From Efficient Coding to Information Gain: Information-Theoretic Principles in Models of Human Decision Making
- Mikaela Akrenius, Cognitive Science, Indiana University Bloomington, Bloomington, Indiana, United States
- Laurence Maloney, Psychology, New York University, New York, New York, United States
- Jonathan D. Nelson,, University of Surrey, Guildford, United Kingdom
AbstractSoon after the publication of Shannon’s (1948) seminal paper on information theory, the formalization of entropy and efficient coding systems saw applications in a wide range of disciplines ranging from biology and economics to fundamental physics (Shannon, 1956). In mathematical psychology, notions borrowed from information theory were successfully applied to pattern perception (Garner, 1962; Garner & Clement, 1963), proportion estimation (Attneave, 1953), choice reaction times (Hick, 1952), and as tools for data processing (McGill, 1954). Within a couple of decades, however, these applications decreased, partially due to difficulties in quantifying perceptions of uncertainty and in connecting uncertainty with the psychological valence of associated outcomes (Luce, 2003).
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