Attentional influences on affective priming: Does categorisation influence spontaneous evaluations of multiply categorisable objects?

Abstract

Previous research suggests that spontaneous evaluative responses to a stimulus depend on how that stimulus is categorised. The present research indicates that such categorisation effects depend on task-specific aspects of the measure, thereby concealing or overriding effects of unattended category cues. Results showed that affective priming effects in a paradigm based on response interference depended on participants' attention to the category membership of the primes. These effects were reflected in: (a) reduced effect sizes; (b) reduced internal consistencies; and (c) reduced correlations to corresponding self-reports when attention was directed toward alternative categories. Such attention-related decrements were not obtained for a priming paradigm based on affect misattribution, which showed reliable priming effects irrespective of participants' attention to the relevant categories. These results challenge the ubiquity of categorisation effects on spontaneous evaluations, suggesting that the impact of unattended category cues depends on conditions inherent in specific tasks.

Keywords:

• Affect misattribution
• Affective priming
• Categorisation
• Implicit measures
• Reliability
• Response interference

Acknowledgements

This research has been supported by grants from the Canada Research Chairs program (CRC), the Social Sciences and Humanities Research Council of Canada (SSHRC), and the Academic Development Fund of the University of Western Ontario (ADF) to the first author.

We thank Keith Payne for providing the stimulus materials for the Affect Misattribution Procedure; and Xenia Avvakumova, Allison Boyd, William Dunlop, and Arjun Sharma for their help in collecting the data.

Notes

¹Following De Houwer (2006), we use the terms direct and indirect to refer to features of measurement procedures, and the terms implicit and explicit to refer to features of the constructs assessed by a given measurement procedure. Moreover, given ambiguous evidence regarding the nature of the constructs assessed by indirect measurement procedures (De Houwer, 2006; Gawronski, Hofmann, & Wilbur, 2006), we limit our interpretation of the term implicit to “unintentional” (i.e., evaluative responses to an object that do not require an intention to evaluate that object).

²In this context, it is important to distinguish between categorisation effects on evaluation (e.g., Olson & Fazio, 2003) and evaluation effects on categorisation (e.g., Smith, Fazio, & Cejka, 1996). The present study is primarily concerned with categorisation effects on evaluation.

³Even though we consider attention and categorisation as conceptually distinct processes, we treat attention to categories and categorisation as functionally equivalent in the present research, given that attention to a category has been argued to imply categorisation and categorisation implying attention to that category (e.g., Logan, 2002).

⁴Note that both the Stroop task and Fazio et al.'s (1995) affective priming task involve variations in stimulus–response compatibility as well as stimulus–stimulus compatibility (De Houwer, 2003b); however, the actual contribution of stimulus–stimulus compatibility seems relatively minor compared to the contribution of stimulus–response compatibility in the Stroop task (De Houwer, 2003c) as well as Fazio et al.'s (1995) affective priming task (Klauer, Musch, & Eder, 2005), which makes response interference the primary source of Stroop and priming effects in these tasks.

⁵More information about procedural details, data aggregation, and the calculation of preference scores is provided in the Main Study.

⁶To provide further evidence for the AMP's robustness against attentional influences, we ran an additional study that used a 75 ms prime presentation followed by blank screen for 125 ms before the target stimulus appeared, as recommended by Payne et al. (2005). The results of this study were identical, in that the AMP produced reliable priming effects of a given category dimension regardless of whether participants paid attention to race or age.

⁷Note that the RIT Pilot Study did not include a masking stimulus. Therefore, latencies in the Pilot Study were recorded from the onset of the target stimulus. For the Main Study, corresponding latencies from the onset of the target stimulus can be calculated by adding a constant of 100 ms to all latencies (equivalent to the 100 ms target presentation). Results of the resulting priming scores are identical irrespective of whether latencies are scored from the onset of the target or the onset of the masking stimulus. In the following sections, we report the actual data recorded by the software.

⁸Note that the standard procedure for analysing AMP data does not involve an exclusion of trials based on RTs. In the present study, trials with latencies higher than 1250 ms were excluded to avoid a potential confounding resulting from different data treatments. To confirm that the obtained AMP data are independent of the employed exclusion criterion, we also ran our analyses without the employed cutoff of 1250 ms. Results were identical to the ones reported below, with effect sizes and split-half correlations differing only at the level of the second decimal.

⁹Reliability analyses produced the same pattern of results when the trials were divided on the basis of whether they occurred in the first versus second half of the task instead of odd versus even trial numbers.

¹⁰Two participants did not provide an estimate of the number of faces.