2011, Neth, Hansjörg, Payne, Stephen J.

Does using our hands help us to add the value of a set of coins? We test the benefits and costs of direct interaction with a men- tal arithmetic task in a computerized yoked design in which groups of participants vary in their interactive mode (move vs. look) and the initial configuration of coins (pseudo-random vs. another mover’s final layout). By assessing performance and conducting a microgenetic analysis of the strategies employed we argue that the purpose of movement is the result, rather than the process of moving. Participants move coins in order to sort, rather than to mark, and select them by value, rather than by location. They spontaneously create remarkably smart solutions, thereby incidentally creating physical configurations that can help other problem solvers.

2002, Neth, Hansjörg, Payne, Stephen J.

This article explores the concept of epistemic actions in the Tower of Hanoi (ToH) problem. Epistemic actions (Kirsh & Maglio, 1994) are actions that do not traverse the problem space toward the goal but facilitate subse- quent problem solving by changing the actor’s cogni- tive state. We report an experiment in which people re- peatedly solve ToH tasks. An instructional manipulation asked participants to minimize moves either trial by trial or only on the last three of six trials. This manipulation did not have the predicted effect on the trial-by-trial move counts. A second, device manipulation provided some participants with an “exploratory mode” in which move sequences could be tried then undone without affecting the criterion move count. Participants effectively used this mode to reduce moves on each trial, but there was no clear evidence that they used it to learn about the problem across trials. We conclude that there is strong evidence for one sub-type of epistemic action (acting-to-plan) but no evidence for a second sub-type (acting-to-learn).

2007-08, Payne, Stephen J., Duggan, Geoffrey B., Neth, Hansjörg

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 retur - 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.

2001, Neth, Hansjörg, Payne, Stephen J.

Successful problem solving depends on a dynamic interplay of resources between agent, task, and task environment. To illuminate these interactions we studied how participants added a series of single-digit numbers presented on a computer screen. We distinguished between four different user interfaces, each implementing a different mode of interaction with the displayed addends: look only, point, mark, and move. By collecting and analysing complete interaction protocols we were able to integrate overall performance measures with fine-grained behavioural process data on the strategies engendered by the different user interfaces. We discovered reliable differences in the chosen sequences of addends, which can be understood in terms of the cost-benefit structures pro- vided by the interactive resources of the user interfaces.

2007, Carlson, Richard A., Gray, Wayne D., Kirlik, Alex, Kirsh, David, Payne, Stephen J., Neth, Hansjörg