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Cognitive science theories are the explanations and models which describe the mental processes of the human brain. It is an intersection of the disciplines of psychology, neuroscience, computer science, linguistics, economics, epistemology, and the social sciences generally [1]. A sound understanding of Cognitive Science enables researchers and scientists to develop artificial models that mimic human nature and cognition, with the goal of addressing real-world challenges and enhancing various aspects of life on a global scale.
Although cognitive science has been a subject of inquiry for centuries, it was not until 1956, as asserted by the author Herbert Simon, that it emerged as a formal academic discipline. Since then, cognitive science has grown steadily and moderately rapidly, with its central focus being the analysis of the human mind in terms of information processing [2]. Cognitive science forms the foundation of a wide range of artificial intelligence models, ranging from simple rule-based systems to complex deep neural networks. It has also contributed to the development of other disciplines, such as cognitive psychology, neuroscience, and linguistics, through interdisciplinary collaborations. Over the years, cognitive science has made significant contributions to our understanding of the human mind and has enabled the creation of innovative technologies that have transformed various aspects of society.
Cognitive science has offered valuable insights into the workings of the human mind, which has enabled the creation of intelligent systems that can mimic human behavior and cognition. However, replicating the intuitive abilities of humans, such as recognizing familiar faces or interpreting sensory information, has proven to be a formidable challenge [2]. For instance, recognizing a childhood friend in a crowded environment involves a complex interplay of factors, including facial features, gait, and voice. Translating this intuitive ability to machines has required a deep understanding of how humans process information and make decisions. By leveraging cognitive science, researchers have developed AI models that can see, feel, do, and think like humans, enabling machines to seamlessly integrate sensory information and make decisions in complex environments [1]. Thus, cognitive science has played a crucial role in advancing the field of AI and creating intelligent systems that can emulate human intelligence.
Perception and cognition are two essential components of cognitive science that have received much attention in terms of study and theorizing. The act of receiving, interpreting, and organizing sensory information from the environment is referred to as perception, whereas cognition refers to the higher-order mental processes involved in reasoning, problem-solving, remembering, and decision-making. In recent studies, scientists have made breakthroughs using ANN models allowing them to compare these models and brains more directly leading to the discovery that ANN models can bear striking resemblance to representations of the human brain [6].
The ecological theory of perception emphasizes the role of context and environment in determining perception, whereas cognitive theories of perception concentrate on the internal processing of sensory input. The application of this theory of perception and cognition can be applied to artificial intelligence in two parts; the gathering of external information and factors, then the internal processing and connectivity required to replicate the thinking and emotions of a human brain. Finally, scientists finding ways to seamlessly connect the ‘perception’ of AI to the ‘cognition’ of AI could see challenges when testing and teaching the model to perform correctly ensuring high-quality and effective data that is not biased meaning creating a machine that truly ‘thinks’ for itself and does not rely on past data and decisions to determine its future state. Overall, research into perception and cognition has resulted in a variety of hypotheses and views that provide unique insights into the workings of the brain.
Learning and memory are critical components of cognitive science. Memory is the act of storing and retrieving information, whereas learning is the process of acquiring new knowledge and abilities.
Environmental cues shape human behavior through reinforcement learning or in the case of AI models punishment weights. Intelligent systems exhibit their intelligence by achieving goals (e.g., meeting their needs for survival) in the face of different and changing environments. [2]. Whereas cognitive models of learning, such as social cognition theory, focus on cognitive processes like attention, perception, and memory. Memory can be used immediately to inform ongoing behavior, it may be stored to use at a later stage, in which scientists uncover how this is stored and retrieved in the human brain so effortlessly to replicate in models of artificial intelligence.
The cognitive science theory of language and communication investigates how humans learn, process, and communicate using language. Language, according to the cognitive perspective, is a complex cognitive system that is dependent on mental representations and processes. Communication is a dynamic process that incorporates both linguistic and non-linguistic clues, and effective communication. Shannon’s analysis of communication and the definition of information that emerges from it is rooted in a probabilistic conceptual framework which takes considers the likelihood of the signal given a certain hypothesis, the prior probability of the hypothesis, the posterior probability of the hypothesis, and the overall probability of the signal as the bases of communication [7]. Overall, cognitive science ideas provide new insights into the learning and processing of language and communication.
