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Word Root of “Cognition”
The Oxford English Dictionary indicates that the term “cognition” derives from the Latin cognit- implying an act of learning, becoming familiar with, or acquiring knowledge. Further etymological insights are provided by the “Etymological Dictionary of Latin and the Other Italic Languages,” pointing out that the verb cogito/cogitare translating to “I think/to think” or “I know/to know” combines the prefix co- (indicating “with” or “together”) with the verb agitare. “Agitare” means “to stir, to drive, to disturb, to be engaged in, or to aspire” and is derived from ago/agere which signifies “to drive” or “to move.” This suggests cognition is a process of bringing elements together or actively engaging with them. Every life we know, from unicellular bacteria to complex multicellular life forms like humans, engages with various forms of life and elements in its surroundings in different ways. Thus, cognition presents itself in various ways across all the kingdoms of life on earth. Therefore, one cannot have one universal definition of cognition per se which is applicable across all the organisms found in the animal kingdom. Our work involves exploring the possibility of building cognitive fingerprints for humans, which we will explain in later sections. But first, let’s explore human cognition.
Human Cognition
Human cognition has a widely accepted and universal description: “Human cognition refers to the mental processes and abilities unique to humans that enable us to perceive, understand, remember, think, reason, and make decisions about the world around us. It encompasses a wide range of cognitive functions, including perception, attention, memory, language, problem-solving, decision-making, and social cognition.” But what aspect of human cognition is studied varies from one field to another. For example:
- An Evolutionary Biologist investigates the origins and drivers of cognition in animals and its link to reproductive success.
- A Neurobiologist examines brain structures and neural circuits responsible for cognition and their changes due to neurological disorders.
- A Cognitive Psychologist studies cognitive development across the lifespan, individual differences in cognitive abilities, and the cognitive processes underpinning behavior and mental health.
Within this diverse landscape of cognitive research, education stands out as a pivotal area of inquiry due to its profound influence on human cognition. The structured schooling environment imparts knowledge and significantly shapes the cognitive functions it engages. From the development of early literacy skills to advanced problem-solving abilities in higher education, the educational system is instrumental in molding the cognitive processes essential for personal and professional success. As students navigate through various educational stages, they encounter tailored curricular designs that aim to enhance specific cognitive skills, such as critical thinking, logical reasoning, and collaborative problem-solving.
Moreover, the role of education extends beyond traditional academic skills, influencing cognitive attributes such as resilience, adaptability, and emotional intelligence. These competencies are increasingly recognized as crucial in managing the complexities of modern life. By engaging with diverse pedagogical strategies, educators play a key role in optimizing these cognitive and socio-emotional skills, thereby preparing students to thrive in an ever-evolving world. This dynamic interaction between educational practices and cognitive development highlights the importance of exploring how educational experiences contribute to shaping the cognitive landscape of individuals.
We, as educators, aim to research and explore methods for quantifying and representing human cognition as influenced by lifelong learning within a standardized education system. By mapping out cognitive strengths and weaknesses, we intend to provide learners with personalized insights to steer them toward their areas of interest.
The Idea of Cognitive Fingerprint (CF)
To understand this, consider the nature of human fingerprints: they are marked by unique ridges specific to an individual influenced by genetic factors. While the exact genetic variations that create these unique ridge patterns are not fully understood, their distinctiveness is evident. Similarly, a cognitive fingerprint may not be directly traceable to specific genetic variations that define an individual’s unique cognitive abilities. However, just as unique ridges define a fingerprint, some identifiable cognitive signatures or patterns contribute to an individual’s cognitive fingerprint. These cognitive signatures could provide insights into the unique cognitive prowess of a person, much like the unique ridges help in identifying individual fingerprints. In the literature also, it has been well established that human cognition is profoundly individual-specific. While human cognition shares certain universal features, such as the ability for abstract reasoning, language processing, and problem-solving, each person’s cognitive processes are uniquely shaped by a confluence of genetic makeup, environmental and experiential factors. We believe that getting a snapshot of this unique cognitive prowess of an individual can empower individuals to make informed decisions about their education, career paths, and personal growth.
In conclusion, the idea of CF is not to capture the cause/origin of the variations found in human cognition but where a person is in terms of their knowledge, language, decision-making, and problem-solving domains at any particular point in their life. A system that can capture individual variations in human cognition and also represent the holistic picture of cognition they have at a particular time point can be called a CF of a person.
What Characteristics of a Person Can Help Us Arrive at the Cognitive Fingerprint of an Individual?
To build something akin to a “cognitive fingerprint” for humans, we need to consider several key characteristics that define individual cognitive profiles. These include genetic makeup, neurobiological factors, environmental influences, learning experiences, and psychological traits. Each of these characteristics contributes to the unique cognitive patterns observed in individuals. Let’s look into each of these briefly.
Genetic Makeup: Genetics play a crucial role in shaping cognitive abilities and predispositions. Studies have shown that genetic factors can influence various cognitive functions like memory, learning, and problem-solving skills.
Neuroplasticity: The human brain is an organ made of approximately 86 billion neurons. These neurons are, in turn, connected. The number of possible interneuron connections in the human brain is astronomically large, far surpassing the number of neurons themselves. This is due to the complex and intricate nature of neural networks, where each neuron can form connections with thousands of other neurons. Neuroplasticity is defined as the brain’s ability to reorganize itself by creating, strengthening, or pruning neural connections throughout life in response to learning, experience, and injury.
