On the association between connectionism and data: Are a few words necessary?

Besner, Derek; Twilley, Leslie C.; McCann, Robert S.; Seergobin, Ken N.

doi:10.1037/0033-295x.97.3.432

Cited by 237 publications

(232 citation statements)

References 43 publications

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“…With the advent of parallel distributed processing models, some theorists claim that there are no lexicons (e.g., Seidenberg & McClelland, 1989; others dispute this claim (e.g., Besner, Twilley, McCann, & Seergobin, 1990;Coltheart, Curtis, Atkins, & Haller, 1993). The "no lexicon" view would seem to predict that, provided that words and nonwords are matched for orthographic and phonological characteristics, there ought to be no lexical effects in the serial order recall of a patient with no access to semantics.…”

Section: Discussionmentioning

confidence: 99%

Beyond the articulatory loop: A semantic contribution to serial order recall of subspan lists

Bourassa

Besner

1994

Psychonomic Bulletin & Review

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101

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In two experiments, serial order recall of short lists of content and function words under quiet and articulatory suppression conditions was examined in order to assess the hypotheses that (1) semantic attributes of words contribute to short-term-memory performance, and (2) do so independently of effects attributable to the articulatory loop component. In Experiment 1, content words were better recalled than function words; both word types were equally impaired by suppression. This provides support for the notion that semantic coding makes an independent contribution to span performance. This word-class effect disappeared in Experiment 2, when content and function words were matched for imageability. These data suggest that at least some aspects of meaning contribute to serial order recall performance for short lists, independently of the articulatory loop.

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Section: Discussionmentioning

confidence: 99%

Beyond the articulatory loop: A semantic contribution to serial order recall of subspan lists

Bourassa

Besner

1994

Psychonomic Bulletin & Review

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101

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“…What would be needed to see if the combination works is a quantitative examination of whether the values of wordness produced by the lexical model map into drift appropriately (cf. criticism of Seidenberg & McClelland's [1989] model by Besner, Twilley, McCann, & Seergobin [1990]). Thus, the following discussion suggests only possible beginning points for research.…”

mentioning

confidence: 99%

“…If the match value is above a criterion, a "word" response is produced, and if not, a "nonword" response is produced. To account for the effects of type of nonword, Seidenberg and McClelland (1989) proposed that when the nonwords are pseudowords, phonological as well as orthographic output is assessed, but this was not implemented (see the critique by Besner et al, 1990, and the reply by Seidenberg & McClelland, 1990). Error responses come from high-match values when nonwords are presented and low-match values when a word is presented.…”

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A Diffusion Model Account of the Lexical Decision Task.

Ratcliff¹,

Gómez²,

McKoon³

2004

Psychological Review

502

675

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The diffusion model for 2-choice decisions (R. Ratcliff, 1978) was applied to data from lexical decision experiments in which word frequency, proportion of high-versus low-frequency words, and type of nonword were manipulated. The model gave a good account of all of the dependent variables -accuracy, correct and error response times, and their distributions-and provided a description of how the component processes involved in the lexical decision task were affected by experimental variables. All of the variables investigated affected the rate at which information was accumulated from the stimuli-called drift rate in the model. The different drift rates observed for the various classes of stimuli can all be explained by a 2-dimensional signal-detection representation of stimulus information. The authors discuss how this representation and the diffusion model's decision process might be integrated with current models of lexical access.The lexical decision task is one of the most widely used paradigms in psychology. The goal of the research described in this article was to account for lexical decision performance with the diffusion model (Ratcliff, 1978), a model that allows components of cognitive processing to be examined in two-choice decision tasks. Nine lexical decision experiments, manipulating a number of factors known to affect lexical decision performance, are presented. The diffusion model gives good fits to the data from all of the experiments, including mean response times for correct and error responses, the relative speeds of correct and error responses, the distributions of response times, and accuracy rates.In the diffusion model, the mechanism underlying two-choice decisions is the accumulation of noisy information from a stimulus over time. Information accumulates toward one or the other of two decision criteria until one of the criteria is reached; then the response associated with that criterion is initiated. In the lexical decision task, one of the criteria is associated with a word response, the other with a nonword response. The rate with which information is accumulated is called drift rate, and it depends on the quality of information from the stimulus. In lexical decision, some stimuli are more wordlike than others, and so their rate of accumulation of information toward the word criterion is faster; other stimuli, such as random letter strings, are so un-wordlike that information accumulates quickly toward the nonword criterion. For the nine experiments presented below, the drift rates can be summarized quite simply. First, the ordering of the drift rates from largest to smallest is as follows: high-frequency words, low-frequency words, very low-frequency words, pseudowords, and random letter NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript strings. Second, the differences among the drift rates are larger when the nonwords in an experiment are pseudowords than when they are random letter strings.For our framework, Figure 1 outlines the relationships among...

