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LWL | Rhea Chadha

Newest Student Article
Inspiring Indian Women Authors: Creating a Supportive Environment
Rhea Chadha

Published: 09 November 2022 | Download
Student's Published Works


This exploratory research aims to get a high-level understanding of the factors that encourage or discourage the writing efforts of Indian women authors. A list of such factors was first obtained through qualitative secondary research, and both quantitative and qualitative feedback was obtained on these factors by designing a questionnaire that was administered to an illustrative set of Indian women authors. The study was designed to determine the average percentage scores for encouraging and discouraging factors for each respondent, and also whether a majority or minority of respondents experienced each particular factor. The results indicate that a majority of the survey respondents faced an overall unsupportive environment in their creative writing efforts.

Keywords: Learning India, women authors, encouraging/discouraging factors, supportive/unsupportive environment


India has a rich literary tradition. The Mahabharata, The Ramayana, The Gita, numerous Vedas and Upanishads are amongst the most famous texts, but there are countless more. Writing is seeing a boom even in current times. According to an article in the Financial Express by Dar (2022, pp. 5), the report titled ‘Value proposition of Indian publishing’, pegged the Indian publishing industry at ₹500 billion in 2019 and projected it would reach ₹800 billion by 2024. On those lines, it is worth mentioning that according to an article in The Hindu by Salam (2022), Geetanjali Shree has recently become the first Indian author to win the prestigious International Booker Prize for the translation of her Hindi novel, Ret Samadhi.

However, the representation of women authors in Indian publishing remains low. In an article in the Times of India (TNN, 2019), it is mentioned that the HT-Nielsen bestsellers list for India comprises mostly male authors whereas the NY Times’ list comprises mostly women authors. This difference could be due to several factors such as lack of interest, lack of creativity, or perhaps structural biases in the system which discourage Indian women authors. Erica Jong had said, “There is still the feeling that women's writing is a lesser class of writing…that what women know about is a less category of knowledge”. As the author of this study, when I co-authored the book ‘Life as a Teacher’ on learning from all experiences, I did not experience such biases but did encounter a couple of challenges which motivated me to dig deeper into this topic.

The purpose of this research study is to determine factors which either discourage or encourage women authors, and to survey a few women authors to analyse the kind of environment - whether supportive or unsupportive - that they experience.

The hypothesis is that the environment being experienced by these authors is ‘Unsupportive’. If yes, then that would most likely explain the low representation of women in Indian writing.


The study consisted of three steps:

Step 1: Determining factors that encourage or discourage women writers (Secondary Research)

Discussions with a few women authors and a review of articles appearing in papers, journals etc. was conducted to create a high-level list of factors that influence the writing propensities of women authors. These were then classified into two groups: factors that encourage and factors that discourage writing by Indian women authors. The classification resulted in a list of five factors that encourage Indian women writers and seven factors that discourage them.

Group I: List of factors that encourage Indian women authors

The list - and explanations - of factors that provide encouragement includes:

  1. Strong background/family support: Encouragement is provided by the family and domestic engagements are shared.
  2. Availability of mentors/sponsors: People who guide throughout the publishing process and support the author.
  3. Opportunities/programs to encourage development of talent: These comprise workshops at literature festivals, opportunities at schools or colleges and/or government-offered programs to express writing.
  4. Awards/recognition for creative writing efforts: Recognition for being a first-time author or awards by educational bodies.
  5. Technological Advances that are simplifying the publishing process and removing intermediaries: E-publishing options with wider reach and revenue potential.

These factors, taken together, create a ‘Supportive’ environment.

Group II: List of factors that discourage Indian women authors

The list of factors that result in discouragement includes:

  1. Gender bias against women writers: This could be in the form of women not being encouraged to write or being discouraged from pursuing writing.
  2. Lack of role models: Having very few Indian women authors to look up to.
  3. Overall low female representation in various fields: For example, while the proportion of female managers in Asia has increased from 20% to 25%, little progress has been made beyond this since 2016 (Financial Express, 2022). This, by itself, could explain the lower proportion of women writers in the overall population of writers.
  4. Unfavourable publishing ecosystems: Budding women writers may lack knowledge of publishing ecosystems or could experience lack of support. This could perhaps be due to focus on limited genres.
  5. Low importance of literature as a profession: The focus may be more on academics, and reading/writing may be considered a pastime rather than a full-time vocation.
  6. The perception that writing is substandard or unremunerative: Allegedly, Margaret Atwood worried that her failure to have a nervous collapse was a sign of a substandard writer (Anantharaman, 2022). Since writing need not always provide a stable and secure income, it may discourage writing efforts.
  7. Language barriers to writing: Writing in regional languages in a country as diverse as India is unappreciated and regional authors don’t tend to reach the mainstream or masses. Very few have the conviction of Geetanjali Shree to say that their creative writing is exclusively in Hindi (Jha, 2022).

