Short-term memory for serial order: a recurrent neural network model.

By Matthew Botvinick, David C. Plaut. Psychology Review, 2006

380 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/08f2d51806af94338c4cc35024d553b362da797b

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Citations in the corpus

A Usage-Based Approach to Recursion in Sentence Processing (2009, background): “Moreover, SRNs have also been shown to provide good models of nonlinguistic sequence learning (e.g., Botvinick & Plaut, 2004, 2006; Servan-Schreiber, Cleeremans, & McClelland, 1991).”
Neural Evidence for a Distinction Between Short-Term Memory and the Focus of Attention (2011, neutral): “…idea that memory representations can be reactivated during short-delay 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…”
Grandmother cells and localist representations: a review of current thinking (2017, background): “If indeed the Botvinick and Plaut (2006) model supports STM through the coactivation of multiple overlapping distributed patterns, it would undermine this argument.”
Multilevel structure in behaviour and in the brain: a model of Fuster's hierarchy (2007, background): “A number of studies have demonstrated the ability of recurrent networks to address aspects of human behaviour in the domains of language (e.g. Elman 1990), implicit learning (e.g. Cleeremans 1993) and memory (e.g. Botvinick & Plaut 2006).”
Phonological similarity effects in simple and complex span tasks (2011, background): “…than phonologically similar lists, is considered a classic finding in short-term memory research and has been reliably demonstrated in simple span tasks and other tasks that require immediate serial recall (for reviews, see Botvinick & Plaut, 2006; Lewandowsky & Farrell, 2008; Page & Norris, 1998).”
Generalization without Systematicity: On the Compositional Skills of Sequence-to-Sequence Recurrent Networks (2017, neutral): “…generalization, with mixed results (e.g., Christiansen & Chater, 1994; Marcus, 1998; Phillips, 1998; Chang, 2002; van der Velde et al., 2004; Botvinick & Plaut, 2006; Wong & Wang, 2007; Bow-ers et al., 2009; Botvinick & Plaut, 2009; Brakel & Frank, 2009; Frank et al., 2009; Frank, 2014;…”
A revised model of short-term memory and long-term learning of verbal sequences (2006, background): “However, to our knowledge, Burgess and Hitch (1999) is the only model that deals with both serial ordering and the role of STM in long-term learning, but see also (Botvinick & Plaut, 2006; Botvinick & Huffstetler , 2006; Farrell & Lewandowsky, 2002).”
On the biological plausibility of grandmother cells: implications for neural network theories in psychology and neuroscience. (2009, methodology): “A: Presents 24 jitter density plots depicting the activation values of first 24 hidden units (from a total of 200 units) from Botvinick and Plaut’s (2006) model of short-term memory.”
3-D Deep Learning Approach for Remote Sensing Image Classification (2018, background): “The Long Short-term Memory (LSTM) cells [26] is a ﬂexible example of such family that enables long and short range interactions by the use of train-able state gates.”
Revisiting the poverty of the stimulus: hierarchical generalization without a hierarchical bias in recurrent neural networks (2018, methodology): “For all experiments we used the sequence-to-sequence model (Botvinick & Plaut, 2006; Sutskever, Vinyals, & Le, 2014) illustrated in Figure 1.”