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“…Wholeword spelling-to-sound correspondences are stored in the lexicon, whereas subword correspondences are computed by a separate route that converts graphemes to phonemes. Although this assumption remains subject to an ongoing debate (e.g., Balota & Spieler, 1998;Besner, 1999;Besner, Twilley, McCann, & Seergobin, 1990;Plaut, 1999;Seidenberg & McClelland, 1989b;Seidenberg & Plaut, 1998;Spieler & Balota, 1997), it is not unique to the symbolic account of reading. Zorzi, Houghton, and Butterworth (1998) demonstrated that the segregation of the sublexical and the lexical routes facilitates the acquisition of spelling-to-soundregularities by a feedforward twolayer network.…”

Section: Does the Mapping Of Graphemes To Phonemes Appeal To Variables?mentioning

confidence: 99%

“…Contrary to this prediction, connectionist accounts of word reading have gained considerable success in modeling the pronunciation of both words and nonwords. The model of Seidenberg and McClelland (1989a) captures the pronunciation of monosyllabic words, although its ability to account for nonword pronunciation is limited (Besner et al, 1990;Coltheart et al, 1993). However, the subsequent models of Plaut et al (1996) and Harm and Seidenberg (1999) account quite well for both monosyllabicword and nonword pronunciation (but see Balota & Spieler, 1998;Besner, 1999;Plaut, 1999;Spieler & Balota, 1997).…”

Section: The Success Of Pattern Associator Accounts Of Readingmentioning

confidence: 99%

Can connectionist models of phonology assembly account for phonology?

Berent

2001

Psychonomic Bulletin & Review

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Connectionist models have gained considerable success as accounts of how printed words are named. Their success challenges the view of grapheme-to-phoneme correspondences (GPCs) as rules. By extension, however, this challenge is sometimes interpreted also as evidence against linguistic rules and variables. This inference tacitly assumes that the generalizations inherent in reading (specifically, GPCs) are similar in their scope to linguistic generalizations and that they are each reducible to token associations. I examine this assumption by comparing the scope of generalizationsrequired for mapping graphemes to phonemes and several linguistic phonological generalizations. Marcus (1998b) distinguishes between two types of generalizations: those that fall within a model's training space and those that exceed it. The scope of generalizations is determined by the model's representational choicesspecifically, the implementation of operations over mental variables. An analysis of GPCs suggests that such generalizations do not appeal to variables; hence, they may not exceed the training space. Likewise, certain phonological regularities,such as syllable phonotactic constraints and place assimilation, may be captured by an associativeprocess. In contrast, other phonological processes appeal to variables; hence, such generalizations potentially exceed the training space. I discuss one such case, the obligatory contour principle. I demonstrate that speakers conform to this constraint and that their behavior is inexplicable by the statistical structure of the language. This analysis suggests that, unlike GPCs, phonological generalizations may exceed the training space. Thus, despite their success in modeling GPCs, eliminative connectionist models of phonology assembly may be unable to provide a complete account for phonology. To the extent that reading is subject to phonological constraints, its modeling may require implementing operations over variables.

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On the association between connectionism and data: Are a few words necessary?

Cited by 237 publications

References 43 publications

Beyond the articulatory loop: A semantic contribution to serial order recall of subspan lists

Beyond the articulatory loop: A semantic contribution to serial order recall of subspan lists

A Diffusion Model Account of the Lexical Decision Task.

Can connectionist models of phonology assembly account for phonology?

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