Student's Published Works

LWL | Brain Implant Maintain Memories

PSYCHOLOGY
Can a Brain Implant Maintain an Alzheimer's Patient's Memories?
Santiago Lobos Melazzo


Published:
05 September 2022 | Download
Student's Published Works


Abstract

Alzheimer's Disease is a neurological degenerative disease that starts in the limbic system and proceeds to decay the rest of the brain. There’s no pinpointed cause for the disease but autopsies discovered a correlated malfunction of Tau and Beta-Amyloid proteins in the neurons. The disease has no cure but is constantly being studied with the minimum intent of slowing it down. The brain has various devices that deal with translating some of the senses into electrical impulses to artificially inform the brain of each particular sense. Technology has also advanced to store large amounts of information in the smallest of devices. Combining the current neuro implants with the technology of memory banks, a device can in theory be made so that it sends information to the cortex of the brain so that patients recognize the things they witness at a basic level. Implants would recognize objects and people and activate files with data to inform the patient of what they’re presented.


Keywords: Alzheimer's, memory, neurological, degenerative, technology

Introduction

Alzheimer’s Disease is a degenerative disease that affects the brain. The proteins Tau and Beta-Amyloid lose control of the neurons and start making them decay. The first area where this disease strikes is the limbic system and the first sign that announces its arrival is the memory loss and functions related to memory failure. The concept being questioned is if an implant can help relieve the symptoms caused by Alzheimer’s. A memory chip or drive that can alternate the functions the limbic system performs in the brain, to keep the patients conscious and the relatives alleviate for a little longer until the disease evolves to the next phases.

The Disease

The degeneration has unknown causes, but autopsies have provided a common denominator between diseased Alzheimer’s patients. The brains of these people had the proteins Tau and Beta-Amyloid in uncanny situations (Jockers, 2021; U.S. Department of Health and Human Services, 2017). The Tau proteins are macromolecules mainly found in the microtubules of the neurons. When people develop the Alzheimer’s disease, the tau proteins unravel from the structure of the microtubules and the unstructured parts of the microtubules start spreading up and pailing in different areas of the neuron (Drummond et al., 2020; Ellison, J. M., 2021; Polan, S., 2020). The Beta-Amyloid comes from the breakdown of a larger protein called Amyloid Prosecutor Protein (APP). The APP is at the neuron’s membranes and it is naturally sliced by two enzymes, Beta and Gamma Secretase (ALZFORUM, n.d.). Beta Secretase cleavages the APP through the outer membrane, and the Gamma Secretase cleavages it through the inner membrane. The remaining part of the APP that was cut between the two enzymes is called Beta-Amyloid. It is left with 38 to 40 long amino acids peptide. It floats in between the neurons to be decomposed by the brain’s nutrients. In Alzheimer’s patients, there is an inexplicable mutation in the Gamma Secretase enzyme which modifies the cut length of the Beta-Amyloid. Instead of being 38 to 40, it becomes 42 amino acids long peptide, thereby being unable to be absorbed by the outer cell environment (Haugabook, S. & Sambamurti, K., 2007). Eventually, these 42 long Beta Amyloids start to opulate outside the neurons and start forming plaques, called Amyloid Plaques. These two protein malfunctions, with unknown causes and no discovered correlation, are the more prominent characteristics of a brain with Alzheimer’s disease. When both errors develop, neurons start dying one by one, mainly located in the limbic system, starting with the hippocampus and the entorhinal cortex. The decay of these areas causes the first symptoms to be memory loss, and the further the decaying disease expands the more neural functions patients start to lose (Fidel Romano et al., 2007). The inevitable outcome of suffering the disease is death. There are some treatments to try and slow down the progress but no cure, yet (Long, J. M., & Holtzman, D. M., 2019).

Technology

Technology nowadays is being implemented in various ways into the brain. There are not only brain implants but also robots that can perform neurosurgery, in development. Currently, there are brain implants aiding sight and hearing, with more technology under development. The Sight implant was developed in order to allow the blind to see.