These factors, taken together, create an ‘Unsupportive’ environment.

Step 2: Scoring each factor (Primary Research)

The output of Step 1 was used to develop a survey questionnaire to which detailed responses were sought either over a call or via email from 30 Indian women authors. Responses were received from 15 women authors. For each respondent, the factors were scored in the following manner:

  1. The factor was scored as ‘1’ if it resonated with the respondent’s personal experience.
  2. It was scored as ‘0.5’ if it partially resonated with the respondent’s experience.
  3. It was scored ‘0’ if the factor did not resonate with the respondent’s experience.

Step 3: Analysis Design to determine whether women authors experience a ‘Supportive’ or ‘Unsupportive’ environment

Step 3.1 Analysis for each respondent across factors

The responses from each respondent were then consolidated in a worksheet. For each respondent, Average (percentage) Scores were calculated:

  1. The list of 5 encouraging factors (Group I) [Average Percentage Supportive Score], and
  2. The list of 7 discouraging factors (Group II) [Average Percentage Unsupportive Score].

Step 3.2 Definition and Implication of ‘Net Score’

The ‘Net Score’ was then calculated as the difference between the Average Percentage Supportive Score (APSS) and the Average Percentage Unsupportive Score (APUS).

Net Score = APSS - APUS

The implications of the Net Score are as follows:

  1. A positive Net Score will imply that the respondent, on average, experienced a ‘Supportive’ environment.
  2. A zero Net Score will imply that the respondent experienced a Net Neutral environment.
  3. A negative Net Score will imply that the respondent experienced an ‘Unsupportive’ environment.

Moreover, if the Net Score (positive or negative) has a magnitude of 25% or higher, then the degree of impact of the Net Score would be considered ‘High’, else it would be considered ‘Low’.

Step 3.3 Analysis for each factor across the entire group of respondents

The percentage of respondents who experienced each factor was determined (‘Total Respondent Percentage’ or TRP). if:

  1. TRP > 66%, the factor was experienced by a ‘Majority’ of the respondents
  2. TRP between 33% and 66%, the factor was experienced by a ‘Moderate’ number of the respondents
  3. TRP < 33%, the factor was experienced by a ‘Minority’ of the respondents

The results were then analysed.


The results are as described below:

Analysis of Net Score

A majority of the respondents (73%) had a ‘Net Score’ that was negative, implying that a majority of Indian women authors experienced an overall ‘Unsupportive’ environment in their writing efforts. The analysis also indicates that 33% of the respondents experienced a ‘High’ level of discouragement, and an additional 40% experienced a ‘Low’ level of discouragement. The remaining 27% of respondents experienced ‘High’ and ‘Low’ levels of encouragement in equal measure i.e., 13.5% experienced ‘High’ levels of encouragement and 13.5% experienced ‘Low’ levels of encouragement.

Group of Factors that Encourage

The analysis indicates that within the group of factors that encouraged Indian women authors, the two factors that were experienced by a ‘Majority’ of the respondents are:

  1. ‘Strong background/family support’. One of the respondents mentioned, “Family was always there”, and a similar sentiment was shared by 80% of the respondents.
  2. ‘Technological Advances’

The factors experienced by a ‘Moderate’ number of respondents include ‘Availability of mentors/sponsors’, ‘Awards/recognition for creative writing efforts’ and ‘Opportunities/programs to encourage development of talent’. A respondent commented that they had “no clear idea of who to approach, where to go, what to do and when”.

Group of Factors that Discourage

The results indicate that within the group of factors that discouraged Indian women authors, the following factors were experienced by a ‘Majority’ of the respondents:

  1. ‘Overall low female representation in various fields’. A key sentiment from the respondents was “organisations should go by merit, and not by other considerations”.
  2. ‘Writing perceived as unremunerative’
  3. ‘Language barriers to writing’
  4. ‘Low importance of literature as a profession’. One respondent exclaimed “Books are seen as pastimes and efforts to write are frowned upon”.
  5. ‘Lack of role models’

The factor ‘Unfavourable publishing ecosystems’ was experienced by a ‘Moderate’ number of respondents.

