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Journal articles on the topic 'Time on task'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

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1

Chan, Alan H. S., and Errol R. Hoffmann. "Subjective Estimation of Task Time and Task Difficulty of Simple Movement Tasks." Journal of Motor Behavior 49, no. 2 (September 3, 2016): 185–99. http://dx.doi.org/10.1080/00222895.2016.1169984.

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2

Tsang, Pamela S. "Display/Control Integrality and Time-Sharing Performance." Proceedings of the Human Factors Society Annual Meeting 30, no. 5 (September 1986): 445–49. http://dx.doi.org/10.1177/154193128603000507.

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Time-sharing performance was investigated as a function of the display and response integrality of the time-shared tasks. A manual step-tracking task was time-shared with a Stroop task that could be responded to manually or by speech. A secondary task technique was employed to manipulate the resource allocation between the two tasks. Display integrality was manipulated by: (1) contingent processing of the different dimensions of the Stroop task, and (2) the “objectness” of the dual task display. Response integrality was manipulated by the number of responses required of the dual task and the response modality of the Stroop task. A prevalent resource competition effect between the manual responses of the two tasks were observed, supporting the concept of multiple resources. Results were also in concordance with Kahneman's object file model of attention; demonstrating that irrelevant elements within an object were difficult to ignore. The findings demonstrated the interactive effects of resource competition and task integrality on time-sharing performance.

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Wang, Endong, Fan Ni, Jicheng Chen, Hongwei Wang, and Yihan Li. "Cache-Aware Cooperative Task Mapping in Multi-core Real-Time Systems." International Journal of Information and Electronics Engineering 6, no. 2 (2016): 72–78. http://dx.doi.org/10.18178/ijiee.2016.6.2.598.

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Rączka, Krzysztof. "Zadaniowy czas pracy." Praca i Zabezpieczenie Społeczne 2019, no. 2 (February 20, 2019): 18–21. http://dx.doi.org/10.33226/0032-6186.2019.2.4.

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5

Nayeem, Razia V., Tal Oron-Gilad, and P. A. Hancock. "Operators' Time Perception Under Stress." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 51, no. 4 (October 2007): 151–55. http://dx.doi.org/10.1177/154193120705100402.

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Time perception is extremely important to the understanding, design and use of complex military systems. This experiment focused on differences in time estimation, navigation performance, and monitoring tasks. In a between-subjects experiment, participants navigated through a ground scenario while monitoring a screen and listening to white noise at either 55dBA or 85dBA. Performance data was collected throughout the task for both the navigation and monitoring tasks. Participants also completed the NASA-TLX and the DSSQ-S. Statistical analyses showed that the noise condition did not significantly affect workload, monitoring abilities, task completion and time estimates for the dual task. However, the noise did affect subjective state questionnaires. These results suggest that the dual task was not demanding enough and the stress was not adequate to push participants out of the comfort range and experience a performance decrement.

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6

Karweit, Nancy. "Time-on-Task: The Second Time Around." NASSP Bulletin 72, no. 505 (February 1988): 31–39. http://dx.doi.org/10.1177/019263658807250507.

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7

Lei, Zhenyang, Xiangdong Lei, and Jun Long. "Memory-Aware Scheduling Parallel Real-Time Tasks for Multicore Systems." International Journal of Software Engineering and Knowledge Engineering 31, no. 04 (April 2021): 613–34. http://dx.doi.org/10.1142/s0218194021400106.

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Shared resources on the multicore chip, such as main memory, are increasingly becoming a point of contention. Traditional real-time task scheduling policies focus on solely on the CPU, and do not take in account memory access and cache effects. In this paper, we propose parallel real-time tasks scheduling (PRTTS) policy on multicore platforms. Each set of tasks is represented as a directed acyclic graph (DAG). The priorities of tasks are assigned according to task periods Rate Monotonic (RM). Each task is composed of three phases. The first phase is read memory stage, the second phase is execution phase and the third phase is write memory phase. The tasks use locks and critical sections to protect data access. The global scheduler maintains the task pool in which tasks are ready to be executed which can run on any core. PRTTS scheduling policy consists of two levels: the first level scheduling schedules ready real-time tasks in the task pool to cores, and the second level scheduling schedules real-time tasks on cores. Tasks can preempt the core on running tasks of low priority. The priorities of tasks which want to access memory are dynamically increased above all tasks that do not access memory. When the data accessed by a task is in the cache, the priority of the task is raised to the highest priority, and the task is scheduled immediately to preempt the core on running the task not accessing memory. After accessing memory, the priority of these tasks is restored to the original priority and these tasks are pended, the preempted task continues to run on the core. This paper analyzes the schedulability of PRTTS scheduling policy. We derive an upper-bound on the worst-case response-time for parallel real-time tasks. A series of extensive simulation experiments have been performed to evaluate the performance of proposed PRTTS scheduling policy. The results of simulation experiment show that PRTTS scheduling policy offers better performance in terms of core utilization and schedulability rate of tasks.

