Date of Award

Spring 1-1-2012

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Psychology & Neuroscience

First Advisor

Charles Judd

Second Advisor

Irene Blair

Third Advisor

Bernadette Park

Abstract

This dissertation addresses the general assumption that the implicit evaluative associations people might hold with social groups (i.e., usually referred to as “implicit attitudes”) are “unconscious” and introspectively unavailable. In the work presented in the current dissertation I directly asked participants to predict their results on five future IATs. I consistently found that participants were highly accurate in their predictions, regardless of whether the IATs were described as revealing true attitudes or cultural associations (Studies 1 and 2); whether predictions were made in the form of specific response patterns (“ease of responding” in Study 1) or a more conceptual response (“your implicit attitude” in Studies 2-5); regardless of how much experience or explanation participants received before making their predictions (Study 4); and regardless of how much deliberation about the attitude targets participants engaged in prior to predicting their implicit attitudes (Study 5). Results also suggested that participants had unique insight into their own implicit attitudes above assumptions of normative responses, as their predictions were accurate even when controlling for predictions they made about the implicit attitude scores for a typical other participant in the study (Study 3). Even as participants accurately predicted their implicit attitudes, they reported distinct explicit attitudes. Interestingly, although participants showed impressive accuracy in predicting how their own implicit attitude scores would relate to each other, they showed somewhat limited insight into how their implicit attitudes compared to those of other people. These results fit theoretical dual-process models on attitudes, and they have several theoretical and practical implications.

Share

COinS