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The TRACE model of speech perception
Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory.
The account presented here suggests that memories are first stored via synaptic changes in the hippocampal system, that these changes support reinstatement of recent memories in the neocortex, that neocortical synapses change a little on each reinstatement, and that remote memory is based on accumulated neocorticals changes.
Understanding normal and impaired word reading: computational principles in quasi-regular domains.
Analysis of the ability of networks to reproduce data on acquired surface dyslexia support a view of the reading system that incorporates a graded division of labor between semantic and phonological processes, and contrasts in important ways with the standard dual-route account.
The time course of perceptual choice: the leaky, competing accumulator model.
The time course of perceptual choice is discussed in a model of gradual, leaky, stochastic, and competitive information accumulation in nonlinear decision units that captures choice behavior regardless of the number of alternatives, and explains a complex pattern of visual and contextual priming in visual word identification.
A distributed, developmental model of word recognition and naming.
A parallel distributed processing model of visual word recognition and pronunciation is described, which consists of sets of orthographic and phonological units and an interlevel of hidden units and which early in the learning phase corresponds to that of children acquiring word recognition skills.
An interactive activation model of context effects in letter perception: I. An account of basic findings.
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
The fundamental principles, basic mechanisms, and formal analyses involved in the development of parallel distributed processing (PDP) systems are presented in individual chapters contributed by…
On the control of automatic processes: a parallel distributed processing account of the Stroop effect.
A model of attention is presented within a parallel distributed processing framework, and it is proposed that the attributes of automaticity depend on the strength of a processing pathway and that strength increases with training.
An interactive activation model of context effects in letter perception: part 1.: an account of basic findings
Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
It is shown that deep linear networks exhibit nonlinear learning phenomena similar to those seen in simulations of nonlinear networks, including long plateaus followed by rapid transitions to lower error solutions, and faster convergence from greedy unsupervised pretraining initial conditions than from random initial conditions.