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How individuals manage, organize, and complete their tasks is central to operations management. Recent research in operations focuses on how under conditions of increasing workload individuals can decrease their service time, up to a point, to complete work more quickly. As the number of tasks increases, however, workers may also manage their workload by a different process—task selection. Drawing on research on workload, individual discretion, and behavioral decision making, we theorize and then test that under conditions of increased workload, individuals may choose to complete easier tasks to manage their load. We label this behavior task completion preference (TCP). Using six years of data from a hospital emergency department, we find that physicians engage in TCP, with implications for their performance. Specifically, TCP helps physicians manage variance in service times; however, although it initially appears to improve shift-level throughput volume, after adjusting for the complexity of the work completed, TCP is related to worse throughput. Moreover, we find that engaging in easier tasks compared with hard ones is related to lower learning in service times. We then turn to the laboratory to replicate conceptually the short-term task selection effect under increased workload and show that it occurs because of both fatigue and the sense of progress individuals get from task completion. These findings provide another mechanism for the workload-speedup effect from the literature. We also discuss implications for both the research and the practice of operations in building systems to help people succeed. This paper was accepted by Serguei Netessine, operations management .

[This corrects the article DOI: 10.3389/fnins.2024.1375225.].

We present a new method, Soloist, that uses transfer learning and machine teaching to build task bots at scale. We parameterize classical modular task-oriented dialog systems using a Transformer-based auto-regressive language model, which subsumes different dialog modules into a single neural model. We pre-train, on heterogeneous dialog corpora, a task-grounded response generation model, which can generate dialog responses grounded in user goals and real-world knowledge for task completion. The pre-trained model can be efficiently adapted to accomplish new tasks with a handful of task-specific dialogs via machine teaching, where training samples are generated by human teachers interacting with the system. Experiments show that (i) Soloist creates new state-of-the-art on well-studied task-oriented dialog benchmarks, including CamRest676 and MultiWOZ; (ii) in the few-shot fine-tuning settings, Soloist significantly outperforms existing methods; and (iii) the use of machine teaching substantially reduces the labeling cost of fine-tuning. The pre-trained models and codes are available at https://aka.ms/soloist.

Effective task scheduling has become the key to optimizing resource allocation, reducing operation costs, and enhancing the user experience. The complexity and dynamics of cloud computing environments require task scheduling algorithms that can flexibly respond to multiple computing demands and changing resource states. Therefore, we propose an enhanced Red-tailed Hawk algorithm (named ERTH) based on multiple elite policies and chaotic mapping, while applying this approach in conjunction with the proposed scheduling model to optimize the efficiency of task scheduling in cloud computing environments. We apply the ERTH algorithm to a real cloud computing environment and conduct a comparison with the original RTH and other conventional algorithms. The proposed ERTH algorithm has better convergence speed and stability in most cases of small and large-scale tasks and performs better in minimizing the task completion time and system load cost. Specifically, our experiments show that the ERTH algorithm reduces the total system cost by 34.8% and 36.4% relative to the traditional algorithm for tasks of different sizes. Further, evaluations in the IEEE Congress on Evolutionary Computation (CEC) benchmark test sets show that the ERTH algorithm outperforms the traditional or emerging algorithms in several performance metrics such as mean, standard deviation, etc. The proposal and validation of the ERTH algorithm are of great significance in promoting the application of intelligent optimization algorithms in cloud computing.

The present paper explores the use of pragmatic strategies by Iranian EFL learners in fulfilling the speech act of request. Accordingly, the analytical classification of request strategies suggested by Blum-Kulka & Olshtain (1984) and the cross-cultural speech act realization patterns (CCSARP) coding manual (Blum-Kulka et al., 1989) were used to examine the corresponding features of pragmatic strategies employed by 22 upper-intermediate Iranian EFL learners in performing the speech act of request. To this end, this descriptive study analyzed the participants’ utterances in both informal and formal situations using a Written Discourse Completion Task (WDCT). The outcomes revealed that among nine request strategies classified by Blum-Kulka & Olshtain (1984), Iranian EFL learners most frequently employed the “query preparatory strategy” as a conventional indirectness strategy on request in informal and formal situations. However, they used the direct strategy of “want statement” in informal contexts.

Standard approaches for identifying task-completion strategies, such as precrastination and procrastination, reduce behavior to single markers that oversimplify the process of task completion. To illustrate this point, we consider three task-completion strategies and introduce a new method to identify their use. This approach was tested using an archival data set (N = 8,655) of the available electronic records of research participation at Kansas State University. The approach outperformed standard diagnostic approaches and yielded an interesting finding: Several strategies were associated with negative outcomes. Specifically, both procrastinators and precrastinators struggled to finish tasks on time. Together, these findings underscore the importance of using holistic approaches to determine the relationship among task characteristics, individual differences, and task completion.

