Abstract not found

Neuropsychological dissociations between regular and irregular past tense verb processing have been explained in two ways: (a) separate mechanisms of a rule-governed process for regular verbs and a lexical-associative process for irregular verbs; (b) a single system drawing on phonological and semantic knowledge. The latter account invokes phonological impairment as the basis of poorer performance for regular than irregular past tense forms, due to greater phonological complexity of the regular past. In 10 nonfluent aphasic patients, the apparent disadvantage for the production of regular past tense forms disappeared when phonological complexity was controlled. In a same-different judgment task on spoken words, all patients were impaired at judging regular stem and past-tense verbs like man/ manned to be different, but equally poor at phonologically matched non-morphological discriminations like men/mend. These results indicate a central phonological deficit that is not limited to speech output nor to morphological processing; under such a deficit, distinctions lacking phonological salience, as typified by regular past tense English verbs, become especially vulnerable.

Optimality Theory (OT) has had a lot of attention from the linguistics research community but also still largely lacks cognitive grounding. We used the ACT-R cognitive architecture to gain greater insight into the cognitive grounding issues that OT needs to address, most notably the GEN process and the learning of the constraint ranking. A generic ACT-R 5.0 model was developed guided by OT principles. The generic model was instantiated in two specific models, one for syllabification and one for past tense formation. Realistic perception data was used to train the models, both were successful in learning the correct constraint ranking for their domain. This result partly bridges the gap between Optimality Theory and ACT-R, providing OT with a better cognitive grounding and ACT-R with better linguistic capabilities.

We present evidence that graded constraints determine the occurrence rates of the different rhyme types found in the ensemble of simple uninflected words in the English language. The rhyme types are defined in terms of vowel length (long vs. short), presence of particular post-vocalic elements, and their place of articulation. The rhyme types in the corpus (uninflected monosyllabic lemmas found in CELEX, which uses Southern British ‘Received Pronunciation’) conform to a template defined by a small number of absolute or categorical constraints. Among those forms consistent with the template, several graded constraints are identified, including constraints favoring short vowels, fewer segments, coronal places of articulation, and, when stops are present, absence of voicing. Such constraints induce a partial ordering over the expected rates of occurrence of different rhyme types; these have as special cases a pattern of implications for whether or not a form occurs at all (if form X occurs, then form Y should occur; if Z does not occur, then W should not occur). The constraints can be incorporated into a monotonic function characterizing the expected frequencies of occurrence of different

Allen Newell, typically a cheery and optimistic man, often expressed frustration over the state of progress in cognitive science. He would point to such things as the “schools ” of thought, the changes in fashion, the dominance of controversies,

statistics and lexical processing: semantic density, information complexity, sex,

We present an extension of the ACT-R model by Taatgen and Anderson (2002) of learning the English past tense that can take into account the production of irregularization errors. These errors are produced by using examples of irregular verbs as the basis for analogy. The relative rareness of irregularization errors puts constraints on the rule generalization process The model explains this by the fact that the probability for a particular irregularization is too low to establish a rule.

Learning the English past tense is characterized by a U-shaped learning function for the irregular verbs. Existing cognitive models rely on a sudden increases in vocabulary, a high token-frequency of regular verbs, and convoluted schemes of feedback in order to model this phenomenon. All these assumptions are at odds with empirical data. In this paper a hybrid ACT-R model is presented that shows U-shaped learning without direct feedback, changes in vocabulary, or unrealistically high rates of regular verbs. The model is capable of learning the default rule, even if regular forms are infrequent. It can also help explore the question of why there is a distinction between regular and irregular verbs in the first place, by examining the costs and benefits of both types of verbs.

The English past tense is a quasi-regular system, in that many of the irregular verbs share characteristics with regular items. Among high-frequency exceptions, in particular, several have the regular /d/ or /t / ending but with either a reduction of the vowel (did, said) or a deletion of a stem consonant (had, made). Such forms suggest that many so-called irregular verbs reflect a joint influence of the systematic past-tense pattern captured in fully regular items together with a pressure to be short or simple. We adapt familiar neural network formalisms to show how such forms can arise if the phonological content of word forms are constrained (a) to support accurate communication of the word’s meaning and (b) to be simple.

perspective