Figure 1: Second Sight Orion 1

It uses an external camera where one version has the camera installed in sunglasses. It is connected to a transmitter located in the head in direct contact with the implant in the brain. The device passes the information from what the camera sees into electrical pulses that the implant translates and sends through the cortex. Through this aid, the blind has reported their newly acquired ability to see, although reports also express the need for constant maintenance of the device (Second Sight Orion 1, n.d.). There are also hearing aids that work in a similar way (Devlin, H., 2019); an external device to capture the sound waves and an internal device that translates them into pulses and sends them to the respective part of the brain.

Figure 2: Coclear Implant

Besides these two implants that assist the senses to be felt, there is a new technology developed with the aim to implement synchronization with other technologies, no longer limiting the brain's activity to human functions but to robotic ones as well. Ideas for implants that will have the ability to operate external technologies like phones and/or computers already seem concrete and possible (Kennedy, R., 2021). Though, with all of these brain devices and the complexity of the brain itself, robots are being made in order to operate through the complexities and reduce the risk that could be man-made. The science working with these robots designates it as Precision Automated Neurosurgery, and it is also a work in progress (Qureshi, Y. A. & Mohammadi, B., 2018).

Idea

The question is if an implant could possibly maintain the memories of Alzheimer’s patients to alleviate the first phase of the disease. Combining the current and future technologies, it might be possible. It is all theoretical but here is how one could possibly see it function: the idea would be to have a memory chip or a transmitter connected to external digital memory, in which the brain can look for information when trying to remember. This would be done by storing information in forms that vision and hearing implants can activate upon detecting similar information. Since the implants can translate information from what they see and hear, information would be stored that could be triggered by what one sees or hears. For example, once someone sees his/her daughter, the visual implant would activate the memory file about the daughter and this file would indicate to the patient that the one he/she sees is his/her daughter. A similar process would be followed with sounds.

The technology has already been tested. We can send information through the brain and make it understand things based on electrical pulses. Additionally, we have devices that can store information in different files. A combination of these can make patients aware of their surroundings.

The device would call upon stored information by visual and sonar activation. However, as technology advances, it could be upgraded into something more. Once it’s advanced enough, the implant could allow the download of every lived experience into an upgraded memory bank and provide the patients with an artificial eidetic memory. Although this level of technology would be ideal, first, the principle of the initial device should be successful in its functions; something that won’t be easy to accomplish as the complexity of both the brain and the disease can bring numerous conflicts. The brain itself is complex, but the disease apports its own level of unpredictability due to the absence of information. It is unknown how the disease would affect the artificial electrical impulses or how the beta-amyloid plaques might prevent its proper function. However, if nothing unprecedented occurs, the device could work and bring forth tranquillity to the patient’s families, allowing the sick to live their lives at their basics until the next phase of the disease takes over the rest of the brain.

Conclusion

The degenerative Alzheimer’s Disease, as mysterious as it is, can in theory be appeased by brain implants. The combination of current and future neuro devices allows for the innovation of a single complex device implanted in the brain that could indulge information about the things a patient witnesses, essentially giving the person a sense of recognition as the limbic system decays.

 

References:

Jockers. (2021). Alzheimer's disease: Symptoms, causes and natural support strategies. DrJockers.com.
https://drjockers.com/alzheimers-disease/

U.S. Department of Health and Human Services. (2017). What happens to the brain in Alzheimer's disease? National Institute on Aging.
https://www.nia.nih.gov/health/what-happens-brain-alzheimers-disease

Drummond, E., Pires, G., MacMurray, C., Askenazi, M., Nayak, S., Bourdon, M., Safar, J., Ueberheide, B., & Wisniewski, T. (2020). Phosphorylated Tau Interactome in the human Alzheimer's disease brain. OUP Academic. Available at: https://academic.oup.com/brain/article/143/9/2803/5893817

Ellison, J. M. (2021). Tau protein and Alzheimer's disease: What's the connection? Tau Protein and Alzheimer's Disease: What's the Connection? | BrightFocus Foundation. https://www.brightfocus.org/alzheimers/article/tau-protein-and-alzheimers-disease-whats-connection

Polan, S. (2020). The study reveals how renegade protein interrupts Brain Cell function in Alzheimer's disease. NYU Langone News. https://nyulangone.org/news/study-reveals-how-renegade-protein-interrupts-brain-cell-function-alzheimers-disease

Haugabook, S., & Sambamurti, K. (2007). Gamma secretase. Gamma Secretase - an overview | ScienceDirect Topics. https://www.sciencedirect.com/topics/neuroscience/gamma-secretase

