The Experts below are selected from a list of 9744 Experts worldwide ranked by ideXlab platform
Franklin Chang - One of the best experts on this subject based on the ideXlab platform.
-
meaningful questions the acquisition of auxiliary inversion in a Connectionist Model of sentence production
2017Co-Authors: Hartmut Fitz, Franklin ChangAbstract:Nativist theories have argued that language involves syntactic principles which are unlearnable from the input children receive. A paradigm case of these innate principles is the structure dependence of auxiliary inversion in complex polar questions (Chomsky, 1968, 1975, 1980). Computational approaches have focused on the properties of the input in explaining how children acquire these questions. In contrast, we argue that messages are structured in a way that supports structure dependence in syntax. We demonstrate this approach within a Connectionist Model of sentence production (Chang, 2009) which learned to generate a range of complex polar questions from a structured message without positive exemplars in the input. The Model also generated different types of error in development that were similar in magnitude to those in children (e.g., auxiliary doubling, Ambridge, Rowland, & Pine, 2008; Crain & Nakayama, 1987). Through Model comparisons we trace how meaning constraints and linguistic experience interact during the acquisition of auxiliary inversion. Our results suggest that auxiliary inversion rules in English can be acquired without innate syntactic principles, as long as it is assumed that speakers who ask complex questions express messages that are structured into multiple propositions.
-
the p chain relating sentence production and its disorders to comprehension and acquisition
2014Co-Authors: Gary S. Dell, Franklin ChangAbstract:This article introduces the P-chain, an emerging framework for theory in psycholinguistics that unifies research on comprehension, production and acquisition. The framework proposes that language processing involves incremental prediction, which is carried out by the production system. Prediction necessarily leads to prediction error, which drives learning, including both adaptive adjustment to the mature language processing system as well as language acquisition. To illustrate the P-chain, we review the Dual-path Model of sentence production, a Connectionist Model that explains structural priming in production and a number of facts about language acquisition. The potential of this and related Models for explaining acquired and developmental disorders of sentence production is discussed.
-
learning to order words a Connectionist Model of heavy np shift and accessibility effects in japanese and english
2009Co-Authors: Franklin ChangAbstract:Languages differ from one another and must therefore be learned. Processing biases in word order can also differ across languages. For example, heavy noun phrases tend to be shifted to late sentence positions in English, but to early positions in Japanese. Although these language differences suggest a role for learning, most accounts of these biases have focused on processing factors. This paper presents a learning-based account of these word order biases in the form of a Connectionist Model of syntax acquisition that can learn the distinct grammatical properties of English and Japanese while, at the same time, accounting for the cross-linguistic variability in processing biases in sentence production. This account demonstrates that the incremental nature of sentence processing can have an important effect on the representations that are learned in different languages.
-
symbolically speaking a Connectionist Model of sentence production
2002Co-Authors: Franklin ChangAbstract:The ability to combine words into novel sentences has been used to argue that humans have symbolic language production abilities. Critiques of Connectionist Models of language often center on the inability of these Models to generalize symbolically (Fodor & Pylyshyn, 1988; Marcus, 1998). To address these issues, a Connectionist Model of sentence production was developed. The Model had variables (role-concept bindings) that were inspired by spatial representations (Landau & Jackendoff, 1993). In order to take advantage of these variables, a novel dual-pathway architecture with event semantics is proposed and shown to be better at symbolic generalization than several variants. This architecture has one pathway for mapping message content to words and a separate pathway that enforces sequencing constraints. Analysis of the Model's hidden units demonstrated that the Model learned different types of information in each pathway, and that the Model's compositional behavior arose from the combination of these two pathways. The Model's ability to balance symbolic and statistical behavior in syntax acquisition and to Model aphasic double dissociations provided independent support for the dual-pathway architecture.
Lisa M. Saksida - One of the best experts on this subject based on the ideXlab platform.
-
why does brain damage impair memory a Connectionist Model of object recognition memory in perirhinal cortex
2006Co-Authors: Rosemary A Cowell, Timothy J. Bussey, Lisa M. SaksidaAbstract:Object recognition is the canonical test of declarative memory, the type of memory putatively impaired after damage to the temporal lobes. Studies of object recognition memory have helped elucidate the anatomical structures involved in declarative memory, indicating a critical role for perirhinal cortex. We offer a mechanistic account of the effects of perirhinal cortex damage on object recognition memory, based on the assumption that perirhinal cortex stores representations of the conjunctions of visual features possessed by complex objects. Such representations are proposed to play an important role in memory when it is difficult to solve a task using representations of only individual visual features of stimuli, thought to be stored in regions of the ventral visual stream caudal to perirhinal cortex. The account is instantiated in a Connectionist Model, in which development of object representations with visual experience provides a mechanism for judgment of previous occurrence. We present simulations addressing the following empirical findings: (1) that impairments after damage to perirhinal cortex (Modeled by removing the “perirhinal cortex” layer of the network) are exacerbated by lengthening the delay between presentation of to-be-remembered items and test, (2) that such impairments are also exacerbated by lengthening the list of to-be-remembered items, and (3) that impairments are revealed only when stimuli are trial unique rather than repeatedly presented. This study shows that it may be possible to account for object recognition impairments after damage to perirhinal cortex within a hierarchical, representational framework, in which complex conjunctive representations in perirhinal cortex play a critical role.
