Simplicity: a unifying principle in cognitive science?

By N. Chater, Paul M. B. Vitányi. Trends in Cognitive Sciences, 2003

458 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/7851c51ee09329e0a023349602f4143331aa1705

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Citations in the corpus

Explanation and understanding (2015, background): “Simplicity and complexity are notoriously slippery notions (e.g., Chater & Vitanyi 2003, Sober 1975), and as noted earlier, logical inference as a deductive chain rarely characterizes real life cognition either in scientists or in laypeople.”
Discovering latent causes in reinforcement learning (2015, background): “[17,18]) favoring latent structures with a small number of latent causes.”
Causal Learning (2012, background): “When this assumption is violated, if an alternative cause (part of a) is observable, the independent infl uence assumption can be given up for the observable alternative cause, and progressively more complex causes can be evaluated using the same distal approach that represents causal powers (see No”
Evaluation in artificial intelligence: from task-oriented to ability-oriented measurement (2017, methodology): “Chaitin suggested the application of AIT to the “definition of intelligence and measures of its varios components” (Chaitin, 1982), but the use of AIT for (artificial) intelligence evaluation started with a variant of the Turing Test that featured compression problems (Dowe and Hajek, 1997, 1998), t”
The Effort Paradox: Effort Is Both Costly and Valued (2018, background): “In a science with very few laws [6,7], psychology has offered the law of least work: given a choice between similarly rewarding options, organisms learn to avoid those that require more work or effort [8].”
Précis of Bayesian Rationality: The Probabilistic Approach to Human Reasoning (2009, background): “Copeland (2006) has also recently shown that PHM provides better fits than mental models and mental logic for extended syllogisms involving three quantified premises.”
A MDL-based Model of Gender Knowledge Acquisition (2008, background): “Chater & Vitányi (2003) view it as a unifying principle in cognitive science to solve the problem of induction in which infinitely many patterns are compatible with any finite set of data.”
EXPECTATION IN MELODY: THE INFLUENCE OF CONTEXT AND LEARNING (2006, background): “The use of forward selection and the preference for feature deletions over additions may be justified by the observation that simplicity appears to be a powerful and general organizing principle in perception and cognition (Chater, 1999; Chater & Vitányi, 2003).”
Simplicity in Explanation (2004, background): “…(AIC) (e.g. Sober, forthcoming), Bayesian Occam’s razor (e.g. Jeffreys & Berger, 1992), Minimum Description Length (MDL) and Kolmogorov Complexity (e.g. Chater & Vitanyi, 2003), nonetheless succeed in precisely specifying a metric for simplicity in the language of statistics and computer science.”
Optimal Behavioral Hierarchy (2014, background): “Some encouragement for this possibility comes from previous work in which related formal principles have been proposed to underlie learning in other domains, including vision [34,35], working memory [36,37], language [38], and others [39,40].”