Fidel Romano, M., Daniela Nissen, M., Maria Del Huerto Paredes, N., & Alberto Parquet, C. (2007). Enfermedad de Alzheimer - UNNE. https://med.unne.edu.ar/revistas/revista175/3_175.pdf

Long, J. M., & Holtzman, D. M. (2019). Alzheimer's disease: An update on pathobiology and treatment strategies. Cell. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6778042/

Second Sight Orion 1. (n.d.). Retrieved from https://www.infotecnovision.com/wp-content/uploads/2017/09/Ori%C3%B3n-I-300x300.jpg.

Medgadget. (2016). Retrieved from https://www.medgadget.com/2016/10/second-sights-orion-brain-implant-bypasses-visual-system-let-blind-see.html

Devlin, H. (2019). Scientists create mind-controlled hearing aid. The Guardian. https://www.theguardian.com/society/2019/may/15/scientists-create-mind-controlled-hearing-aid

Cochlear Implant. (n.d.). Retrieved from https://www.mayoclinic.org/-/media/kcms/gbs/patient-consumer/images/2013/08/26/11/03/an01963_ds00172_im03853_ans7_cochlearimplant09thu_jpg.jpg

Kennedy, R. (2021). What is Neuralink? how does the technology work and what can a person do with it? Republic World. https://www.republicworld.com/technology-news/other-tech-news/what-is-neuralink-how-does-the-technology-work-and-what-can-a-person-do-with-it.html

Qureshi, Y. A., & Mohammadi, B. (2018). Robotic oesophagogastric cancer surgery. Annals of the Royal College of Surgeons of England. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5956573/

LWL | Psychology, Machine Learning, and AI

PSYCHOLOGY
Application of Psychology in Artificial Intelligence and Machine Learning
Siddarth Gupta


Published:
05 September 2022 | Download
Student's Published Works


Abstract

Classical and operant conditioning is the basis of learning in psychology and is imperative to the learning habits of humans. One of the objectives of Artificial Intelligence (AI) is to mimic human learning for the creation of a fully functioning artificial human mind and brain. This research paper aims to induce conditioning into Machine Learning (ML) algorithms to design machine intelligence with commonsense and high learning capabilities. We see how stimulus-response and rewards & punishment play an imperious role in reinforcement learning and deep learning - a subpart of machine learning. Although reinforcement learning is similar to operant conditioning, there are still a lot of complexities of the human mind which pose a hindrance to the invention of General Artificial intelligence which has been featured in science fiction stories for over a century. We discover how building algorithms using logic and theoretical knowledge can be influential in the answer to integrating common sense into the environment of machine learning. When we are using reinforcement learning, it explores and exploits the data in the environment and learns by mistake and error. When we apply classical conditioning, the machine can use a loop into three subparts: the first one associates events that occur simultaneously, the second part collects the evidence that a made association is important, and the third recognizes the important association. The ability to recognize prior associations allows for the formation of both evidence for and against the existence of a given relationship, as well as the creation of more complicated associations by recognizing occurrences of strongly associated event pairs as events in and of themselves.


Keywords: Psychology, AI, Machine Learning

Introduction

I begin the paper with a preface to conditioning and its types like classical conditioning and instrumental conditioning. Further on I explore the idea of machine learning and various types of machine learning which can be incorporated with conditioning such as reinforcement learning. The paper aims to explore the idea of reward, punishment, and stimulus in Artificial Intelligence for the development of common sense and human-like minds and learning habits. The hypothesis that is proposed in the research paper relates to the fact that humans learn from stimulus-to-stimulus association. And in the second part how reinforcement learning is similar to operant conditioning. I have come up with essentially two hypotheses which outline the project in the paper

Hypothesis A

Classical conditioning can be used as a model of its environment to incorporate common sense partially or completely in a machine learning algorithm.

