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Evidence that ageing yields improvements as well as declines across attention and executive functions

Nature Human Behaviour volume 6, pages 97–110 (2022)Cite this article

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A Publisher Correction to this article was published on 27 August 2021

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Abstract

Many but not all cognitive abilities decline during ageing. Some even improve due to lifelong experience. The critical capacities of attention and executive functions have been widely posited to decline. However, these capacities are composed of multiple components, so multifaceted ageing outcomes might be expected. Indeed, prior findings suggest that whereas certain attention/executive functions clearly decline, others do not, with hints that some might even improve. We tested ageing effects on the alerting, orienting and executive (inhibitory) networks posited by Posner and Petersen’s influential theory of attention, in a cross-sectional study of a large sample (N = 702) of participants aged 58–98. Linear and nonlinear analyses revealed that whereas the efficiency of the alerting network decreased with age, orienting and executive inhibitory efficiency increased, at least until the mid-to-late 70s. Sensitivity analyses indicated that the patterns were robust. The results suggest variability in age-related changes across attention/executive functions, with some declining while others improve.

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Fig. 1: Linear effects of age on the efficiencies of the three attentional networks.
Fig. 2: The nonlinear effect of age on the efficiency of the executive network.
Fig. 3: Comparison between a recent qualitative review of age effects on the three attentional networks and the findings from the present study.
Fig. 4: A neurocognitive account of age effects on the three attentional networks.

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Data availability

The anonymized data (with accompanying documentation) have been uploaded to the Open Science Framework. They can be found at https://osf.io/59er2/.

Code availability

Commented analysis scripts (in the R programming language) for all statistical models reported in this paper have been uploaded to the Open Science Framework. They can be found at https://osf.io/59er2/.

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Acknowledgements

This work was supported by the Deutsche Forschungsgemeinschaft (German Research Foundation) Project ID 317633480 (SFB 1287, Project Q) (University of Potsdam, to J.V.); NIH R01 AG016790 (Princeton University, to N.G.), with a subcontract to M.T.U. at Georgetown University; NIH R01 AG016661 (Georgetown University, to M.W.); NSF BCS 1940980 (Georgetown University, to M.T.U.); and the Graduate School of Arts and Sciences, Georgetown University (to M.W.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank the Health Promotion Administration at the Ministry of Health in Taiwan for their support of this project; M. Pullman and L. Babcock for task preparation and testing; and M. Posner, M. Riesenhuber, M. Rugg, D. Fernandez-Duque and especially D. Glei for helpful comments.

Author information

Authors and Affiliations

  1. Center of Linguistics, School of Arts and Humanities, University of Lisbon, Lisbon, Portugal

    João Veríssimo

  2. Department of Linguistics, University of Potsdam, Potsdam, Germany

    João Veríssimo

  3. School of Psychology, Georgia Institute of Technology, Atlanta, GA, USA

    Paul Verhaeghen

  4. Office of Population Research, Princeton University, Princeton, NJ, USA

    Noreen Goldman

  5. Center for Population and Health, Georgetown University, Washington, DC, USA

    Maxine Weinstein

  6. Department of Neuroscience, Georgetown University, Washington, DC, USA

    Michael T. Ullman

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  1. João Veríssimo
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  2. Paul Verhaeghen
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  3. Noreen Goldman
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  4. Maxine Weinstein
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  5. Michael T. Ullman
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Contributions

The study was conceived by M.T.U. and M.W. and designed by M.T.U., as part of the larger SEBAS project led by N.G. and M.W. J.V. performed the data preparation and analysis. J.V. and M.T.U. wrote the paper, with contributions from P.V. as well as N.G. and M.W. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to João Veríssimo or Michael T. Ullman.

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The authors declare no competing interests.

Additional information

Peer review information Nature Human Behaviour thanks Gregory Samanez-Larkin and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Demographic and cognitive information for participants, presented in 5-year age brackets.

