The Strategic Use of Memory for Frequency and Recency in Search Control

The Strategic Use of Memory for Frequency and Recency in Search Control Andrew Howes (HowesA@cardiff.ac.uk) School of Psychology, Cardiff University, Cardiff, CF10 3YG, Wales, UK Stephen J. Payne (PayneS@cardiff.ac.uk) School of Psychology, Cardiff University, Cardiff, CF10 3YG, Wales, UK Abstract A requirement of an information processing account of human problem solving is that it includes a mechanism by which people remember which goals and operators have been evaluated and which still need to be evaluated. One might expect that these are issues of such fundamental importance that they must have been solved or at least addressed by the two architectural accounts of cognition (Soar and ACT-R), but in fact it is an issue that is glossed in both. We identify two problems: (1) Soar and ACT-R guarantee information about goals, and (2) ACT-R combines measures of frequency and recency into a single representation of activation. In this paper we report a model of how people search simple binary trees. The model demonstrates the cognitive plausibility of a search algorithm that is supported by a memory system that delivers independent estimates of frequency and recency. Introduction A requirement of an information processing account of human problem solving is that it includes a mechanism by which people remember which goals and operators have been evaluated and which still need to be evaluated. Whether the task is the Tower of Hanoii, a waterjugs problem, a world-wide web search problem or a spatial navigation task, a person engaged in search examines the consequences of applying an operator to a state by trying it out and perceiving to which state it, and subsequent operators, lead. At some point in the future, the person may, through backup, or because of loops, find themselves in a visited state. Recognition that the state has already been visited and/or that the operator has already been applied to this state, will in the long-term help prune the search space and thereby constrain the effort spent on attaining the goal. This constraint has been used in a number of models of human problem solving (Atwood & Polson, 1976; Jeffries, Polson., Razran, & Atwood, 1977; Anderson, 1993; Howes, 1994). Atwood & Polson's model of human performance on the waterjugs problem, built up a representation of the 'familiarity' of states that was factored into the operator selection process. The more familiar an operator then the less likely it was to be selected. One might expect that these are issues of such fundamental importance that they must have been solved or at least addressed by the two substantial architectural accounts of cognition (ACT-R, Anderson, 1998; Soar, Newell, 1990), but in fact it is an issue that is glossed in both. In Soar, the architecture automatically ensures that operators that have already been applied to a particular state in pursuit of a particular goal (on the goal stack) on a particular trial will not be reselected. In ACT-R the goal stack has privileged status. Items posted on the stack are not subject to the constraints of memory, i.e. they do not have decaying activation and cannot therefore be forgotten (Altman and Trafton, 1999). Another resource for supporting decisions about which operator to apply is memory for previous attempts at a goal (either successful or failed). If a goal has been achieved prior to the current attempt then memories that indicate that an operator is familiar may be taken as evidence that it is more likely to lead to the goal than an unfamiliar operator (Payne, Richardson, Howes, 2000). However, an issue for the problem solver is how to determine the source of the familiarity. If the source is the current trial then the operator should be rejected, if it is a previous trial then perhaps it should be selected. Payne, Richardson, Howes (2000) investigated the role of familiarity (Jacoby, 1991) in controlling interactive search. They tested the hypothesis (Aasman & Akyurek, 1992; Howes, 1994) that people help control search merely by recognising the actions that have been tried before and found that the familiarity of items could affect decisions about which item to select. Moreover familiarity was used strategically. When participants had information indicating that familiarity would be more likely to indicate that an operator would lead to the goal, they were more likely to use familiarity to guide selection. Again, one might expect that this issue would have been addressed in architectural theories of cognition. However, while Soar’s chunking mechanism is flexible, the issue of whether it can provide a mechanism for representing the episodic familiarity of an operator has only recently started to be explored (Altmann and John, 1999). The situation for ACT-R is more complex.