Analyses proceeded in three stages. First, we looked at whether the term ‘rare’ provided any additional mitigation in the three genes conditions on guilty verdicts and beliefs in the underlying nature of the ‘True self’. This was accomplished by fitting two 3x2 ANOVAs with genes conditions (Genes for; Genes: mutation; Genes: variant) as one factor and whether ‘rare’ was included as the crossing factor. We predicted no significant effect for rarity and a main effect for ‘type’ of genes description but no interaction. This first test was done with the following code: BOOTSTRAP /SAMPLING METHOD=SIMPLE /VARIABLES TARGET=guilty INPUT=rare genesgroup /CRITERIA CILEVEL=95 CITYPE=PERCENTILE NSAMPLES=10000 /MISSING USERMISSING=EXCLUDE. UNIANOVA guilty BY rare genesgroup /METHOD=SSTYPE(3) /INTERCEPT=INCLUDE /POSTHOC=genesgroup(LSD) /EMMEANS=TABLES(OVERALL) /EMMEANS=TABLES(rare) COMPARE ADJ(LSD) /EMMEANS=TABLES(genesgroup) COMPARE ADJ(LSD) /EMMEANS=TABLES(rare*genesgroup) /PRINT=DESCRIPTIVE /CRITERIA=ALPHA(.05) /DESIGN=rare genesgroup rare*genesgroup. **Results**: As predicted, there was no effect of 'rarity' on guilty verdicts, nor did rarity interact with the genes conditions (*p* > .12 for main effect, *p* > .78 to interaction). There was a main effect for group condition, however (*F* (3, 1491) = 5.05, *p* < .002). Consistent with our pre-registration plan, we continue all analyses collapsingRobust standard errors are used in all analyses as the control group is half the size of the collapsed groups. We found, consistent with our prediction, that the genetic effect being labeled as a 'mutation' caused people to find the arsonist less guilty than if the cause of their self-control failure was being born with 'genes for' low self control (*b* = -.314, *p* < .006, 95%CI = .537 to .091), less guilty if they had a 'genetic variant' (*b* = -.273, *p* < .012, 95%CI = 1.142 to .854), and less guilty than if no genetic explanation was given (*b* = -.595, *p* < .001, 95%CI = .894 to .296). probit guilty ib3.genesgroup, vce(robust) Iteration 0: log pseudolikelihood = -515.34762 Iteration 1: log pseudolikelihood = -505.92723 Iteration 2: log pseudolikelihood = -505.82828 Iteration 3: log pseudolikelihood = -505.82823 Probit regression Number of obs = 1498 Wald chi2(3) = 18.44 Prob > chi2 = 0.0004 Log pseudolikelihood = -505.82823 Pseudo R2 = 0.0185 ------------------------------------------------------------------------------ | Robust guilty | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- genesgroup | 0 | .5952396 .1525018 3.90 0.000 .2963415 .8941376 1 | .3140036 .1137171 2.76 0.006 .0911222 .536885 2 | .2733272 .109356 2.50 0.012 .0589934 .4876611 | _cons | .9979785 .0735565 13.57 0.000 .8538104 1.142147 ------------------------------------------------------------------------------ In addition, the 'genetic variant' person was found less guilty than if no genetic explanation was given (*b* = -.322, *p* < .039, 95%CI = -.628 to -.016) but not significantly different than the 'genes for' group (*p* > .07). margins genesgroup Adjusted predictions Number of obs = 1498 Model VCE : Robust Expression : Pr(guilty), predict() ------------------------------------------------------------------------------ | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- genesgroup | 0 | .9444444 .0149792 63.05 0.000 .9150856 .9738031 1 | .9052369 .014631 61.87 0.000 .8765607 .9339131 2 | .89819 .0143884 62.42 0.000 .8699893 .9263908 3 | .8408551 .0178345 47.15 0.000 .8059001 .8758101 ------------------------------------------------------------------------------ This overall pattern of results was identical for determinations of whether to send the person to prison or probation. The geneitc mutation caused less prison sentences than all other conditions (all *p*s < .012), with 'genetic variant' being less likely to recommend