A large-scale analysis of task switching practice effects across the lifespan

Abstract

An important feature of human cognition is the ability to flexibly and efficiently adapt behavior in response to continuously changing contextual demands. We leverage a large-scale dataset from Lumosity, an online cognitive-training platform, to investigate how cognitive processes involved in cued switching between tasks are affected by level of task practice across the adult lifespan. We develop a computational account of task switching that specifies the temporal dynamics of activating task-relevant representations and inhibiting task-irrelevant representations and how they vary with extended task practice across a number of age groups. Practice modulates the level of activation of the task-relevant representation and improves the rate at which this information becomes available, but has little effect on the task-irrelevant representation. While long-term practice improves performance across all age groups, it has a greater effect on older adults. Indeed, extensive task practice can make older individuals functionally similar to less-practiced younger individuals, especially for cognitive measures that focus on the rate at which task-relevant information becomes available.

Origin of perseveration in the trade-off between reward and complexity

“…We have made very few assumptions about the underlying cognitive mechanisms that produce a particular point on the reward-complexity curve. This contrasts with the modeling that was previously applied to the same data sets (Collins, 2018;Steyvers et al, 2019), which explored detailed mechanistic hypotheses. These different approaches have different advantages and disadvantages.…”

“…The second data set, reported in Steyvers et al (2019), consists of 1000 subjects playing the task-switching game "Ebb and flow" on the Lumosity platform ( Figure 1B). On each trial, subjects viewed moving leaves on a display and reported either the motion or pointing direction of the leaves.…”

“…The marginal action probability P (a) was estimated using a 5-trial moving average (truncated at boundaries between blocks). For the Steyvers et al (2019) data set, we assumed that subjects had full knowledge of Q(s, a) and simply hard-coded it into the policy. As with the Collins data set, the marginal action probability was estimated using a 5-trial moving average.…”

“…Reward-complexity trade-off, applied to data fromSteyvers et al (2019). The solid line shows the optimal trade-off function, and the circles correspond to individual subjects.…”

“…An evidence of 0 indicates equal support for M1 and M2. The top panels show the results for the data fromCollins (2018); the bottom panels show the results for the data fromSteyvers et al (2019). Parameter recovery from simulated data.…”