The cognitive science theory of consciousness and attention seeks to understand how the mind processes information and directs attention. The relationship between selective attention and consciousness is an intimate one [3].
Consciousness refers to the awareness of one’s thoughts, feelings, and surroundings. When we attend to an object, we become conscious of its attributes; when we shift attention away, the object fades from consciousness [3]. The global workspace hypothesis posits that consciousness arises when information is integrated and made available for processing in different brain regions [3].
Attention is the cognitive process of selectively focusing on information and ignoring irrelevant stimuli. No attention, no consciousness [3]. The cognitive model of attention suggests that attention involves a hierarchy of processing stages, including selection, enhancement, and maintenance. The orienting reflex theory suggests that attention is guided by automatic responses to salient stimuli [3]. The study of consciousness and attention in cognitive science has led to a range of theories and models, each offering insights into how the mind processes information and directs attention.
Deep Learning techniques such as Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) have been utilized in literature to detect disaster events from news articles and social media. These techniques are adept at identifying event triggers and detecting corresponding event types by analyzing temporal occurrences. Among the models discussed, the most effective approach involves a Hierarchical Deep Learning Network (HDLN) that uses supervised learning techniques to categorize data sets into two classes — those reporting disaster events and those reporting non-disaster events. By taking into account the title and first 10 sentences of the article and running 20 interactions with a batch size of 20 and binary classification methods, this model can provide an accurate output.
While this model accurately detects disaster events, the impact of predicting and preventing disasters is potentially much greater. School shootings, robberies, or terrorist attacks often have a significant impact on society. The ability to detect and prevent such events using HDLNs has the potential to revolutionize disaster prevention and may be an avenue for future research in this field.
This project relates to cognitive science as it discusses the use of deep learning techniques, specifically RNNs, and CNNs, in detecting disaster events from news articles and social media. Deep learning is a subfield of machine learning that is inspired by the structure and function of the human brain and is related to cognitive science. Hierarchical Deep Learning Network (HDLN) is a type of deep learning architecture that is specifically designed for sequential data processing, and this type of architecture is relevant to cognitive science as it mimics the way that the human brain processes and stores information. The potential impact of using HDLNs to detect and prevent disasters is a topic of great interest to cognitive scientists who study decision-making, perception, and human behavior in response to stress and danger [5].
Robotic technology has become ubiquitous in modern society, offering various applications, from smart homes to the automation of dangerous or labor-intensive tasks. Integrating robots into our daily lives can assist humans and reduce the effort required to complete a task. However, the development and maintenance of robots can be costly.
To address this issue, a robotics project was developed using a microprocessor and human interaction to control a Bluetooth-connected robotic arm with motor movement and sensors to detect obstacles or hazards. A mobile app is used to control the robot, which can be activated by voice commands or gestures, allowing the user to communicate with the robot.
While this project is a significant advancement in integrating robots into human life, robotics technology has advanced considerably since its development. Innovations such as robotic dogs and vacuums have become mainstream in capitalist societies. The next frontier for robotics technology lies in developing generative artificial intelligence that enables machines to think, act, and feel like humans. This would take robotics to a whole new level, allowing for more advanced applications and uses in society, particularly in medicine, security, and cognitive science.
In India, uncertain weather patterns and crop loss can sometimes result in farmers being unable to repay their loans, leading to dire consequences such as suicide. Additionally, the nutrition of infants and teenagers is at risk due to the inadequate availability of nutritious food throughout the year. To address these issues, the implementation of greenhouses has become a popular solution to increase crop yield and provide nutrients to citizens. However, the creation of an artificial environment for plants presents a difficult challenge in ensuring success, as factors such as temperature, humidity, moisture, light intensity, and carbon dioxide must be carefully monitored and controlled. While systems currently exist to monitor greenhouse conditions, they require human intervention to make adjustments [5].
