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The history of computational thinking as a concept dates back at least to the 1950s but most ideas are much older. [6] [3] Computational thinking involves ideas like abstraction, data representation, and logically organizing data, which are also prevalent in other kinds of thinking, such as scientific thinking, engineering thinking, systems thinking, design thinking, model-based thinking, and ...
The computational theory of mind asserts that not only cognition, but also phenomenal consciousness or qualia, are computational. That is to say, CTM entails CTC. That is to say, CTM entails CTC. While phenomenal consciousness could fulfill some other functional role, computational theory of cognition leaves open the possibility that some ...
Computational cognition (sometimes referred to as computational cognitive science or computational psychology or cognitive simulation) is the study of the computational basis of learning and inference by mathematical modeling, computer simulation, and behavioral experiments. In psychology, it is an approach which develops computational models ...
Computational representational understanding of mind (CRUM) is a hypothesis in cognitive science which proposes that thinking is performed by computations operating on representations.
A computational model uses computer programs to simulate and study complex systems [1] using an algorithmic or mechanistic approach and is widely used in a diverse range of fields spanning from physics, [2] engineering, [3] chemistry [4] and biology [5] to economics, psychology, cognitive science and computer science.
A central idea in computability is that of a (computational) problem, which is a task whose computability can be explored. There are two key types of problems: A decision problem fixes a set S , which may be a set of strings, natural numbers, or other objects taken from some larger set U .
Data thinking is a product design framework that combines data science with the design process. It draws on computational thinking, statistical thinking, and domain-specific knowledge to steer the creation of data-driven solutions. Data thinking guides the exploration, design, development, and validation of data-driven solutions in product ...
Computational complexity – P ≠ NP (the P versus NP problem); Cryptographic – One-way functions exist. There are several different approaches to computational learning theory based on making different assumptions about the inference principles used to generalise from limited data.