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8

Khatami, Mostafa, and Amir Salehipour. "Coupled task scheduling with time-dependent processing times." Journal of Scheduling 24, no. 2 (January 21, 2021): 223–36. http://dx.doi.org/10.1007/s10951-020-00675-2.

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9

Bisson, Nicolas, and Simon Grondin. "Time Estimates of Internet Surfing and Video Gaming." Timing & Time Perception 1, no. 1 (2013): 39–64. http://dx.doi.org/10.1163/22134468-00002002.

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Time estimation researchers have long been interested in the effects exerted by the time estimation paradigms, the task’s target duration and the nature of the task on the precision and variability of time estimates. Only a few studies though have focused on how these results can be applied to daily life tasks. Hence, the present study aims at exploring these effects with two different tasks: surfing the Internet and playing a video game. Results show that the time estimation paradigms, target duration and task effects normally observed in short and simple tasks can be extended to daily life tasks. Of relevance, the video gaming task was significantly underestimated compared to the Internet surfing task. These findings are discussed in the light of the current time estimation literature.

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10

Mahan, Robert P. "Time-on-Task Effects within a Probabilistic Task Environment." Perceptual and Motor Skills 72, no. 1 (February 1991): 307–14. http://dx.doi.org/10.2466/pms.1991.72.1.307.

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11

Marshall, Michael J., and William E. Wilsoncroft. "Time Perception and the Stroop Task." Perceptual and Motor Skills 69, no. 3_suppl (December 1989): 1159–62. http://dx.doi.org/10.2466/pms.1989.69.3f.1159.

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Three experiments were conducted to assess the effects of the Stroop task (color-word incongruities) on observers' estimates of 30-sec. inspection periods. The experiments differed in psychophysical procedure; the three classic methods of production, reproduction, and verbal estimation were employed. Observers underestimated the passage of time, compared to doing nothing, when they were engaged on the Stroop task. However, judgments of duration on the Stroop task were shorter than those made in the control condition of naming color dots only when the method of production was employed. These findings are similar to results with mental arithmetic tasks and contribute to the understanding of the relationship between cognitive processing and time perception.

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12

Marshall, Michael J., and William E. Wilsoncroft. "Time Perception and the Stroop Task." Perceptual and Motor Skills 69, no. 3-2 (December 1989): 1159–62. http://dx.doi.org/10.1177/00315125890693-217.

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Three experiments were conducted to assess the effects of the Stroop task (color-word incongruities) on observers’ estimates of 30-sec. inspection periods. The experiments differed in psychophysical procedure; the three classic methods of production, reproduction, and verbal estimation were employed. Observers underestimated the passage of time, compared to doing nothing, when they were engaged on the Stroop task. However, judgments of duration on the Stroop task were shorter than those made in the control condition of naming color dots only when the method of production was employed. These findings are similar to results with mental arithmetic tasks and contribute to the understanding of the relationship between cognitive processing and time perception.

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13

Hamid, Rashida Hakim, and Preeti Shah. "EFFECT OF DUAL TASK EXERCISES ON REACTION TIME IN SCHOOL BASKETBALL PLAYERS." International Journal of Physiotherapy and Research 8, no. 6 (December 11, 2020): 3688–92. http://dx.doi.org/10.16965/ijpr.2020.176.

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Background: Reaction time is the time taken to respond to a stimulus. Reaction time is a pre-requisite of any sports player. A short reaction time is an indicative of swift movements and attentiveness on field of the player. A player on field should have the ability to multitask. This ability is strengthened using dual task exercises. Method: Participants- 27 school basketball players of 13-16 years were included in the study. Hand dominance was assessed using the handedness questionnaire and leg dominance was assessed by asking the participant to kick the ball. Reaction time was assessed using the reaction timer and dynamic balanced was assessed using the Y balance test. Both the parameters were recorded as a pretest and posttest after intervention of dual task exercises. As an intervention 3 dual task exercises throwing and catching a ball while walking, spot marching and jump up to reach targets and side marching and passing the ball were used. Each exercise was done for a period of 8-10 mins respectively. During this time their regular basketball practice and physical fitness exercises were continued in school respectively. Results: The data was analyzed using SPSS version 24.0. A significant change was found in the reaction time of basketball players with p Value obtained as 7.26E-06. The balance component showed a significant improvement as well. P Values obtained for Anterior direction is 0.048, for posteromedial direction is 0.053and for posterolateral direction is 0.014. Conclusion: Dual task exercises along with basketball training were effective in improving the reaction time and dynamic balance in basketball players. KEY WORDS: Reaction time, dual task exercises, Dynamic balance, Y balance test, School basketball players.