-
The organization of visual object representations: a Connectionist Model of effects of lesions in perirhinal cortex.
2002Co-Authors: Timothy J. Bussey, Lisa M. SaksidaAbstract:We have developed a simple Connectionist Model based on the idea that perirhinal cortex has properties similar to other regions in the ventral visual stream, or 'what' pathway. The Model is based on the assumption that representations in the ventral visual stream are organized hierarchically, such that representations of simple features of objects are stored in caudal regions of the ventral visual stream, and representations of the conjunctions of these features are stored in more rostral regions. We propose that a function of these feature conjunction representations is to help to resolve 'feature ambiguity', a property of visual discrimination problems that can emerge when features of an object predict a given outcome (e.g. reward) when part of one object, but predict a different outcome when part of another object. Several recently reported effects of lesions of perirhinal cortex in monkeys have provided key insights into the functions of this region. In the present study these effects were simulated by comparing the performance of Connectionist networks before and after removal of a layer of units corresponding to perirhinal cortex. The results of these simulations suggest that effects of lesions in perirhinal cortex on visual discrimination may be due not to the impairment of a specific type of learning or memory, such as declarative or procedural, but to compromising the representations of visual stimuli. Furthermore, we propose that attempting to classify perirhinal cortex function as either 'perceptual' or 'mnemonic' may be misguided, as it seems unlikely that these broad constructs will map neatly onto anatomically defined regions of the brain.
-
effects of similarity and experience on discrimination learning a nonassociative Connectionist Model of perceptual learning
1999Co-Authors: Lisa M. SaksidaAbstract:This article describes a novel Connectionist Model of perceptual learning (PL) that provides a mechanism for nonassociative differentiation (J. J. Gibson & E. J. Gibson, 1955). The Model begins with the assumption that 2 processes--1 that decreases associability and 1 that increases discriminability--operate during preexposure (S. Channell & G. Hall, 1981). In contrast to other Models (e.g., I. P. L. McLaren, H. Kaye, & N. J. Mackintosh, 1989), in the current Model the mechanisms for these processes are compatible with a configural Model of associative learning. A set of simulations demonstrates that the present Model can account for critical PL phenomena such as exposure learning and effects of similarity on discrimination. It is also shown that the Model can explain the paradoxical result that preexposure to stimuli can either facilitate or impair subsequent discrimination learning. Predictions made by the Model are discussed in relation to extant theories of PL.
Mark S Seidenberg - One of the best experts on this subject based on the ideXlab platform.
-
phonology and syntax in specific language impairment evidence from a Connectionist Model
2003Co-Authors: Marc F Joanisse, Mark S SeidenbergAbstract:Difficulties in resolving pronominal anaphora have been taken as evidence that Specific Language Impairment (SLI) involves a grammar-specific impairment. The present study explores an alternative view, that grammatical deficits in SLI are sequelae of impaired speech perception. This perceptual deficit specifically affects the use of phonological information in working memory, which in turn leads to poorer than expected syntactic comprehension. This hypothesis was explored using a Connectionist Model of sentence processing that learned to map sequences of words to their meanings. Anaphoric resolution was represented in this Model by recognizing the semantics of the correct antecedent when a bound pronoun was input. When the Model was trained on distorted phonological inputs—simulating a perceptual deficit—it exhibited marked difficulty resolving bound anaphors. However, many other aspects of sentence comprehension were intact; most importantly, the Model could still resolve pronouns using gender information. In addition, the Models deficit was graded rather than categorical, as it was able to resolve pronouns in some sentences, but not in others. These results are consistent with behavioral data concerning syntactic deficits in SLI. The Model provides a causal demonstration of how a perceptual deficit could give rise to grammatical deficits in SLI.
-
impairments in verb morphology after brain injury a Connectionist Model
1999Co-Authors: Marc F Joanisse, Mark S SeidenbergAbstract:The formation of the past tense of verbs in English has been the focus of the debate concerning Connectionist vs. symbolic accounts of language. Brain-injured patients differ with respect to whether they are more impaired in generating irregular past tenses (take–took) or past tenses for nonce verbs (wug–wugged). Such dissociations have been taken as evidence for distinct “rule” and “associative” memory systems in morphology and against the Connectionist approach in which a single system is used for all forms. We describe a simulation Model in which these impairments arise from damage to phonological or semantic information, which have different effects on generalization and irregular forms, respectively. The results provide an account of the bases of impairments in verb morphology and show that these impairments can be explained within Connectionist Models that do not use rules or a separate mechanism for exceptions.
