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...

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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...

This article presents norms of valence/pleasantness, activity/arousal, power/dominance, and age of acquisition for 4,300 Dutch words, mainly nouns, adjectives, adverbs, and verbs. The norms are based on ratings with a 7-point Likert scale by independent groups of students from two Belgian (Ghent and Leuven) and two Dutch (Rotterdam and Leiden-Amsterdam) samples. For each variable, we obtained high split-half reliabilities within each sample and high correlations between samples. In addition, the valence ratings of a previous, more limited study (Hermans & De Houwer, Psychologica Belgica, 34:115-139, 1994) correlated highly with those of the present study. Therefore, the new norms are a valuable source of information for affective research in the Dutch language.

Much emotion research has focused on the end result of the emotion process, categorical emotions, as reported by the protagonist or diagnosed by the researcher, with the aim of differentiating these discrete states. In contrast, this review concentrates on the emotion process itself by examining how (a) elicitation, or the appraisal of events, leads to (b) differentiation, in particular, action tendencies accompanied by physiological responses and manifested in facial, vocal, and gestural expressions, before (c) conscious representation or experience of these changes (feeling) and (d) categorizing and labeling these changes according to the semantic profiles of emotion words. The review focuses on empirical, particularly experimental, studies from emotion research and neighboring domains that contribute to a better understanding of the unfolding emotion process and the underlying mechanisms, including the interactions among emotion components. Expected final online publication date for the Annual Review of Psychology Volume 70 is January 4, 2019. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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Learning has been defined functionally as changes in behavior that result from experience or mechanistically as changes in the organism that result from experience. Both types of definitions are problematic. We define learning as ontogenetic adaptation-that is, as changes in the behavior of an organism that result from regularities in the environment of the organism. This functional definition not only solves the problems of other definitions, but also has important advantages for cognitive learning research.

Standard dual-process models in the action domain postulate that stimulus-driven processes are responsible for suboptimal behavior because they take them to be rigid and automatic and therefore the default. We propose an alternative dual-process model in which goal-directed processes are the default instead. We then transfer the dual- process logic from the action domain to the emotion domain. This reveals that emotional behavior is often attributed to stimulus-driven processes. Our alternative model submits that goal-directed processes could be the primary determinant of emotional behavior instead. We evaluate the type of empirical evidence required for validating our model and we consider implications of our model for behavior change, encouraging strategies focused on the expectancies and values of action outcomes.