- Antipsychotic drug fights resistant bacteria
- Forensics Talk | Exploring the Mind of a Psychopath - Funzing
- Psychopathy: What Mental Health Professionals Need to Know
Forensics Talk Exploring the Mind of a Psychopath 4.
Jennifer Rees. Follow Me. Previously, she has been a trainer on the Certific This Funzing Experience:. Psychopathic children — how early do the signs emerge? Is animal cruelty always a reliable indicator of a future killer? What are the causes of psychopathy? Jennifer will also explore the neuroscience behind the condition, including how the psychopathic brain functions. Are You a Psychopath? Take a screener on the night for fun!
Is your boss a psychopath? Jennifer will provide you with some tips for dealing with them if you think so! Can psychopaths cheat lie detector tests? Jennifer will review the research. She will also explore why we need psychopaths in our society.
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Can you guess which professions have a high prevalence for psychopaths? Jennifer will also provide you with some top psychopath self-help tips: what you can learn from psychopaths to improve your life! You will also get opportunities to watch videos of various psychopaths in action, giving you the chance to observe how they behave and think. Read Less. Psychopathic children — how early do Read More. Subscribe for an Unlimited Funzing Talks plan and enjoy as many lectures as you like! Valid on all events in the Funzing Talks category taking place in the one month period following payment date.
Cancel at any time; cancellation applies from the next billing period. A Wise subscription plan grants 1 ticket per each talk booked. Advanced booking can be done for up to two upcoming events at one time. Data were segmented in ms time intervals with a pre-stimulus baseline of ms.
After baseline correction, we performed an automatic artifact detection. All trials with an incensement of more than 3. Additionally, we discarded trials with reaction times below ms and above ms. This led to an average exclusion of Then, the data were z -standardized within individuals to overcome large individual differences in general EMG activity [ 70 ]. Finally, we excluded all incorrect trials on average 6. For the indicators of the measurement model, we averaged mean activity in the to ms time window for one-third of the trials within each emotion category.
This resulted in three indicators per emotion category for one latent factor: an1-an3 for the anger factor ANG , ha1-ha3 for the happiness factor HAP , sa1-sa3 for the sadness factor SAD , with an1-an3, ha1-ha3, sa1-sa3, and ne1-ne3 as indicators of the general face factor FACE. This time window has been used in a previous study [ 30 ] and seems to adequately reflect the emotion effect in this study too see Fig 1 for the time course of the EMG responses for each emotion category or see [ 71 ] for a comparable design and corrugator effect.
For the measurement models, ha1-ha3 were reversed so that high values for all indicators indicate more congruent facial muscle responses. Reliable averaged EMG responses required at least 5 trials per indicator per participant.
Antipsychotic drug fights resistant bacteria
Five participants failed to meet this criterion and hence were excluded from later analyses. Errors bars depicts standard errors. All statistical analyses were performed in R [ 69 ]. We tested mean differences in accuracy and reaction time scores for the emotion categorization task and corrugator activity, as assessed with the EMG, in a repeated-measures ANOVA with group membership as the between-subject factor and emotion as the within-subject factor. The dependent variable was corrugator activity during the pre-defined time window of to ms after stimulus onset [ 30 ].
Type I error rates in post-hoc comparisons were Bonferroni-corrected, at a familywise error rate of. We applied CFA [ 54 ] to model individual differences in corrugator activity. CFA models were estimated with the sem function of the lavaan package [ 72 ] using the maximum likelihood ML estimator. The measurement model is depicted in Fig 2.
Forensics Talk | Exploring the Mind of a Psychopath - Funzing
Squares represent the indicators i. The latent factors predict the manifest variables, indicated by direct paths, e. These regressions weights are called factor loadings and represent the strength of the relationship between the latent and the manifest variables. Two-headed arrows represent correlations among the latent factors, e.
Latent factors were scaled with effects coding [ 73 ] with the sum of all indicators within one factor fixed to one and the sum of the intercepts within one factor fixed to zero. This allows factor loadings, factor variances, and factor means to be freely estimated and for the factor means to be estimated in the unit of the manifest indicators.