To tackle these challenges, artificial intelligence has been implemented through the use of Raspberry Pi, a critical component that connects inputs from sensors and a Pi camera. The sensors trigger the software to engage controllers and optimize conditions once the parameters reach a certain threshold. This system allows for the automatic monitoring and control of greenhouse conditions, resulting in reduced maintenance fees and food prices, as well as remote monitoring for farmers. Real-time data is easily accessible through a monitor, and alerts can be sent via SMS. [5]
This application of artificial intelligence relates to cognitive science because it involves the use of artificial intelligence to monitor and control greenhouse conditions, which in turn affects the cognitive processes of farmers and their ability to sustain themselves and their communities. The project involves the collection and analysis of sensory data, which is then used to optimize environmental conditions for plant growth. This requires an understanding of the relationship between environmental factors and plant growth, which falls under the domain of cognitive science. Additionally, the use of Raspberry Pi and other technologies to automate this process demonstrates the integration of cognitive science and computer science in solving real-world problems.
In summary, Cognitive Science is a multidisciplinary field that aims to explain and model the mental processes of the human brain. Its history dates back to 1956, and since then, it has been a central focus of study in fields such as psychology, neuroscience, linguistics, and computer science. Cognitive Science has enabled the development of Artificial Intelligence (AI) models that can mimic human behavior and cognition, making it a crucial field in advancing AI.
Perception, cognition, learning, memory, language, communication, consciousness, and attention are key theories in Cognitive Science. There is no one theory more important than the other as in parallel to the human brain the workings are interconnected and vital for function and development. The learning and development of cognitive models have aided in the education of the human brain and its workings. Although there is still much unknown, an effective way to learn more about the human brain may be to leverage the power of artificial intelligence to in return tell us more about the workings of the human brain. We are basing cognitive models off the brain, but artificial intelligence can learn exponentially quicker than humans, so when is the turning point for us to try to replicate AI models in humans?
Cognitive Science has provided insights into the workings of the human mind, resulting in innovative technologies that have transformed various aspects of our lives. The future of Cognitive Science and AI seems bright, with ongoing research focusing on further understanding the human mind and developing intelligent systems that can better serve humanity. However, challenges such as replicating the intuitive abilities of humans in machines still exist, and more interdisciplinary collaborations may be necessary to overcome them. Overall, Cognitive Science is a rapidly growing field with immense potential for innovation and technological advancement which must be done with duty and care to ensure human lives are being enhanced through its applications.
V. References:
- Chen, M., Herrera, F., & Hwang, K. (2018). Cognitive Computing: Architecture, Technologies and Intelligent Applications. IEEE Access, 6, 19774–19783. https://doi.org/10.1109/access.2018.2791469
- SIMON, H. (1981). Cognitive science: The newest science of the artificial. Cognitive Science, 4(1), 33–46. https://doi.org/10.1016/s0364-0213(81)80003-1
- Koch, C., & Tsuchiya, N. (2007). Attention and consciousness: two distinct brain processes. Trends in Cognitive Sciences, 11(1), 16–22. https://doi.org/10.1016/j.tics.2006.10.012
- Reverdy, P. B. Decision mechanisms from cognitive science for human-robot learning.
- Vinit Kumar Gunjan, Zurada, J. M., Balasubramanian Raman, Gangadharan, G. R., & Springerlink (Online Service. (2020). Modern Approaches in Machine Learning and Cognitive Science: A Walkthrough : Latest Trends in AI. Springer International Publishing, Imprint Springer.
- Richards, B., Tsao, D., & Zador, A. (2021, July 21). The application of artificial intelligence to biology and neuroscience [Review of The application of artificial intelligence to biology and neuroscience]. Cellpress; Cell Press.
1McGill University, Montreal, QC, Canada 2Mila, Montreal, QC, Canada 3CIFAR, Toronto, ON, Canada 4University of California, Berkeley, Berkeley, CA, USA 5Howard Hughes Medical Institute, USA 6Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA *Correspondence: zador@cshl.edu https://doi.org/10.1016/j.cell.2022.06.047 - Gallistel, C. R., & King, A. P. (2011). Memory and the Computational Brain: Why Cognitive Science will Transform Neuroscience. In Google Books. John Wiley & Sons. https://books.google.com.au/books?hl=en&lr=&id=o0jpHcgwkEoC&oi=fnd&pg=PT8&dq=theories+of+learning+and+memory+cognitive+science&ots=3BqqjBCRY6&sig=6ZTlpLrQw1Lmra4c5EWieHQ1Ew0#v=onepage&q=theories%20of%20learning%20and%20memory%20cognitive%20science&f=false