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14

Venanzi, Matteo, John Guiver, Pushmeet Kohli, and Nicholas R. Jennings. "Time-Sensitive Bayesian Information Aggregation for Crowdsourcing Systems." Journal of Artificial Intelligence Research 56 (July 28, 2016): 517–45. http://dx.doi.org/10.1613/jair.5175.

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Many aspects of the design of efficient crowdsourcing processes, such as defining workers bonuses, fair prices and time limits of the tasks, involve knowledge of the likely duration of the task at hand. In this work we introduce a new timesensitive Bayesian aggregation method that simultaneously estimates a tasks duration and obtains reliable aggregations of crowdsourced judgments. Our method, called BCCTime, uses latent variables to represent the uncertainty about the workers completion time, the tasks duration and the workers accuracy. To relate the quality of a judgment to the time a worker spends on a task, our model assumes that each task is completed within a latent time window within which all workers with a propensity to genuinely attempt the labelling task (i.e., no spammers) are expected to submit their judgments. In contrast, workers with a lower propensity to valid labelling, such as spammers, bots or lazy labellers, are assumed to perform tasks considerably faster or slower than the time required by normal workers. Specifically, we use efficient message-passing Bayesian inference to learn approximate posterior probabilities of (i) the confusion matrix of each worker, (ii) the propensity to valid labelling of each worker, (iii) the unbiased duration of each task and (iv) the true label of each task. Using two real- world public datasets for entity linking tasks, we show that BCCTime produces up to 11% more accurate classifications and up to 100% more informative estimates of a tasks duration compared to stateoftheart methods.

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15

Hsieh, Shulan. "Task Shifting in Dual-Task Settings." Perceptual and Motor Skills 94, no. 2 (April 2002): 407–14. http://dx.doi.org/10.2466/pms.2002.94.2.407.

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When a participant is asked to perform two tasks in alternation, their mean reaction times were slower than when they performed the same tasks repeatedly. This “shift cost” has been hypothesized to reflect the time course of a single central executive that exerts control over thought and actions in task shifting. This study attempted to test this hypothesis using dual-task methodology. Participants were asked to carry out both a subtracting task and a rule-shifting task simultaneously. The main interest is to examine the effect of dual task on the magnitude of shift cost. The results showed that performing a concurrent subtracting task significantly interfered with the shifting operation resulting in over-additive time cost for shifting of task set. We further suggest that such interference may arise from the competition between activations of various rules.

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Cho, Moon-Haeng, Soong-Yeol Lee, Won-Yong Lee, Geun-Jae Jeong, Yong-Hee Kim, and Cheol-Hoon Lee. "Deterministic Real-Time Task Scheduling." Journal of the Korea Contents Association 7, no. 1 (January 28, 2007): 73–82. http://dx.doi.org/10.5392/jkca.2007.7.1.073.

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Wohldmann, Erica L., Alice F. Healy, and Lyle E. Bourne. "Task integration in time production." Attention, Perception, & Psychophysics 72, no. 4 (May 2010): 1130–43. http://dx.doi.org/10.3758/app.72.4.1130.

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18

O'Malley, Michael. "Time, Work and Task Orientation." Time & Society 1, no. 3 (September 1992): 341–58. http://dx.doi.org/10.1177/0961463x92001003002.

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19

Coviello, Decio, Andrea Ichino, and Nicola Persico. "Time Allocation and Task Juggling." American Economic Review 104, no. 2 (February 1, 2014): 609–23. http://dx.doi.org/10.1257/aer.104.2.609.

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A single worker allocates her time among different projects which are progressively assigned. When the worker works on too many projects at the same time, the output rate decreases and completion time increases according to a law which we derive. We call this phenomenon “task juggling” and argue that it is pervasive in the workplace. We show that task juggling is a strategic substitute of worker effort. We then present a model where task juggling is the result of lobbying by clients, or coworkers, each seeking to get the worker to apply effort to his project ahead of the others’. (JEL J22, M12, M54)

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20

Dyson, Anne Haas. "The Value of "Time Off Task": Young Children's Spontaneous Talk and Deliberate Text." Harvard Educational Review 57, no. 4 (December 1, 1987): 396–421. http://dx.doi.org/10.17763/haer.57.4.j3743l47g0k60m59.

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Anne Haas Dyson analyzes primary students' spontaneous, unsanctioned talk in the classroom and argues that these interactions — often regarded as "time off task" — can become occasions for engaging in intellectually demanding tasks. Drawing upon research conducted over a two-year period in an urban elementary school, the author presents an overview of the accomplishments of children who, without explicit directions from their teacher, collaborated with one another to create extended stories and critique the logic of those stories. Dyson maintains that these examples of spontaneous talk supported the intellectual development of these beginning writers, thereby extending conventional definitions of students' "on" and "off" task behavior.

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Nikiforov. "Task scheduling in time-triggered real-time systems." SPIIRAS Proceedings 1, no. 1 (March 17, 2014): 129. http://dx.doi.org/10.15622/sp.1.10.

