An introduction to linear algebra in parallel distributed processing
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Mechanisms of implicit learning: connectionist models of sequence processing
Mechanisms of implicit learning: connectionist models of sequence processing
Testing the role of associative interference and compound cues in sequence memory
CNS '96 Proceedings of the annual conference on Computational neuroscience : trends in research, 1997: trends in research, 1997
Judgments of frequency and recency in a distributed memory model
Journal of Mathematical Psychology
An Autoassociative Neural Network Model of Paired-Associate Learning
Neural Computation
Neural Networks - Special issue: Computational theories of the functions of the hippocampus
2005 Special issue: Hippocampal mechanisms for the context-dependent retrieval of episodes
Neural Networks - Special issue: Computational theories of the functions of the hippocampus
Journal of Cognitive Neuroscience
Providing good memory cues for people with episodic memory impairment
Proceedings of the 9th international ACM SIGACCESS conference on Computers and accessibility
Neural evidence for a distinction between short-term memory and the focus of attention
Journal of Cognitive Neuroscience
A scale-invariant internal representation of time
Neural Computation
Neural evidence for a distinction between short-term memory and the focus of attention
Journal of Cognitive Neuroscience
Integrating memory context into personal information re-finding
FDIA'08 Proceedings of the 2nd BCS IRSG conference on Future Directions in Information Access
The successor representation and temporal context
Neural Computation
Scaling laws of associative memory retrieval
Neural Computation
TRANSDISCIPLINARY SYNTHESIS AND COGNITION FRAMEWORKS
Journal of Integrated Design & Process Science
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The principles of recency and contiguity are two cornerstones of the theoretical and empirical analysis of human memory. Recency has been alternatively explained by mechanisms of decay, displacement, and retroactive interference. Another account of recency is based on the idea of variable context (Estes, 1955; Mensink & Raaijmakers, 1989). Such notions are typically cast in terms of a randomly fluctuating population of elements reflective of subtle changes in the environment or in the subjects' mental state. This random context view has recently been incorporated into distributed and neural network memory models (Murdock, 1997; Murdock, Smith, & Bai, 2001). Here we propose an alternative model. Rather than being driven by random fluctuations, this formulation, the temporal context model (TCM), uses retrieval of prior contextual states to drive contextual drift. In TCM, retrieved context is an inherently asymmetric retrieval cue. This allows the model to provide a principled explanation of the widespread advantage for forward recalls in free and serial recall. Modeling data from single-trial free recall, we demonstrate that TCM can simultaneously explain recency and contiguity effects across time scales.