prison than the no-explkanation group (*p* < .001) but not the 'genes for' group (*p* > .08). probit prison ib3.genesgroup, vce(robust) Iteration 0: log pseudolikelihood = -999.87197 Iteration 1: log pseudolikelihood = -982.97488 Iteration 2: log pseudolikelihood = -982.96815 Iteration 3: log pseudolikelihood = -982.96815 Probit regression Number of obs = 1498 Wald chi2(3) = 33.45 Prob > chi2 = 0.0000 Log pseudolikelihood = -982.96815 Pseudo R2 = 0.0169 ------------------------------------------------------------------------------ | Robust prison | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- genesgroup | 0 | .5577934 .1043248 5.35 0.000 .3533206 .7622661 1 | .3806661 .090142 4.22 0.000 .2039911 .5573412 2 | .2239637 .0886577 2.53 0.012 .0501977 .3977296 | _cons | -.5470811 .0645452 -8.48 0.000 -.6735874 -.4205747 ------------------------------------------------------------------------------ margins genesgroup Adjusted predictions Number of obs = 1498 Model VCE : Robust Expression : Pr(prison), predict() ------------------------------------------------------------------------------ | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- genesgroup | 0 | .5042735 .0326957 15.42 0.000 .440191 .568356 1 | .4339152 .024758 17.53 0.000 .3853904 .48244 2 | .3733032 .0230141 16.22 0.000 .3281964 .4184099 3 | .2921615 .0221709 13.18 0.000 .2487074 .3356156 ------------------------------------------------------------------------------ . probit prison ib0.genesgroup, vce(robust) Iteration 0: log pseudolikelihood = -999.87197 Iteration 1: log pseudolikelihood = -982.97488 Iteration 2: log pseudolikelihood = -982.96815 Iteration 3: log pseudolikelihood = -982.96815 Probit regression Number of obs = 1498 Wald chi2(3) = 33.45 Prob > chi2 = 0.0000 Log pseudolikelihood = -982.96815 Pseudo R2 = 0.0169 ------------------------------------------------------------------------------ | Robust prison | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- genesgroup | 1 | -.1771272 .1033299 -1.71 0.086 -.37965 .0253956 2 | -.3338297 .1020376 -3.27 0.001 -.5338197 -.1338396 3 | -.5577934 .1043248 -5.35 0.000 -.7622661 -.3533206 | _cons | .0107123 .0819608 0.13 0.896 -.1499279 .1713525 ------------------------------------------------------------------------------ In contrast to our previous findings, we did find evidence that all genetic explanations caused an increase in perceived self-control the person used before lighting the car on fire (all *p*s < .027 compared to the control) . regress sc ib0.genesgroup, vce(robust) Linear regression Number of obs = 1498 F( 3, 1494) = 4.16 Prob > F = 0.0060 R-squared = 0.0071 Root MSE = 1.207 ------------------------------------------------------------------------------ | Robust sc | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- genesgroup | 1 | .2093271 .0943583 2.22 0.027 .0242383 .3944159 2 | .3034188 .0918602 3.30 0.001 .1232302 .4836074 3 | .2739103 .0923687 2.97 0.003 .0927242 .4550964 | _cons | 1.581197 .0709573 22.28 0.000 1.44201 1.720383 ------------------------------------------------------------------------------ margins genesgroup Adjusted predictions Number of obs = 1498 Model VCE : Robust Expression : Linear prediction, predict() ------------------------------------------------------------------------------ | Delta-method | Margin Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- genesgroup | 0 | 1.581197 .0709573 22.28 0.000 1.44201 1.720383 1 | 1.790524 .0621976 28.79 0.000 1.66852 1.912528 2 | 1.884615 .0583382 32.31 0.000 1.770182 1.999049 3 | 1.855107 .0591357 31.37 0.000 1.739109 1.971105 ------------------------------------------------------------------------------ There was no difference, however, between the different genetic explanations groups (all *p*s > .45). As predicted, there were significnat differences in the extent to