Estimating the contributions of associations and recoding in the Implicit Association Test: The ReAL model for the IAT.

Meissner, Franziska; Rothermund, Klaus

doi:10.1037/a0030734

Cited by 116 publications

(201 citation statements)

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“…For the current experiment, we hypothesized to replicate recent findings concerning the underlying processes of the flower-insect IAT (Meissner & Rothermund, 2013): Recoding should play a significant role and the A parameters should reflect the typical preference for flowers over insects. Following our assumption of a higher probability of recoding if targets and attributes are similar, we expected an interaction of target and attribute modality on the recoding parameter: Re should be larger in case of a modality match compared to a mismatch.…”

Section: Methodsmentioning

confidence: 83%

“…Previous studies confirmed the validity of the ReAL model parameters as measures of the respective processes (Meissner & Rothermund, 2013: While the role of recoding is reflected in the parameter Re (with higher values indicating a higher probability of responding based on task recoding in the compatible block), the strength of evaluative associations is measured via the A parameters. Two of those A parameters are estimated for each attitude IAT, one for each of the two target categories.…”

Section: Methodsmentioning

confidence: 87%

“…To provide more direct evidence for the mediating role of recoding, we employed the ReAL model, a multinomial model for the IAT (Meissner & Rothermund, 2013).…”

Section: Methodsmentioning

confidence: 99%

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A Thousand Words Are Worth More Than a Picture? The Effects of Stimulus Modality on the Implicit Association Test

Meissner

Rothermund

2015

Social Psychological and Personality Science

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Implicit Association Tests (IATs) employing pictures as target stimuli usually yield smaller scores than those with verbal target stimuli, suggesting weaker attitudes in the former compared to the latter case. As the attribute dimension of attitude IATs is typically represented by words, we hypothesized the target modality effect to be actually due to a modality match between targets and attributes. We manipulated stimulus modality independently for targets and attributes and confirmed our assumption in two attitude IATs (Experiment 1: flower/insect, Experiment 2: old/young): Employing verbal attributes, IAT scores were smaller for pictorial compared to verbal targets. However, the target modality effect disappeared or was even reversed if pictures were employed as attributes. Applying the ReAL model revealed that this modality match effect is mediated by recoding processes. Importantly, evaluative associations remained unaffected by modality match and reflected equal or even stronger preferences for pictorial compared to verbal targets.

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Section: Methodsmentioning

confidence: 83%

Section: Methodsmentioning

confidence: 87%

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A Thousand Words Are Worth More Than a Picture? The Effects of Stimulus Modality on the Implicit Association Test

Meissner

Rothermund

2015

Social Psychological and Personality Science

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“…The Quad model does not provide an exhaustive account of IAT performance, which is certainly influenced by processes that are not included in the model, including recoding (e.g., Chang & Mitchell, 2011;De Houwer et al, 2005;Kinoshita & Peek-O'Leary, 2005, 2006Meissner & Rothermund, 2013;Rothermund et al, 2009;Rothermund & Wentura, 2001, 2004Rothermund et al, 2005), task-set shifts and task-set simplification (e.g., Mierke & Klauer, 2001Klauer et al, 2010), and speed-accuracy tradeoffs (e.g., Brendl et al, 2001;Klauer et al, 2007). Klauer and colleagues have already demonstrated method variance in IAT performance related to task switching ability Mierke & Klauer, 2001.…”

Section: Resultsmentioning

confidence: 99%

Attitudinal and Non-Attitudinal Components of IAT Performance

Calanchini

Sherman

Klauer

et al. 2014

Pers Soc Psychol Bull

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The Implicit Association Test (IAT) was designed to measure automatically activated attitudinal associations, free of the influence of processes that affect their expression. Subsequent research has shown that IAT performance also is influenced by non-associative processes, but the extent to which these non-associative processes are content-specific or if they operate similarly regardless of the attitude being measured has largely gone unexamined. In the current research, participants completed pairs of IATs that varied in conceptual overlap: Tests shared a high, moderate, or low degree of overlap in the measured attitudes. The Quad model was applied to estimate the contributions of four processes to IAT performance. Evidence was found for two relatively general, non-attitudinal processes and two relatively attitude-specific processes. Implications are discussed for interpretation of IAT scores, individual differences in IAT scores, and IAT score malleability.

