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Investigating the Other-Race Effect using Multinomial Processing Tree Models

Investigating the Other-Race Effect using Multinomial Processing Tree Models. Henrik Singmann David Kellen Christoph Klauer Johannes Falck. Learning Phase:. HOUSE. BOAT. FOOD. SEAL. RAIN. …. Test phase :. HEAVEN. RAIN. CAT. KING. FOOD. SEAL. old ? new ?.

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Investigating the Other-Race Effect using Multinomial Processing Tree Models

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  1. Investigating the Other-Race Effect using Multinomial Processing Tree Models Henrik Singmann David Kellen Christoph Klauer Johannes Falck

  2. Learning Phase: HOUSE BOAT FOOD SEAL RAIN … Test phase: HEAVEN RAIN CAT KING FOOD SEAL old? new? old? new? old? new? old? new? old? new? old? new? …

  3. Learning Phase: HOUSE BOAT FOOD SEAL RAIN … Old Word New Word RAIN SEAL HEAVEN CAT KING FOOD Hit old? new? old? new? old? new? old? new? old? new? old? new? Miss CorrectRejection False Alarm …

  4. Learning Phase: HOUSE P(hits) + P(misses) = 1 P(falsealarms) + P(correctrejections) = 1 = twoindependentdatapoints BOAT FOOD SEAL RAIN … Old Word New Word FOOD CAT KING SEAL HEAVEN RAIN Hit old? new? old? new? old? new? old? new? old? new? old? new? Miss CorrectRejection False Alarm

  5. Two item classes: central-europeanfaces turkish/arabicfaces Learning phase: Test phase: old? new? old? new? old? new? old? new? old? new? old? new?

  6. Two item classes: central-europeanfaces turkish/arabicfaces Learning phase: Test phase: > < Other RaceEffect (ORE)(Meissner & Brigham, 2001) old? new? old? new? old? new? old? new? old? new? old? new?

  7. Research Questions • ORE in Germany withTurkish/Arabicfaces? • Turks/Arabslargestethnicminority (Statistisches Bundesamt, 2011) • most-researchedother-race: Blacks (USA) • Source for ORE: memory- orresponseprocesses? • Memory processes: bettermemory = ↑ hits + ↓ falsealarmworsememory= ↓ hits + ↑falsealarm • Response processes:bias "old" = ↑ hits + ↑falsealarmbias"new" = ↓hits + ↓falsealarm

  8. Extended 2HTM Do "old" Old Items gunsure "unsure" 1 - Do gold/new "old" 1 – gunsure 1 – gold/new "new" Dn "new" New Items gunsure "unsure" 1 - Dn gold/new "old" 1 – gunsure 1 – gold/new "new"

  9. Extended 2HTM Do "old" Old Items gunsure "unsure" 1 - Do 1. detection gold/new "old" 1 – gunsure 1 – gold/new "new" Dn "new" New Items gunsure "unsure" 1 - Dn gold/new "old" 1 – gunsure 1 – gold/new "new"

  10. Extended 2HTM Do "old" Old Items gunsure "unsure" 1 - Do 1. detection gold/new "old" 1 – gunsure 1 – gold/new "new" 2. uncertainty Dn "new" New Items gunsure "unsure" 1 - Dn gold/new "old" 1 – gunsure 1 – gold/new "new"

  11. Experiments 1 (n = 42) & 2 (n = 36) • 100 pictureseach (turkish/arabicandwhite): • fromwebsitesofturkish, arabic, orcentraleuropeanfootballteams (noknownleagues) • Matched after pretest: ethnicity, valence, distinctivness Exp. 1: Singmann, Kellen, & Klauer (2013), CogSciProceedings

  12. †: p < .1 *: p < .05 **: p < .01 ***: p < .001 Response Proportions Exp. 2: Exp. 1: *** † * *** * *** *** ** ** errorbars: 95%-Cosineau-Morey-Baguleywithin-subjects CIs

  13. Exp. 1: HierarchicalBayesian (posteriorgrand-meanμ) * Exp. 2: *

  14. Experiment 3 (n = 37) • 100 pictureseach (blackandwhite): • Color FERET (Facial Recognition Technology) database

  15. Response Proportions * *** * ** ** errorbars: 95%-Cosineau-Morey-Baguleywithin-subjects CIs

  16. HierarchicalBayesian (posteriorgrand-meanμ) *

  17. Research Questions: Take home Message • ORE in Germany withTurkish/Arabicfaces? • YES, but otherthanexpected(replicatedfor Black faces) • Source for ORE: memory- orresponseprocesses? • Someevidenceforinfluencememoryprocesses. • Strongerevidencefordifferences in responseprocesses(stronger "old"-bias forother-racefaces) • Preliminaryresultspointtowards different ORE groups.

  18. Thanks to my Collaborators

  19. Exp 1 & 2 (n = 78) * †

  20. *: p < .05 **: p < .01 ***: p < .001 Exp 2: Response Proportions Exp. 2 (ternary): Exp. 2 (binary): *** * *** *** *** ** ** errorbars: 95%-Cosineau-Morey-Baguleywithin-subjects CIs

  21. Correlations p< .05 p< .1

  22. Exp. 1 MLE (individual fits) *** Hierar. Bayesian(posteriorμ) *

  23. Exp. 2 MLE (individual fits) *** Hierar. Bayesian(posteriorμ) *

  24. Exp. 3 MLE (individual fits) *** Hierar. Bayesian(posteriorμ) *

  25. Results Experiment 1 HierarchicalBayesian (posteriorgrand-meanμ) *

  26. Results Experiment 2 HierarchicalBayesian (posteriorgrand-meanμ) *

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