Final Research Analysis

Research Analysis on Consciousness and Decision-Making University PSYC 234 Research Design & Statistics = 1\*ROMAN \* MERGEFORMAT I Teacher Research Analysis on Consciousness and Decision-Making Identifying the link between conscious awareness and decision-making has posed a significant task in neuroscience and psychology. Liljenström (2021) has explored this intricate interrelation in his study and has developed a computational model that captures the neuronal activities involved in decision-making, primarily considering the amygdala, orbitofrontal cortex, and lateral prefrontal cortex functions. Thus, evaluating the strengths of the emotional and rational factors influencing the decisions, the study provides a diverse insight into how these cognitions are linked with the brain. The research is relatively novel in its use of computational simulations to test theoretical ideas that are pretty difficult to measure in other ways. From the critical evaluation of the research design, ethical issues, and the credibility of the computational models, the study offers new knowledge on the neural basis of human decision-making and future research recommendations. Identification of Variables In this study, it is necessary to give the concept definitions to explain and record its variables. OFC, LPFC, and amygdala are the areas of the brain that are involved in decision-making. The amygdala regulates emotions, the OFC for immediate consumption, and the LPFC for strategic thinking and planning (Liljenström, 2021). Brain imaging, such as fMRI and EEG, gives reliable data, making the study valid. These methods are widely used in neuroscientific studies and provide excellent temporal and spatial resolution to reveal brain activity patterns. Several components are examined to comprehend consciousness, decision-making, and volition. This group includes independent and dependent variables. Current analysis focuses on brain components and decision-making mechanisms. The particular brain areas responsible for decision-making, including the amygdala, OFC, and LPFC, are characterized as brain structures. These variables can be easily quantified using brain mapping techniques like fMRI or EEG, which provide real-time data on brain function (Liljenström, 2021). Emotions pertain to the aspect of decision-making as a process. In the case of concrete variables such as decision outcomes, the information is obtained directly from subjects’ actions in the tasks; however, in the case of abstract variables like the dominance of emotions or rationality, the data is based on models that mimic human decision-making given specific inputs and conditions (Liljenström, 2021). The main topics of interest are the consequences of decisions and the share of rational factors. Decision outcomes are concrete variables because they are operationally defined as subjects’ choices during tasks. Often, such decisions are documented based on conditions and can be either observation-based or based on the subject’s report (Liljenström, 2021). When comparing the two variables, emotions versus dominance in decision-making, the activity of the brain and the time taken to make decisions are considered. Emotions are faster than thinking, while rationality takes time to be put into action (Liljenström, 2021). Therefore, it is imperative to ensure that reliability and validity are achieved in these measurements. Techniques used in brain imaging for tracking activities are reliable because the results are highly consistent and repeatable. The model’s validity is determined by its ability to closely and precisely mimic actual life decision-making processes (Liljenström, 2021). These simulations’ success relies on the extent to which the operational definitions depict the main features of the variables of interest. Although neuroimaging techniques are used to measure brain activity, translating this information to determine the presence of affective and cognitive processes requires theoretical assumptions’ confirmation. The model’s validity is based on the fact that it imitates recognized decision-making behaviors and can determine the likely outcome of decisions when dealing with new conditions. There are various ways in which the author provided validity in the study. They ensured that the measures regarding variables like the brain regions and decision-making processes aligned with the actual theoretical constructs being examined (Liljenström, 2021). Theoretical frameworks and existing research supported the distinction between emotional and rational work. Thus, the author verified that their computational model reflected known decision-making behaviors by comparing its predictions to behaviors observed in empiri...