Modeling Underlying Mechanisms of the Implicit Association Test

Modeling Underlying Mechanisms of the Implicit Association Test Boon-Kiat Quek (boonkiat.quek@northwestern.edu) Andrew Ortony (ortony@northwestern.edu) Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore, and Northwestern University, Evanston, IL 60208, USA Abstract The Implicit Association Test (IAT) is designed to measure implicit attitudes, and is often claimed to reveal prejudicial attitudes that are at odds with explicit attitudes. Numerous proposals as to the information processing mechanisms under- lying human performance on the IAT have been suggested, many or all of which may well play a role. This makes it dif- ficult to study them and their interactions experimentally in an efficient manner. We describe a localist connectionist model that simulates human performance on the IAT and that allows us to explore many of the proposed explanations, by compar- ing the results with observations from actual experiments with human subjects. By simulating the performance of virtual subjects, the model also makes it possible to conduct “theo- retical” experiments that could not be undertaken with real subjects in the real world. Keywords: Associations; attitudes; simulation; localist con- nectionist networks. Introduction The Implicit Association Test (IAT; Greenwald, McGhee & Schwartz, 1998) is a computer-administered test (see e.g., https://implicit.harvard.edu/implicit/) designed to measure automatic associations between concepts, where such asso- ciations are assumed to underlie implicit attitudes towards attitude objects (Greenwald & Banaji, 1995). It has been used in a variety of domains, including age, race, self- esteem, sexual orientation, and so on. By being implicit, these attitudes are assumed to be beyond conscious control (Greenwald & Banaji, 1995) and thus cannot be assessed with explicit measures such as self-reports. Instead, the IAT seeks to measure implicit attitudes by recording subjects’ response times as they complete a number of tasks, the performance on which is assumed to involve accessing automatic associations. Such tasks involve pressing a Left or Right key on a keyboard to classify sequentially presented stimuli into one of two categories. Each category comprises one of two target concepts (e.g., flower or insect) crossed with one of two evaluative attributes (e.g., pleasant or un- pleasant). This results in four possible pairings, two of which are, by hypothesis, incompatible (e.g. insect and pleasant) and two of which are compatible. A central assumption of the IAT is that the stronger the associations between an attitude object and its evaluative attributes, the stronger the implicit attitude. Thus, a person with a negative attitude towards insects is assumed to have a correspondingly strong association between the concept insect and some general concept for negativity. The idea is that the strength of these associations will be reflected in people’s response times on the classification tasks, with longer latencies for incompatible pairings than for compati- ble pairings. This difference in response times between trials for incompatible and compatible pairings is known as the IAT effect (Greenwald, McGhee & Schwartz, 1998). Although the IAT is widely used (Greenwald, Poehlman, Uhlmann & Banaji, 2009), its history is rife with contro- versy. Questions have been raised as to whether measuring automatic associations is a valid way of measuring implicit attitudes (Blanton & Jaccard, 2006), and whether it is indeed measuring automatic associations at all (De Houwer, 2001; Brendl, Markman & Messner, 2001). Researchers have also wondered whether IAT effects might be partially due to other factors such as differences in stimulus familiarity (Dasgupta et al, 2000; Ottaway, Hayden & Oakes, 2001) or salience asymmetries between target concepts (Rothermund & Wentura, 2004), and whether they might merely be a reflection of prevailing cultural norms rather than of genu- ine attitudes (Karpinski & Hilton, 2001). Given these various proposals, several accounts of the processing mechanisms underlying the IAT have been ad- vanced. These include the figure-ground asymmetry model (Rothermund & Wentura, 2001), a random-walk model (Brendl, Markman & Messner, 2000), a diffusion model (Klauer, Voss, Schmitz & Teige-Mocigemba, 2007), an acquired meaning of response keys account (De Houwer, 2001) and a suggestion that longer response latencies are the result of task-set switching overheads (Mierke & Klauer, 2001; Klauer & Mierke, 2005) occasioned by moving be- tween compatible and incompatible task blocks. Finally, a quad model of implicit task performance (Conrey, Sherman, Gawronski, Hugenberg & Groom, 2005) postulates that multiple processes are at work – some automatic and others more controlled (Schneider & Shiffrin, 1977; Shiffrin & Schneider, 1977). These differing explanations are not necessarily mutually exclusive. It might be that some, or even all of them, play a role, in which case one would need a model that could, among other things, account for (i) the interaction between automatic and control processes (e.g., Mierke & Klauer, 2001; Herd, Banich & O'Reilly, 2006), (ii) cue and response competition induced by the interference of competing stim- uli in incompatible trials (e.g., Praamstra & Seiss, 2005), (iii) resolution of these conflicts (e.g., through the mutual inhibition of competing action tendencies, Atkinson & Birch, 1970), and (iv) increase in cognitive processing (e.g., Snyder-Tapp & Dale, 2009) and reduction in speed and accuracy (Klauer & Mierke, 2005) on incompatible trials. To investigate some of these issues, we developed a com- putational model intended to emulate human performance on the IAT. The model handles the generation of output behavior, and allows us to investigate such issues as re-