Hypothesis B

Operant conditioning can be used as a model of its principle to replicate the reward and punishment phenomenon in reinforcement learning

Beginning with the psychology aspect, conditioning is a behavioural process whereby certain feedback becomes more frequent/predictable in a given surrounding as a result of reinforcements (Elmer, 2020). Reinforcements typically are a stimulus or reward for the preferred response. It mainly has two types: Classical and Operant conditioning. Classical conditioning is a type of learning that happens unconsciously. When you learn through classical conditioning, an automatic conditioned response is paired with a specific stimulus (Elmer, 2020). (This creates a behaviour, a very famous example is Pavlov’s dog experiment in which he explores the concept of dog salivation when seeing food to create a conditioned stimulus in which the dog salivates when he hears a bell or some sound that makes him expect a stimulus like food (Elmer, 2020). Operant conditioning is a method of learning that employs rewards and punishments for behaviour (Cherry, 2020). Through operant conditioning, an association is made between a behaviour and a consequence (whether negative or positive) for that behaviour. For illustration, when lab rats press a switch when a green light is on, they admit a food bullet as a price. When they press the switch when a red light is on, they admit a mild electric shock. As a result, they learn to press the switch when the green light is on and avoid the red light (Cherry, 2020).

In the technological aspect, Artificial Intelligence (AI) is the creation of the stimulation of human-level intelligence in a machine to mimic human beings in real-time (Education, 2022). Machine learning (ML) is referred to as the process by which a machine learns and reacts to its environment and surroundings depending on the data it has been provided and the data it has collected (Education, 2022a). There are various types of machine learning for example supervised learning, unsupervised learning, and reinforcement learning (Education, 2022a). In the paper, reinforcement learning is used as the basis of the research considering that it is a model of the reward and punishment mechanism. In reinforcement learning, an agent (the machine/algorithm) explores and exploits its environment through trial and error to receive the desired results (Education, 2022a). Reinforcement learning is mainly used in autonomous driving, healthcare, and various other fields (Education, 2022a).

Classical conditioning application in Artificial intelligence

Classical conditioning can be considered the model of its surroundings and the stimulus, then common sense knowledge can be designed as a model of classical conditioning to be later incorporated into reinforcement learning from stimulus to stimulus (Anirudh, 2019). By building algorithms of associations, we can essentially build a model of the environment and common sense (Anirudh, 2019). The agent - which is the machine - can be conditioned to react to the reward (being the common-sense knowledge) by forming an interdependence between unconditioned stimulus and conditioned stimuli. We see the feedback loop in which the significant associations are made through three steps of the hierarchy: the first phase connects events that happen at the same time, the second portion gathers evidence that a created connection is significant, and the third party acknowledges the significant connection. The capacity to detect past connections allows for the construction of both evidence for and against the presence of a particular link, as well as the creation of more sophisticated associations by treating the occurrences of highly related event pairs as separate events (Anirudh, 2019). This also creates a hierarchical model of data.

Instrumental learning in Reinforcement learning

Knowing that reinforcement learning is used in situations where there is a reward and punishment, instrumental conditioning is the most ideal learning style present in the field of psychology which can be integrated with machine learning (Poddiachyi, 2020). By principle on the surface, both the phenomena, in theory, follow the same ideology of trial and error, as we see the agent learns through exploration of the environment what to avoid and what to exploit in a given situation. The Law of Effect highlights two key aspects of animal learning that are replicated in RL algorithms (Poddiachyi, 2020). To begin, an algorithm must be selectional, which means it must test many actions and choose one based on the results. Second, the algorithms must be associative, which means that they must link specific situations (states) to actions discovered during the selection phase.

Model

The system is implemented as four modules that reflect the system's key processing phases. These modules are pre-processing, recognition, association, and significance. The pre-processing module converts object movement data into predefined events from which patterns are learned. Existing event patterns are recognized by the recognition module, which creates tokens that reflect those patterns. To create new candidate patterns, the association module associates the pattern tokens depending on their time. Finally, the significance module gathers fresh pattern evidence and evaluates whether a pattern is considered to exist based on classical conditioning. The system is built around a feedback loop that connects the pattern recognition, association, and significance modules. The modules and the data that is exchanged between them are depicted in Figure 1.

The system gradually takes time-frames that include bounding boxes for each object of interest in the observed scene as input. The first module examines these boxes and makes a record of the items' spatial connections. The module then identifies certain differences in spatial connections between each frame and utilises these differences as the fundamental event instances that the recognition system uses to recognize patterns of those events.

