Login / Signup

Repeated evaluative pairings and evaluative statements: How effectively do they shift implicit attitudes?

Benedek KurdiMahzarin R Banaji
Published in: Journal of experimental psychology. General (2017)
Six experiments, involving a total of 6,492 participants, were conducted to investigate the relative effectiveness of repeated evaluative pairings (REP; exposure to category members paired with pleasant or unpleasant images), evaluative statements (ES; verbally signaling upcoming pairings without actual exposure), and their combination (ES + REP) in shifting implicit social and nonsocial attitudes. Learning modality (REP, ES, and ES + REP) was varied between participants and implicit attitudes were assessed using an Implicit Association Test (IAT). Study 1 (N = 675) used fictitious social groups (NIFFs and LAAPs), Study 2 (N = 1,034) used novel social groups (humans with long vs. square faces), Study 3 (N = 1,072) used nonsocial stimuli (squares vs. rectangles), and Study 4 (N = 848) and Study 5 (N = 958) used known social groups (young vs. elderly; American vs. foreign). ES were more effective than REP and no less superior than ES + REP in producing implicit attitude change. Results were robust across social and nonsocial domains and for known and novel groups. Study 6 (N = 1,905) eliminated time on intervention, levels of construal, and expectancy effects as possible explanations for these findings. Associative theories of implicit evaluation posit that implicit attitudes should shift piecemeal over time; yet, in these experiments, one-shot language-based learning led to larger shifts in implicit attitude than exposure to stimulus pairings. Moreover, the redundancy observed in REP + ES suggests that attitude acquisition from repeated pairings and evaluative instructions may rely on shared mental representations. (PsycINFO Database Record
Keyphrases
  • mental health
  • healthcare
  • randomized controlled trial
  • systematic review
  • deep learning
  • autism spectrum disorder
  • optical coherence tomography
  • electronic health record