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“…Concerning measures of implicit attitudes, several authors proposed to rely on MPT models to analyze response frequencies instead of analyzing mean RTs in the implicit association test (IAT) and the go/no-go association task (e.g., Conrey et al, 2005;Meissner & Rothermund, 2013;Nadarevic & Erdfelder, 2011). These MPT models disentangle cognitive processes such as activation of associations and stimulus discrimination (Conrey et al, 2005) or recoding of response categories (Meissner & Rothermund, 2013). However, to do so, these models sacrifice information about the speed of processes and only rely on response frequencies.…”

Section: Rt Extensions Of More Complex Mpt Modelsmentioning

confidence: 99%

Extending multinomial processing tree models to measure the relative speed of cognitive processes

Heck

Erdfelder

2016

Psychon Bull Rev

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Multinomial processing tree (MPT) models account for observed categorical responses by assuming a finite number of underlying cognitive processes. We propose a general method that allows for the inclusion of response times (RTs) into any kind of MPT model to measure the relative speed of the hypothesized processes. The approach relies on the fundamental assumption that observed RT distributions emerge as mixtures of latent RT distributions that correspond to different underlying processing paths. To avoid auxiliary assumptions about the shape of these latent RT distributions, we account for RTs in a distribution-free way by splitting each observed category into several bins from fast to slow responses, separately for each individual. Given these data, latent RT distributions are parameterized by probability parameters for these RT bins, and an extended MPT model is obtained. Hence, all of the statistical results and software available for MPT models can easily be used to fit, test, and compare RT-extended MPT models. We demonstrate the proposed method by applying it to the two-high-threshold model of recognition memory. Many substantive psychological theories assume that observed behavior results from one or more latent cognitive processes. Because these hypothesized processes can often not be observed directly, measurement models are important tools to test the assumed cognitive structure and to obtain parameters quantifying the probabilities that certain underlying processing stages take place or not. Multinomial processing tree models (MPT models; Batchelder & Riefer, 1990) provide such a means by modeling observed, categorical responses as originating from a finite number of discrete, latent processing paths. MPT models have been successfully used to explain behavior in many areas such as memory (Batchelder & Riefer, 1986, 1990, decision making (Erdfelder, Castela, Michalkiewicz, & Heck, 2015;Hilbig, Erdfelder, & Pohl, 2010), reasoning (Klauer, Voss, Schmitz, & Teige-Mocigemba, 2007), perception (Ashby, Prinzmetal, Ivry, & Maddox, 1996), implicit attitude measurement (Conrey, Sherman, Gawronski, Hugenberg, & Groom, 2005;Nadarevic & Erdfelder, 2011), and processing fluency (Fazio, Brashier, Payne, & Marsh, 2015;Unkelbach & Stahl, 2009). Batchelder & Riefer (1999 and Erdfelder et al. (2009) reviewed the literature and showed the usefulness and broad applicability of the MPT model class. In the present paper, we introduce a simple but general approach to include information about response times (RTs) into any kind of MPT model. KeywordsAs a running example, we will use one of the most simple MPT models, the two-high-threshold model of recognition memory (2HTM; Bröder & Schütz, 2009;Snodgrass & Corwin, 1988). The 2HTM accounts for responses in a binary recognition paradigm. In such an experiment, participants first learn a list of items and later are prompted to categorize old and new items as such. Hence, one obtains frequencies of hits (correct old), misses (incorrect new), false alarms (incorrect ol...

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Estimating the contributions of associations and recoding in the Implicit Association Test: The ReAL model for the IAT.

Cited by 116 publications

References 78 publications

A Thousand Words Are Worth More Than a Picture? The Effects of Stimulus Modality on the Implicit Association Test

A Thousand Words Are Worth More Than a Picture? The Effects of Stimulus Modality on the Implicit Association Test

Attitudinal and Non-Attitudinal Components of IAT Performance

Extending multinomial processing tree models to measure the relative speed of cognitive processes

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