A distributed representation of temporal context

By Marc W Howard, Michael J. Kahana. 2002

1,213 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/9b1df5b85c7e13edc350b68173eeb2b7840dffb1

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Citations in the corpus

Output interference in recognition memory (2011, methodology): “We use list-context information to refer to the internal and external factors that comprise the situation in which learning occurs or the to-be-remembered information was presented, other than information about other words (cf, Howard & Kahana, 2002).”
Representing word meaning and order information in a composite holographic lexicon. (2007, background): “Although they function quite well on small finite-state grammars, to date, none has scaled up to unconstrained natural language (but see Burgess & Lund, 2000, and Howard & Kahana, 2002, for similar large-scale models).”
Deep Successor Reinforcement Learning (2016, background): “For instance, in reinforcement learning (RL), the task is to find a policy that maximizes expected future discounted rewards (value).”
Adaptive Memory: Evolutionary Constraints on Remembering (2010, background): “For example, theories of free recall lean heavily on a construct called ''temporal context'' because free recall requires people to remember information in the absence of explicit cues (e.g., Howard & Kahana, 2002; Raaijmakers & Shiffrin, 1981).”
Models of Semantic Memory (2015, methodology): “Howard et al. (2011) trained a predictive version of TCM (pTCM) on a text corpus to compare to established semantic models.”
Hippocampal mechanisms for the context-dependent retrieval of episodes (2005, positive): “The model of temporal context presented here was directly inspired by the work of Howard and Kahana on the role of temporal context in human verbal memory function (Howard & Kahana, 2002), and Howard’s work on place cell responses in entorhinal cortex (Howard et al., 2005).”
The demise of short-term memory revisited: empirical and computational investigations of recency effects. (2005, methodology): “The presence of a lagrecency effect in immediate, delayed, and continuous-distractor free recall motivated Howard and Kahana (1999, 2002) to develop a single-store model that accounts for all these effects (but see Kahana, 1996, who used a short-term buffer to interpret lagrecency effects in immedia”
Temporal associations and prior-list intrusions in free recall. (2006, methodology): “Recently published models of free recall, including the temporal-context model (Howard, Fotedar, Datey, & Hasselmo, 2005; Howard & Kahana, 2002a) and the context-activation model (Davelaar, Goshen-Gottstein, Ashkenazi, Haarmann, & Usher, 2005) have neglected to include such a mechanism.”
Hybrid computing using a neural network with dynamic external memory (2016, background): “Human ‘free recall’ experiments demonstrate the increased probability of item recall in the same order as first pre-sented—a hippocampus-dependent phenomenon accounted for by the temporal context model 25 , bearing some similarity to the formation of temporal links (Methods).”
Note on the power law of forgetting (2017, background): “Bower’s (1967) multicomponent model also assumes exponential strength-decay of the individual components.”
Neural Evidence for a Distinction Between Short-Term Memory and the Focus of Attention (2011, neutral): “…tests of STM is anticipated in neuralnetwork models of serial order recall (Botvinick & Plaut, 2006; Burgess & Hitch, 2006; Farrell & Lewandowsky, 2002; Burgess & Hitch, 1999) and in retrieved context models of memory search (Polyn et al., 2009; Sederberg et al., 2008; Howard & Kahana, 2002).”
Memory search and the neural representation of context. (2008, background): “Using the telephone poles analogy, Howard and Kahana suggest that recalling an item is akin to jumping back to the location of a telephone pole from the road behind, and continuing to search memory in the vicinity of that pole.”