Does aging amplify the rule-based efficiency effect in action selection?

Abstract

When it comes to the selection of adequate movements, people may apply varying strategies. Explicit if-then rules, compared to implicit prospective action planning, can facilitate action selection in young healthy adults. But aging alters cognitive processes. It is unknown whether older adults may similarly, profit from a rule-based approach to action selection. To investigate the potential effects of aging, the Rule/Plan Motor Cognition (RPMC) paradigm was applied to three different age groups between 31 and 90 years of age. Participants selected grips either instructed by a rule or by prospective planning. As a function of age, we found a general increase in a strategy-specific advantage as quantified by the difference in reaction time between plan- and rule-based action selection. However, in older age groups, these differences went in both directions: some participants initiated rule-based action selection faster, while for others, plan-based action selection seemed more efficient. The decomposition of reaction times into speed of the decision process, action encoding, and response caution components suggests that rule-based action selection may reduce action encoding demands in all age groups. There appears a tendency for the younger and middle age groups to have a speed advantage in the rule task when it comes to information accumulation for action selection. Thus, one influential factor determining the robustness of the rule-based efficiency effect across the lifespan may be presented by the reduced speed of information uptake. Future studies need to further specify the role of these parameters for efficient action selection.

Keywords: action planning; action selection; drift diffusion; end-state comfort; implementation intentions; motor cognition.

FIGURE 1

Graphical representation of the drift diffusion model (DDM).

FIGURE 2

Plan- and rule-task mean reaction times (RT) per age group are given in milliseconds (ms). Error bars represent 95% confidence intervals. Significant median differences within age groups are marked with *.

FIGURE 3

The figure depicts task-specific (upper panel) and rule efficiency (lower panel) reaction time (RTs) distributions, demonstrating a change in strategy advantages with increasing age. While young participants appeared quicker in rule-based action selection, older adults varied more strongly in which approach was more advantageous. (Upper panel) Mean plan- and rule-task RT for each participant, plotted against each participant’s age in years. (Lower panel) Rule efficiency effect (mean rule-task RT subtracted from mean plan-task RT) for each participant plotted against each participant’s age. See Supplementary Figure 2 for a breakdown of the (lower panel) by hand and first task.

FIGURE 4

Drift diffusion model (DDM) parameter estimates from simulated datasets. Error bars represent 95% confidence intervals. [*] denotes uncorrected p-values < 0.05; *** denotes Bonferroni corrected p-values < 0.001 of within-group t-test task comparisons. (A) DDM boundary separation parameter (a) by age group and task. (B) DDM drift rate parameter (v) by age group and task. (C) DDM non-decision time parameter (t₀) in seconds by age group and task. Drift rate, v: the rate of information accumulation (speed of the decision process), boundary separation, a: the distance between decision thresholds (response caution), and the duration of non-decision components t₀: stimulus encoding, preparation of motor response, visualization (action encoding).