Six experiments that were designed to test the adequacy of criterion bias explanations of the word frequency effect and the semantic priming effect are reported. It was found that criterion bias models correctly predicted higher error rates in a lexical decision task for nonwords that were misspelled versions of high-frequency words (e.g., MOHTER), rather than low-frequency words (e.g., BOHTER). Also correct was the prediction of increased error rates for misspelled words preceded by a semantically related word (e.g., NURSE-DOTCOR). However, in a misspelling decision task (in which the subject must decide whether the stimulus is a word, a misspelled word, or a nonword), it can be argued that criterion bias should be inoperative, since correct responses must be delayed until all orthographic information has been checked; this should eliminate both frequency and semantic priming effects. This was found not to be the case; clear frequency and priming effects were obtained for both words and misspelled words.The notion of bias plays a key role in current attempts to explain many of the major characteristics of the word recognition process. The essence of this notion is that the perceptual system is biased in some way so that cere tain percepts are more easily formed than others. One form of bias is referred to as criterion bias and was first proposed by Broadbent (1967) to explain how common words were identified more readily than were lowfrequency words. He suggested that subjects were "biased in such a way as to accept a smaller amount of evidence before decidingin favour of a probable word" (Broadbent, 1967, p. 3). Morton's (1969Morton's ( , 1970 logogen theory incorporates such a mechanism to account for the effects of both linguistic context and word frequency.A quite different form of bias has been proposed within the context of ordered-search models of lexical access. In these models, recognition involves comparing a representation of the stimulus with a series of lexical entries in a mental dictionary (Forster, 1976;Rubenstein, Garfield, & Millikan, 1970; Stanners & Forbach, 1973). Bias is introduced into this system if the search through the set of lexical entries deviates in any way from a purely random search. Thus if there is a bias to search through the entries for high-frequency words first, the frequency effect can be explained. Or, if there is a bias to search through the entries for words that are semantically related to a context word, then the effects of semantic priming can be explained (Forster, 1976). This second kind of bias we will refer to as search sequence bias. The purpose of the present paper is to explore the differences between these two theoretical devices in terms of their capacity to explain the effects of frequency and semantic relatedness on both latencies and error rates in word classification tasks.In Morton's model (Morton, 1970), word recognition is accomplished by a system of logogens, or word detecCopyright 1981 Psychonomic Society, Inc. 78 tors, each of which is capable ...

The spread of Covid-19 and the variety of government responses opens space for policy learning. Traditional policy analysis tools would lead us to explain policy variation using population size, ease of closing jurisdictional borders, governance arrangements, available resources, and system capacity. Following the approach of the Narrative Policy Framework, we suggest narrative has played a key role in the relative effectiveness of responses to Covid-19. We illustrate the dynamics at play using evidence from the state level in the United States, where there has been considerable variation in policy actions and rates of infection and death. After reviewing the national context, we explain our selection of cases. We note differences in state-level policy narratives and how they influenced policy development and implementation. The findings provide compelling reason for policy designers everywhere to routinely integrate narrative development and control into their advising practices.

Royal commissions are established at the Commonwealth and state levels in Australia to investigate matters of significant public concern. These distinctive entities are highly respected both by government and the community. They are typically expected to produce reports that will have significant impact. Although a large number are created primarily to determine why specific events happened and where accountabilities lie, many are expected to generate findings and recommendations that will lead to policy change. We review common strategies that actors in and around government deploy with the purpose of shaping public policy. This leads to an exploration of the influence strategies deployed by three Australian royal commissions that achieved high levels of policy impact. Lessons are drawn for how future royal commissions might create policy legacies that deliver high public value.