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Do Additional Features Help or Hurt Category Learning? The Curse of Dimensionality in Human Learners
Vong, Wai Keen; Hendrickson, Andrew T.; Navarro, Danielle J.; Perfors, Amy – Cognitive Science, 2019
The curse of dimensionality, which has been widely studied in statistics and machine learning, occurs when additional features cause the size of the feature space to grow so quickly that learning classification rules becomes increasingly difficult. How do people overcome the curse of dimensionality when acquiring real-world categories that have…
Descriptors: Learning Processes, Classification, Models, Performance
Landy, David; Charlesworth, Arthur; Ottmar, Erin – Cognitive Science, 2017
How do people stretch their understanding of magnitude from the experiential range to the very large quantities and ranges important in science, geopolitics, and mathematics? This paper empirically evaluates how and whether people make use of numerical categories when estimating relative magnitudes of numbers across many orders of magnitude. We…
Descriptors: Undergraduate Students, Computation, Mathematics Skills, Number Concepts
Jarecki, Jana B.; Meder, Björn; Nelson, Jonathan D. – Cognitive Science, 2018
Humans excel in categorization. Yet from a computational standpoint, learning a novel probabilistic classification task involves severe computational challenges. The present paper investigates one way to address these challenges: assuming class-conditional independence of features. This feature independence assumption simplifies the inference…
Descriptors: Classification, Conditioning, Inferences, Novelty (Stimulus Dimension)
Hannagan, Thomas; Grainger, Jonathan – Cognitive Science, 2012
It has been recently argued that some machine learning techniques known as Kernel methods could be relevant for capturing cognitive and neural mechanisms (Jakel, Scholkopf, & Wichmann, 2009). We point out that "String kernels," initially designed for protein function prediction and spam detection, are virtually identical to one contending proposal…
Descriptors: Brain, Word Recognition, Visual Discrimination, Orthographic Symbols
Lee, Michael D.; Vanpaemel, Wolf – Cognitive Science, 2008
This article demonstrates the potential of using hierarchical Bayesian methods to relate models and data in the cognitive sciences. This is done using a worked example that considers an existing model of category representation, the Varying Abstraction Model (VAM), which attempts to infer the representations people use from their behavior in…
Descriptors: Computation, Inferences, Cognitive Science, Models
Smolensky, Paul – Cognitive Science, 2006
In this article, I survey the integrated connectionist/symbolic (ICS) cognitive architecture in which higher cognition must be formally characterized on two levels of description. At the microlevel, parallel distributed processing (PDP) characterizes mental processing; this PDP system has special organization in virtue of which it can be…
Descriptors: Cognitive Processes, Computation, Research Projects, Theories