceptual environments. Proceedings of the National Academy of Sciences of the United States of America, 107(11), 5232-5237. doi: 10.1073/pnas.0911972107 pmid: 20194768
[24]	Noorani, I., & Carpenter, R. H. (2016). The LATER model of reaction time and decision. Neuroscience and Biobehavioral Reviews, 64, 229-251.
[25]	Park, D. C., Lautenschlager, G., Hedden, T., Davidson, N. S., Smith, A. D., & Smith, P. K. (2002). Models of visuospatial and verbal memory across the adult life span. Psychology and Aging, 17(2), 299-320. pmid: 12061414
[26]	Park, D. C., & Reuter-Lorenz, P. (2009). The adaptive brain: Aging and neurocognitive scaffolding. Annual Review of Psychology, 60, 173-196. doi: 10.1146/annurev.psych.59.103006.093656 pmid: 19035823
[27]	Peltsch, A., Hemraj, A., Garcia, A., & Munoz, D. P. (2011). Age-related trends in saccade characteristics among the elderly. Neurobiology of Aging, 32(4), 669-679. doi: 10.1016/j.neurobiolaging.2009.04.001 pmid: 19414208
[28]	Poletti, M., Rucci, M., & Carrasco, M. (2017). Selective attention within the foveola. Nature Neuroscience, 20(10), 1413-1417. doi: 10.1038/nn.4622 pmid: 28805816
[29]	Ramzaoui, H., Faure, S., & Spotorno, S. (2021). Top-down and bottom-up guidance in normal aging during scene search. Psychology and Aging, 36(4), 433-451. doi: 10.1037/pag0000485 pmid: 34124920
[30]	Salthouse, T. A. (1996). The processing-speed theory of adult age differences in cognition. Psychological Review, 103(3), 403-428. doi: 10.1037/0033-295x.103.3.403 pmid: 8759042
[31]	Schütz, A. C., Braun, D. I., & Gegenfurtner, K. R. (2011). Eye movements and perception: A selective review. Journal of Vision, 11(5), 9.
[32]	Schütz, A. C., Trommershäuser, J., & Gegenfurtner, K. R. (2012). Dynamic integration of information about salience and value for saccadic eye movements. Proceedings of the National Academy of Sciences of the United States of America, 109(19), 7547-7552.
[33]	Seidler, R. D. (2006). Differential effects of age on sequence learning and sensorimotor adaptation. Brain Research Bulletin, 70(4-6), 337-346.
[34]	Shadmehr, R., Smith, M. A., & Krakauer, J. W. (2010). Error correction, sensory prediction, and adaptation in motor control. Annual Review of Neuroscience, 33, 89-108. doi: 10.1146/annurev-neuro-060909-153135 pmid: 20367317
[35]	Siegel, M., Buschman, T. J., & Miller, E. K. (2015). Cortical information flow during flexible sensorimotor decisions. Science, 348(6241), 1352-1355. doi: 10.1126/science.aab0551 pmid: 26089513
[36]	Spreng, R. N., DuPre, E., Selarka, D., Garcia, J., Gojkovic, S., Mildner, J.,... Turner, G. R. (2014). Goal-congruent default network activity facilitates cognitive control. The Journal of Neuroscience, 34(42), 14108-14114.
[37]	Sugiura, M. (2016). Functional neuroimaging of normal aging: Declining brain, adapting brain. Ageing Research Reviews, 30, 61-72. doi: 10.1016/j.arr.2016.02.006 pmid: 26988858
[38]	Teufel, C., & Fletcher, P. C. (2020). Forms of prediction in the nervous system. Nature Reviews Neuroscience, 21(4), 231-242.
[39]	Trommershäuser, J., Glimcher, P. W., & Gegenfurtner, K. R. (2009). Visual processing, learning and feedback in the primate eye movement system. Trends in Neurosciences, 32(11), 583-590. doi: 10.1016/j.tins.2009.07.004 pmid: 19729211
[40]	Valsecchi, M., Billino, J., & Gegenfurtner, K. R. (2018). Healthy aging is associated with decreased risk-taking in motor decision-making. Journal of Experimental Psychology: Human Perception and Performance, 44(1), 154-167. doi: 10.1037/xhp0000436 pmid: 28504524
[41]	Wang, F., Huang, J., Lv, Y., Ma, X., Yang, B., Wang, E., Du, B., Li, W., & Song, Y. (2016). Predicting perceptual learning from higher-order cortical processing. NeuroImage, 124(Pt A),682-692. doi: S1053-8119(15)00830-7 pmid: 26391126
[42]	Wiebel, C. B., Valsecchi, M., & Gegenfurtner, K. R. (2013). The speed and accuracy of material recognition in natural images. Attention, Perception & Psychophysics, 75(5), 954-966.
[43]	Wolpert, D. M., & Flanagan, J. R. (2016). Computations underlying sensorimotor learning. Current Opinion in Neurobiology, 37, 7-11. doi: S0959-4388(15)00181-6 pmid: 26719992
[44]	Wynn, J. S., Ryan, J. D., & Buchsbaum, B. R. (2020). Eye movements support behavioral pattern completion. Proceedings of the National Academy of Sciences of the United States of America, 117(11), 6246-6254. doi: 10.1073/pnas.1917586117 pmid: 32123109