We successfully replicated the VO effect using new distractor tasks. regress accurate i.verbalize, vce(robust) Linear regression Number of obs = 1096 F( 1, 1094) = 4.75 Prob > F = 0.0295 R-squared = 0.0043 Root MSE = .44804 ------------------------------------------------------------------------------ | Robust accurate | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- 1.verbalize | -.0591244 .0271253 -2.18 0.029 -.1123478 -.005901 _cons | .3095685 .0200434 15.44 0.000 .2702406 .3488964 ------------------------------------------------------------------------------ Only one of our filler tasks moderated the results (complete results at bottom of page). this was whether someone was more likely to remember soeone's face after meeting them. Verbal Overshadowing was present for those people but not for name rememberers or people equally likely to remember faces or names. interflex accurate verbalize facerememberer facerverbazlie, vce(robust) p value of Wald test: 0.0001 regress accurate i.verbalize if facerememberer == 1, vce(robust) Linear regression Number of obs = 666 F( 1, 664) = 4.82 Prob > F = 0.0284 R-squared = 0.0073 Root MSE = .45542 ------------------------------------------------------------------------------ | Robust accurate | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- 1.verbalize | -.0778573 .0354516 -2.20 0.028 -.1474681 -.0082465 _cons | .336478 .0265366 12.68 0.000 .2843722 .3885838 ------------------------------------------------------------------------------ . regress accurate i.verbalize if facerememberer != 1, vce(robust) Linear regression Number of obs = 430 F( 1, 428) = 0.60 Prob > F = 0.4389 R-squared = 0.0014 Root MSE = .43572 ------------------------------------------------------------------------------ | Robust accurate | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- 1.verbalize | -.0325581 .0420244 -0.77 0.439 -.1151579 .0500417 _cons | .2697674 .0303402 8.89 0.000 .2101331 .3294018 ------------------------------------------------------------------------------ **Exploratory Analyses** People were more likely to say the robber was 'not present' if they verbalized his description regress notpresent verbalize ,vce(robust) Linear regression Number of obs = 1098 F( 1, 1096) = 24.98 Prob > F = 0.0000 R-squared = 0.0221 Root MSE = .42354 ------------------------------------------------------------------------------ | Robust notpresent | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- verbalize | .1271615 .0254437 5.00 0.000 .0772377 .1770854 _cons | .17603 .0164958 10.67 0.000 .143663 .2083969 ------------------------------------------------------------------------------ People were also more confident in their judgments when they said the robber was 'not present' or when they were wrong when they verbalized his description. When they wetre correct, however, verbalizing had no affect on confidence. regress confidence verbalize , vce(robust) Linear regression Number of obs = 1096 F( 1, 1094) = 10.74 Prob > F = 0.0011 R-squared = 0.0098 Root MSE = 25.717 ------------------------------------------------------------------------------ | Robust confidence_1 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- verbalize | 5.104449 1.557317 3.28 0.001 2.048783 8.160115 _cons | 53.07317 1.155354 45.94 0.000 50.80621 55.34013 ------------------------------------------------------------------------------ regress confidence verbalize if whodunnit == 9 , vce(robust) Linear regression Number of obs = 265 F( 1, 263) = 15.89 Prob > F = 0.0001 R-squared = 0.0608 Root MSE = 28.386 ------------------------------------------------------------------------------ | Robust confidence_1 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- verbalize | 15.03888 3.772799 3.99 0.000 7.610148 22.46762 _cons | 45.8617 3.151695 14.55 0.000 39.65594 52.06747 ------------------------------------------------------------------------------ . regress confidence verbalize if whodunnit == 6 , vce(robust) Linear regression Number of obs = 306 F( 1, 304) = 0.17 Prob > F = 0.6795 R-squared = 0.0006 Root MSE = 23.45 ------------------------------------------------------------------------------ | Robust confidence_1 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- verbalize | -1.110251 2.684358 -0.41 0.679 -6.392526 4.172023 _cons | 62.06061 1.845466 33.63 0.000 58.4291 65.69211 ------------------------------------------------------------------------------ regress confidence verbalize if whodunnit !