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22

Masci, Frank. "Time for Time on Task and Quality Instruction." Middle School Journal 40, no. 2 (November 2008): 33–41. http://dx.doi.org/10.1080/00940771.2008.11461670.

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23

Furst, David M., and Gershon Tenenbaum. "Influence of Attentional Focus on Reaction Time." Psychological Reports 56, no. 1 (February 1985): 299–302. http://dx.doi.org/10.2466/pr0.1985.56.1.299.

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It was hypothesized that attention could be directed to the emphasized task regardless of its spatial location. 20 subjects performed a simple RT to a stimulus located in foveal vision and a simple RT to four surrounding stimuli set in the visual periphery. Importance of task was manipulated through instructions. Analysis of variance showed subjects had shorter RTs to the emphasized area regardless of its spatial location. The attentional demands of the tasks and the importance assigned to the tasks were critical factors in response speed. This may help to explain the results of visual-narrowing experiments which have indicated narrowing after placing an attention-demanding task in foveal vision.

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Ezrati-Vinacour, Ruth, and Iris Levin. "Time Estimation by Adults Who Stutter." Journal of Speech, Language, and Hearing Research 44, no. 1 (February 2001): 144–55. http://dx.doi.org/10.1044/1092-4388(2001/013).

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In view of the fact that stuttering involves time pressure in communicative contexts, the aim of this investigation was to study the effect of stuttering on time estimation. Two matching groups of 47 adults each, one consisting of stutterers and the other of fluent speakers, estimated the duration of four verbal tasks—two that involved speaking and two that did not. Two methods of time estimation were used: production and reproduction. In production, participants were required to perform a task, terminating it when they felt that the specified interval had elapsed. In reproduction, participants were asked to estimate the duration of a task immediately after being stopped by the experimenter. The results reveal that the differences in time estimation between adults who stutter and fluent speakers were task dependent, with the stutterers estimating time less accurately on oral verbal tasks. The conversation task in particular highlighted the inaccuracy of their time estimation. Furthermore, severe stutterers estimated time less accurately than mild stutterers. The greatest inaccuracy was displayed by severe stutterers when estimating the time of an oral task by the production method. The results are discussed in terms of cognitive models based on attention to time, method measurement, and mental workload. Clinical implications are also suggested.

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Deng, Chao, Shi Cao, Chaozhong Wu, and Nengchao Lyu. "Modeling Driver Take-Over Reaction Time and Emergency Response Time using an Integrated Cognitive Architecture." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 12 (July 16, 2019): 380–90. http://dx.doi.org/10.1177/0361198119842114.

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Drivers’ take-over reaction time in partially automated vehicles is a fundamental component of automated vehicle design requirements, and take-over reaction time is affected by many factors such as distraction and drivers’ secondary tasks. This study built cognitive architecture models to simulate drivers’ take-over reaction time in different secondary task conditions. Models were built using the queueing network-adaptive control of thought rational (QN-ACTR) cognitive architecture. Drivers’ task-specific skills and knowledge were programmed as production rules. A driving simulator program was connected to the models to produce prediction of reaction time. Model results were compared with human results in both single-task and multi-task conditions. The models were built without adjusting any parameter to fit the human data. The models could produce simulation results of take-over reaction time similar to the human results in take-over conditions with visual or auditory concurrent tasks, as well as emergency response time in a manual driving condition. Overall, R square was 0.96, root mean square error (RMSE) was 0.5 s, and mean absolute percentage error (MAPE) was 9%. The models could produce simulation results of reaction time similar to the human results from different task conditions. The production rules are plausible representations of drivers’ strategies and skills. The models provide a useful tool for the evaluation of take-over alert design and the prediction of driver performance.

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Gutzwiller, Robert S., Christopher D. Wickens, and Benjamin A. Clegg. "The Role of Time on Task in Multi-task Management." Journal of Applied Research in Memory and Cognition 5, no. 2 (June 2016): 176–84. http://dx.doi.org/10.1016/j.jarmac.2016.04.003.

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Johnson, R. S., L. B. Lempke, J. D. Schmidt, and R. C. Lynall. "Dynamic Reaction Time: Jumping into the Future of Concussion Management." Archives of Clinical Neuropsychology 34, no. 5 (July 2019): 788. http://dx.doi.org/10.1093/arclin/acz026.58.