System Flow Chart

Figure 1: Model System

Materials and Research Methodology

This chapter explores the methodology of research. Papers related to the hypothesis was researched on the internet, particularly over Google Scholar. I have formed results and conclusions through them and attempted to build algorithms by creating hypotheses to be integrated into the machine and deep learning processes to build better reinforcement learning.

Results

As result, it is discovered that hypothesis A doesn't fail due to the fact that the presumptions that the positive correlation of exactness between the classical conditioning model and how well the model performed in certain situations failed but some results were in line with the expectations. The model of the three systems did not necessarily fail as the model worked out of the classical conditioning model of fidelity to learn through association. While the data do include some evidence in favour of the hypothesis, it is not clear enough to proclaim the hypothesis as standing or to declare the hypothesis to be disproved. This opens room for further research and speculation through various algorithms, systems, and models. It is also discovered that the model can be used to teach machine learning about common sense knowledge partially. Furthermore, we discover that hypothesis B is more successful than hypothesis A as the reinforcement learning model is based on rewards and punishments. Reinforcement learning has improved further from the law of effort which is the principle based on instrumental learning (Poddiachyi, 2020). We see that the model has been very successful in terms of instrumental learning but also has some aspects which do not concern the operant conditioning model. The exploration phase for an RL algorithm is the selection process, and there are numerous ways to do it. For instance, consider the greedy policy, which stipulates that an agent selects a random action with a chance of and then chooses greedily (the action that provides the greatest immediate reward) with a probability of 1 (Anirudh, 2019). We're attempting to answer The Exploration-Exploitation Dilemma, which states, to put it simply, when one should stop exploring and start exploiting, by steadily reducing during training. Another intriguing aspect is motivation. It is what impacts the intensity and direction of behaviour in instrumental conditioning (Anirudh, 2019). The food was left outside the box in Thorndike's experiment. When the cat escapes from the box, it gets the food, which reinforces the activities it took to get out. Of course, it has nothing to do with reality, but the reward signal is essentially motivation, and the goal is to make the agent's experience gratifying.

Reinforcement learning is the study of how artificial systems may handle challenges involving instrumental conditioning (Anirudh, 2019). Perhaps less clear is the link between reinforcement learning and classical conditioning. Learning to act in a way that maximizes rewards and minimizes punishments, on the other hand, necessitates the capacity to foresee future rewards and punishments (Anirudh, 2019). As a result, most reinforcement-learning systems include this capability. One method for predicting future reinforcements based on temporal differences accounts well for behavioural (e.g., Sutton & Barto, 1990) and neural (e.g., McClure, Berns, & Montague, 2003; O’Doherty, Dayan, Friston, Critchley, & Dolan, 2003; O’Doherty et al., 2004; Schultz, 1998, 2002; Schultz, Dayan, & Montague, 1997; Schultz & Dickinson, 2000; Suri, 2002) findings on classical conditioning.

Conclusion

As hypothesis A is not a complete failure but partial success, the model can be improved and the results can have a large variety of applications, especially in the field of self-driving cars and where stimuli - stimuli response can be a determining factor and help with the development of AI and creating a general AI or even super-powered one eventually.

Hypothesis B can be used incorporated where reinforcement learning is used and directly be used to create an ecosystem for the machine learning program to learn quickly

 

References

Yechiam, E., Busemeyer, J. R., Stout, J. C., Bechara, A. (2005). Using cognitive models to map relations between neuropsychological disorders and human decision-making deficits. Psychological Science, 16(12), 973–978.

Alonso, Eduardo & Schmajuk, Nestor A. (September 2012). “Special Issue on Computational Models of Classical Conditioning Guest Editors’ Introduction”. Learning and Behaviour, 40(3), pages 231–240.

MAIA, T. I. A. G. O. (2009). Reinforcement learning, conditioning, and the brain: Successes and challenges. https://link.springer.com/. Retrieved June 10, 2022, from https://link.springer.com/content/pdf/10.3758%2FCABN.9.4.343.pdf

Cherry, K. (2020, June 4). What is operant conditioning and how does it work? Verywell Mind. Retrieved June 10, 2022, from https://www.verywellmind.com/operant-conditioning-a2-2794863

Elmer, J. (2020, January 8). Classical conditioning: How it works and how it can be applied. Healthline. Retrieved June 10, 2022, from https://www.healthline.com/health/classical-conditioning