=6 & whodunnit != 9, vce(robust) Linear regression Number of obs = 525 F( 1, 523) = 4.64 Prob > F = 0.0317 R-squared = 0.0087 Root MSE = 24.68 ------------------------------------------------------------------------------ | Robust confidence_1 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- verbalize | 4.629904 2.148925 2.15 0.032 .4083186 8.851489 _cons | 50.13504 1.545832 32.43 0.000 47.09823 53.17184 ------------------------------------------------------------------------------ There was also the possibility that there wa sa higher-order interaction between mental rotaiton ability and being a face rememberer, such that among face rememberers, verbal overshadowing was stronger for those of lower ability, where there was no such interaction among name rememberers or those equally likely to remember a face and name. regress accurate verbalize mrts mrtsverbazlie if facerememberer == 1, vce(robust) Linear regression Number of obs = 666 F( 3, 662) = 2.99 Prob > F = 0.0304 R-squared = 0.0143 Root MSE = .45449 ------------------------------------------------------------------------------- | Robust accurate | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- verbalize | -.1821699 .0638083 -2.85 0.004 -.307461 -.0568788 mrts | -.0095582 .004793 -1.99 0.047 -.0189694 -.0001469 mrtsverbazlie | .012855 .0064868 1.98 0.048 .0001178 .0255922 _cons | .4151076 .0492395 8.43 0.000 .3184232 .5117921 ------------------------------------------------------------------------------- regress accurate verbalize mrts mrtsverbazlie if facerememberer !=1, vce(robust) Linear regression Number of obs = 430 F( 3, 426) = 2.01 Prob > F = 0.1112 R-squared = 0.0135 Root MSE = .43409 ------------------------------------------------------------------------------- | Robust accurate | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- verbalize | -.037568 .0620088 -0.61 0.545 -.1594493 .0843134 mrts | .0075174 .0053583 1.40 0.161 -.0030145 .0180494 mrtsverbazlie | .001356 .007256 0.19 0.852 -.012906 .0156181 _cons | .2147678 .0471204 4.56 0.000 .1221504 .3073852 ------------------------------------------------------------------------------- **non-interactions** None of the interactions seemed to work, however. Still need to test mental rotation and crt. The only one may have been an interaction with face rememberers . regress accurate i.verbalize agec agecverbalize if Finished ==1, vce(robust) Linear regression Number of obs = 1096 F( 3, 1092) = 7.69 Prob > F = 0.0000 R-squared = 0.0206 Root MSE = .44477 ------------------------------------------------------------------------------- | Robust accurate | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- 1.verbalize | -.0601372 .0651815 -0.92 0.356 -.1880322 .0677579 agec | -.0035637 .0012319 -2.89 0.004 -.0059809 -.0011466 agecverbalize | .0001567 .0016572 0.09 0.925 -.0030949 .0034084 _cons | .4283544 .0474332 9.03 0.000 .3352839 .5214249 ------------------------------------------------------------------------------- . interflex accurate i.verbalize agec agecverbalize if Finished ==1, vce(robust) factor variables and time-series operators not allowed r(101); . interflex accurate verbalize agec agecverbalize if Finished ==1, vce(robust) p value of Wald test: 0.1675 . regress accurate i.verbalize white whiteverbalize if Finished ==1, vce(robust) Linear regression Number of obs = 1096 F( 3, 1092) = 2.32 Prob > F = 0.0735 R-squared = 0.0060 Root MSE = .44808 -------------------------------------------------------------------------------- | Robust accurate | Coef. Std. Err. t P>|t| [95% Conf. Interval] ---------------+---------------------------------------------------------------- 1.verbalize | -.1307562 .0597395 -2.19 0.029 -.2479733 -.013539 white | -.0337632 .0503732 -0.67 0.503 -.1326024 .065076 whiteverbalize | .0891206 .0670375 1.33 0.184 -.0424162 .2206574 _cons | .3363636 .0451302 7.45 0.000 .2478119 .4249153 -------------------------------------------------------------------------------- . interflex accurate verbalize white whiteverbalize if Finished ==1, vce(robust) p value of Wald test: . . regress accurate i.verbalize wsp wspverbalize if Finished ==1, vce(robust) Linear regression Number of obs = 1096 F( 3, 1092) = 2.34 Prob > F = 0.0721 R-squared = 0.0065 Root MSE = .44796 ------------------------------------------------------------------------------ | Robust accurate | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- 1.verbalize | .0476937 .0789266 0.60 0.546 -.1071711 .2025586 wsp | .0091382 .0058963 1.55 0.121 -.0024312 .0207076 wspverbalize | -.0113591 .0079926 -1.42 