Connectionist models of language production: lexical access and grammatical encoding

Dell, George H.

doi:10.1016/s0364-0213(99)00014-2

Cited by 104 publications

(107 citation statements)

References 39 publications

(59 reference statements)

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“…There is broad agreement that the probability with which a word or structure will be selected is determined by its relative activation compared to other candidates. For example, models of lexical access typically describe the probability that a word (also known as lexical, lemma, or L-level) node is selected as function of the target lemma's relative activation and the relative activation of all other lemma nodes (Dell, 1986;Dell et al, 1999;Levelt, Roelofs, & Meyer, 1999;Mackay, 1982;Roelofs, 2002;Stemberger, 1985). The relative activation of each node is the result of both its relative ''starting activation'' and top-down activation that spreads from concepts to lexical nodes.…”

Section: 2relating the Constant Entropy Hypothesis To Language Promentioning

confidence: 99%

“…We propose an alternative model of cue effectiveness and test it against data from 12 languages. Second, it has so far remained unclear how the concepts evoked in the information theoretic account by Genzel and Charniak (2002) relate to the activation-based architecture underlying most contemporary mechanistic models of language production (Dell, 1986;Dell, Chang, & Griffin, 1999;Levelt, Roelofs, & Meyer, 1999;Mackay, 1982;Roelofs, 2002;Stemberger, 1985). Here, we aim to address these two points.…”

Section: Introductionmentioning

confidence: 99%

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Cue Effectiveness in Communicatively Efficient Discourse Production

Qian

Jaeger

2012

Cognitive Science

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Recent years have seen a surge in accounts motivated by information theory that consider language production to be partially driven by a preference for communicative efficiency. Evidence from discourse production (i.e., production beyond the sentence level) has been argued to suggest that speakers distribute information across discourse so as to hold the conditional per-word entropy associated with each word constant, which would facilitate efficient information transfer (Genzel & Charniak, 2002). This hypothesis implies that the conditional (contextualized) probabilities of linguistic units affect speakers' preferences during production. Here, we extend this work in two ways. First, we explore how preceding cues are integrated into contextualized probabilities, a question which so far has received little to no attention. Specifically, we investigate how a cue's maximal informativity about upcoming words (the cue's effectiveness) decays as a function of the cue's recency. Based on properties of linguistic discourses as well as properties of human memory, we analytically derive a model of cue effectiveness decay and evaluate it against cross-linguistic data from 12 languages. Second, we relate the information theoretic accounts of discourse production to well-established mechanistic (activation-based) accounts: We relate contextualized probability distributions over words to their relative activation in a lexical network given preceding discourse.

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Section: 2relating the Constant Entropy Hypothesis To Language Promentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cue Effectiveness in Communicatively Efficient Discourse Production

Qian

Jaeger

2012

Cognitive Science

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“…A number of connectionist models of language processing (e.g. Dell, 1986;Norris, 1994;Gaskell et al, 1995;Dell et al, 1999;see Christiansen & Chater, 1999, 2001a,b for reviews) adopt a 'localist' approach to the problem, whereby one node of the network does not represent a pool of cortical neurones but a phonological feature, a phoneme or even a whole word. A localist approach would have given us several advantages, including reduced computational load and easier implementation.…”

Section: Network Structure and Functionmentioning

confidence: 99%

A neuroanatomically grounded Hebbian‐learning model of attention–language interactions in the human brain

Garagnani¹,

Wennekers²,

Pulvermüller³

2008

Eur J of Neuroscience

118

173

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Meaningful familiar stimuli and senseless unknown materials lead to different patterns of brain activation. A late major neurophysiological response indexing 'sense' is the negative component of event-related potential peaking at around 400 ms (N400), an event-related potential that emerges in attention-demanding tasks and is larger for senseless materials (e.g. meaningless pseudowords) than for matched meaningful stimuli (words). However, the mismatch negativity (latency 100-250 ms), an early automatic brain response elicited under distraction, is larger to words than to pseudowords, thus exhibiting the opposite pattern to that seen for the N400. So far, no theoretical account has been able to reconcile and explain these findings by means of a single, mechanistic neural model. We implemented a neuroanatomically grounded neural network model of the left perisylvian language cortex and simulated: (i) brain processes of early language acquisition and (ii) cortical responses to familiar word and senseless pseudoword stimuli. We found that variation of the area-specific inhibition (the model correlate of attention) modulated the simulated brain response to words and pseudowords, producing either an N400-or a mismatch negativity-like response depending on the amount of inhibition (i.e. available attentional resources). Our model: (i) provides a unifying explanatory account, at cortical level, of experimental observations that, so far, had not been given a coherent interpretation within a single framework; (ii) demonstrates the viability of purely Hebbian, associative learning in a multilayered neural network architecture; and (iii) makes clear predictions on the effects of attention on latency and magnitude of event-related potentials to lexical items. Such predictions have been confirmed by recent experimental evidence.

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“…Syntactic-sequential states could, instead, correspond to the hidden-unit activation patterns of a simple recurrent network that learns to be sensitive to the distribution of words in utterances (Elman, 1990(Elman, , 1993Elman et al, 1996;Tabor & Tanenhaus, 1999). (See also Chang, 2002;Chang, Dell, Bock, & Griffin, 2000;Dell, Chang, & Griffin, 1999 for examples of this approach in production models. )…”

Section: Lexical Retrieval In Language Productionmentioning

confidence: 99%

Learning to divide the labor: an account of deficits in light and heavy verb production

2003

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Theories of sentence production that involve a convergence of activation from conceptual-semantic and syntactic-sequential units inspired a connectionist model that was trained to produce simple sentences. The model used a learning algorithm that resulted in a sharing of responsibility (or "division of labor") between syntactic and semantic inputs for lexical activation according to their predictive power. Semantically rich, or "heavy", verbs in the model came to rely on semantic cues more than on syntactic cues, whereas semantically impoverished, or "light", verbs relied more on syntactic cues. When the syntactic and semantic inputs were lesioned, the model exhibited patterns of production characteristic of agrammatic and anomic aphasic patients, respectively. Anomic models tended to lose the ability to retrieve heavy verbs, whereas agrammatic models were more impaired in retrieving light verbs. These results obtained in both sentence production and single-word naming simulations. Moreover, simulated agrammatic lexical retrieval was more impaired overall in sentences than in single-word tasks, in agreement with the literature. The results provide a demonstration of the division-of-labor principle, as well as general support for the claim that connectionist learning principles can contribute to the understanding of non-transparent neuropsychological dissociations.

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Connectionist models of language production: lexical access and grammatical encoding

Cited by 104 publications

References 39 publications

Cue Effectiveness in Communicatively Efficient Discourse Production

Cue Effectiveness in Communicatively Efficient Discourse Production

A neuroanatomically grounded Hebbian‐learning model of attention–language interactions in the human brain

Learning to divide the labor: an account of deficits in light and heavy verb production

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