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Abstract Purpose To explore the relationship between reaction time (RT) on a commonly used post-concussion computerized neurocognitive assessment and dynamic RT during sport-like movements, with and without a cognitive task. Methods Fourteen healthy individuals (7 females; age=22.3±2.5yrs, height=169.1±14.0cm, mass=71.1±16.8kg) completed a computerized Stroop task (CNS Vital Signs), where the individual presses the space bar when the color of the word does not match the word, and two dynamic RT tasks (jump landing [4 trials], anticipated cutting [8 trials]). Dynamic RT tasks were performed with (dual-task) and without (single-task) a cognitive task (subtracting by 6’s or 7’s). Participants jumped off of a 30cm box and then either performed a maximum vertical jump landing or an anticipated cut at 45. Dynamic RT, recorded using high-speed 3D-motion capture (Qualisys), was the average time (seconds) between visual stimulus and when participants’ sacrum moved >3cm in the sagittal or transverse plane. Pearson correlation coefficients (α=0.05) were calculated between all RT measures. Results Stroop RT (0.67±0.01s) was not significantly correlated with jump landing single-task (p=0.45; r=0.22), jump landing dual-task (p=0.10; r=0.45), anticipated cutting single-task (p=0.7; r=0.11), or anticipated cutting dual-task (p=0.85; r=0.06) RTs. We found positive correlations between all single- and dual-task dynamic RTs (p=.03, r=.56). Conclusion Stroop RT was not correlated with dynamic RTs, which may more closely reflect RT associated with sport participation. Commonly used computerized RT assessments may not fully represent the dynamic RT athletes need in sport tasks. Further investigation is warranted in order to develop the most appropriate RT assessments for post-concussion return-to-play.

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Nauert, Elliot, and Douglas J. Gillan. "Individual Measures of Time Perception Predict Performance in a Timed Reaching Task." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 61, no. 1 (September 2017): 1380–84. http://dx.doi.org/10.1177/1541931213601829.

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In temporally-constrained reaching tasks, participants make rapid movements to a target while making their movements last a designated length of time. It has been well-established that effective target width, a measure of spatial accuracy, increases linearly with movement speed. This study sought to understand how individual differences in temporal sensitivity affect this speed-accuracy tradeoff. It was found that time sensitivity did not affect spatial components of the timed reaching task, but it was related to temporal components of the task. Ideas regarding the role of time perception in movement planning as well as differences in movement strategies for short and long target intervals are discussed.

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Ohmura, Yu, Iku Tsutsui-Kimura, and Mitsuhiro Yoshioka. "Assessment of attentional function and impulsivity using 5-choice serial reaction time task/3-choice serial reaction time task." Folia Pharmacologica Japonica 134, no. 3 (2009): 137–41. http://dx.doi.org/10.1254/fpj.134.137.

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Wang, Hui, Cheng Xu, Li Ning Zeng, and Yan Liu. "A Mixed Scheduling Algorithm about Hard Periodic and Soft Aperiodic Real-Time Tasks on Heterogeneous Multiprocessor." Advanced Materials Research 950 (June 2014): 209–13. http://dx.doi.org/10.4028/www.scientific.net/amr.950.209.

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This paper studies the problem about scheduling composition of periodic real-time tasks and aperiodic soft real-time tasks in heterogeneous multiprocessor environment. It analyzes the response time of each task in periodic real-time task set and the influence factor about the response time of aperiodic soft real-time task. We use a new mixed scheduling algorithm--UEDF and Task-Centric with Slack Defragmentation algorithm (TCSD) to schedule hybrid task set which consist of the periodic real-time tasks and aperiodic soft real-time tasks. It can improve the timeliness of aperiodic soft real-time tasks response, so the ratio of aperiodic tasks to meet soft deadline will increase.

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van der Lubbe, Rob H. J., Piotr Jaśkowski, Bernd Wauschkuhn, and Rolf Verleger. "Influence of Time Pressure in a Simple Response Task, a Choice-by-Location Task, and the Simon Task." Journal of Psychophysiology 15, no. 4 (October 2001): 241–55. http://dx.doi.org/10.1027//0269-8803.15.4.241.

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Abstract The influence of strategy was examined for a simple response task, a choice-by-location task, and the Simon task by varying time pressure. Besides reaction time (RT) and accuracy, we measured response force and derived two measures from the event-related EEG potential to form an index for attentional orienting (posterior contralateral negativity: PCN) and the start of motor activation (the lateralized readiness potential: LRP). For the choice-by-location task and the Simon task, effects of time pressure were found on the response-locked LRP, but not on the onset of the PCN and the stimulus-locked LRP. Thus, strategy influences processing after the start of motor activation in choice tasks. A small effect of time pressure was found on the peak latency of the PCN in the Simon task, which suggests that time pressure may affect attentional orienting. In the simple response task, time pressure reduced the amplitude of the PCN. This finding suggests that strategy affects attentional orienting to stimuli when these stimuli are not highly relevant. Finally, the effect of time pressure on RT was much larger in the simple response task than in the other tasks, which may be ascribed to the possibility of preparing the required response in the simple response task.

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Soltanlou, Mojtaba, Mohammad Ali Nazari, Parisa Vahidi, and Parvin Nemati. "Explicit and Implicit Timing of Short Time Intervals: Using the Same Method." Perception 49, no. 1 (November 20, 2019): 39–51. http://dx.doi.org/10.1177/0301006619889554.

