An interactive activation model of context effects in letter perception: I. An account of basic findings.

By James L. McClelland, David E. Rumelhart. 1981

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Relational inductive biases, deep learning, and graph networks (2018, background): “We represent complex systems as compositions of entities and their interactions 1 (Navon, 1977; McClelland and Rumelhart, 1981; Plaut et al., 1996; Marcus, 2001; Goodwin and Johnson-Laird, 2005; Kemp and Tenenbaum, 2008), such as judging whether a haphazard stack of objects is stable (Battaglia et…”
Inductive biases for deep learning of higher-level cognition (2020, background): “Successful perception in humans clearly relies on both top-down and bottom-up signals (Buschman and Miller, 2007; Beck and Kastner, 2009; McMains and Kastner, 2011; Kinchla and Wolfe, 1979; Rauss and Pourtois, 2013; McClelland and Rumelhart, 1981).”
Beyond dual-process models: A categorisation of processes underlying intuitive judgement and decision making (2010, background): “Automatic-intuitive processes are, for instance, activated in visual perception (McClelland & Rumelhart, 1981; Wertheimer, 1938a, 1938b) and social perception (Bruner & Goodman, 1947; Read & Miller, 1998; Read, Vanman, & Miller, 1997) to structure information in our environment and to quickly form…”
Connectionist models of development (2003, methodology): “For example, within connectionist approaches dynamics at many scales is essential since it allows for gradual integration over constraints at various spatial and temporal scales (McClelland and Rumelhart, 1981; Spivey and Dale, 2005).”
To transform or not to transform: using generalized linear mixed models to analyse reaction time data (2015, background): “For example, processing rate or speed of evidence accumulation is assumed to be slower for visually degraded as opposed to clearly presented words in activation models (e.g., McClelland and Rumelhart, 1981), thus yielding the slower RT typically observed for these conditions.”
Nonlinear neural networks: Principles, mechanisms, and architectures (1988, unknown): “McCl.elland (1987) has proposed that models of this class be renamed the "interactive activation framework" after the interactive activation model that was introduced by McClelland and Rumelhart (1981) and Rumelhart and McClelland (1982).”
Learning Deep Architectures for AI (2007, background): “Note that connectionist cognitive psychologists have been studying for long time the idea of neural computation organized with a hierarchy of levels of representation corresponding to different levels of abstraction, with a distributed representation at each level [67, 68, 123, 122, 124, 157].”
A critical look at the embodied cognition hypothesis and a new proposal for grounding conceptual content. (2008, unknown): “In contrast , such data can be explained if it is assumed that activation within the speech production system can spread beyond levels of processing at which information has been selected for output – the cascaded processing assumption (e.g., McClelland and Rumelhart , 1981).”
The effect of bimodal input on implicit and explicit memory: An investigation into the benefits of within-language subtitling (2002, background): “(See also McClelland & Rumelhart, 1981, and Grainger & Ferrand, 1996, for slightly different versions of the same basic model.)”
Automatic semantic activation of embedded words: Is there a hat in that? (2005, methodology): “For example, ed. according to the interactive activation model of McClelland and Rumelhart (1981), processing of the word lamp should automatically result in the partial activation of the high frequency word camp.”