Toward a universal law of generalization for psychological science.

By Roger N. Shepard. 1987

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Citations in the corpus

Generalization Through the Recurrent Interaction of Episodic Memories (2012, background): “To test this hypothesis, we wished to examine overall levels of activity in the conjunctive layer during simulated AB and BC study trials, at weight strengths known to support either good or poor generalization ability (based on the simulation results discussed above).”
SUSTAIN: a network model of category learning. (2004, background): “Table 2: The logical structure of the six classification problems tested in Shepard et al. (1961) is shown.”
A distributed representation of temporal context (2002, methodology): “Our rule for mapping the activations onto probability of recall is a variant on several well-known models of stimulus classification and category learning (Nosofsky, 1986, 1987; Shepard, 1987).”
An instance theory of attention and memory. (2002, background): “This implication is consistent with Shepard’s (1987) claim that an exponential distance function is a universal law of generalization supported by a wide variety of empirical generalization gradients (e.g., Shepard, 1958, 1987).”
Reconciling intuitive physics and Newtonian mechanics for colliding objects. (2013, methodology): “The problem of finding the best possible interpretation of noisy sensory information is one that we can solve by following the rational analyses of Shepard (1987) and Anderson (1990) and the ideal observer in visual and auditory research (de Vries, 1943; Geisler, 1989; Green & Swets, 1966;…”
On the Evolutionary Origin of Prospect Theory Preferences (2008, unknown): “Skinner (1952) who wholly disregarded the importance of cognitive processing in human action.”
The Rational Basis of Representativeness (2001, methodology): “Rational models have been successfully applied to a number of cognitive capacities (Shepard, 1987; Anderson, 1990; Oaksford & Chater, 1998) but not previously to analyz-”
Learning Semantic Image Representations at a Large Scale (2014, background): “Tenenbaum and colleagues [104, 105] proposed mathematical abstractions for the concept learning problem, building on previous work on models of generalization by Shepard [101].”
Dissociations Between Categorization and Recognition in Amnesic and Normal Individuals: An Exemplar-Based Interpretation (1998, background): “According to the exemplar model, similarity ( s ) between a pair of objects is an exponential decay function of their psychological distance ( d ), s = exp(– c • d ), where c is a sensitivity parameter (Nosofsky, 1984; Shepard, 1987).”
PRIMIR: A Developmental Framework of Infant Speech Processing (2005, methodology): “In this way the planes available in PRIMIR allow the listener the advantages of generalization that categorization makes possible (Shepard, 1987) while at the same time permitting access to subcategorical detail, in some cases all the way back to the exemplars that stimulate the formation of similar”