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Up until now, there has been no study conducted in the field of time perception using very short intervals for a direct comparison between implicit and explicit timing tasks in order to uncover plausibly different underlying mechanisms. Therefore, the aim of this study was to compare human time estimation during implicit and explicit timing tasks with short intervals and the same method. A total of 81 adults were divided into three groups and completed two tasks with one of three different intervals: 500, 1,000, and 2,000 ms. The results revealed an overestimation for all three intervals of the implicit timing task, while participants overestimated 500 ms but underestimated 1,000 and 2,000 ms intervals of the explicit timing task. Moreover, explicit time estimation was more precise than implicit time estimation. We observed the opposite pattern as compared to a few previous studies with long intervals: Short intervals were perceived longer in the implicit timing task as compared to the explicit timing task. We concluded that nontemporal contents represent passing time during the implicit timing task but unlike temporal dimension during the explicit timing task. Therefore, even the same method of measurement led to a different performance in implicit and explicit timing tasks.

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Haga, Shigeru, Haruo Shinoda, and Mitsuteru Kokubun. "Effects of task difficulty and time-on-task on mental workload." Japanese Psychological Research 44, no. 3 (August 6, 2002): 134–43. http://dx.doi.org/10.1111/1468-5884.00016.

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Tanaka, Daisuke, and Yuka Mizuuji. "Subjective time evaluation during probabilistic serial reaction time task." Proceedings of the Annual Convention of the Japanese Psychological Association 82 (September 25, 2018): 3EV—053–3EV—053. http://dx.doi.org/10.4992/pacjpa.82.0_3ev-053.

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35

Bregman, Robert L. "Managing project task time uncertainty with probabilistic time buffers." International Journal of Advanced Operations Management 3, no. 1 (2011): 40. http://dx.doi.org/10.1504/ijaom.2011.040659.

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36

EKERDT, DAVID J., and CATHERYN KOSS. "The task of time in retirement." Ageing and Society 36, no. 06 (June 10, 2015): 1295–311. http://dx.doi.org/10.1017/s0144686x15000367.

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ABSTRACTRetirees' encounter with time has long interested social scientists, especially the negotiation of such an open-ended status. Pursuing theoretical suggestions that daily activities anchor a narrative of self-identity, this project examined the coherence of retirees' representations of their time use. Information is drawn from interviews with 30 retirees in the Midwestern United States of America who were invited to discuss their daily lives and activities. The retirees valued time sovereignty and accounted for their time use by describing schedules of activities in some detail. Daily time was not presented as improvised but rather as structured into routines. Recurring behaviours flowed from situations and structures in which people were implicated, such as body care and living with others. Even in replies to a specific question about the preceding day, people slipped into language about what they typically do. Retirees' ready narratives about routines were also accounts of who they are not. Our findings suggest, first, that daily routines are instrumental for retirees in economising thought and behaviour. Second, the assertion of a routine is an assurance that one's life is ordered and proceeds with purpose, thus solving the task of time. Third, routines can be a means to signal conformity with ideals of active ageing.

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Schuster, John W., and Ann K. Griffen. "Using Time Delay with Task Analyses." TEACHING Exceptional Children 22, no. 4 (July 1990): 49–53. http://dx.doi.org/10.1177/004005999002200413.

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Maqsood, Maria, Saima Anwar Lashari, Murtaja Ali Saare, Sari Ali Sari, Yaqdhan Mahmood Hussein, and Hatem Oday Hatem. "Minimization Response Time Task scheduling Algorithm." IOP Conference Series: Materials Science and Engineering 705 (December 2, 2019): 012008. http://dx.doi.org/10.1088/1757-899x/705/1/012008.

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Hardy, Colin A. "Teacher communication and time on-task." Research in Education 49, no. 1 (May 1993): 29–38. http://dx.doi.org/10.1177/003452379304900104.

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Hemamalini, M., and M. V. "Response Time Minimization Task Scheduling Algorithm." International Journal of Computer Applications 145, no. 1 (July 15, 2016): 9–14. http://dx.doi.org/10.5120/ijca2016910532.

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Leppink, Jimmie. "When I say … time on task." Medical Education 51, no. 11 (April 5, 2017): 1101–2. http://dx.doi.org/10.1111/medu.13298.

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Dufour, André, Pascale Touzalin, and Victor Candas. "Time-on-task effect in pseudoneglect." Experimental Brain Research 176, no. 3 (December 5, 2006): 532–37. http://dx.doi.org/10.1007/s00221-006-0810-2.

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Sussman, Rachel, Robert Sekuler, Mercedes Villalonga, and Hannah Snyder. "Flanker task under (perceived) time pressure." Journal of Vision 21, no. 9 (September 27, 2021): 2953. http://dx.doi.org/10.1167/jov.21.9.2953.

