Estimation strategies and the judgment of event frequency.

Abstract

Processes underlying judgments of absolute event frequency were investigated in 3 experiments. In all 3, word pairs consisting of a target (a category label, e.g., CITY) and context (a category exemplar, e.g., London) were presented in a different-or same-context study list. In the different-context condition, each target was paired with a new context on each presentation; in the same-context condition, a target always appeared with the same context. Verbal protocols (Experiment 1) and response times (Experiments 2 and 3) indicate that multiple estimation strategies were used and that strategy selection was related to memory contents. In particular, different-context participants often enumerated, and same-context participants did not. Also, because range information only affected same-context estimates (Experiment 3), it appears that a numerical conversion process was necessary when nonenumeration strategies were used. Judgments of absolute frequency are collected in most experiments concerned with the encoding and representation of event frequency (for reviews see Hasher & Zacks, 1979, 1984; Hintzman, 1976, 1988; Howell, 1973). The research described in this article was aimed at understanding how these judgments are produced. In particular, I argue that participants use multiple strategies when estimating event frequency; that strategy selection is determined, in part, by the contents of memory; and that the magnitude and accuracy of participants' frequency estimates are related to the strategy they select. The multiple-strategy perspective adopted in this article has numerous precedents. It is well established that people use multiple strategies to perform a wide variety of simple and not-so-simple cognitive tasks. Among these are recognition (e.g.

Real-world estimation: Estimation modes and seeding effects

“…Finally, an analogue to adjusted retrieval has been seen in the lab. Here it is common for participants to generate frequency estimates by retrieving some subset of relevent instances and then adjusting their enumerated counts to account for the unretrieved items (Brown, 1995). Thus, although numerical strategies appear to be uncommon in many realworld estimation tasks, people can and do retrieve, adjust, and transform numerical values when they are available.…”

“…On the other hand, because real-world knowledge is complex, and relevant between-domain differences are large, estimation strategies vary from task to task, and different strategies often compete within the same task. It follows that the study of real-world estimation should produce information about the range of estimation strategies people use, the factors that influence strategy selection, and the ways that people coordinate competing sources of information (Brown, 1995(Brown, , 1997Brown & Siegler, 1993;Conrad, Brown, & Cashman, 1998). In brief, because performance on many real-world estimation tasks reflects the systematic interplay of process and content, it is possible to employ these tasks to investigate both.…”

Estimating the frequency of events from unnatural categories

“…We have called this the multiple strategy perspective (Brown, 1995(Brown, , 1997(Brown, , 2002Conrad, Brown, & Cashman, 1998). The basic idea is that, although many strategies are potentially available, Some of the material contained in this article was presented at the 1999 conference of the American Association for Public Opinion Research.…”

“…We (Brown, 1995(Brown, , 1997Conrad et al, 1998) have observed people using all of these strategies when the test categories were familiar (e.g., cities, musical instruments, and trips to the grocery store) and probably matched people's encoding of events (this match was ensured for laboratory participants by presenting events that consisted of an instance and its category, the same category on which participants were later tested). But on what basis do people respond when they have not encoded the events as instances of the test categories, presumably because the instances fit more naturally with other categories?…”

“…People are surprisingly skilled at determining how often events occur (e.g., Alba, Chromiak, Hasher, & Attig, 1980;Brown, 1995Brown, , 1997Burton & Blair, 1991;Means & Loftus, 1991). However, their ability to estimate the frequency of events defined by properties (e.g., color, size, smell, or shape) is quite poor.…”