Sentence examples for computational explanation from inspiring English sources

According to Egan, computational explanation should describe the visual system as computing a particular mathematical function that carries particular mathematical inputs into particular mathematical outputs.

In traditional computational models of the human mind, it is assumed that mental processes respect the semantics of mental states, and the only computational explanation for such mental processes is a computing mechanism that manipulates symbols related to the semantic properties of mental states [14].

The model provided a computational explanation for the mechanisms of lateral control skill learning.

Computational explanation decomposes the system into parts and describes how each part helps the system process the relevant vehicles.

The computational explanation is just a part of that complete representational explanation (Miłkowski 2013), which also includes my mental representation and contents.

But the existence of a computational explanation does not exclude the possibility that that there is a deeper cause of the words on the screen.

In the face of these objections, limited pancomputationalists are likely to maintain that the explanatory force of computational explanations does not come from the claim that a system is computational simpliciter.

Computational explanations have been offered of, among other mental phenomena, belief (Fodor 1975, 2008 Field 1978), visual perception (Marr 1982, Osherson, et al. 1990), rationality (Newell and Simon 1972, Fodor 1975, Johnson-Laird and Wason 1977), language learning and use (Chomsky 1965, Pinker 1989), and musical comprehension (Lerdahl and Jackendoff 1983).

Only some pixel configurations correspond to meaningful statements, so while formal computational explanations explain why a particular configuration appeared, they do not explain why it was one that corresponds to a meaningful statement, the one that has a truth-value.

Computational complexity advances an explanation to this apparent paradox: (1) only a small portion of instances of such problems are actually hard, and (2) successful heuristics exploit structural properties of the typical instance to selectively improve parts that are likely to be sub-optimal.

We suppose that the topological structure is supporting blind flow, and we measure the load as random-walk betweenness [35] that counts all possible routes assuming that information wanders at random until it finds the target (see Materials and Methods for further details and the explanation of computational procedures).