This table is for informational purposes only; we remind readers that all analyses were performed with age as a continuous variable. For each age bracket, the columns show first of all the sample size (total, with number of females in parentheses) and the mean (and SD) of years of education. Thus, these two columns display the age distributions of sex and education, the two variables covaried out in our analyses; for discussion of these distributions see refs. 73,109. The subsequent columns display means (and SDs) of the previously published cognitive measures of working memory (n-back task; mean d’ over 1-back and 2-back109) and declarative memory (recognition memory task; mean d’ over real and novel objects73) for this sample. Note that the sample sizes in each 5-year age bracket are slightly different for the working memory scores (Supplementary Table 1 in ref. 109) and the declarative memory scores (Table 1 in Ref. 73) than for the data in the present paper (for example, due to slightly different subsets of participants having valid performance measures in the respective tasks). For a more general cognitive measure obtained in this sample, see Ref. 232. N: number of participants; NA: not available; SD: standard deviation.

Extended Data Fig. 2 Results from the mixed-effects logistic regression model on the accuracy of responses.

We examined accuracy using generalized linear mixed-effects regression with a logit link function. We analysed correct/incorrect responses produced prior to the timeout period (number of data points: 49,980), for the same 702 participants as in the main analysis on RTs. The same fixed-effect predictors were included as in the main analysis. Models included a random by-participant intercept only, because models with random slopes did not converge. Model convergence was reached only by applying a weakly informative Bayesian prior on the effects233 (priors on fixed effects were normal distributions with mean=0 and SD = 3 for the intercept, and mean=0 and SD = 0.4 for slopes). Effect sizes are reported as unstandardized estimates (b-values) in the logit scale with 95% confidence intervals, together with z-values; p-values are reported as two-tailed, with exact values to three digits. The significant interaction between age and the executive effect indicated that age was associated with increasing executive efficiency, parallel to the finding of increasing efficiency in the main analysis on RTs. Follow-up analyses indicated that there was an interference cost on accuracy for incongruent flankers at the minimum age of 58 years (back-transformed accuracy in percent correct for congruent, 99.70% vs. incongruent, 99.38%; b = -0.7302 [-0.9756, -0.4847], z = -5.83, p < .001), but no significant difference between incongruent and congruent flankers at the maximum age of 98 years (back-transformed accuracy for congruent, 98.10% vs. incongruent, 97.94%; b = -0.0825 [-0.4693, 0.3043], z = -0.42, p = .676). Likewise, there was no significant executive effect at age 90 (b = -0.2127 [-0.5150, 0.0897], z = -1.38, p = .168). The executive effect was almost nine times larger at the minimum than maximum age, as revealed by the regression coefficients in the logit scale (b values: 58 years: -0.7302 vs. 98 years: -0.0825), and twice as large in back-transformed accuracy (0.32% vs. 0.16%). Education effect: higher education was associated with higher accuracy (across all ages and all cues and flankers).

Extended Data Fig. 3 Results from the linear mixed-effects regression model on log RTs.

P-values were obtained from t-tests with 49,163 degrees of freedom, calculated as the number of data points (that is, 49,176) minus the number of fixed effect estimates (that is, 13)234. Here and elsewhere, effect sizes of linear mixed-effects models are reported as unstandardized estimates (b-values) with 95% confidence intervals, together with t-values; p-values are reported as two-tailed, with exact values to three digits. Education effect: higher education was associated with faster responses (across all ages and all cues and flankers). Trial effect: later trials were associated with faster responses. Follow-up analyses to the two network interactions (Alerting X Executive, Orienting X Executive) were performed. Note that both of these interactions, and the general patterns found in their follow-up analyses, are commonly reported for the ANT in both younger and older adults122,138,140,144,145,146,152,235. First, the alerting effect was significant in trials with congruent flankers (b = 0.0127 [0.0070, 0.0184], t = 4.37, p < .001), but not in trials with incongruent flankers (b = -0.0032 [-0.0089, 0.0026], t = -1.08, p = .279). Second, the orienting effect was larger in trials with incongruent flankers (b = 0.0227 [0.0169, 0.0284], t = 7.76, p < .001) than in those with congruent flankers (b = 0.0094 [0.0037, 0.0151], t = 3.24, p = .001), but was significant in both. We also followed up on both interactions by examining the executive effect in the different cue types involved in alerting and orienting. The executive effect was larger in trials with a central cue (b = 0.0777 [0.0713, 0.0841], t = 23.79, p < .001) than in trials with no cue (b = 0.0618 [0.0554, 0.0682], t = 18.95, p < .001) (Alerting X Executive). The executive effect was also larger in trials with a central cue (see just above) than in trials with a spatial cue (b = 0.0644 [0.0580, 0.0708], t = 19.75, p < .001) (Orienting X Executive). CI: confidence interval.

