Scaling of the Parameters for Cost Balancing in Self-Organized Task Switching

Task performance errors and rewards affect voluntary task choices

Humans are remarkably flexible in adapting their behavior to current demands. It has been suggested that the decision which of multiple tasks to perform is based on a variety of factors pertaining to the rewards associated with each task as well as task performance (e.g., error rates associated with each task and/or error commission on the previous trial). However, further empirical investigation is needed to examine whether task performance still influences task choices if task choices are rewarded but task performance is not. Accordingly, we exposed participants to a novel reward-varying voluntary task switching paradigm where the reward for the performed task gradually decreased while the reward associated for the alternative task was unchanged. Importantly, we rewarded participants' task choices before participants performed the task to investigate the effect of rewards independent from task performance. We examined the effect of (i) reward, (ii) error rates associated with each of the two tasks, and (iii) error commission in the previous trial on voluntary task choices. As expected, we found that participants' task selection was influenced by reward differences between task choices. In addition, error rates associated with a task also influenced task selection, with participants requiring larger reward differences to switch to a task associated with relatively higher error rates, compared to switching to a task with relatively lower error rates. However, errors in n - 1 did not influence participants' probability to switch to the alternative task. These findings contribute to an ongoing discussion on the influence of task performance on task selection.

Individual Strategies of Response Organization in Multitasking Are Stable Even at Risk of High Between-Task Interference

Recently, reliable interindividual differences were found for the way how individuals process multiple tasks (at a cognitive level) and how they organize their responses (at a response level). Previous studies have shown mixed results with respect to the flexibility of these preferences. On the one hand, individuals tend to adjust their preferred task processing mode to varying degrees of risk of crosstalk between tasks. On the other, response strategies were observed to be highly stable under varying between-resource competition. In the present study, we investigated whether the stability of response strategies also persists with increased risk of crosstalk or whether individuals adjust their choice of response strategy, similar to what has been found at the level of task processing modes. Besides, related differences in multitasking efficiency were assessed. For this purpose, 53 participants performed the Free Concurrent Dual-Tasking (FCDT) paradigm, which allows them to control their task scheduling and response organization. The participants completed the FCDT paradigm under two conditions including task pairs characterized by either low or high levels of risk of crosstalk. The free choice of task scheduling resulted in the previously found distinct response patterns, best described as blocking, switching or response grouping. Remarkably, we did not find any notable adjustments of strategies of response organization to the extent of crosstalk. However, we observed suspected performance decrements of a switching strategy in the condition of high risk of crosstalk. The results suggest that individual strategies of response organization are stable habits. Further, they illustrate disadvantages of switching vs. blocking strategies of response organization in case of high task similarity.

Evaluating students' engagement with an online learning environment during and after COVID-19 related school closures: A survival analysis approach

Background

Due to the COVID-19 pandemic schools all over the world were closed and thereby students had to be instructed from distance. Consequently, the use of online learning environments for online distance learning increased massively. However, the perseverance of using online learning environments during and after school closures remains to be investigated.

Method

We examined German students’ (n ≈ 300,000 students; ≈ 18 million computed problem sets) engagement in an online learning environment for mathematics by means of survival analysis.

Results

We observed that the total number of students who registered increased considerably during and after school closures compared to the previous three years. Importantly, however, the proportion of students engaged also decreased more rapidly over time.

Conclusion

The application of survival analysis provided valuable insights into students’ engagement in online learning - or conversely students’ increased dropout rates - over time. Its application to educational settings allows to address a broader range of questions on students’ engagement in online learning environments in the future.

The self-organized task switching paradigm: Movement effort matters

The self-organized task switching paradigm enables to investigate the link between task performance and task selection in a voluntary task switching setting that benefits task switches over task repetitions. For example, waiting for a repetition-related stimulus onset denotes environmental costs, which are balanced with internal task-switch costs. Here we extent this research by asking whether movement effort also plays a crucial role for task selection. In detail, we investigate how motor-related consequences, i.e., increasing force for task repetitions, influence task-switching behavior. Participants voluntarily switched between a number (i.e., even or odd) or letter task (i.e., vowel or consonant) using a robot system for response execution. With consecutive task repetitions the robot system was harder to move to the response target as we systematically added a damping load. We found that switch rate correlated with cognitive switch costs (i.e., costs in: reaction time, r = -0.741, and error rate, r = -0.545), and motor repetition cost represented by movement-time increment, r = 0.414. Interestingly, switch rate also correlated with individual force maximum, r = -0.480. However, switch rate did not correlate with movement-impulse increment, r = -0.033. Stepwise multiple regression analyses across participants revealed that 66% of variance are explained including all predicting factors. Yet, only cognitive costs and individual force maximum reached significant importance in the regression model. Hence, we extended switch-rate analyses using linear regression on a within-subject level, and thus, keeping individual force maximum constant. We found about 84% of variance explained by motor and cognitive costs. Thereby, movement impulse predicted task selection more than reaction time and more than movement time. Thus, we demonstrated that both cognitive and motor consequences influence task-switch behavior. Furthermore, we showed that task selection is importantly modulated by motor effort related to individual motor skills.