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Lodinger, Natalie R., and Patricia R. DeLucia. "Does automated driving affect time-to-collision judgments?" Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1 (September 2018): 1833. http://dx.doi.org/10.1177/1541931218621417.

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Automation presumably frees cognitive resources because drivers do not have to control the vehicle. Those resources may be reallocated to processing visual information relevant to driving, such as optic flow, which is relevant for judgments of time-to-collision (TTC). On the other hand, drivers may not use cognitive resources freed during automation to process information relevant to the driving task and improve performance. Drivers may choose to allocate cognitive resources freed during automation to non-driving, secondary tasks (Merat, Jamson, Lai, & Carsten, 2012; Rudin-Brown & Parker, 2004). Therefore, automated driving may lead to performance decrements, particularly when drivers need to resume manual control of the vehicle (Strand, Nilsson, Karlsson, & Nilsson, 2014). The current study compared TTC judgments between automated and manual driving, using a prediction-motion (PM) task which presumably relies on cognitive resources (Tresilian, 1995). We included a braking task to determine whether we could replicate prior reports that drivers brake later during automated driving compared to manual driving (Rudin-Brown & Parker, 2004; de Winter, Happee, Martens & Stanton, 2014). Including PM and braking tasks let us determine whether automation affected only responses (i.e., brake reaction time) or also affected visual perception (i.e., TTC estimation). We hypothesized that automation would affect perceptual judgments rather than solely responses. We expected TTC judgments to be more accurate during automated driving compared to manual driving. We also expected that adding a secondary task that demands cognitive resources would be more detrimental to TTC judgments during automation because the driver would place more cognitive resources on the secondary task during automation than when manually controlling the vehicle. With a driving simulator, participants completed eight drives using manual or automated driving. During half of the drives, participants completed a secondary task, the twenty questions task (TQT), in addition to driving. The TQT is presumably similar to a cell phone conversation because it uses a “question and answer” format (Horrey, Lesch, & Garabet, 2009; Merat et al., 2012, p. 765). At the end of each drive, a critical incident occurred. A vehicle directly in front of the participant’s vehicle decelerated at a rate faster than the automation was capable of braking. Therefore, the automation did not respond to this vehicle’s deceleration. In the braking task, participants used the brake pedal to avoid collision with the lead vehicle. In the PM task, the lead vehicle decelerated for between 0.24 and 3.04 s and then the screen went black. Participants pressed a button to indicate when they thought their vehicle would have hit the lead vehicle if the vehicles’ motions continued in the same manner after the screen went black. Results suggest that automation can affect perceptual judgments in addition to driving responses (e.g., braking). TTC judgments were more accurate, and brake reaction time was faster, during automated driving than manual driving. This occurred even while performing a cognitively-demanding secondary task, suggesting that participants used resources freed by automation to process visual information relevant to TTC judgments rather than complete non-driving tasks. To realize this safety benefit, it is important to design automated systems so that freed cognitive resources are assigned to information relevant to the driving task and not to non-driving tasks.

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Zhang, Guoxi, and Robert Feyen. "Hierarchical Task Prioritization Behavior in Two- and Four-Task Scenarios." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 51, no. 4 (October 2007): 191–95. http://dx.doi.org/10.1177/154193120705100410.

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This paper describes an empirical study conducted to validate a computational model of dynamic task prioritization based on a framework proposed by Zhang and Feyen (2005). Three key factors in task prioritization were manipulated: processing time, available time, and task valence. Because earlier studies did not investigate how people prioritize tasks when valence and temporal characteristics conflict, this study examined how these conflicts are resolved. 20 subjects completed 54 time-limited task scenarios. Each scenario consisted of two or four concurrent tasks, each assigned a point value for completion. Subjects were instructed to maximize points scored. Results indicated that, although valence was predominant in determining task selection, it failed to explain all instances. Instead, a hierarchy of task prioritization was revealed in which subjects first checked what tasks were doable (e.g., self-efficacy), then applied rules first regarding valence, then temporal characteristics, and then others (e.g., task location).

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Froger, Grégory, Colin Blättler, Emilien Dubois, Cyril Camachon, and Nathalie Bonnardel. "Time-Interval Emphasis in an Aeronautical Dual-Task Context: A Countermeasure to Task Absorption." Human Factors: The Journal of the Human Factors and Ergonomics Society 60, no. 7 (July 16, 2018): 936–46. http://dx.doi.org/10.1177/0018720818783946.

