American Psychological Association, Inc. Choice, Similarity, and the Context Theory of Classification

Citations in the corpus

• Learning From Others (2012, unknown): “Yet most models assume that learning depends on this sort of random sampling (Anderson, 1991; Goodman, Tenenbaum, Feldman, & Griffiths, 2008; Kruschke, 1992; Love, Medin, & Gureckis, 2004; Medin & Schaffer, 1978; Nosofsky, 1984; Nosofsky, Palemeri, & McKinley, 1994; Pothos & Chater, 2002).”
• Similarity-Dissimilarity Competition in Disjunctive Classification Tasks (2013, methodology): “Effectively, RSM computes the probability of putting each example in each category, assuming that the classification of an example is determined by its similarity to the stored category exemplars (Medin and Schaffer, 1978; Nosofsky, 1984).”
• An instance theory of attention and memory. (2002, background): “Nosofsky (1984) generalized the context model by assuming that the dimensional similarities were exponential functions of distance (i.e., h(i, j) exp[ s dhij], where dhij is the distance between objects i and j on dimension h) in a multidimensional similarity space with a city-block distance metric…”
• The Role of Typicality in Object Classification: Improving The Generalization Capacity of Convolutional Neural Networks (2016, background): “Figure 1 shows examples of these atypical images, which human subjects categorize correctly,”
• Instance-based learning in dynamic decision making (2003, background): “Nosofsky (1984) extends the context model to modeling choice and similarity.”
• Learning Phonology With Substantive Bias: An Experimental and Computational Study of Velar Palatalization (2006, background): “…well-known finding that perceptual similarity η ij between two points x i and x j falls exponentially as the distance between the points increases (Nosofsky 1984, 1986, Shepard 1957, 1987). η ij = exp(−d ij ) (2) Nosofsky (1986) gives a more general version of this equation, in which the…”
• Vector-based Models of Semantic Composition (2008, methodology): “Under this framework, we introduce a wide range of composition models which we evaluate empirically on a sentence similarity task.”