0.156 -.0270416 .0043234 _cons | .2234326 .0577199 3.87 0.000 .1101782 .336687 ------------------------------------------------------------------------------ . interflex accurate verbalize wsp wspverbalize if Finished ==1, vce(robust) p value of Wald test: 0.6192 . regress accurate i.verbalize faithintu faithinuverbalize if Finished ==1, vce(robust) Linear regression Number of obs = 1096 F( 3, 1092) = 1.88 Prob > F = 0.1314 R-squared = 0.0053 Root MSE = .44824 ----------------------------------------------------------------------------------- | Robust accurate | Coef. Std. Err. t P>|t| [95% Conf. Interval] ------------------+---------------------------------------------------------------- 1.verbalize | -.1202523 .1584032 -0.76 0.448 -.4310614 .1905567 faithintu | -.0270407 .0312207 -0.87 0.387 -.0883 .0342185 faithinuverbalize | .015936 .0408981 0.39 0.697 -.0643116 .0961837 _cons | .4127088 .1212174 3.40 0.001 .1748635 .6505541 ----------------------------------------------------------------------------------- . interflex accurate verbalize faithintu faithinuverbalize if Finished ==1, vce(robust) p value of Wald test: 0.7613 . regress accurate i.verbalize nfc nfcverbalize if Finished ==1, vce(robust) Linear regression Number of obs = 1096 F( 3, 1092) = 1.95 Prob > F = 0.1192 R-squared = 0.0051 Root MSE = .44828 ------------------------------------------------------------------------------ | Robust accurate | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- 1.verbalize | -.0211093 .0981303 -0.22 0.830 -.2136544 .1714359 nfc | -.0072689 .0245449 -0.30 0.767 -.0554294 .0408916 nfcverbalize | -.0133697 .0328807 -0.41 0.684 -.0778861 .0511467 _cons | .3302371 .0728541 4.53 0.000 .1872873 .473187 ------------------------------------------------------------------------------ . interflex accurate verbalize nfc nfcverbalize if Finished ==1, vce(robust) p value of Wald test: 0.1420 . regress accurate i.verbalize restraint restraintverbalize if Finished ==1, vce(robust) Linear regression Number of obs = 1096 F( 3, 1092) = 2.48 Prob > F = 0.0597 R-squared = 0.0064 Root MSE = .44798 ------------------------------------------------------------------------------------ | Robust accurate | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------------+---------------------------------------------------------------- 1.verbalize | .1362881 .1276934 1.07 0.286 -.1142641 .3868404 restraint | .0360418 .0343748 1.05 0.295 -.0314064 .1034899 restraintverbalize | -.0686581 .0438521 -1.57 0.118 -.154702 .0173859 _cons | .2084419 .0978056 2.13 0.033 .0165338 .4003501 ------------------------------------------------------------------------------------ . interflex accurate verbalize restraint restraintverbalize if Finished ==1, vce(robust) p value of Wald test: 0.4686 . regress accurate i.verbalize impulsive impulsiveverbalize if Finished ==1 , vce(robust) Linear regression Number of obs = 1096 F( 3, 1092) = 2.01 Prob > F = 0.1104 R-squared = 0.0056 Root MSE = .44816 ------------------------------------------------------------------------------------ | Robust accurate | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------------+---------------------------------------------------------------- 1.verbalize | -.1257343 .0680811 -1.85 0.065 -.2593188 .0078502 impulsive | -.0243365 .0212467 -1.15 0.252 -.0660254 .0173524 impulsiveverbalize | .0316504 .0296588 1.07 0.286 -.0265443 .0898451 _cons | .3611089 .0501158 7.21 0.000 .2627748 .4594429 ------------------------------------------------------------------------------------ . interflex accurate verbalize impulsive impulsiveverbalize if Finished ==1 , vce(robust) p value of Wald test: 0.2117 . regress accurate i.verbalize vizualizer vizualizerverbazlie if Finished ==1, vce(robust) Linear regression Number of obs = 1096 F( 3, 1092) = 1.81 Prob > F = 0.1429 R-squared = 0.0050 Root MSE = .44829 ------------------------------------------------------------------------------------- | Robust accurate | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------------+---------------------------------------------------------------- 1.verbalize | -.0046113 .0704638 -0.07 0.948 -.142871 .1336484 vizualizer | .012281 .0164927 0.74 0.457 -.0200799 .0446419 vizualizerverbazlie | -.0181877 .0217146 -0.84 0.402 -.0607948 .0244194 _cons | .2729328 .0526558 5.18 0.000 .1696148 .3762508 ------------------------------------------------------------------------------------- . interflex accurate verbalize vizualizer vizualizerverbazlie if Finished ==1, vce(robust) p value of Wald test: 0.8826 . regress accurate i.verbalize vrblzr vrblzrvizualize if Finished ==1, vce(robust) Linear regression Number of obs = 1096 F( 3, 1092) = 1.72 Prob > F = 0.1614 R-squared = 0.0047 Root MSE = .44838 --------------------------------------------------------------------------------- | Robust accurate | Coef. Std. Err. t P>|t| [95% Conf. Interval] ----------------+---------------------------------------------------------------- 1.verbalize | -.0293702 .0631153 -0.47 0.642 -.1532111 .0944707 vrblzr | .0030219 .0152 0.20 0.842 -.0268026 .0328465 vrblzrvizualize | -.0106627 .0202195 -0.53 0.598 -.0503362 .0290108 _cons | .301013 .0473587 6.36 0.000 .2080886 .3939374 --------------------------------------------------------------------------------- . interflex accurate verbalize vrblzr vrblzrvizualize if Finished ==1, vce(robust) p value of Wald test: 0.9149 . tab facerememberer After meeting someone for the | first time, on the second time | meeting them, are y | Freq. Percent Cum. -----------------------------------+----------------------------------- More likely to remember their name | 239 13.88 13.88 .5 | 574 33.33 47.21 More likely to remember their face | 909 52.79 100.00 -----------------------------------+----------------------------------- Total | 1,722 100.00 . gen facerverbazlie = facerememberer*verbalize (1040 missing values generated) . regress accurate i.verbalize facerememberer facerverbazlie if Finished ==1, vce(robust) Linear regression Number of obs = 1096 F( 3, 1092) = 4.64 Prob > F = 0.0031 R-squared = 0.0121 Root MSE = .44671 -------------------------------------------------------------------------------- | Robust accurate | Coef. Std. Err. t P>|t| [95% Conf. Interval] ---------------+---------------------------------------------------------------- 1.verbalize | .0270115 .059644 0.45 0.651 -.0900182 .1440412 facerememberer | .1610305 .0518463 3.11 0.002 .0593009 .2627602 facerverbazlie | -.1161638 .0741176 -1.57 0.117 -.2615928 .0292651 _cons | .1893243 .0408195 4.64 0.000 .1092308 .2694178 -------------------------------------------------------------------------------- . interflex accurate verbalize facerememberer facerverbazlie if Finished ==1, vce(robust) p value of Wald test: 0.0001 . gen weedverbalize = weed*verbalize (1040 missing values generated) . regress accurate i.verbalize weed weedverbalize if Finished ==1, vce(robust) Linear regression Number of obs = 1096 F( 3, 1092) = 1.95 Prob > F = 0.1202 R-squared = 0.0053 Root MSE = .44824 ------------------------------------------------------------------------------- | Robust accurate | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- 1.verbalize | -.0688635 .0288998 -2.38 0.017 -.125569 -.0121581 weed | -.0270517 .0609119 -0.44 0.657 -.1465693 .0924659 weedverbalize | .0789239 .0838504 0.94 0.347 -.0856023 .24345 _cons | .312766 .0214243 14.60 0.000 .2707285 .3548035 ------------------------------------------------------------------------------- regress accurate i.verbalize crt2 crt2verbalize if Finished ==1, vce(robust) Linear regression Number of obs = 1096 F( 3, 1092) = 1.78 Prob > F = 0.1501 R-squared = 0.0049 Root MSE = .44832 ------------------------------------------------------------------------------- | Robust accurate | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- 1.verbalize | -.0474418 .0457748 -1.04 0.300 -.1372583 .0423747 crt2 | .0118325 .0166779 0.71 0.478 -.0208919 .0445569 crt2verbalize | -.0063733 .0229394 -0.28 0.781 -.0513836 .038637 _cons | .289411 .0343253 8.43 0.000 .2220601 .3567619 ------------------------------------------------------------------------------- . interflex accurate verbalize crt2 crt2verbalize if Finished ==1, vce(robust) p value of Wald test: 0.9724 regress accurate i.verbalize mrts mrtsverbazlie if Finished ==1, vce(robust) Linear regression Number of obs = 1096 F( 3, 1092) = 2.86 Prob > F = 0.0360 R-squared = 0.0073 Root MSE = .44779 ------------------------------------------------------------------------------- | Robust accurate | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- 1.verbalize | -.1155726 .0451606 -2.56 0.011 -.204184 -.0269611 mrts | -.0017995 .0035641 -0.50 0.614 -.0087927 .0051937 mrtsverbazlie | .0075181 .0048126 1.56 0.119 -.0019249 .0169611 _cons | .3237112 .0347603 9.31 0.000 .2555067 .3919157 ------------------------------------------------------------------------------- interflex accurate verbalize mrts mrtsverbazlie if Finished ==1, vce(robust) p value of Wald test: 0.2227