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Objective: We tested a training method intended to prevent unsafe aeronautical behavior (i.e., too much time spent gazing inside the cockpit) induced by the modern cockpit, by teaching individuals to perform a task complementing the see-and-avoid mandatory safety task within a limited time interval. Background: Aeronautical activities led crews to perform several tasks simultaneously in an ergonomic environment under constant change. See and avoid remains one of the main safety tasks during visual flight. However, modern cockpits induce absorption and impair performance of this safety task. Many laboratory studies showed the relevance of training methods for managing dual-task situations and estimating time intervals. Method: A specific virtual environment was developed to expose participants to a dual-task situation in which time-interval emphasis was provided in real time. Two types of emphasis training were tested: a permissive one that allowed participants to pursue the inside-cockpit task beyond the time limit and a nonpermissive one that did not. Results: The best time-interval acquisition, with retention up to 24 hr later, was observed in the nonpermissive condition, but task performances immediately after the training sessions were equivalent across conditions. Conclusion: Time-emphasis training appears to be an efficient means of promoting absorption resistance while preserving task performance. Transferability of time-interval estimation skills has yet to be tested. Application: Most areas of application for absorption resistance (aviation, shipping, rail, road, etc.) could benefit from this type of training to manage multitask situations.

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Chen, Da Ren, and You Shyang Chen. "Efficient Scheduling for Real-Time Pinwheel Tasks on DVS Processors." Applied Mechanics and Materials 479-480 (December 2013): 901–5. http://dx.doi.org/10.4028/www.scientific.net/amm.479-480.901.

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In this paper, we focus on the pinwheel task model for a variable voltage processor with d discrete voltage/speed levels. We propose an intra-task DVS algorithm which constructs a minimum energy schedule for k tasks in O(d+ k log k) time. Previous approaches solve this problem by generating a canonical schedule beforehand and adjusting the tasks' speed in O(dn log n) or O(n3) time. However, the length of a canonical schedule depends on the hyperperiod of those task periods and is of exponential length in general. In our approach, the tasks with arbitrary periods are first transformed into harmonic periods and then profile their key features. Afterward, an optimal discrete voltage schedule can be computed directly from those features.

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Yang, You Dong, Wei Qing Guo, and Xia Xia Hu. "Time Window Constraint Conflict Resolution Algorithm for Collaborative Design Process Scheduling." Advanced Materials Research 102-104 (March 2010): 822–26. http://dx.doi.org/10.4028/www.scientific.net/amr.102-104.822.

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Aim at complex relationships among design tasks after task grouping for the collaborative product design process scheduling, the constraint conflicts of the task time windows would be generated in the task scheduling process, and the reasons of constraint conflicts are analyzed. Based on the least waiting times and time, a time window constraint conflict resolution algorithm is put forward, and the high parallel requirement is implemented for the collaborative design process.

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Spitz, Gabriel. "Flexibility in Resource Allocation and the Performance of Time-Sharing Tasks." Proceedings of the Human Factors Society Annual Meeting 32, no. 19 (October 1988): 1466–70. http://dx.doi.org/10.1177/154193128803201934.

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The extent and nature of the ability to control the allocation of mental resources between the components of a dual task was investigated in three separate experiments. Using a variable priority (demand) methodology it was found that subjects could manipulate their performance level, however their ability to meet specific demand levels was limited. Training subjects under single or dual-task conditions using a wide range of task demand significantly improved dual task performance and degree of control over resource allocation as compared to performance following practice under a narrow range of task demands or under single task fixed demand conditions. Single task performance among all groups improved to the same degree. It was concluded that training subjects under a wide range of task demands increases the range of performance levels over which mental resources can be flexibly allocated for those tasks and improves time sharing performance. Implications for the design of training for complex task performance are discussed.

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Shi, Lei, Jing Xu, Lunfei Wang, Jie Chen, Zhifeng Jin, Tao Ouyang, Juan Xu, and Yuqi Fan. "Multijob Associated Task Scheduling for Cloud Computing Based on Task Duplication and Insertion." Wireless Communications and Mobile Computing 2021 (April 28, 2021): 1–13. http://dx.doi.org/10.1155/2021/6631752.

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With the emergence and development of various computer technologies, many jobs processed in cloud computing systems consist of multiple associated tasks which follow the constraint of execution order. The task of each job can be assigned to different nodes for execution, and the relevant data are transmitted between nodes to complete the job processing. The computing or communication capabilities of each node may be different due to processor heterogeneity, and hence, a task scheduling algorithm is of great significance for job processing performance. An efficient task scheduling algorithm can make full use of resources and improve the performance of job processing. The performance of existing research on associated task scheduling for multiple jobs needs to be improved. Therefore, this paper studies the problem of multijob associated task scheduling with the goal of minimizing the jobs’ makespan. This paper proposes a task Duplication and Insertion algorithm based on List Scheduling (DILS) which incorporates dynamic finish time prediction, task replication, and task insertion. The algorithm dynamically schedules tasks by predicting the completion time of tasks according to the scheduling of previously scheduled tasks, replicates tasks on different nodes, reduces transmission time, and inserts tasks into idle time slots to speed up task execution. Experimental results demonstrate that our algorithm can effectively reduce the jobs’ makespan.

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