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Aggressive Realism and Anxiety Articles Summary 

DeAnna C. Fleming 

Grand Canyon University: PSY-260  

June 13, 2020 

 

 

 

 

 

 

 

 

 

 

 

 

Aggressive Realism and Anxiety Articles Summary 

1. Aggressive Realism: More Efficient Processing of Anger in Physically Aggressive Individuals article summary.  

    The article proposes that the aggressive individuals' inclination to resolve that ambiguous faces are angry is a causative factor to the negative social behavior aggressive people exhibit (Brennan, & Baskin-Sommers, 2020). Even though this is strongly linked to bias, there are other factors that result in patterns of behavior shown by aggressive individuals. To investigate this, a study is carried out using diffusion modelling to analyze how other cognitive processes affect decision making. The article looks at factors such as bias and other aspects such as how individuals can efficiently accumulate information and the extent to which information is accumulated. The study subjects are 90 imprisoned males, where diffusion modelling was applied to emotion identification (Brennan, & Baskin-Sommers, 2020). The study results revealed the several aspects that are associated with physical aggression and those that are not. The factors associated with physical aggression include drift rate; for anger, and it intermediated the relationship between being aggressive and a higher identification to anger. From the study, how efficiently an individual can efficiently process information relating to anger is the factor that influences abnormal levels of aggression.  

This study was experimental. In conducting the study, diffusion modelling was applied, which is a scientific method. This method was applied to find out the cause and effect relationship among the variables that are likely to lead to aggressive behavior (Brennan, & Baskin-Sommers, 2020). Even though the study was not carried out in an actual lab, the study had nothing to do with a laboratory setting. Enough measures were put in place to point out and control the variables. The independent variables were manipulated to determine the role of the dependent variables in the study. In the study, the independent variable was physical aggression which was being altered in its level to determine the effect it was having on the dependent variables. The dependent variables included the drift rate between anger-fear blended faces and anger-happiness blended faces. 

    In conducting the study, participants were to meet the guideline of the study, including being in proper health. The research observed guidelines to ensure subject privacy was respected. Under the research methods, the measures that were applied included filling the Buss-Perry Aggression Questionnaire (BPAQ). This is an assessment that contains 29 measures of self-report to measure aggression. The four subscales of the questionnaire are verbal aggression, hostility, anger and physical aggression (Brennan, & Baskin-Sommers, 2020). The Range and Differentiation of Emotional Experience Scale (RDEES) was also as a self-report measure that could measure how broad a person’s emotional experience is by rating the items listed to a scale of five. In the subscale, the score can range from 7 to 35.  

Additionally, the subjects also responded to two alternative tasks involving pointing out the emotion shown on several unclear faces in the Ambiguous emotion-identification task. The stimuli involved in this were face images from models of different faces expressing a range of emotions. The subjects would use computer keys to state the emotion that the stimuli were exhibiting. After this, the control emotion identification task followed where subjects completed a control emotion identification task which analyzed the accuracy of the task so as to inspect how valid a particular emotion was in the stimuli (Brennan, & Baskin-Sommers, 2020). Task validation followed these steps. After collecting the data, it was subjected to diffusion modelling, strictly observing the guidelines. 

The results of the study revealed that individuals who are physically aggressive have a higher anger identification ability for anger fear faces. Additionally, physical aggression was linked with a higher level of anger identification for excessively ambiguous faces (Brennan, & Baskin-Sommers, 2020). Task aggression was, however, not linked with the overall task accuracy. As a conclusion to the study, the author states that from the study results, physically aggressive individuals have better processing of anger-related cues and not from prejudice or thoughtless responding. Aggressive individuals are also more accurate under high ambiguity. These individuals have a higher ability to accumulate information that has a connection to anger, especially in ambiguous conditions. 

2. Anxiety Impedes Adaptive Social Learning under Uncertainty article summary  

    Generally, the area of understanding how individuals acquire information during situations of uncertainty in the social context is generally unexplored. Often social information is not clear, and it is often confusing humans have become adapted to social situations, and this is mainly the case to those who are sensitive to uncertainty (Lamba, Frank, & FeldmanHall, 2020).  Anxious people are intolerant to uncertainty, and the situation is worse in social contexts. This study attempts to find out the dynamics of reward learning in various social and non-social situations using the Bayesian-RL model. From the study findings, healthy people learn in negative situations well. The case is opposite in people with anxiety, and they also have a problem in changing their rates of learning as situations continue to transform (Lamba et al., 2020). Due to human beings being tuned to negative social uncertainty, they can adapt their learning based on the social situation.  

    Experimental research was adopted in the research where scientific methods were applied to find out the cause and effect relation in the variable of the study. Alterations were made to the independent variable to establish the effects they would have on the dependent variables (Lamba et al., 2020). For the study, the dependent variables were the investments that were being done on the Trust Game (TG) and the Slot Machine (SM). Also, the biggest dependent variable was the ability of the research subjects to adjust their learning in the Trust Game. These investments were being made to influence the change of points that resulted in positive or negative prediction errors. The independent variables were the anxiety levels of the research subjects. Under methods, the experimental design was applied to test the predictions of learning on all non-social contexts. This applied a modified version of the TG standard. Additionally, the computational model was also applied. Three models were compared, which varied in how relevant they were psychologically. The model of interest in this was the 6-parameter Dynamic Bayesian model that observes how flexible learning is, especially in how an individual behaves in an inconstant environment. 

The study revealed that subjects with anxiety invested overly in TG players as compared to the health people this was the case especially when the dynamics of reward were going down (Lamba et al., 2020). This was a behavioral result showing people with anxiety had more money during negative valence block. By means of Bayesian model selection, the relative fit was compared, which revealed that the healthy subjects were better off. The Bayesian model was also better in capturing the behavior of anxious subjects. From these results, context only affects the impact of the valence of learning that involves rewards among healthy subjects in a selective and moderate manner. Additionally, posthoc pairwise comparisons also reveal that a significant alteration in the speed of decay between healthy and anxious subjects in the TG while on the other hand, there were no differences that appeared in the slot machine game. 

In summation, the article concludes by stating that even though learning and uncertainty are strongly tied together. There is not enough that is known about this relationship in the social setting (Lamba et al., 2020). Understanding how learning occurs, especially when there is improbability is important mainly because social interaction is not clear. From past work, the overlying of neural circuitry governs or rules social and non-social learning. Consequently, it is revealed that social and non-social ability to process information is the result of parallel calculations (Lamba et al., 2020). The results presented in the study are, however, not absolute. When social and non-social learning were paralleled in a condition of uncertainty, the individuals who were healthy showed different learning profiles. Therefore, the healthy people also altered the strategies they were using for learning based on the environment. This study is, however, the first of its case in showing that people alter their learning depending on the surrounding in fear of being exploited. 

 

 

 

 

 

 

 

References 

Brennan, G. M., & Baskin-Sommers, A. R. (2020). Aggressive Realism: More Efficient Processing of Anger in Physically Aggressive Individuals. Psychological science, 0956797620904157. 

Lamba, A., Frank, M. J., & FeldmanHall, O. (2020). Anxiety Impedes Adaptive Social Learning Under Uncertainty. Psychological Science, 0956797620910993. 

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AGGRESSIVE REALISM AND ANXIETY ARTICLES SUMMARY