which people believed the 'true self' of the arsonist was a dangerous person. the only significant difference was that those reading about the 'genetic mutation' explanation thought the arsonist was less dangerous than all other conditions. regress ts ib3.genesgroup, vce(robust) Linear regression Number of obs = 1498 F( 3, 1494) = 4.36 Prob > F = 0.0046 R-squared = 0.0091 Root MSE = 1.4612 ------------------------------------------------------------------------------ | Robust ts | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- genesgroup | 0 | .3435552 .1183868 2.90 0.004 .1113333 .5757772 1 | .3377897 .1049726 3.22 0.001 .1318804 .543699 2 | .1947367 .100188 1.94 0.052 -.0017874 .3912608 | _cons | 4.472684 .0746453 59.92 0.000 4.326263 4.619105 ------------------------------------------------------------------------------ . margins genesgroup Adjusted predictions Number of obs = 1498 Model VCE : Robust Expression : Linear prediction, predict() ------------------------------------------------------------------------------ | Delta-method | Margin Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- genesgroup | 0 | 4.816239 .0918886 52.41 0.000 4.635995 4.996484 1 | 4.810474 .073806 65.18 0.000 4.665699 4.955248 2 | 4.667421 .066826 69.84 0.000 4.536338 4.798504 3 | 4.472684 .0746453 59.92 0.000 4.326263 4.619105 ------------------------------------------------------------------------------ How much people thought the victim should blame the purse snatcher followed the same pattern as before, with the 'genetic mutation' being significantly less blameworthy than all other groups, and 'genetic variant' less blameworthy than the no-explanation control but not the 'genes for' group. regress blame ib3.genesgroup, vce(robust) Linear regression Number of obs = 1498 F( 3, 1494) = 10.96 Prob > F = 0.0000 R-squared = 0.0229 Root MSE = 1.1295 ------------------------------------------------------------------------------ | Robust blame | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- genesgroup | 0 | .4612136 .0993766 4.64 0.000 .2662812 .6561461 1 | .3807524 .0784018 4.86 0.000 .2269631 .5345417 2 | .1869552 .0765457 2.44 0.015 .0368067 .3371037 | _cons | 3.684086 .0571321 64.48 0.000 3.572018 3.796153 ------------------------------------------------------------------------------ . margins genesgroup Adjusted predictions Number of obs = 1498 Model VCE : Robust Expression : Linear prediction, predict() ------------------------------------------------------------------------------ | Delta-method | Margin Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- genesgroup | 0 | 4.145299 .0813119 50.98 0.000 3.985801 4.304797 1 | 4.064838 .0536914 75.71 0.000 3.959519 4.170156 2 | 3.871041 .0509428 75.99 0.000 3.771114 3.970968 3 | 3.684086 .0571321 64.48 0.000 3.572018 3.796153 ------------------------------------------------------------------------------ . regress blame ib0.genesgroup, vce(robust) Linear regression Number of obs = 1498 F( 3, 1494) = 10.96 Prob > F = 0.0000 R-squared = 0.0229 Root MSE = 1.1295 ------------------------------------------------------------------------------ | Robust blame | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- genesgroup | 1 | -.0804612 .0974392 -0.83 0.409 -.2715934 .110671 2 | -.2742584 .0959521 -2.86 0.004 -.4624735 -.0860433 3 | -.4612136 .0993766 -4.64 0.000 -.6561461 -.2662812 | _cons | 4.145299 .0813119 50.98 0.000 3.985801 4.304797 ------------------------------------------------------------------------------ Other results pasted below regress again ib3.genesgroup, vce(robust) Linear regression Number of obs = 1498 F( 3, 1494) = 2.85 Prob > F = 0.0362 R-squared = 0.0059 Root MSE = 20.839 ------------------------------------------------------------------------------ | Robust again | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- genesgroup | 0 | -2.167844 1.736306 -1.25 0.212 -5.573701 1.238013 1 | 2.719691 1.472222 1.85 0.065 -.1681508 5.607533 2 | 1.210235 1.397271 0.87 0.387 -1.530585 3.951056 | _cons | 71.47981 1.022896 69.88 0.000 69.47334 73.48628 ------------------------------------------------------------------------------ . regress again ib0.genesgroup, vce(robust) Linear regression Number of obs = 1498 F( 3, 1494) = 2.85 Prob > F = 0.0362 R-squared = 0.0059 Root MSE = 20.839 ------------------------------------------------------------------------------ | Robust again | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- genesgroup | 1 | 4.887535 1.757715 2.78 0.005 1.439683 8.335388 2 | 3.378079 1.695432 1.99 0.047 .052399 6.70376 3 | 2.167844 1.736306 1.25 0.212 -1.238013 5.573701 | _cons | 69.31197 1.403012 49.40 0.000 66.55988 72.06405 ------------------------------------------------------------------------------ . regress humanu ib3.genesgroup, vce(robust) Linear regression Number of obs = 1498 F( 3, 1494) = 1.69 Prob > F = 0.1671 R-squared = 0.0035 Root MSE = 23.299 ------------------------------------------------------------------------------ | Robust humanu | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- genesgroup | 0 | -3.442424 1.888651 -1.82 0.069 -7.147113 .2622644 1 | .7155034 1.660006 0.43 0.667 -2.540687 3.971694 2 | .0731613 1.534394 0.05 0.962 -2.936633 3.082956 | _cons | 42.24584 1.104684 38.24 0.000 40.07895 44.41274 ------------------------------------------------------------------------------ . regress humanu ib0.genesgroup, vce(robust) Linear regression Number of obs = 1498 F( 3, 1494) = 1.69 Prob > F = 0.1671 R-squared = 0.0035 Root MSE = 23.299 ------------------------------------------------------------------------------ | Robust humanu | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- genesgroup | 1 | 4.157928 1.970272 2.11 0.035 .2931356 8.02272 2 | 3.515586 1.865667 1.88 0.060 -.1440191 7.175191 3 | 3.442424 1.888651 1.82 0.069 -.2622644 7.147113 | _cons | 38.80342 1.531886 25.33 0.000 35.79854 41.8083 ------------------------------------------------------------------------------ The third stage will use the same number of groups (and whether to condition on ‘rarity) defined in the first stage and construct the indirect effects model of mitigation on guilty verdicts, prison recommendations, and blame, indirectly through true-self judgments. This provides the first pre-registered true-self analysis in this manuscript as well. This was done using the following model: Analysis: TYPE = GENERAL; ESTIMATOR = ML; BOOTSTRAP = 10000; ITERATIONS= 10000; Model: guilty ON genesfor (cdashgf); guilty ON variant (cdashvariant); guilty ON mutation (cdashmutate); guilty ON trueself (b1); prison ON trueself (b2); blame ON trueself (b3); prison ON genesfor ; prison ON variant ; prison ON mutation ; blame ON genesfor ; blame ON variant ; blame ON mutation ; trueself ON genesfor (agf); trueself ON variant (avar); trueself ON mutation (amutate); MODEL INDIRECT: guilty IND genesfor ; guilty IND variant ; guilty IND mutation ; prison IND genesfor ; prison IND variant ; prison IND mutation ; blame IND genesfor ; blame IND variant ; blame IND mutation ; OUTPUT: STAND CINT(bcbootstrap); Results are as follows: MODEL RESULTS Two-Tailed Estimate S.E. Est./S.E. P-Value GUILTY ON GENESFOR -0.575 0.352 -1.634 0.102 VARIANT -0.628 0.345 -1.819 0.069 MUTATION -1.095 0.334 -3.282 0.001 TRUESELF 0.229 0.053 4.288 0.000 PRISON ON TRUESELF 0.633 0.051 12.306 0.000 GENESFOR -0.341 0.176 -1.936 0.053 VARIANT -0.526 0.173 -3.046 0.002 MUTATION -0.859 0.185 -4.638 0.000 BLAME ON TRUESELF 0.236 0.020 11.926 0.000 GENESFOR -0.079 0.093 -0.847 0.397 VARIANT -0.239 0.093 -2.573 0.010 MUTATION -0.380 0.097 -3.906 0.000 TRUESELF ON GENESFOR -0.006 0.117 -0.049 0.961 VARIANT -0.149 0.114 -1.300 0.193 MUTATION -0.344 