How should we think about implicit measures and their empirical “anomalies”?

Abstract

Based on a review of several "anomalies" in research using implicit measures, Machery (2021) dismisses the modal interpretation of participant responses on implicit measures and, by extension, the value of implicit measures. We argue that the reviewed findings are anomalies only for specific-influential but long-contested-accounts that treat responses on implicit measures as uncontaminated indicators of trait-like unconscious representations that coexist with functionally independent conscious representations. However, the reviewed findings are to-be-expected "normalities" when viewed from the perspective of long-standing alternative frameworks that treat responses on implicit measures as the product of dynamic processes that operate on momentarily activated, consciously accessible information. Thus, although we agree with Machery that the modal view is empirically unsupported, we argue that implicit measures can make a valuable contribution to understanding the complexities of human behavior if they are used wisely in a way that acknowledges what they can and cannot do.

Testing for implicit bias: Values, psychometrics, and science communication

“…The reclassification argument may be similar to the response from Gawronski and colleagues: they emphasize that their original stance was that tests of implicit bias measure behavior rather than “trait‐like constructs that are highly stable over time” (Gawronski et al, 2022, p. 2). This stance aligns with other discussions about measuring and manipulating “implicitly biased behavior ” (Byrd, 2019, italics added): “responses on implicit measures are behaviors, and these behaviors should not be equated with their underlying mental constructs” (Gawronski et al, 2022, p. 4) This allows Gawronski and colleagues to accommodate many of Machery's claims without accepting the illegitimacy argument's conclusion.…”

“…Those who expect implicit bias to vary between contexts (like blood pressure ) are not necessarily concerned about unreliability (e.g., Byrd, 2019, section 2.3; Gawronski et al, 2022). However, those who expect implicit bias to be stable across contexts (like blood type), take unreliability to indicate a problem with tests of implicit bias (e.g., Machery, 2021b).…”

“…Machery (2021b, p. 6) notes that evidence produced by the AMP is unlikely relevant to claims about implicit attitudes. Gawronski et al (2022, p. 2) do take evidence about the internal consistency of the AMP to be relevant to claims about the psychometric qualities of indirect measures of implicit bias. Both views recognize the importance of employing multiple methods to examine the same phenomenon, outcome, or result (a strategy called “triangulation”), but they come to different conclusions about the extent to which different methods produce evidence that is relevant to claims about implicit attitudes or biases.…”

It's time to do more research on the attitude–behavior relation: A commentary on implicit attitude measures

“…The recent commentaries by Brownstein, Madva, and Gawronski (Brownstein et al, 2019; Gawronski et al, 2022)and by Machery (Machery, 2021; Machery, 2022) lead us to recommend increasing research attention to the correlation between implicit attitudes and overt behavior. In their present form, implicit measures are excellent instruments to gauge evaluatively relevant associations in specific contexts.…”

“…Specifically, Machery highlights what he describes as the “anomalies” of implicit measures, including low validity, low reliability, low predictive power, and limited casual efficacy. These “anomalies” are addressed point‐by‐point in a follow‐up response from Gawronski et al (2022), who conclude that each “anomaly” is to be expected because implicit measures capture transient states instead of traits. Machery (2022) responds unconvinced, highlighting that the field of implicit attitudes has failed to accomplish its initial goal of measuring hidden attitudes in a reliable and valid way.…”