Several theoretical views of automaticity are discussed. Most of these suggest that automaticity should be diagnosed by looking at the presence of features such as unintentional, uncontrolled/uncontrollable, goal independent, autonomous, purely stimulus driven, unconscious, efficient, and fast. Contemporary views further suggest that these features should be investigated separately. The authors examine whether features of automaticity can be disentangled on a conceptual level, because only then is the separate investigation of them worth the effort. They conclude that the conceptual analysis of features is to a large extent feasible. Not all researchers agree with this position, however. The authors show that assumptions of overlap among features are determined by the other researchers' views of automaticity and by the models they endorse for information processing in general. : automatic, unintentional, uncontrolled, autonomous, unconscious Automaticity is a concept with a long-standing history in psychology (e.g., James, 1890;Wundt, 1903). It has been invoked in domains as diverse as perception (MacLeod, 1991), memory (Jacoby, 1991), social cognition (Wegner & Bargh, 1998), learning (Cleeremans & Jiménez, 2002), motivation (Carver & Scheier, 2002), and emotion (Scherer, 1993). Despite its central nature, there is no consensus about what automaticity means. The aim of this article is to provide an in-depth analysis of the concept and, in particular, the features that have been subsumed under the term. We also discuss implications of this analysis for future research. KeywordsIn the first section, we consider several contrasting views of automaticity. This overview reveals that most scholars analyze automaticity in terms of one or more features. The implication is that automaticity can be diagnosed by looking for the presence of these features in performance or processes. 1 Contemporary approaches to automaticity, moreover, have begun to argue for the separate investigation of automaticity features. In the second section, we discuss the most essential features in greater detail. One purpose is to examine whether they can be separated on a conceptual level. We analyze the features to a point at which the overlap among them is minimized. Another purpose is to show that assumptions of overlap are pervasive in the research literature despite the recent claim that individual features should be investigated separately. These assumptions arise from authors' specific views of automaticity, as well as from the specific underlying information-processing model that they endorse. Approaches to AutomaticityJames (1890), Jastrow (1906), and Wundt (1896/1897, 1903 offered some of the earliest descriptions of automaticity, and many of their ideas have reemerged in contemporary accounts. The views presented below are also based on early studies of skill development (Bryan & Harter, 1899) and early dual-task studies (Solomons & Stein, 1896; see review by Shiffrin, 1988). Automaticity as Processing With No or Minimal AttentionC...

Evaluative conditioning refers to changes in the liking of a stimulus that are due to the fact that the stimulus has been paired with other, positive or negative stimuli. Although evaluative conditioning appears to be subjected to certain boundary conditions, significant evaluative conditioning effects have been obtained using a large variety of stimuli and procedures. Some data suggest that evaluative conditioning can occur under conditions that do not support other forms of Pavlovian conditioning, and several models have been proposed to account for these differences. In the present article, the authors summarize the available literature, draw conclusions where possible, and provide suggestions for future research.

This article presents a meta-analysis of research on evaluative conditioning (EC), defined as a change in the liking of a stimulus (conditioned stimulus; CS) that results from pairing that stimulus with other positive or negative stimuli (unconditioned stimulus; US). Across a total of 214 studies included in the main sample, the mean EC effect was d ϭ .52, with a 95% confidence interval of .466 -.582. As estimated from a random-effects model, about 70% of the variance in effect sizes were attributable to true systematic variation rather than sampling error. Moderator analyses were conducted to partially explain this variation, both as a function of concrete aspects of the procedural implementation and as a function of the abstract aspects of the relation between CS and US. Among a range of other findings, EC effects were stronger for high than for low contingency awareness, for supraliminal than for subliminal US presentation, for postacquisition than for postextinction effects, and for self-report than for implicit measures. These findings are discussed with regard to the procedural boundary conditions of EC and theoretical accounts about the mental processes underlying EC.Keywords: evaluative conditioning, affective learning, attitude learning, associative learning, propositional learning One of the most influential ideas in psychology is that human behavior is, to a large extent, governed by likes and dislikes (Allport, 1935;Martin & Levey, 1978). For instance, people prefer the company of people they like and try to avoid those they do not like; people buy and consume products they like rather than those they dislike; and they vote for and support politicians and ideas that they find sympathetic rather than repelling. Furthermore, preferences influence attention, memory, and judgments and form the basis of our emotional life (Fox, 2009). Given the pervasive impact of preferences on behavior, it is vital for our discipline to understand how preferences are formed and how they can be influenced. Although some likes and dislikes may be genetically determined (Poulton & Menzies, 2002), the vast majority of our preferences are learned rather than innate (Rozin & Millman, 1987). But precisely how humans acquire their likes and dislikes continues to be the subject of vigorous debate (Rozin, 1982;De Houwer, Thomas, & Baeyens, 2001).The present article provides a meta-analysis of research on one possible manner in which likes and dislikes can be learned: evaluative conditioning (EC), which may be best defined as an effect that is attributed to a particular core procedure. Specifically, EC refers to a change in the valence of a stimulus (the effect) that is due to the pairing of that stimulus with another positive or negative stimulus (the procedure) (De Houwer, 2007a;De Houwer et al., 2001). The first stimulus is often referred to as the conditioned stimulus (CS), and the second stimulus is often referred to as the unconditioned stimulus (US). Typically, a CS becomes more positive when it has been paired with a posit...

