The Bayesian reader: explaining word recognition as an optimal Bayesian decision process.

By Dennis Norris. Psychology Review, 2006

397 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Developmental and Educational Psychology, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/3557b5315ad8b605831722562f8b141efb6e3d8f

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Citations in the corpus

An amorphous model for morphological processing in visual comprehension based on naive discriminative learning. (2011, result): “In what follows, we therefore compare the naive discriminative reader in some detail with the Bayesian Reader of Norris (2006), and briefly discuss other models of word recognition.”
A stimulus sampling theory of letter identity and order (2010, methodology): “The Bayesian Reader (Norris, 2006) is a stimulus sampling model, based on the assumption that readers approximate optimal Bayesian decision makers.”
Frequency in lexical processing (2016, background): “Such effects are outside the scope of interactive activation models, as well as of parallel distributed processing (Harm and Seidenberg, 1999) or Bayesian (Norris, 2006) models.”
Troubles with Bayesianism: An introduction to the psychological immune system (2018, background): “I agree with the general moral Danks draws, but in the specific case of Bayesian processing theorists are in fact explicitly committed to the optimality condition (see, e.g., Oaksford & Chater, 1994; Weiss, Simoncelli & Adelson, 2002; Griffiths & Tenenbaum, 2006; Norris, 2006; Bogacz, 2007; Feldman,”
What do we mean by prediction in language comprehension? (2015, background): “…and cognitive resources she has at her disposal in a rational fashion (e.g. Griffiths, Lieder, & Goodman, 2015; Howes, Lewis, & Vera, 2009; Simon, 1956 for applications and discussion in relation to language processing, see for example, Bicknell et al., 2015; Lewis et al., 2014; Norris, 2006).”
Demythologizing the word frequency effect: A discriminative learning perspective (2010, methodology): “Consider, by way of example, the Bayesian Reader model of Norris (2006).”
The spatial coding model of visual word identification. (2010, methodology): “A theoretical approach to orthographic input coding that is very similar to the overlap model has been adopted in the Bayesian reader model (Norris, 2006; Norris & Kinoshita, 2008).”
The Overlap Model: A Model of Letter Position Coding (2008, neutral): “For example, the original implementation of the Bayesian reader model (Norris, 2006) assumes position-specific letter coding.”
The effect of word predictability on reading time is logarithmic (2013, methodology): “There are two theories which predict the precise logarithmic effect we found: The Bayesian Reader (Norris, 2006; Norris, 2009) and the incremental processing account.”
Bayesian just-so stories in psychology and neuroscience. (2012, background): “…to provide a better and more principled account of a range of phenomena, including the impact of word frequency on response times to identify words (Norris, 2006), the impact of form similarity on word identification in different tasks (Norris et al., 2010), and various priming phenomena (Norris &…”
Methodological and empirical developments for the Ratcliff diffusion model of response times and accuracy (2009, methodology): “The lexical decision task is one of the most often used tasks in the field of visual word recognition, and several models have been proposed to account for its key findings (e.g., Norris, 2006; Plaut, 1997; Wagenmakers et al., 2004).”
A Bayesian Recognitional Decision Model (2009, background): “These include decisions about episodic memory (REM, Shiffrin & Steyvers, 1997; BCDMEM, Dennis & Humphreys, 2001), semantic knowledge (REM-II, Mueller & Shiffrin, 2006), perceptual judgment (ROUSE, Huber et al., 2001), and word recognition (the bayesian reader, Norris, 2006).”
Shortlist B: a Bayesian model of continuous speech recognition. (2008, methodology): “Relative to Shortlist B, Merge B is a simplified Bayesian model based on the sampling process used in the Bayesian Reader (Norris, 2006).”