However, only a ‘Minority’ of the respondents considered the factor ‘Gender bias against women writers’ was relevant in their context.


This is an exploratory study and hence the survey was conducted with a focused set of participants. The initial results do seem to indicate that Indian women authors experience an ‘Unsupportive’ environment. The study should be expanded to a wider base of participants to validate the findings.

Also, the sample of authors consisted of upper middle-class women living in a metropolis. It is not completely representative of the universe of Indian women authors, but is illustrative. Future studies can choose a more diverse set of participants.

The list of factors that encourage and discourage Indian women authors was based on a review of articles in general purpose magazines and newspapers because the literature review did not lead to any scholarly articles which had such a list of relevant factors. When asked, the responding women authors validated these factors and mentioned they couldn’t think of any additional ones to add to the list. Moreover, the strength of the factors within each group was considered to be equal and then a simple average score for each group was determined. A research study can be undertaken to determine the comprehensiveness of these factors and their relative strengths.


The study is illustrative of the challenges that Indian women authors experience. All the women authors who responded to the survey mentioned that this topic resonates with them. They mentioned that they would like to see the final research report and would be interested in supporting efforts to promote Indian women authors.

The study emphasizes the need to create an enabling ecosystem for women authors to actualize their writing potential. Steps like instituting rewards are very simple to implement, yet can have a tremendous impact in terms of encouraging emerging talent. This talent should be encouraged from a young age and school programs should be instituted to enable more girl students in their creative writing journeys. To paraphrase Ayn Rand, the question for Indian women authors shouldn’t be who is going to let them write, it should be who is going to stop them.

Also, as the overall representation of women in various fields goes up, their contribution to writing efforts in those fields should commensurately increase. Hence, the overall efforts for women empowerment need to be strengthened.


Anantharaman, Latha (2022) Making Everything Illuminated. India Today, May 10-16

Dar, Vaishali (2022) Words & Figures. The Financial Express, May 29

FE Bureau (2022) Higher female representation in management results in positive impact. The Financial Express, March 7

Jha, Aditya Mani (2022) The Past Comes First. India Today, June 7-13

Khan, Faizal (2022) ‘It’s important kids find some mirror in literature’. The Financial Express

Salam, Ziya Us (2022) Geetanjali Shree’s ‘Tomb of Sand’ first Hindi novel to win International Booker Prize. The Hindu, May 27,

Times News Network (2019) Women writers and gender disparity in the publishing world.,


I would like to acknowledge all the accomplished Indian women authors who participated in the study not only for providing responses to the survey questions, but also for the detailed discussion and qualitative inputs they provided for enhancing the quality of this paper.

LWL | Brain Implant Maintain Memories

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

05 September 2022 | Download
Student's Published Works


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


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 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).


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.


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.



Jockers. (2021). Alzheimer's disease: Symptoms, causes and natural support strategies.

U.S. Department of Health and Human Services. (2017). What happens to the brain in Alzheimer's disease? National Institute on Aging.

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:

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.

Polan, S. (2020). The study reveals how renegade protein interrupts Brain Cell function in Alzheimer's disease. NYU Langone News.

Haugabook, S., & Sambamurti, K. (2007). Gamma secretase. Gamma Secretase - an overview | ScienceDirect Topics.

Fidel Romano, M., Daniela Nissen, M., Maria Del Huerto Paredes, N., & Alberto Parquet, C. (2007). Enfermedad de Alzheimer - UNNE.

Long, J. M., & Holtzman, D. M. (2019). Alzheimer's disease: An update on pathobiology and treatment strategies. Cell.

Second Sight Orion 1. (n.d.). Retrieved from

Medgadget. (2016). Retrieved from

Devlin, H. (2019). Scientists create mind-controlled hearing aid. The Guardian.

Cochlear Implant. (n.d.). Retrieved from

Kennedy, R. (2021). What is Neuralink? how does the technology work and what can a person do with it? Republic World.

Qureshi, Y. A., & Mohammadi, B. (2018). Robotic oesophagogastric cancer surgery. Annals of the Royal College of Surgeons of England.

LWL | Psychology, Machine Learning, and AI

Application of Psychology in Artificial Intelligence and Machine Learning
Siddarth Gupta

05 September 2022 | Download
Student's Published Works


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


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.


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.


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.


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



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