![]() ![]() Such models are also used in modern robotic applications. The foundations of these models are derived from Bayes’ theorem, which associates probability factors with human understanding. Bayesian modelsĬognitive science relies heavily on Bayesian models to understand psychological traits such as vision, motor control, social cognition, learning, and others. Specifically, the field combined inferential rules and neural networks to build models that could simulate far more mental functions than individual rule-based, or connectionist models could achieve. Moreover, these models also helped better understand human emotions associated with the brain’s amygdala region. This helped decode the decision-making process, an intricate process (high-level reasoning) happening in the prefrontal cortex region of the brain. Moreover, with the emergence of theoretical neuroscience, computational models were developed that could visualize the firing neurons (brain activity) within the brain. This happened in the 1990s and 2000s as it was when cognitive science got linked to neuroscience with the development of brain-related technologies and instruments such as magnetic resonance imaging (MRI) and functional MRI (fMRI) that could observe brain activity in real-time in experimental setups. The development of theoretical neuroscience came to the fore with the ultimate integration of the rule-based model and the connectionism approach. It is also used to comprehend psychological aspects that include language learning, which is key to deciphering human thinking. Thus, the connectionist model is used in modern facial recognition applications. However, connectionism is more about simultaneously satisfying several criteria and data-processing constraints. Typically, rule-based systems use inference rules to simulate thinking phenomena. In contrast to rule-based approaches, connectionist models run parallel computational processes rather than in a serial fashion. These use artificial neural networks (ANN) to model neural structures in the brain to simulate human thinking. The connectionist approach emerged in the 1980s and referred to parallel-distributed processing models. Such rule-based approaches have been extensively used in the medical field to develop expert systems for practical purposes. Rule-based models have a critical role in modeling the complex facets of human thinking such as language use or problem-solving. On using another rule, ‘IF you suffer from injuries, THEN your body should rest for 10 hours to recover’, to the latter symbol, it yields the symbol ‘your body should rest for 10 hours to recover’. Applying this rule to the symbol ‘you ran too fast in a full marathon’ gives the output symbol ‘you will suffer from injuries’. According to this model, thinking constitutes the application of inference rules of the kind ‘IF…THEN…’ to symbols to represent the structure of language sentences.įor example, consider the rule ‘IF you run too fast in a full-marathon, THEN you will suffer from injuries’. The rule-based approach has typically been around since the 1970s. The most commonly used methods include: 1. Here, mental representations are similar to computer data structures, while the computational procedures are analogous to computational algorithms that operate on the said data structures.Ĭognitive science encompasses several approaches to reveal the nature of mental representations and computational procedures. įundamentally, cognitive science relies on developing representative structures of the mind and analyzing computational procedures that run on those structures to understand better how the thinking process unfolds within the human brain. The history of cognitive science dates back to the 1950s, coinciding with the emergence of artificial intelligence. It focuses on comprehending the nature of the human mind and how it uses mental representations to realize, process, transform, and manipulate knowledge.Ĭognitive researchers aim to develop a deeper understanding of human intelligence and behavior by investigating the functions of nervous systems that involve critical mental faculties such as perception, memory, emotional experience, learning, reasoning, problem-solving, decision-making, and language. Top 5 Applications of Cognitive ScienceĬognitive science refers to the field of study that interfaces multiple disciplines such as neuroscience, computer science, psychology, artificial intelligence (AI), philosophy, linguistics, and anthropology to understand the cognitive functioning of the human mind and the underlying mental processes. ![]()
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