Furze, T. A. (2013, July). The Application of Classical Conditioning to the Machine Learning of a Commonsense Knowledge of Visual Events. core.ac.UK. Retrieved June 10, 2022, from https://core.ac.uk/download/pdf/53141335.pdf

Manchev, N. (2021, October). Reinforcement learning introduction: Foundations and use-cases. Reinforcement Learning Introduction: Foundations and Use-Cases. Retrieved June 10, 2022, from https://blog.dominodatalab.com/introduction-to-reinforcement-learning-foundations

Poddiachyi, A. (2020, May 3). Reinforcement learning, brain, and psychology: Classical and instrumental conditioning. Medium. Retrieved June 10, 2022, from https://towardsdatascience.com/reinforcement-learning-brain-and-psychology-part-2-classical-and-instrumental-conditioning-217a4f0a989

The Unlikely Techie. (2020, July 26). Conditioning algorithms: Reinforcement learning - an introduction. The Unlikely Techie. Retrieved June 10, 2022, from https://www.unlikelytechie.com/post/conditioning-algorithms-reinforcement-learning-an-introduction

VK, Anirudh. (2019, July 11). The brains behind AI: How pavlov's Dogs & Weight Loss Tips influenced reinforcement learning. Analytics India Magazine. Retrieved June 10, 2022, from https://analyticsindiamag.com/the-brains-behind-ai-how-pavlovs-dogs-weight-loss-tips-influenced-reinforcement-learning/

Vélez, J. I. (1970, January 1). Machine Learning Based Psychology: Advocating for a data-driven approach. International Journal of Psychological Research. Retrieved June 10, 2022, from https://www.redalyc.org/journal/2990/299067861001/html/

Scott, J. zu. (2019, September 16). Combining AI's power with self-centered human nature could be dangerous. Chatham House – International Affairs Think Tank. Retrieved June 11, 2022, from https://www.chathamhouse.org/2019/03/combining-ais-power-self-centered-human-nature-could-be-dangerous

Frankenfield, J. (2022, March 16). How artificial intelligence works. Investopedia. Retrieved June 11, 2022, from https://www.investopedia.com/terms/a/artificial-intelligence-ai.asp#:~:text=Artificial%20intelligence%20(AI)%20refers%20to,as%20learning%20and%20problem%2Dsolving.

in, B. (2021). Artificial Intelligence. BuiltIn. Retrieved June 11, 2022, from https://builtin.com/artificial-intelligence

Bajaj, P. (2022, June 3). Reinforcement learning. GeeksforGeeks. Retrieved June 11, 2022, from https://www.geeksforgeeks.org/what-is-reinforcement-learning/

Osiński, B. (2022, February 15). What is reinforcement learning? The Complete Guide. deepsense.ai. Retrieved June 11, 2022, from https://deepsense.ai/what-is-reinforcement-learning-the-complete-guide/

B, W. (1913). Apa PsycNet. American Psychological Association. Retrieved June 12, 2022, from https://psycnet.apa.org/record/1926-03227-001

Education, I. C. (2022, July 7). Artificial Intelligence (AI). What-Is-Artificial-Intelligence. https://www.ibm.com/cloud/learn/what-is-artificial-intelligence

Education, I. C. (2022a, July 6). Machine Learning. What Is Machine Learning? https://www.ibm.com/cloud/learn/machine-learning

LWL | Anxiety Disorder in Gen Z

PSYCHOLOGY
A societal change in dynamics: The trigger of GAD for GENZs during COVID
Audrey Yu


Published:
21 June 2022 | Download
Student's Published Works


Abstract

With over 497,846,771 cases from the deadly virus of COVID-19, and over 6 million deaths, it is easy to grasp the magnitude of damage this virus has caused. Restrictions on free movement and regulations for isolation are 2 factors that heavily affected our generation, making it difficult for us to predict the changes that are yet to occur. The global pandemic has made unprecedented changes in our world, from the daily use of masks and hand sanitizers to the essential vaccination cards when traveling. However, there are also underlying problems taking place that are frequently overlooked - mental health conditions - for instance. ‘The Sacrificed Generation’ is the eye-catching title of a news article by The Guardian that emphasizes the profoundly negative impact of the pandemic on the current young generations that are being forced to keep up with the ever-shifting drawbacks. Mental health issues including depression, major anxiety issues and addiction were already present previously, alongside self-esteem issues or burdensome stress loads. The severity of COVID-19 has clearly caused numerous disruptions within our society including major evident mental health. This has triggered various symptoms of generalized anxiety disorder for the GEN- Z generation in the past few years.