With the iterative ML method model parameters are estimated in way that maximizes the similarity between the covariance matrix implied by the assumed model and the observed covariance matrix. Higher similarity indicates a better model fit, for which we report the following indices: Acceptable model fit would be considered present if the Comparative Fit Index CFI value was above. We tested measurement invariance of the measurement model across our three groups using multiple-group confirmatory factor analysis MCFA.
First, the assumed measurement model was estimated for all three groups separately to ensure factorial validity [ 55 ]. Measurement invariance was then tested by comparing nested models with an increasing number of model constraints i. Strong factorial invariance is given when factor-indicator relationships configural invariance , factor loadings metric invariance , and indicator intercepts scalar invariance are equal across groups [ 77 , 78 ], which is sufficient to interpret latent means across groups [ 79 ].
Finally, we compared models with equality constraints on the factor means to test our hypothesis. Table 1 depicts the descriptive statistics for performance accuracy, reaction time, and mean EMG activity during the time-window of to ms for each emotion category per group. Pairwise comparisons showed that happy expressions were better recognized than the other expressions.
Identification of sad expressions was lower than that for the other expressions, while anger and neutral expressions were recognized equally well.
There was no significant effect of group and no significant interaction between emotion and group. Happy expressions were recognized the fastest while sad expressions were recognized the slowest. There was significantly reduced corrugator activity while viewing happy facial expressions, in comparison to all other conditions, which supports our decision to reverse the ha1- ha3 indicators for the later measurement model, so that high values for all indicators means more congruent facial muscle responses.
The corrugator measurement model for the whole sample is depicted in Fig 2. The model contains one general face response factor FACE , which controls for emotion-unspecific variance in all indicators, and three correlated emotion-specific factors representing emotion specific facial responses to angry ANG , happy HAP , and sad SAD facial expressions. The factor correlations were also significant. Before we could test our hypothesis, we first needed to establish that the corrugator measurement model was invariant between the three groups.
Next, we modeled a MCFA with configural invariance and successively applied constraints on the factor loadings metric invariance and indicator intercepts scalar invariance. We found invariance at each step see Table 2 for the fit indices and test statistics for the invariance comparisons across the three groups. Thus, the model displayed in Fig 2 fit the three groups equally well. Consequently, further comparisons between the groups on factors means were admissible.
Next, in order to find out whether there were significant differences between the groups on the FACE and the emotion-specific corrugator response factors, we set all factor means to equality. Note that all scores are positive, since happy scores were reversed before they were included in the CFA analysis. Higher values indicate higher emotion congruent facial responsivity. Complementary analyses using a continuous latent variable of psychopathy mirrored the results of the group comparisons please see supporting information S1 Fig. In the present study, we investigated corrugator activity in response to facial expressions of emotion in non-offenders and in criminal offenders who had varying levels of psychopathy.
First, we aimed to replicate a measurement model of immediate facial responses in corrugator activity to emotional expressions [ 30 ]. Then, we tested measurement invariance across groups, including comparisons of latent means between the three groups in order to test differences in the size of facial responses to emotional expressions. This model has the core advantage of controlling for general facial responsiveness to facial emotional expressions, which then allows one to derive purer estimates of emotion-specific facial muscle responses.
The means of the emotion-specific factors were then compared between the three groups. The amount of corrugator activity did not differ significantly between the groups, disconfirming our first hypothesis. This means that in our study emotion simulation on the facial muscle level, seen as a constitutive component of emotional concern for others [ 8 , 9 , 10 ], was not disrupted for psychopaths.
Thus, emotional detachment, a core component of psychopathy, seems to be unrelated to immediate facial muscle responses toward angry, happy, or sad facial expressions.
Psychopathy: What Mental Health Professionals Need to Know
Our lack of group differences contradicts Hagenmuller et al. However, the sample tested by Hagenmuller et al. Likewise, our results contradict the findings of de Wied et al. The stimulus material used in the current study, however, differed substantially from the material employed in the study by de Wied and colleagues [ 25 ].