heuristics (not biases), rules of thumb, mental shortcuts, strategies ignoring information
(…) 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 |
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 |
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.
You can’t play 20 questions with nature and win. |
Allen Newell (1973) |
Hansjörg Neth, Chris R. Sims, Vladislav D. Veksler, Wayne D. Gray
Abstract: To investigate people’s ability to update memory in a dynamic task environment we use the experimental card game TRACS^tm (Burns, 2001). In many card games card counting is a component of optimal performance. However, for TRACS, Burns (2002a) reported that players exhibited a baseline bias: rather than basing their choices on the actual number of cards remaining in the deck, they chose cards based on the initial composition of the deck. Both a task analysis and computer simulation show that a perfectly executed memory update strategy has minimal value in the original game, suggesting that a baseline strategy is a rational adaptation to the demands of the original game. We then redesign the game to maximize the difference in performance between baseline and update strategies. An empirical study with the new game shows that players perform much better than could be achieved by a baseline strategy. Hence, we conclude that people will adopt a memory update strategy when the benefits outweigh the costs.