The past 50 years have seen an accumulation of evidence suggesting that associative learning depends on high-level cognitive processes that give rise to propositional knowledge. Yet, many learning theorists maintain a belief in a learning mechanism in which links between mental representations are formed automatically. We characterize and highlight the differences between the propositional and link approaches, and review the relevant empirical evidence. We conclude that learning is the consequence of propositional reasoning processes that cooperate with the unconscious processes involved in memory retrieval and perception. We argue that this new conceptual framework allows many of the important recent advances in associative learning research to be retained, but recast in a model that provides a firmer foundation for both immediate application and future research.

Implicit measures can be defined as outcomes of measurement procedures that are caused in an automatic manner by psychological attributes. To establish that a measurement outcome is an implicit measure, one should examine (a) whether the outcome is causally produced by the psychological attribute it was designed to measure, (b) the nature of the processes by which the attribute causes the outcome, and (c) whether these processes operate automatically. This normative analysis provides a heuristic framework for organizing past and future research on implicit measures. The authors illustrate the heuristic function of their framework by using it to review past research on the 2 implicit measures that are currently most popular: effects in implicit association tests and affective priming tasks.Keywords: implicit measures, automaticity, IAT, affective priming Most psychologists would argue that a full understanding of the behavior of an individual requires knowledge not only of the external situation in which the individual is present but also of the internal psychological attributes of the individual. Throughout the history of psychology, researchers have therefore attempted to measure interindividual differences in the psychological attributes of people (e.g., Anastasi, 1958;Eysenck & Eysenck, 1985;Mischel & Shoda, 1995). During the past decade, a major development in this research has been the introduction of so-called implicit measures. These measures were originally put forward mainly within the social psychology literature (e.g., Fazio, Jackson, Dunton, & Williams, 1995;Greenwald, McGhee, & Schwartz, 1998) but have since then spread to various other subdisciplines of psychology, including differential psychology (e.g., Asendorpf, Banse, & Mücke, 2002), clinical psychology (e.g., Gemar, Segal, Sagrati, & Kennedy, 2001, consumer psychology (e.g., Maison, Greenwald, & Bruin, 2004), and health psychology (e.g., Wiers, van Woerden, Smulders, & de Jong, 2002).Despite the widespread use of implicit measures, the actual meaning of the term implicit measure is rarely defined. On the basis of the work of Borsboom (Borsboom, 2006;Borsboom, Mellenbergh, & van Heerden, 2004) and De Houwer (De Houwer, 2006;, we first provide a normative analysis of the concept "implicit measure." The analysis is normative in the sense that it stipulates the properties that an ideal implicit measure should have. As such, the analysis provides a heuristic framework for reviewing and evaluating existing research on implicit measures. By examining the extent to which a particular implicit measure exhibits these normative properties, one can clarify the way in which the measure is an implicit measure and highlight those issues on which further research is required. In the second part of this article, we perform this exercise with regard to the two types of implicit measures that are currently most popular: effects in implicit association tests (IATs;Greenwald et al., 1998) and affective priming tasks (Fazio et al., 1995). Before we present and ap...