Keywords: Anxiety, COVID

Introduction

The COVID 19 virus has gone through several mutations over the past 2 years, with deterioration of symptoms for patients (Frellick, 2022). Most recognizable of these variants have been named Alpha, Beta, and Omicron. The virus is known for its light to very heavy diverse symptoms; including fever, cough, chest pains, loss of taste and/or smell, sore throat and more (DePietro, 2021). With its high transmission rate, the number of confirmed cases has rapidly increased in the course of 2020 until now (Visual and Journalism team, 2022). Hence, each country’s government has implemented distinct regulations according to their country's situation. With over 100 countries having imposed a full or partial lockdown and tight regulations implemented in each city, majority sections of the population globally were showing symptoms of panic, as seen by hoarding of toilet paper and panic-buying daily essentials.

Generalized Anxiety Disorder

What is GAD?

Generalized anxiety disorder, also known as GAD is a long-term condition where exaggerated worry, tension and hyperventilating is displayed by one (Wandler, 2022). Patients of GAD experience constant anxiety, causing them to feel anxious due to an extensive variety of situations and problems. Some common symptoms of GAD include feeling dizzy, having difficulty concentrating or sleep and health palpitations (Wandler, 2022). The physical signs and symptoms may comprise muscle tensions, trembling, twitching and sweating (Wandler, 2022). Research from the National Health Service of the UK has shown that GAD could be due to overactivity in the areas of our brain which focuses on emotions and behaviors, inherited genes and painful or traumatic experiences previously (Margo, 2015). This condition has affected over 6.8 million adults and due to its difficult nature of treating, it has a relatively low treatment rate (Wandler, 2022).

Factors affecting GAD

Generalized anxiety disorder can be diagnosed through a mental health clinician where diagnosis and a comprehensive assessment will be conducted to help clarify the diagnosis (Casarella, 2021). There are various factors that could spark this disorder. The common factors include family background, genetic predisposition, social influences, and different lifestyle factors (Meek, 2021). Genetics could play a strong role as certain genetic markers could have been passed down. Research has shown that first degree relatives or family members of individuals with GAD have a high chance of developing anxiety disorder and mood swings (Wandler, 2022). Life experiences such as experiencing trauma and uncomfortable situations could also lead to a development of GAD (Meek, 2021). Life events connected to deep emotions such as humiliation, feelings of loss, danger and more are important events which act as predictors of GAD (Wandler, 2022). Heavy workload, stress and caffeine are a part of the daily routine for a majority of people and may affect the degree of GAD experienced by them. These factors may be powerful triggers with immense impact on individuals and should accordingly be considered more often when relying on the treatment of GAD. Studies have also shown that GAD is twice as common in women than men, as there is a significant difference in terms of anxious thoughts between males and females (Bahrami, 2011). Results from PubMED Central (2014) have shown that females are more likely to be anxious as they have more metacognitive beliefs, having a strong mindset that the worries need to be avoided (Catuzzi & Beck, 2014).

Impact of COVID 19 on anxiety disorders

As mentioned previously, COVID restrictions have caused an immense effect on different households as well as governments in each and every country. However, to what extent have these regulations affected the global prevalence of anxiety levels? News article from WHO published in March 2022 amplifies that only within the first year of COVID, the global level of anxiety and depression spiked by an enormous 25%. According to WHO (2022), the main factors affecting such change include gaps in health care, multiple stress factors and much more. The stress levels may be caused by enormous changes like the large unemployment rate, feeling powerless, the continuous updates on fatalistic news etc. COVID-19 became a tremendous challenge for multitudinous countries. As a result, many local and international economies were struggling (WHO, 2022). Students around the world have also reportedly felt more stress during situations such as online learning, since there is an overestimation and pressure from teachers and/or parents.

