Minimization of Boolean complexity in human concept learning

By J. Feldman. 2000

505 citations recorded by the source corpus.

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

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Citations in the corpus

Interpretable Two-level Boolean Rule Learning for Classification (2015, background): “This paper considers learning two-level Boolean rules from datasets, with the joint criteria of both classi-ﬁcation accuracy and human interpretability measured by the total number of features used (i.e. sparsity) [3].”
Interactive machine learning for health informatics: when do we need the human-in-the-loop? (2016, background): “Also more recent characterizations of concepts as prototypes rather than logical rules leave it unsolved [61, 62].”
A Rational Analysis of Rule-Based Concept Learning (2008, background): “Indeed, Feldman (2001) found that the Boolean complexity is a good predictor of the difficulty of remembering a wide variety of binary concepts.”
Simplicity and probability in causal explanation. (2007, unknown): “Bayesian models of category learning ( Feldman, 2000; GriYths, Christian, & Kalish, 2006) and causal induction (Lu, Yuille, Liljeholm, Cheng, & Holyoak, 2006) likewise provide support for the claim that simpler hypotheses are accorded a higher probability.”
The first crank of the cultural ratchet: Learning and transmitting concepts through language (2019, methodology): “The five rule types we used in our experiment cover a range of complexity in terms of description length (Feldman, 2000), which manifests in variable performance in test accuracy across types (Figure 2).”
Optimal Behavioral Hierarchy (2014, background): “This is no coincidence: It is a well established result from learning theory, echoed in empirical observations of human behavior, that ease of learning is directly related to descriptive complexity [27,28].”
Human Evaluation of Models Built for Interpretability (2019, methodology): “These are supported by insights from the psychology and interpretability literature (Freitas, 2014; Feldman, 2000; Gottwald and Garner, 1972; Miller, 1956; Treisman and Gelade, 1980), and were found in pilot studies to have larger effects than reasonable alternatives.”
A catalog of Boolean concepts (2003, methodology): “In retrospect, this seems to have led to some erroneous generalizations about psychological mechanisms (Feldman, 2000).”
SUSTAIN: a network model of category learning. (2004, background): “Examples of computational level approaches include Corter and Gluck (1992), Feldman (2000), and Gosselin and Schyns (2001).”
Cognitive Control (2012, unknown): “Kolmogorov complexity is closely related to the intuitive notion of conceptual difficulty [33], [34].”
Similarity-Dissimilarity Competition in Disjunctive Classification Tasks (2013, background): “…seeking similarities between members of a category is more typical of a rule-based process (considering that a rule is usually based on positive information; e.g., Feldman, 2000), so a manipulation that draws all stimuli closer to one another might be expected to improve rule-based categorization.”
TIP: Typifying the Interpretability of Procedures (2017, background): “Feldman [9] finds that the subjective difficulty of a concept is directly proportional to its Boolean complexity, the length of the shortest logically equivalent propositional formula.”
Evidence in Practice: A Framing of the Terrain (2007, background): “organizational routines are often portrayed as the nemesis of change, innovation, and growth (Freeman & Hannan, 1983), this view has been challenged by scholars who believe that routines actually increase the flexibility of organizations (Feldman, 2000; Feldman & Pentland, 2003; Suchman, 1983).”
The New C Standard (Excerpted material) An Economic and Cultural Commentary (2008, unknown): “A study by Feldman[ 3 ] found that the subjective difficulty of a concept (e.g., classifying colored polygons”
Simplicity: a unifying principle in cognitive science? (2003, background): “well predicted by the shortest code length for those concepts[ 16 ].”
Generalization and similarity in exemplar models of categorization: Insights from machine learning (2008, background): “For example, what is the role of the complexity of the category to be learned (Alfonso-Reese et al., 2002; Briscoe & Feldman, 2006; Fass & Feldman, 2003; Feldman, 2000)?”