The three factors mentioned above are crucial for decision making. First, inferences about other mental states are a key driver of morality (Baez et al., 2017; Guglielmo, 2015; Yoder & Decety, 2014) and legal (Buckholtz and Faigman, 2014; Greely, 2011). In particular, intentional damage is punished more severely than identical accidental damage, classified as morally inferior and judged as more significant harm (Alter et al., 2007; Cushman, 2008; Darley and Pittman, 2003; Koster-Hale et al., 2013; Young et al., 2010, 2007). This distortion effect of intentionality on the quantification of harm persists even in the face of economic incentives to be objective (Ames and Fiske, 2013). In legal contexts, guilt is assessed, among other things, by the mental state that accompanies an illegal act (Buckholtz & Faigman, 2014). In addition, criminal provisions require that conclusions be drawn about the beliefs, intentions and motivations of the potential perpetrator (Buckholtz & Marois 2012). Second, emotionally arousing elements, such as the use of cruel language (GL) to describe harm, can influence decision-making. GL elicits significantly greater emotional responses (e.g., stress, anxiety, shock) (Nuã±ez et al., 2016), which promotes harsher sanctions and increases activity in the amygdala (Treadway et al., 2014), a key brain region for emotional processing and damage coding (Bright & Goodman-Delahunty, 2006; Hesse et al., 2016; Salerno and Peter-Hagene, 2013; Shenhav and Greene, 2014). The effects of emotionally arousing elements have even been reported in legal contexts (Bright & Goodman-Delahunty, 2006). Cruel evidence (e.g. Autopsy images of serious injuries) generally provoke anger or disgust (Bright & Goodman-Delahunty, 2006; Salerno and Peter-Hagene, 2013; Treadway et al., 2014) and may influence jury mock verdicts on the guilt or punishment of accused persons (Bright & Goodman-Delahunty, 2006, 2011; Whalen and Blanchard, 1982).
However, the impact of these elements on decision-making has not been assessed by legal experts. Third, emotional responses as a whole are controlled by persistent physiological states that also shape decision-making processes (Greifeneder et al., 2011; Lerner and Keltner, 2000; Winkielman et al., 2007). The first linear model included group, language, EFs, LF performance and age as predictors, and moral scores (mean of moral scores for intentional and random injuries) as dependent variables. We used average moral scores on levels of intentionality, as we had previously found that this factor did not interact with group or language. The overall model was statistically significant (F7.78 = 2.79, p = 0.0013, R2 = 22). Age (tâ=â0.034, pâ=â0.20, î²â=â0.02) was not a significant predictor. As expected, the model had significant interaction between groups and languages. This interaction showed that controls rated actions as morally inferior to those of judges and lawyers, but only when participants were exposed to GL (GLââ0 GLâJuges: tâ=â2.16, pa=â0.034, î²â=â0.455; Plâaattorneys: tâ=â0.44, pâ=â0.66, βâ=â0.133; PlâÂJuges: tâ=â1.34, pâ=â0.18, βâ=ââ0.291). These differences between GL groups were confirmed by follow-up tests of mean morale scores (lawyer checks: t25.4â=â4.18, pa=â0.0045; Control magistrate: t19.9â=â2.92, pâ=â0.0084; Advocate-judge: T29.4â=â0.98, PA=â0.34; Method of adjusting the p-value: HolmâBonferroni). Resolving a legal claim, including a health care liability claim, involves answering 2 types of questions: legal issues and questions of fact. To fully understand the role of the judge in litigation, it is necessary to examine the nature of these issues. However, statutory decision-making authority is not directly related to the time allotted to a parent or other person.
Instead, it is about who has the authority to act on behalf of the child and make decisions that are in the best interests of the child. For example, the Supreme Court in Daubert clarified that trial judges must decide whether witnesses were qualified to give the opinions they gave and whether the subject matter of their testimony was relevant and reliable or not. Texas adopted this interpretation of the rules of evidence and the judge`s role as guardian in 1995 in E.I. DuPont de Nemours v. Robinson (14). Given the expertise of judges and lawyers in deciding transgressions, we expected that their moral choices would be more in line with the offender`s intentions and less dependent on emotional reactions and peripheral physiological signals. Consistent with these predictions, our results showed that the transgressor`s mental state was a key determinant of moral decision-making (Guglielmo, 2015; Yoder and Decety, 2014). In particular, we found that, as with controls, judges and lawyers overestimated the harm caused by intentional damage compared to accidental damage. However, judges and lawyers were less biased when it came to criminal assessments and the severity of damage in the face of accidental damage. Unlike witnesses, speech manipulation and physiological arousal did not have a significant impact on the decisions of judges or lawyers.
Consistent with this, moral ratings in response to GL manipulations were only predicted in controls by physiological signals. This suggests that legal decision-makers may rely less on physiological signals than on bystanders to assess violations, although they remain biased by the «harm enhancement effect» (Ames & Fiske, 2013, 2015; Baez, Herrera et al., 2017), which shows that people overestimate the harm caused by intentional damage compared to accidental damage, even though the two are identical. Taken together, these results suggest that specific expertise developed in legal situations can partially eliminate strong biases related to the assessment of the mental states of others, the affective states induced by GL and the physiological state of one`s own body. The three groups in this subsample did not differ statistically in terms of years of education, sex, overall cognitive function, and executive function (see Table S1). Nevertheless, lawyers were significantly younger than judges and witnesses. Therefore, we calculated mixed ANCOVA models as covariates for all assessments, including years of education and age. We fitted an additional linear model that included group, EFs, LF performance and age as predictors and penalty scores for random harm as a dependent variable (the significant variable in previous assessment results). Results showed that age (tâ=â0.76, pae=â0.45, î²â=â0.113), EFs (tâ=â0.47, pa=â0.64, βâ=â0.01) and LF performance (tâ=ââ1.89, pâ=â0.062, βâ=â0.04) were not significant predictors of punishment scores, although the overall model was statistically significant (F6, 79â=â3.41, pâ=â0.0048, R2â=â0, 15).
Of the differences between the groups, only the slope of the group of judges was significant (lawyers: tâ=âââ1.51, pa=â0.14, î²â=â0.37; Judges: tâ=ââ4,04, pâ=â0,00012, βâ=âââ1,03; Controls were the reference group). Therefore, the results of punishment do not appear to be explained by age, HRV or EF. A question of law is often a question of duty. In many recent cases, the issue has been whether or not health care providers have an obligation to act in certain situations, such as whether a health care provider has an obligation to a child`s parents not to misdiagnose parental abuse (2) or whether an on-call specialist`s conversations with an emergency physician about a potential patient are sufficient to create a doctor-patient relationship 3). The judge makes those decisions. There are three standard legal decision agreements in Arizona: We also examined the role of two potential modulators of participants` decisions on regression models, including measures of EF and heart rate variability (HRV) in a subsample of participants (nâ=â86) consisting of 30 lawyers, 27 controls, and 29 judges. The groups in this subsample did not differ in terms of years of schooling or sex, but differed considerably by age (see «Methods» section and Table S1). To test the possible association between this variable and measures in which we found differences between groups, age was included as an additional predictor in all regression models. We estimated the low-frequency (LF) band power of participants` ECGs, given the relevance of this measure as an indicator of emotional arousal (Castaldo, 2015; Kop et al., 2011; Mccraty et al., 1995). We calculated the percentage change in performance in this task reference band (Sloan et al., 1995) (see «methods»; and SI: «materials and methods»).