Influence of Motor and Cognitive Tasks on Time Estimation

Abstract

The passing of time can be precisely measured by using clocks, whereas humans' estimation of temporal durations is influenced by many physical, cognitive and contextual factors, which distort our internal clock. Although it has been shown that temporal estimation accuracy is impaired by non-temporal tasks performed at the same time, no studies have investigated how concurrent cognitive and motor tasks interfere with time estimation. Moreover, most experiments only tested time intervals of a few seconds. In the present study, participants were asked to perform cognitive tasks of different difficulties (look, read, solve simple and hard mathematical operations) and estimate durations of up to two minutes, while walking or sitting. The results show that if observers pay attention only to time without performing any other mental task, they tend to overestimate the durations. Meanwhile, the more difficult the concurrent task, the more they tend to underestimate the time. These distortions are even more pronounced when observers are walking. Estimation biases and uncertainties change differently with durations depending on the task, consistent with a fixed relative uncertainty. Our findings show that cognitive and motor systems interact non-linearly and interfere with time perception processes, suggesting that they all compete for the same resources.

Keywords: clock speed; cognitive load; cognitive–motor interaction; duration estimation; time perception; walking.

Figure 1

Experimental design. (a) Experimental set-up in sitting (left panel) and walking condition (right panel). (b) Number of trials per condition, time interval and cognitive task. Observers performed 10 trials for all possible combinations of time intervals and cognitive tasks in each motor condition. (c) Trial randomization in a block. Example of a block of 10 trials. Grey: look task; blue: read task; yellow: solve simple task; orange: solve hard task.

Figure 2

Procedure and tasks. (a) Time ruler. Ruler shown at the end of each trial to allow participants to express how much time had passed while they were performing the task. In the example shown, the participant estimated 25 s. (b) Look task. (c) Read task. (d) Solve task. Example of solve simple task. The solve hard task follows the same procedure but consists of harder mathematical sums. Red words report the task to be performed in the trial. The green circle informs the observers to start estimating the passing of time; the red circle informs the observers to stop estimating the passing of time.

Figure 3

Time estimations during different tasks. (a) Sitting condition: different panels represent all data in different tasks (see different colors in the legend). (b) Walking condition: different panels represent all data in different tasks (see different colors in the legend). (c) Sitting condition: estimation bias averaged over time intervals and participants. The dashed lines represent best fits calculated on data averaged on smaller time intervals (2 s, see text). Look: intercept = 6.4 ± 0.9, slope = 0.005 ± 0.2, χ² (35) = 45.9. Read: intercept = 4.8 ± 0.8, slope = −0.09 ± 0.2, χ² (35) = 50.1. Solve simple: intercept = 2.8 ± 0.6, slope = −0.2 ± 0.2, χ² (35) = 57.8. Solve hard: intercept = 1.2 ± 0.4, slope = −0.3 ± 0.01, χ² (35) = 36.1 (d) Walking condition: estimation bias averaged over time intervals and participants. Solid lines represent best-fit curves, calculated as in c). Look: intercept = 8.9 ± 0.8, slope = 0.01 ± 0.2, χ² (35) = 35.6. Read: intercept = 5.7 ± 0.8, slope = −0.1 ± 0.2, χ² (35) = 41.6. Solve simple: intercept = 3.3 ± 0.6, slope = −0.3 ± 0.2, χ² (35) = 40. Solve hard: intercept = 1.8 ± 0.4, slope = −0.4 ± 0.01, χ² (35) = 36.7. Error bars are SE across participants.

Figure 4

Estimation uncertainty as a function of duration. Averaged RMSE computed on 2 s intervals for all tasks and conditions with their best-fit curves. Different panels correspond to different tasks (see colors in the legend). Sitting condition: open symbols—dashed lines; walking condition: solid symbols—solid lines. Sitting condition—Look: slope = 0.14 ± 0.02, intercept = 4.6 + 0.2; Read: slope = 0.16 ± 0.01, intercept = 4.1 + 0.4; Solve simple: slope = 0.22 ± 0.02, intercept = 1.7 + 0.1; Solve hard: slope = 0.21 ± 0.01, intercept = 0.8 + 0.1. Walking condition—Look: slope = 0.22 ± 0.1, intercept = 4.9 + 0.1; Read: slope = 0.23 ± 0.01, intercept = 3.2 + 0.2; Solve simple: slope = 0.21 ± 0.01, intercept = 1.2 + 0.1; Solve hard: slope = 0.23 ± 0.02, intercept = 0.9 + 0.2.

Figure 5

Coefficient of variation for different tasks in sitting and walking conditions. Data shown and their errors are the results of best linear fitting of binned RMSE as a function of estimated time.

Figure 6

Comparison of time estimation during walking and while sitting for all tasks. (a) Slopes of best-fit lines, shown in Figure 3c,d. Asterisks mark statistically significant differences with z-tests: * p < 0.05, *** p < 0.001. (b) Y-axis intercepts of best-fit lines, shown in Figure 3c,d. (c) Estimation bias averaged over time intervals and participants. Asterisks mark statistically significant pairwise post hoc comparisons: * p < 0.05, ** p < 0.01, *** p < 0.001.

Figure 7

Performance in the math tasks while walking and sitting. (a) Percentage of correct solutions. (b) Response time to operations. Responses for correct and incorrect solutions are considered. Asterisks mark statistically significant differences with ANOVAs: * p < 0.05, ** p < 0.01. Error bars are SE across participants.