Additive and interactive effects of word frequency, stimulus quality, and semantic priming have been used to test theoretical claims about the cognitive architecture of word-reading processes. Additive effects among these factors have been taken as evidence for discrete-stage models of word reading. We present evidence from linear mixed-model analyses applied to 2 lexical decision experiments indicating that apparent additive effects can be the product of aggregating over-and underadditive interaction effects that are modulated by recent trial history, particularly the lexical status and stimulus quality of the previous trial's target. Even a simple practice effect expressed as improved response speed across trials was powerfully modulated by the nature of the previous target item. These results suggest that additivity and interaction between factors may reflect trial-to-trial variation in stimulus representations and decision processes rather than fundamental differences in processing architecture.Keywords: additive and interactive effects, effects of trial history, lexical decision, linear mixed models Many formal models of word-reading processes assume the existence of separate processing modules that are responsible for computing different types of information (e.g., deriving orthography from visual input, computing phonology from orthographic patterns, selecting semantic information that corresponds to a presented word). A fundamental issue in the design of such models is the manner in which information is shared between the constituent modules. It is commonly assumed that input between processing modules is cascaded, so that partial information moves between modules even before a module has completed its operations (e.g., McClelland, 1979), and most current models of visual word identification include feedback from higher (e.g., lexical) to lower (e.g., letter) levels of processing (e.g., Coltheart, Rastle, Perry, Langdon, & Ziegler, 2001;Perry, Ziegler, & Zorzi, 2007;Plaut & Booth, 2000).These architectural features play an important role in allowing computational models to simulate some fundamentally important behavioral results involving word-identification tasks and, in particular, interactions between independent variables that influence the speed with which words are identified. In word-identification tasks such as naming aloud or lexical decision, semantic priming of target words is enhanced if the targets are presented in a visually degraded form such as low contrast (e.g., Becker, 1979;Borowsky & Besner, 1991Stanovich & West, 1983). The typical form of this interaction is overadditive, whereby the influence of semantic priming is greater when performance is slowed by a lowcontrast stimulus. In general, an overadditive interaction is one in which the simultaneous influence of two independent variables is larger than what would be expected by a simple summation of their individual effects. For example, unrelated primes and low stimulus quality both slow responding relative to related primes and high...