Environmental Influences: The environment in which a person grows and lives, including educational and socio-economic backgrounds, significantly impacts cognitive development.
Learning Experiences: Learning experiences encompass a broad range of activities and interactions. They include formal education in schools and universities, informal learning through personal interests and hobbies, and experiential learning through real-world experiences. Each type of learning experience can contribute to the development of various cognitive skills, such as critical thinking, problem-solving, and adaptability.
Psychological Traits: Psychological aspects, including personality traits, emotional intelligence, and mental health status, also influence cognitive functioning. The interplay between psychological factors and cognition is a key area of study in psychology and neuroscience.
Exploring Learning Experiences for Building a Preliminary Form of CF
The traits discussed earlier each represent a significant field of inquiry, warranting dedicated research to fully understand their implications. Simultaneously exploring all these traits can complicate the clarity of results concerning their individual impacts on cognitive fingerprints. To ensure precision in our findings, it is prudent to isolate one trait as a focal point initially, thereby establishing a foundational analysis of its effect on the cognitive fingerprint.
Among the broader range of traits, our research will specifically explore the learning experiences of individuals, narrowing our focus to academic learning contexts.
Various factors, such as pedagogical methods, levels of learner engagement, and environmental influences, significantly shape how individuals learn in academic environments. Understanding these factors is crucial for delving into the complexities of individual learning experiences, and that can be done by capturing a wide variety of learner characteristics, including knowledge levels, skills, learning preferences, errors, motivations, and emotional responses.
This figure represents various characteristics relevant to making a person’s cognitive fingerprint. It is important to note the cyclical nature of cognitive fingerprint here as it accommodates the changes observed in the cognitive prowess of a person with time.
An adaptive learning system presents an ideal tool for effectively monitoring and comprehending these characteristics. By adapting to individual learners’ needs and responses, such systems have the potential to provide valuable insights into the complexity of academic learning experiences. Adaptive learning systems not only assess learners’ knowledge levels and learning patterns but also dynamically adapt to meet their specific educational needs. This involves identifying and addressing learning gaps, which in turn contributes to a more personalized learning experience that mirrors the learner’s cognitive fingerprint, with a particular focus on their academic learning.
The cornerstone of such an adaptive learning system is the development of a comprehensive student model. The student model is pivotal in gathering, representing, and utilizing information about the learner to facilitate the identification and remediation of learning gaps. The effectiveness of this system hinges on our ability to answer critical questions about the learner’s profile: What characteristics should be modeled? How can these characteristics be accurately captured and represented? And how can this model be used to tailor the learning experience effectively?
Among the plethora of student modeling techniques, two stand out for their relevance and efficacy: the Overlay Method and the Perturbation Method.
Overlay Method: The Overlay Method focuses on mapping the learner’s knowledge landscape, both their prior understanding and current level, across various domains. By dissecting the subject matter into finer concepts and assessing the learner’s familiarity with each through targeted questions, this method identifies knowledge gaps.
Perturbation Method: Simply knowing what learners don’t know isn’t enough for effective learning. Often, learners might answer incorrectly due to misconceptions they’ve developed. The Perturbation Method goes further by identifying these false beliefs in addition to knowledge gaps, making it a deeper extension of the Overlay Method. It helps correct these misconceptions, offering a more robust solution to learning challenges.
By focusing on both the acquisition of new knowledge and the correction of misconceptions, adaptive learning systems can offer a highly personalized education that aligns closely with the learner’s unique cognitive profile, ultimately enriching their learning journey.
Correlation Versus Causation
Creating an accurate portrayal of an individual’s cognitive profile is challenging due to the prevalence of tacit knowledge—information that individuals possess but is not readily accessible or visible in society and even less so in structured public databases. To tackle this, one approach involves collecting data points based on responses within our educational system and verifying these using indirect methods. Additionally, assessing an individual’s social cognitive abilities is crucial, which can be effectively examined through gamified elements in app development. This integration helps to authenticate the so-called cognitive fingerprint of an individual. Nevertheless, caution must be exercised when establishing such correlations. Relying on data that includes these interactions raises important questions about the causal relationships inherent in our assertions about an individual’s cognitive capabilities.
Overlaying various data points on the empirically connected concepts(white dots) within various knowledge domains (optics, thermodynamics, classical mechanics) shows the uniqueness of cognitive fingerprints for two users.
Closing Statement
The initial exploration into learning experiences to develop a preliminary model of Cognitive Fingerprint (CF) highlights the vast potential that CF holds in revolutionizing personalized education. By incorporating academic learning experiences into CF, we can enhance personalized education by offering tailored support to students. This enables them to make well-informed decisions about their education based on a detailed understanding of their academic strengths and weaknesses, backed by data. The potential for applying CF in various ways will expand as we start to capture and analyze even a few of the many traits that influence human cognition. Currently, the concept of CF is in its nascent stages. However, as we expand our research to include more traits and their impacts on cognition, and as we develop appropriate models and technologies to incorporate these insights into CF, our comprehension of CF’s full potential and its varied applications will deepen. In essence, we are only beginning to scratch the surface—there is a vast ‘ocean’ of possibilities that remains to be explored in this exciting field.
Author –
- Apoorva and Kavita
