multi-tasking (discretionary interleaving, task switching, dual task performance, exploration vs. exploitation)
|If an organism is confronted with the problem of behaving approximately rationally,
or adaptively, in a particular environment, the kinds of simplifications that are suitable
may depend not only on the characteristics—sensory, neural, and other—of the organism,
but equally on the nature of the environment.
|H.A. Simon (1956), Rational choice and the structure of the environment, p. 130|
[Copyright neth.de, 2008]:
Hans Neth, Sunny Khemlani, Wayne Gray (2008)
Feedback design for the control of a dynamic multitasking system: Dissociating outcome feedback from control feedback. Human Factors Journal, 2008.
Hansjörg Neth, Sangeet S. Khemlani, Wayne D. Gray
Objective: We distinguish outcome feedback from control feedback to show that suboptimal performance in a dynamic multitasking system may be caused by limits inherent to the information provided rather than human resource limits.
|(…) we make search in our memory for a forgotten idea, just as we rummage our house for a lost object. In both cases we visit what seems to us the probable neighborhood of that which we miss. We turn over the things under which, or within which, or alongside of which, it may possibly be;
and if it lies near them, it soon comes to view.
|William James (1890), The Principles of Psychology, p. 654|
[Copyright neth.de, 2007–2014]:
Steve Payne, Geoff Duggan, Hans Neth (2007).
Discretionary task interleaving: Heuristics for time allocation in cognitive foraging.
Journal article in JEP:G.
Stephen J. Payne, Geoffrey B. Duggan, Hansjörg Neth
Abstract: When participants allocated time across 2 tasks (in which they generated as many words as possible from a fixed set of letters), they made frequent switches. This allowed them to allocate more time to the more productive task (i.e., the set of letters from which more words could be generated) even though times between the last word and the switch decision (“giving-up times”) were higher in the less productive task. These findings were reliable across 2 experiments using Scrabble tasks and 1 experiment using word-search puzzles. Switch decisions appeared relatively unaffected by the ease of the competing task or by explicit information about tasks’ potential gain. The authors propose that switch decisions reflected a dual orientation to the experimental tasks. First, there was a sensitivity to continuous rate of return — an information-foraging orientation that produced a tendency to switch in keeping with R. F. Green’s (1984) rule and a tendency to stay longer in more rewarding tasks. Second, there was a tendency to switch tasks after subgoal completion. A model combining these tendencies predicted all the reliable effects in the experimental data.
|Doing two things at once, like singing while you take a shower,
is not the same as instant messaging while writing a research report.
Don’t fool yourself into thinking you can multitask jobs that need your
full attention. You’re not really having a conversation while you write;
you’re shifting your attention back and forth between the two activities quickly.
You’re juggling. When you juggle tasks, your work suffers AND takes longer
— because switching tasks costs.
|Gina Trapani, Work Smart, FastCompany.com|
[Copyright neth.de, 2006]:
Hans Neth, Brittney Oppermann, Sunny Khemlani, Wayne Gray (2006)
Juggling multiple tasks: A rational analysis of multitasking in a synthetic task environment.
Paper presented at HFES 2006, San Francisco, CA, USA.
Hansjörg Neth, Sangeet S. Khemlani, Brittney Oppermann, Wayne D. Gray
Abstract: Tardast (Shakeri 2003; Shakeri & Funk, in press) is a new and intriguing paradigm to investigate human multitasking behavior, complex system management, and supervisory control. We present a replication and extension of the original Tardast study that assesses operators’ learning curve and explains gains in performance in terms of increased sensitivity to task parameters and a superior ability of better operators to prioritize tasks. We then compare human performance profiles to various artificial software agents that provide benchmarks of optimal and baseline performance and illustrate the outcomes of simple heuristic strategies. Whereas it is not surprising that human operators fail to achieve an ideal criterion of performance, we demonstrate that humans also fall short of a principally achievable standard. Despite significant improvements with practice, Tardast operators exhibit stable sub-optimal performance in their time-to-task allocations.