Dissociating implicit wanting from implicit liking: Development and validation of the Wanting Implicit Association Test (W-IAT)

Koranyi, Nicolas; Grigutsch, Laura Anne; Algermissen, Johannes; Rothermund, Klaus

doi:10.1016/j.jbtep.2016.08.008

Cited by 21 publications

(26 citation statements)

References 23 publications

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“…This detail in the results underpins the assumption that purely semantic “wanting” measures fail to dissociate themselves from comparable measures of “liking” (c.f., Tibboel et al, 2011, 2015a). The findings of Koranyi et al (2017) thus suggest that an implicit measure of “wanting” should establish the motivational quality of relevant responses.…”

Section: Issue 2: Distinguishing Between Liking and Wantingmentioning

confidence: 95%

“…Obviously, changing the attribute categorization task on a merely semantic level by simply replacing the category labels cannot transform the IAT into an implicit measure of “wanting.” If anything, these IATs most likely reflect semantic associations, or a “cognitive form of wanting” (Tibboel et al, 2015b, p. 189). Recently, however, a new Wanting-IAT was introduced (Koranyi et al, 2017) that can be considered a more promising way forward in multiple respects.…”

Section: Issue 2: Distinguishing Between Liking and Wantingmentioning

confidence: 99%

“…The basic idea of the Wanting-IAT (W-IAT, Koranyi et al, 2017) consists in endowing the attribute discrimination task with motivational character. More precisely, execution of one of the attribute responses should come to equal execution of a “wanting”-triggered consummatory response.…”

Section: Issue 2: Distinguishing Between Liking and Wantingmentioning

confidence: 99%

“…The potential of this new W-IAT was illustrated in a study on attraction in a mating context (Koranyi et al, 2017). Heterosexual male participants completed the previously described W-IAT procedure as well as a standard valence IAT (i.e., positive vs. negative attribute dimension).…”

Section: Issue 2: Distinguishing Between Liking and Wantingmentioning

confidence: 99%

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Predicting Behavior With Implicit Measures: Disillusioning Findings, Reasonable Explanations, and Sophisticated Solutions

et al. 2019

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Two decades ago, the introduction of the Implicit Association Test (IAT) sparked enthusiastic reactions. With implicit measures like the IAT, researchers hoped to finally be able to bridge the gap between self-reported attitudes on one hand and behavior on the other. Twenty years of research and several meta-analyses later, however, we have to conclude that neither the IAT nor its derivatives have fulfilled these expectations. Their predictive value for behavioral criteria is weak and their incremental validity over and above self-report measures is negligible. In our review, we present an overview of explanations for these unsatisfactory findings and delineate promising ways forward. Over the years, several reasons for the IAT’s weak predictive validity have been proposed. They point to four potentially problematic features: First, the IAT is by no means a pure measure of individual differences in associations but suffers from extraneous influences like recoding. Hence, the predictive validity of IAT-scores should not be confused with the predictive validity of associations. Second, with the IAT, we usually aim to measure evaluation (“liking”) instead of motivation (“wanting”). Yet, behavior might be determined much more often by the latter than the former. Third, the IAT focuses on measuring associations instead of propositional beliefs and thus taps into a construct that might be too unspecific to account for behavior. Finally, studies on predictive validity are often characterized by a mismatch between predictor and criterion (e.g., while behavior is highly context-specific, the IAT usually takes into account neither the situation nor the domain). Recent research, however, also revealed advances addressing each of these problems, namely (1) procedural and analytical advances to control for recoding in the IAT, (2) measurement procedures to assess implicit wanting, (3) measurement procedures to assess implicit beliefs, and (4) approaches to increase the fit between implicit measures and behavioral criteria (e.g., by incorporating contextual information). Implicit measures like the IAT hold an enormous potential. In order to allow them to fulfill this potential, however, we have to refine our understanding of these measures, and we should incorporate recent conceptual and methodological advancements. This review provides specific recommendations on how to do so.

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Section: Issue 2: Distinguishing Between Liking and Wantingmentioning

confidence: 95%

Section: Issue 2: Distinguishing Between Liking and Wantingmentioning

confidence: 99%

Section: Issue 2: Distinguishing Between Liking and Wantingmentioning

confidence: 99%

Section: Issue 2: Distinguishing Between Liking and Wantingmentioning

confidence: 99%

See 2 more Smart Citations

Predicting Behavior With Implicit Measures: Disillusioning Findings, Reasonable Explanations, and Sophisticated Solutions

et al. 2019

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“…Attraction has frequently been theorized to produce approach (e.g., Mehrabian, 1970;Zajonc, 2000) and has been measured using approach indices (e.g., Van Straaten, Engels, Finkenauer, & Holland, 2009). For instance, the Go/No-Go task (GNAT) has been adapted to assess behavioral "approach" tendencies (e.g., Koranyi, Grigutsch, Algermissen, & Rothermund, 2017). Such an index not only potentially presumes that attraction is regulated by behaviorism or by cognitive consistency-motivated "approach" principles, but it also leaves unacknowledged what TDMA defines as most critical to the attraction process (i.e., attraction's instrumental nature).…”

Section: Develop Measures Of Affiliation Rather Than Approachmentioning

confidence: 99%

Understanding the attraction process

Montoya

Horton

2020

Social & Personality Psych

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A comprehensive model of interpersonal attraction is presented in which attraction is an emotion expressed to regulate interpersonal relationships. The model proposes that cognitive appraisals, the subjective experience, and the expression of affiliative behavior are fundamental to understanding how attraction operates. Specifically, we discuss how (a) cognitive appraisals (willingness and ability) are fundamental to the subjective experience of attraction, (b) social dilemma considerations affect when and why the subjective experience aligns with the expression of affiliative behavior, (c) the interplay of the components of attraction explain the panoply of extant attraction phenomena, and (d) the attraction process operates in both initial interactions and enduring relationships. We conclude with a discussion of methodological and theoretical directions to fuel future growth.

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