Generation Z

The Generation Z (henceforth, GEN- Z) is called the ‘The most anxious generation’ (Butler, 2021). GEN-Zs are the generation born from 1997 to 2012. Members of this generation are diverse in many perspectives due to the modernization of our society along with medias (Francis & Hoefel, 2018). Our current society heavily depends on social media and constant news coverage, where they were raised around stronger social media platforms. The substantial difference of portrayed life online versus in person has increased GAD to an unparalleled level in history (Silard, 2021). A study conducted in 2018 by Center for Addiction and Mental health (CAMH), has shown that there has been an evident increase in mental health conditions recently; the trend is correlated with the increased use of social media and our dependence on technology. Our prominent reliance on technology has resulted in less social interactions in real life, pressuring adolescents to embody attributes such as putting up false images and upward comparison (Zhao & Zhou, 2021). Furthermore, using social media non-stop could disrupt an individual’s sleep routine, which is a prerequisite for a healthy lifestyle (Weatherspoon, 2019). The continuous excessive amount of content online could also escalate the inner feeling of loneliness and isolation between peers (Silard, 2021), which leads to many unhealthy thoughts or health issues for young people. GEN-Zs can barely leave their devices alone, even knowing the harmful effect it has on their mental health.

COVID-19 impact on GEN-Z

COVID-19 is a significant chronicle of our generation; the strenuous impact of being trapped within homes has immensely changed the normal usage of social media for GEN- Zs. A poll conducted by Web MD (2021) titled ‘Gen Z Most Stressed by Pandemic’, revealed that around 46% of the GEN- Z age group has stated that COVID has become an obstacle for pursuing their education as well as their career goals. Furthermore, about 45% of the respondents found it difficult to maintain good relationships over the hard period of time (Vultaggio, 2021). A study conducted in the UK from March to May 2020 showed how being in lockdown with social media has heightened the withdrawal from social media due to the platforms being too overwhelming (Liu et al., 2021). Numerous researches have also shown how stress was affecting various aspects of each individual’s life. Generation Z expressed their feelings towards the catastrophic impact as ‘a roller coaster’, ‘terrifying’, ‘burnout’ and much more (Butler, 2021). With great demands from the previous generation and lack of support from them, GEN-Zs are often left alone to face their disturbed mental state. The lack of spontaneous socio-physical interactions has now resulted in diverse teenagers or young adults feeling entrapped (“We are facing a global health crisis”, n.d.). An interview of a student at a University in Wales in 2021 unveiled how university students turned to antidepressant drugs due to their persistent decline in mental health (Margo, 2015). However, without professional declaration or medical help, these antidepressants may have long-term after-effects on their health. The long-term effects may lead to serious potential health risks including loss of appetite, dizziness, indigestion, insomnia and many more. These are all due to the slight changes in our brain structure and how it functions, which could result in unhealthy mental effects on our brain (Margo, 2015). A journal published in 2022 titled ‘Neuropharmacology’ has shown that drugs will decrease the volume of principal regions in our brain. The two sectors affected are the anterior cingulate and hippocampus. The anterior cingulate cortex is the area of the brain which controls and regulates our moods, while the hippocampus is where registration as well as consolidation of memory takes place (Margo, 2015).

Conclusion

As part of our generation, actions should be taken to raise awareness about the rise of GAD in the past few years. Individuals with GAD symptoms should not be randomly taking antidepressants; proper official diagnosis from the clinic should be given out before taking medical treatments with long term risks. Although cases are still rising in most countries, governments now have better experience with imposing COVID protocols and policy strategies to help overcome the barriers for social interactions. Local governments are now able to overcome similar challenges, in addition to far better medical healthcare backups. All of this could help assist in encouraging more social interactions to help reduce the time spent on social media. Social media detox should be encouraged to stimulate new generations to have face-to-face interactions as an alternative. The presence of ingroup or outgroups have proved to drastically reduce stress levels, especially being out with friends and close ones, which would allow individuals to feel more accepted. Ingroups refers to individuals who are identified as members of a certain group with a positive attitude being returned by the group. This allows them to have a sense of belonging. To help reduce the drastic change in GAD conditions, opening borders and social events could ensure a safer environment for individuals to interact. Some limitations of this research could be the different factors such as genetics, workload or eating habits could have a higher impact than the effect from the COVID pandemic. Further research regarding the different human behaviors in each generation during the pandemic could possibly provide a better understanding of possible solutions to in improve the degree of generalized anxiety which affects individuals.

 

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