Extended Data Fig. 4 Mean untransformed RTs for each condition, and mean attentional network effects, in milliseconds, presented in 5-year age brackets.

This table displays mean untransformed RTs, by flanker and cue condition (with SDs in parentheses), together with mean attentional network effects (computed as differences between the mean untransformed RTs in each pair of relevant conditions for example, between the central and no cue conditions for alerting), in 5-year age brackets. This table is for informational purposes only; we remind readers that all analyses were performed on log-transformed RTs with age as a continuous variable. Congruent and incongruent flanker types are computed over all three cue types, and each cue type is computed over congruent and incongruent flankers. RTs: response times; N: number of participants; SDs: standard deviations.

Extended Data Fig. 5 Linear effects of age on the efficiencies of the three attentional networks, showing network effects for each of the 702 participants.

The linear age effects are displayed for (a) the alerting network, (b) the orienting network, and (c) the executive network. For each network, each data point reflects the difference between mean log-transformed RTs in the two relevant conditions (for example, between no cue and central cue for alerting) for each participant. The y-axis ranges (maximum minus minimum) of the three panels are identical, while their numerical values differ; specifically, because the executive effect is larger than the other two attentional effects, the numerical values for the y-axis in panel (c) are shifted upwards.

Extended Data Fig. 6 Age effects on RTs shown separately for each of the cue and flanker conditions.

Though age-related slowdowns were observed for all conditions, the RT increases differed among the relevant conditions, that is, among the cue or flanker types. These interactions yielded the observed age effects on efficiency for the three attentional networks. First (panel a), the RT increase with aging was greater for trials preceded by a central cue (solid line; b = 0.0087 [0.0072, 0.0103], t = 11.12, p < .001) than for those with no cue (line with short dashes; b = 0.0080 [0.0064, 0.0095], t = 10.15, p < .001), leading to the observed decrease of alerting efficiency with aging. (The reasons for the detrimental effect of the central cue relative to no cue at later ages remain to be determined; perhaps once the alerting benefit has decreased past a certain point, processing costs associated with the presentation of the cue become predominant.) Second (also in panel a), the RT increase with age was smaller for trials preceded by spatial cues (line with long dashes; b = 0.0081 [0.0065, 0.0096], t = 10.29, p < .001) than for those preceded by a central cue (see just above), yielding the reported increase in orienting efficiency with age (that is, older participants benefited particularly from spatial cues as compared to central cues). Third (panel b), the age-related RT increase in incongruent flanker trials (solid line; b = 0.0077 [0.0061, 0.0092], t = 9.79, p < .001) was smaller than for congruent trials (dashed line; b = 0.0089 [0.0073, 0.0104], t = 11.29, p < .001), leading to the observed increase in executive efficiency with aging.

Extended Data Fig. 7 Density plots for the distributions of untransformed RTs of correct trials.

The plots show these RTs (from the trials analysed in the main regression model) for incongruent flankers (solid lines) and congruent flankers (dashed lines), for younger participants (n = 592; panel a) and older participants (n = 110; panel b), split at the midpoint of the age range (age 78). As can be seen, very few responses for either the incongruent or congruent trials approached the timeout of 1,700ms for either group of participants, arguing against a ‘timeout’ alternative explanation for the age-related increase in efficiency of the executive network (see ‘Linear effects of age: sensitivity analyses’, in Results).

Extended Data Fig. 8 Discovery of the optimal breakpoint model.

This was achieved by comparing model goodness-of-fit (AIC) for regression-with-breakpoints models with breakpoints at successive ages. The AIC of the optimal model (with a breakpoint at age 76) is indicated with the gray arrow.

Extended Data Fig. 9 The nonlinear effect of age on the efficiency of the executive network, showing the executive effect for each of the 702 participants.

The nonlinear age effect is displayed for (a) the model with a quadratic term for age, and (b) the breakpoint model with the optimal breakpoint (age 76). Each data point reflects the difference between the mean log-RTs for incongruent and congruent flankers, for each participant.

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Veríssimo, J., Verhaeghen, P., Goldman, N. et al. Evidence that ageing yields improvements as well as declines across attention and executive functions. Nat Hum Behav 6, 97–110 (2022). https://doi.org/10.1038/s41562-021-01169-7

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