-
learning to segment speech using multiple cues a Connectionist Model
1998Co-Authors: Morten H Christiansen, Joseph A Allen, Mark S SeidenbergAbstract:Considerable research in language acquisition has addressed the extent to which basic aspects of linguistic structure might be identieed on the basis of probabilistic cues in caregiver speech to children. This type of learning mechanism presents classic learnability issues: there are aspects of language for which the input is thought to provide no evidence, and the evidence that does exist tends to be unreliable. We address these issues in the context of the speciec problem of learning to identify lexical units in speech. A simple recurrent network was trained on a phoneme prediction task. The Model was explicitly provided with information about phonemes, relative lexical stress, and boundaries between utterances. Individually these sources of information provide relatively unreliable cues to word boundaries and no direct evidence about actual word boundaries. After training on a large corpus of childdirected speech, the Model was able to use these cues to reliably identify word boundaries. The Model shows that aspects of linguistic structure that are not overtly marked in the input can be derived by efeciently combining multiple probabilistic cues. Connectionist networks provide a plausible mechanism for acquiring, representing, and combining such probabilistic information.
Steffen Hölldobler - One of the best experts on this subject based on the ideXlab platform.
-
Connectionist Model generation: A first-order approach
2008Co-Authors: Sebastian Bader, Pascal Hitzler, Steffen HölldoblerAbstract:Knowledge-based artificial neural networks have been applied quite successfully to propositional knowledge representation and reasoning tasks. However, as soon as these tasks are extended to structured objects and structure-sensitive processes as expressed e.g., by means of first-order predicate logic, it is not obvious at all what neural-symbolic systems would look like such that they are truly Connectionist, are able to learn, and allow for a declarative reading and logical reasoning at the same time. The core method aims at such an integration. It is a method for Connectionist Model generation using recurrent networks with feed-forward core. We show in this paper how the core method can be used to learn first-order logic programs in a Connectionist fashion, such that the trained network is able to do reasoning over the acquired knowledge. We also report on experimental evaluations which show the feasibility of our approach.
-
a fully Connectionist Model generator for covered first order logic programs
2007Co-Authors: Sebastian Bader, Steffen Hölldobler, Pascal Hitzler, Andreas WitzelAbstract:We present a fully Connectionist system for the learning of first-order logic programs and the generation of corresponding Models: Given a program and a set of training examples, we embed the associated semantic operator into a feed-forward network and train the network using the examples. This results in the learning of first-order knowledge while damaged or noisy data is handled gracefully.
-
The Core Method : Connectionist Model Generation
2006Co-Authors: Sebastian Bader, Steffen HölldoblerAbstract:Knowledge based artificial networks networks have been applied quite successfully to propositional knowledge representation and reasoning tasks. However, as soon as these tasks are extended to structured objects and structure-sensitive processes it is not obvious at all how neural symbolic systems should look like such that they are truly Connectionist and allow for a declarative reading at the same time. The core method aims at such an integration. It is a method for Connectionist Model generation using recurrent networks with feed-forward core. After an introduction to the core method, this paper will focus on possible Connectionist representations of structured objects and their use in structure-sensitive reasoning tasks.
Virginia A Marchman - One of the best experts on this subject based on the ideXlab platform.
-
learning from a Connectionist Model of the acquisition of the english past tense
1996Co-Authors: Kim Plunkett, Virginia A MarchmanAbstract:L'A. rappelle les buts du Modele connexionniste du Plunkett et Marchman (1993) concernant l'acquisition de la morphologie des verbes et l'acquisition des temps passes en anglais. Il rejette les critiques formulees par Marcus (1995) a l'encontre de ce Modele. Il demontre, dans le meme sens que Plunkett et Marchman, que la taille limitee du lexique conduit l'enfant a produire correctement a la fois des formes regulieres et irregulieres des temps passes et que la croissance non-lineaire du lexique est un facteur qui contribue a l'enclenchement des sur-regularisations
-
constraints on plasticity in a Connectionist Model of the english past tense
1993Co-Authors: Virginia A MarchmanAbstract:This paper investigates constraints on dissociation and plasticity in a Connectionist Model undergoing random “lesions” both prior to and during training. When networks were trained only on phonological encodings of stem-suhed pairs similar to English regular verbs (e.g., walk x walked), long-term deficits (i.e., “critical period” effects) were not observed, yet there were substantive short-term effects of injury. When training vocabulary reflected the English-like competition between regular (suffixed) and irregular verbs (e.g., go x went, hit x hit), the acquisition of regular verbs became increasingly susceptible to injury, while the irregulars were learned quickly and were relatively impervious to damage. Patterns of generalization to novel forms conflicts with the assumption that this behavioral dissociation is indicative of selective impairment of the learning and generalization of the past tense rule, while the associative lexical-based mechanism is left intact. Instead, we propose a view of network performance in which the regular-irregular dissociation derives from a general reduction in the ability to find a single-mechanism solution when resolving the competition between two classes of mappings. In light of other Models in which “regular” and “irregular” forms compete (e.g., Patterson, Seidenberg, & McClelland, 1989), as well as patterns of performance in normal and disordered English speakers (e.g., Pinker, 1991), two general implications are discussed: (1) critical period effects need not derive from endog-enously determined maturational change, but instead may in part result from learning history in relation to characteristics of the language to be learned (i.e., entrenchment), and (2) selective dissociations can result from general damage in systems that are not modularized in terms of rule-based vs. associative mechanisms.