0.119 -2.894 0.004 Intercepts BLAME 3.007 0.127 23.655 0.000 TRUESELF 4.816 0.092 52.338 0.000 Thresholds GUILTY$1 -1.778 0.384 -4.632 0.000 PRISON$1 3.041 0.292 10.414 0.000 Residual Variances BLAME 1.153 0.050 22.929 0.000 TRUESELF 2.129 0.071 30.061 0.000 TOTAL, TOTAL INDIRECT, SPECIFIC INDIRECT, AND DIRECT EFFECTS Two-Tailed Estimate S.E. Est./S.E. P-Value Effects from GENESFOR to GUILTY Total -0.576 0.352 -1.634 0.102 Total indirect -0.001 0.028 -0.048 0.962 Specific indirect GUILTY TRUESELF GENESFOR -0.001 0.028 -0.048 0.962 Direct GUILTY GENESFOR -0.575 0.352 -1.634 0.102 Effects from VARIANT to GUILTY Total -0.662 0.346 -1.911 0.056 Total indirect -0.034 0.028 -1.217 0.224 Specific indirect GUILTY TRUESELF VARIANT -0.034 0.028 -1.217 0.224 Direct GUILTY VARIANT -0.628 0.345 -1.819 0.069 Effects from MUTATION to GUILTY Total -1.174 0.334 -3.517 0.000 Total indirect -0.079 0.033 -2.393 0.017 Specific indirect GUILTY TRUESELF MUTATION -0.079 0.033 -2.393 0.017 Direct GUILTY MUTATION -1.095 0.334 -3.282 0.001 Effects from GENESFOR to PRISON Total -0.345 0.194 -1.780 0.075 Total indirect -0.004 0.075 -0.049 0.961 Specific indirect PRISON TRUESELF GENESFOR -0.004 0.075 -0.049 0.961 Direct PRISON GENESFOR -0.341 0.176 -1.936 0.053 Effects from VARIANT to PRISON Total -0.621 0.191 -3.256 0.001 Total indirect -0.094 0.074 -1.279 0.201 Specific indirect PRISON TRUESELF VARIANT -0.094 0.074 -1.279 0.201 Direct PRISON VARIANT -0.526 0.173 -3.046 0.002 Effects from MUTATION to PRISON Total -1.077 0.202 -5.338 0.000 Total indirect -0.217 0.078 -2.777 0.005 Specific indirect PRISON TRUESELF MUTATION -0.217 0.078 -2.777 0.005 Direct PRISON MUTATION -0.859 0.185 -4.638 0.000 Effects from GENESFOR to BLAME Total -0.080 0.096 -0.839 0.402 Total indirect -0.001 0.028 -0.049 0.961 Specific indirect BLAME TRUESELF GENESFOR -0.001 0.028 -0.049 0.961 Direct BLAME GENESFOR -0.079 0.093 -0.847 0.397 Effects from VARIANT to BLAME Total -0.274 0.096 -2.866 0.004 Total indirect -0.035 0.027 -1.286 0.198 Specific indirect BLAME TRUESELF VARIANT -0.035 0.027 -1.286 0.198 Direct BLAME VARIANT -0.239 0.093 -2.573 0.010 Effects from MUTATION to BLAME Total -0.461 0.100 -4.599 0.000 Total indirect -0.081 0.029 -2.801 0.005 Specific indirect BLAME TRUESELF MUTATION -0.081 0.029 -2.801 0.005 Direct BLAME MUTATION -0.380 0.097 -3.906 0.000 We found support for this hypothesis. Specifically, there were indirect effects from having a genetic mutation causing low self-control mitigating guilty verdicts for arson mediated through judgments that the true self of the individual was not a violent person (β = -.019, p = .015, 95%CI = -.037 to -.006). There was no indirect effect for either having genes for low-self control (p > .96) or a genetic variant (p > .21). There was also indirect effects mitigating harshness of punishment (i.e. less prison sentencing opposed to probation) through belief that the ‘true self’ was not a violent person when one had a genetic mutation (β = -.047, p = .005, 95%CI = -.08 to -.015) but not having genes for low self control (p > .96) or a genetic variant (p > .19). Furthermore, blame was also reduced when one had a genetic mutation, mediated through judgments that the true self is not violent (β = -.032, p = .005, 95%CI = -.055 to -.01), but not have genes for low self-control (p > .96) or a genetic variant (p > .19). Thus, we found support for the hypothesis that the reason for heterogeneity among genetic biological explanations mitigating punishment is different ways of phrasing the explanation and lead people to think of the individual as supposed to be someone else (their ‘true self’ was not dangerous) but due to a mutation (which intones a change to their core self) they have a surface self different from their true self. Thus, they should not be held as accountable for their criminal activity.