Understanding multitasking through parallelized strategy exploration and individualized cognitive modeling

Zhang, Yunfeng; Hornof, Anthony J.

doi:10.1145/2556288.2557351

Cited by 14 publications

(11 citation statements)

References 17 publications

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“…This notion of flexible strategies has recently also been proposed for threaded cognition models (Nijboer, Borst, van Rijn, & Taatgen, ), although in that study it is proposed as a hallmark of novice behavior. In contrast, in our work, flexible interleaving in response to relative value of tasks is a hallmark of skilled adaptive behavior (similar to models in Howes et al., ; Janssen et al., ; Janssen & Brumby, ; Zhang & Hornof, ).…”

Section: Discussionsupporting

confidence: 72%

Dividing Attention Between Tasks: Testing Whether Explicit Payoff Functions Elicit Optimal Dual‐Task Performance

et al. 2017

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We test people's ability to optimize performance across two concurrent tasks. Participants performed a number entry task while controlling a randomly moving cursor with a joystick. Participants received explicit feedback on their performance on these tasks in the form of a single combined score. This payoff function was varied between conditions to change the value of one task relative to the other. We found that participants adapted their strategy for interleaving the two tasks, by varying how long they spent on one task before switching to the other, in order to achieve the near maximum payoff available in each condition. In a second experiment, we show that this behavior is learned quickly (within 2–3 min over several discrete trials) and remained stable for as long as the payoff function did not change. The results of this work show that people are adaptive and flexible in how they prioritize and allocate attention in a dual‐task setting. However, it also demonstrates some of the limits regarding people's ability to optimize payoff functions.

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Section: Discussionsupporting

confidence: 72%

Dividing Attention Between Tasks: Testing Whether Explicit Payoff Functions Elicit Optimal Dual‐Task Performance

et al. 2017

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“…However, a lesson of cognitive architecture modeling is that even in simple tasks, subjects can adopt subtle strategies that can obscure the underlying mechanisms unless taken into account (Kieras & Meyer 2000;Meyer & Kieras 1999;Zhang & Hornof 2014). In our models of visual search, the task strategy plays a role in determining the object to be next fixated and, when there are no clear candidates for the next saccade, there are a variety of possible decisions that could be made, and these decisions affect the predicted search performance.…”

Section: Laurent Ittimentioning

confidence: 99%

The impending demise of the item in visual search

Hulleman¹,

Olivers²

2015

Behav Brain Sci

102

118

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The way the cognitive system scans the visual environment for relevant information - visual search in short - has been a long-standing central topic in vision science. From its inception as a research topic, and despite a number of promising alternative perspectives, the study of visual search has been governed by the assumption that a search proceeds on the basis of individual items (whether processed in parallel or not). This has led to the additional assumptions that shallow search slopes (at most a few tens of milliseconds per item for target-present trials) are most informative about the underlying process, and that eye movements are an epiphenomenon that can be safely ignored. We argue that the evidence now overwhelmingly favours an approach that takes fixations, not individual items, as its central unit. Within fixations, items are processed in parallel, and the functional field of view determines how many fixations are needed. In this type of theoretical framework, there is a direct connection between target discrimination difficulty, fixations, and reaction time (RT) measures. It therefore promises a more fundamental understanding of visual search by offering a unified account of both eye movement and manual response behaviour across the entire range of observed search efficiency, and provides new directions for research. A high-level conceptual simulation with just one free and four fixed parameters shows the viability of this approach.

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“…To overcome these temporal overlaps, different task strategies have been evaluated and compared in previous studies. There are two types of task strategies that have been discussed and evaluated in much of the research on multitasking performance: serial and parallel strategies (Benbunan-Fich et al, 2011;Brumby, Salvucci, & Howes, 2009;Zhang & Hornof, 2014).…”

Section: Multitasking Strategymentioning

confidence: 99%

“…Previous studies on cognitive modeling also reveal that the application of different multitasking strategies will affect performance and impact safety (Brumby, Howes, & Salvucci, 2007;Brumby et al, 2009;Zhang & Hornof, 2014). Comparison between these two types of task strategies has been explored in some studies on multitasking.…”

Section: Multitasking Strategymentioning

confidence: 99%