Background Although there is an association between stress and cognitive dysfunction in older adults, this research typically uses between‐group comparisons. Advances in remote, smartphone‐based cognitive assessments allow for examination of within‐person variability, including testing how fluctuations in an individual's stress levels influence their own cognitive performance. The current study explored within‐person variability in stress as predictors of cognitive outcomes and examined how these associations differ by amyloid (Aβ) status. Method Participants included 123 cognitively normal adults (60‐81 years old, 66.7% female, 86.2% White, mean education = 16.49 years). A subsample had Aβ PET imaging results available (Aβ+ = 25; Aβ− = 48). Participants rated their stress using a single‐item immediately before completing cognitive assessments on the Mobile Monitoring of Cognitive Change (M2C2) smartphone‐based app. Data were gathered three times daily over eight days. Cognitive tasks included processing speed (PS), visual working memory accuracy (WM), and episodic memory accuracy (EM). Multilevel modeling examined the interaction between within‐person stress and session number on cognitive performance, adjusting for between‐person variability in stress, age, sex, and education. Exploratory analyses stratified by Aβ status (Aβ+ vs. Aβ−). Result An interaction between within‐person stress and session number emerged for PS (p = .01), such that lower stress ratings were associated with quicker task completion over time. For WM, an interaction with session number was observed (p = .04), suggesting that the positive association between performance and session number was more pronounced when participants reported higher stress levels. No significant interactions emerged when examining EM (p = .23). Exploratory analyses revealed that among Aβ+ individuals, an interaction between within‐person stress and session number for WM (p = .02). No other significant associations were found by Aβ status. Conclusion This study highlights the role of stress fluctuations on cognitive performance during remote assessments. Increased stress was linked to slower PS over time, while increased stress was associated with improved WM, particularly among Aβ+ individuals. While chronic stress is typically associated with cognitive dysfunction, intraindividual fluctuations in stress may enhance performance by increasing attentional resources, at least in certain cognitive domains.

Cognitive fatigue (CF) can result from sustained mental effort, is characterized by subjective feelings of exhaustion and cognitive performance deficits, and is associated with slowed simple reaction time (RT). This study determined whether declines in motor preparation underlie this RT effect. Motor preparation level was indexed using simple RT and the StartReact effect, wherein a prepared movement is involuntarily triggered at short latency by a startling acoustic stimulus (SAS). It was predicted that if decreased motor preparation underlies CF-associated RT increases, then an attenuated StartReact effect would be observed following cognitive task completion. Subjective fatigue assessment and a simple RT task were performed before and after a cognitively fatiguing task or non-fatiguing control intervention. On 25% of RT trials, a SAS replaced the go-signal to assess the StartReact effect. CF inducement was verified by significant declines in cognitive performance (p = 0.003), along with increases in subjective CF (p < 0.001) and control RT (p = 0.018) following the cognitive fatigue intervention, but not the control intervention. No significant pre-to-post-test changes in SAS RT were observed, indicating that RT increases resulting from CF are not substantially associated with declines in motor preparation, and instead may be attributable to other stages of processing during a simple RT task.

Respondents in online panels often lack motivation when completing research questionnaires. We propose a method to improve their motivation. In many online survey platforms, questionnaires start with a relatively trivial task (e.g., self-identification or responding to screener demographic questions). We show that bringing attention to the completion of these trivial tasks can increase participants’ motivation in the subsequent main task; without this completion cue, the completion of the trivial task tends to go unnoticed. Using different trivial tasks (e.g., providing demographic information, stapling a questionnaire) and main tasks (e.g., commenting about a situation, recalling information), four experiments demonstrate what we call the “trivial-task motivation effect”. We further show that trivial tasks need not be irritants and may be added to surveys, along with a completion cue, to boost motivation. Our research presents an easily implemented and low-cost method to increase motivation in surveys.

Many children with autism spectrum disorder (ASD) have problem behaviors that interfere with learning and social interaction. This randomized controlled trial compared treatment with functional communication training (FCT) to “treatment as usual” for young children with ASD (n = 38, ages 21–84 months). FCT was conducted by parents with training and real-time coaching provided by behavioral consultants using telehealth. FCT treatment via telehealth achieved a mean reduction in problem behavior of 98% compared to limited behavioral improvement in children receiving “treatment as usual” during a 12-week period. Social communication and task completion also improved. For children with ASD and moderate to severe behavior problems, parent-implemented FCT using telehealth significantly reduced problem behavior while ongoing interventions typically did not.