Different Connotations: Neural Networks Reaction

The human amygdala and its limbic structures play an incredibly important role in the processing and formation of emotional stimuli and pose considerable recent interest to researchers. Typically, the human amygdala is involved in response to negative emotions and the reaction is well understood, but not much is known about its role in positive emotions.

The article examines how people’s neural networks respond to words with different connotations. The study involved fourteen normal, healthy adult male volunteers who had no history of neurological or psychiatric diseases. The purpose of the study was to identify which words trigger amygdalae more. According to Hamann and Mao (2001), the participants’ age varied from 20 to 31 years. The subjects were taught to pay attention to each word as it was uttered and to experience any thoughts or feelings caused by these words (Hamann & Mao, 2001). However, during the scan, the subjects did not give any explicit answers or emotional assessments. There were several trials followed by scanning and memory tests. Then, the emotional ratings were appraised, and pre-and post-ratings were contrasted (Hamann & Mao, 2001). The condition affects were estimated using voxels in the general linear model, while some specific areas were analyzed in the linear contrast model. Different neuroimages were compared in order to establish how amygdalae respond to the triggers.

As a result of the experiment, it was established that the major response in the left amygdala was caused by positive and negative phrases rather than the neutral ones. The dorsal and ventral striatal areas were mostly impacted by the positive and negative affirmations, and the same case was observed in the previous studies with assertive effects. The researchers found that positive and negative words were equally emotionally arousing unlike neutral ones (Hamann & Mao, 2001). This evidence ensures that the primary proof is that the amygdala is vital in emotional responses caused by both kinds of emotional words. Moreover, it identifies that positive words further activate brain regions connected to the reward receiving.

In conclusion, it seems vital to mention that positive words affect the left amygdala and the negative ones elicit the activity in it. Yet, words with a negative shade of meaning trigger the left amygdala more. The structure of the amygdala is complex and its emotional functions have not been fully discovered yet. In my judgment, neuroscience contributes much to the development of treatment for psychosomatic disorders.

Reference

Hamann, S., & Mao, H. (2001). Positive and negative emotional verbal stimuli elicit activity in the left amygdala. Neuro Report, 13(1), 15-19.

Make a reference

Pick a citation style

Reference

PapersGeeks. (2022, July 18). Different Connotations: Neural Networks Reaction. https://papersgeeks.com/different-connotations-neural-networks-reaction/

Work Cited

"Different Connotations: Neural Networks Reaction." PapersGeeks, 18 July 2022, papersgeeks.com/different-connotations-neural-networks-reaction/.

1. PapersGeeks. "Different Connotations: Neural Networks Reaction." July 18, 2022. https://papersgeeks.com/different-connotations-neural-networks-reaction/.


Bibliography


PapersGeeks. "Different Connotations: Neural Networks Reaction." July 18, 2022. https://papersgeeks.com/different-connotations-neural-networks-reaction/.

References

PapersGeeks. 2022. "Different Connotations: Neural Networks Reaction." July 18, 2022. https://papersgeeks.com/different-connotations-neural-networks-reaction/.

References

PapersGeeks. (2022) 'Different Connotations: Neural Networks Reaction'. 18 July.

Click to copy

This paper on Different Connotations: Neural Networks Reaction was created by a student just like you. You are allowed to use this work for academic purposes. If you wish to use a snippet from the sample in your paper, a proper citation is required.

Takedown Request

If you created this work and want to delete it from the PapersGeeks database, send a removal request.