The provided alphabetical entries detail several foundational topics in cognitive science, beginning with an analysis of language universals where cross-linguistic variations are constrained by human processing considerations, especially the difficulty of self-embedded constructions. Furthermore, functional motivations from semantics and pragmatics dictate patterns in language structure, such as the tendency for pronoun systems to make more referential distinctions in the third person because those referents change more frequently within a discourse compared to the fixed first and second persons. The concept of Uncertainty is thoroughly explored, noting that it arises from incomplete information, inaccuracy, disagreement, and linguistic imprecision. To formalize reasoning under uncertainty, various schemes have been developed, most notably Probability, but also alternatives such as Fuzzy Set Theory (which explicitly models imprecision), Nonmonotonic Logics, and interval representations like Dempster-Shafer belief functions. These formalisms are compared based on six criteria, including their epistemology (what kind of uncertainty they represent), their ability to model flawed human reasoning (descriptive validity), their efficiency for knowledge engineering (using tools like Bayesian Networks), and their computational tractability, noting that exact probabilistic inference is potentially intractable for very large systems. Uncertainty is crucial for Rational Decision Making, which links directly to Utility Theory, the branch of decision theory concerning the measurement and representation of preferences. Utility scales must be ordinal when applied to outcomes under certainty, but become cardinal when extended to prospects (distributions over outcomes) under uncertainty, formalized by the Expected Utility principle derived from the Independence Axiom. Although central to economics, this theory has faced critiques, such as the Allais paradox, prompting the development of descriptive models like prospect theory and the quantitative study of properties like risk aversion. Related to linguistic ambiguity is Vagueness, where terms like "tall" or "heap" have borderline cases that are indeterminate, challenging the classical principle of bivalence and leading to the Sorites paradox. Semantic theories addressing vagueness include the Epistemic View (positing sharp but unknowable boundaries), Degree Theory or Fuzzy Logic (employing a spectrum of truth values between 0 and 1), and Supervaluationism (determining truth based on consistency across all ways the term could be made precise, called precisifications). Finally, the section addresses the Unity of Science, noting its historical roots in logical positivism through the attempted reduction of theories to a base science like physics. Modern integration is often viewed as a network of local integration achieved through interfield theories (connecting phenomena between fields) and the sharing of research techniques and tools, exemplified by combining behavioral studies with neuroimaging technologies like POSITRON EMISSION TOMOGRAPHY and function MAGNETIC RESONANCE IMAGING. The entry concludes with Unsupervised Learning, essential for modeling brain plasticity because it allows systems to learn input structure without explicit target outputs. This learning occurs through two main classes: density estimation techniques (building explicit statistical models, such as using a mixture of Gaussians for clustering) and feature extraction techniques, which seek statistical regularities directly, exemplified by projection pursuit and IMAX (which identifies global features by maximizing mutual information).
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