Different writing systems in the world select different units of spoken language for mapping. Do these writing system differences influence how first language (L1) literacy experiences affect cognitive processes in learning to read a second language (L2)? Two groups of college students who were learning to read English as a second language (ESL) were examined for their relative reliance on phonological and orthographic processing in English word identification: Korean students with an alphabetic L1 literacy background, and Chinese students with a (...) nonalphabetic L1 literacy background. In a semantic category judgment task, Korean ESL learners made more false positive errors in judging stimuli that were homophones to category exemplars than they did in judging spelling controls. However, there were no significant differences in responses to stimuli in these two conditions for Chinese ESL learners. Chinese ESL learners, on the other hand, made more accurate responses to stimuli that were less similar in spelling to category exemplars than those that were more similar. Chinese ESL learners may rely less on phonological information and more on orthographic information in identifying English words than their Korean counterparts. Further evidence supporting this argument came from a phoneme deletion task in which Chinese subjects performed more poorly overall than their Korean counterparts and made more errors that were phonologically incorrect but orthographically acceptable. We suggest that cross-writing system differences in L1s and L1 reading skills transfer could be responsible for these ESL performance differences. (shrink)

A growing body of research suggests that children with autism spectrum disorder are at risk of reading and learning difficulties. However, there is mixed evidence on their weaknesses in different reading components, and little is known about how reading skills characterize in ASD. Thereby, the current study aimed to systematically review the research investigating this function in children with ASD. To this purpose, we reviewed 24 studies that compared children with ASD and children with typical development in word and nonword (...) reading performance, children with ASD and normative data of word and nonword reading tests, and the results obtained by children with ASD in word and nonword reading tests. Most of the comparisons contrasting the reading performance of children with ASD and children with TD did not find significant differences between groups in both word and nonword reading. However, all the comparisons that reported standardized results showed that children with ASD had scores that fell within population norms. Regarding the third comparison of interest, about 54% of the studies presented data for both word and nonword reading, but only one study tested the difference between them and showed that children with ASD had higher levels of word than of nonword reading. Despite these results, the heterogeneous and small samples do not allow to draw sound conclusions regarding the strategies that children with ASD use to read words. As consequence, the nature of reading difficulties presented by children with ASD are still unknown, requiring future research conducted with larger and well-characterized samples of ASD and TD, using homogeneous specific tasks designed to assess word reading strategies. (shrink)

It is often assumed that similar domain-specific behavioural impairments found in cases of adult brain damage and developmental disorders correspond to similar underlying causes, and can serve as convergent evidence for the modular structure of the normal adult cognitive system. We argue that this correspondence is contingent on an unsupported assumption that atypical development can produce selective deficits while the rest of the system develops normally (Residual Normality), and that this assumption tends to bias data collection in the field. Based (...) on a review of connectionist models of acquired and developmental disorders in the domains of reading and past tense, as well as on new simulations, we explore the computational viability of Residual Normality and the potential role of development in producing behavioural deficits. Simulations demonstrate that damage to a developmental model can produce very different effects depending on whether it occurs prior to or following the training process. Because developmental disorders typically involve damage prior to learning, we conclude that the developmental process is a key component of the explanation of endstate impairments in such disorders. Further simulations demonstrate that in simple connectionist learning systems, the assumption of Residual Normality is undermined by processes of compensation or alteration elsewhere in the system. We outline the precise computational conditions required for Residual Normality to hold in development, and suggest that in many cases it is an unlikely hypothesis. We conclude that in developmental disorders, inferences from behavioural deficits to underlying structure crucially depend on developmental conditions, and that the process of ontogenetic development cannot be ignored in constructing models of developmental disorders. Key Words: Acquired and developmental disorders; connectionist models; modularity; past tense; reading. (shrink)

Human participants and recurrent (“connectionist”) neural networks were both trained on a categorization system abstractly similar to natural language systems involving irregular (“strong”) classes and a default class. Both the humans and the networks exhibited staged learning and a generalization pattern reminiscent of the Elsewhere Condition (Kiparsky, 1973). Previous connectionist accounts of related phenomena have often been vague about the nature of the networks’ encoding systems. We analyzed our network using dynamical systems theory, revealing topological and geometric properties that can (...) be directly compared with the mechanisms of non-connectionist, rule-based accounts. The results reveal that the networks “contain” structures related to mechanisms posited by rule-based models, partly vindicating the insights of these models. On the other hand, they support the one mechanism (OM), as opposed to the more than one mechanism (MOM), view of symbolic abstraction by showing how the appearance of MOM behavior can arise emergently from one underlying set of principles. The key new contribution of this study is to show that dynamical systems theory can allow us to explicitly characterize the relationship between the two perspectives in implemented models. (shrink)

The recognition of taboo words – i.e. socially inappropriate words – has been repeatedly associated to semantic interference phenomena, with detrimental effects on the performance in the ongoing task. In the present study, we investigated taboo interference in the context of reading aloud, a task configuration which prompts the overt violation of conventional sociolinguistic norms by requiring the explicit utterance of taboo items. We assessed whether this form of semantic interference is handled by habituative or cognitive control processes. In addition (...) to the reading aloud task, participants performed a vocal Stroop task featuring different conditions to dissociate semantic, task, and response conflict. Taboo words were read slower than non-taboo words, but this effect was subject to a quick habituation, with a decreasing interference over the course of trials, which allowed participants to selectively attend to goal-relevant information. In the Stroop task, only semantic conflict was significantly reduced by habituation. These findings suggest that semantic properties can be quickly and flexibly weighed on the basis of contextual appropriateness, thus characterising semantic processing as a flexible and goal-directed component of reading aloud. (shrink)

The forms of words as they appear in text and speech are central to theories and models of lexical processing. Nonetheless, current methods for simulating their learning and representation fail to approach the scale and heterogeneity of real wordform lexicons. A connectionist architecture termed thesequence encoderis used to learn nearly 75,000 wordform representations through exposure to strings of stress‐marked phonemes or letters. First, the mechanisms and efficacy of the sequence encoder are demonstrated and shown to overcome problems with traditional slot‐based (...) codes. Then, two large‐scale simulations are reported that learned to represent lexicons of either phonological or orthographic wordforms. In doing so, the models learned the statistics of their lexicons as shown by better processing of well‐formed pseudowords as opposed to ill‐formed (scrambled) pseudowords, and by accounting for variance in well‐formedness ratings. It is discussed how the sequence encoder may be integrated into broader models of lexical processing. (shrink)

The forms of words as they appear in text and speech are central to theories and models of lexical processing. Nonetheless, current methods for simulating their learning and representation fail to approach the scale and heterogeneity of real wordform lexicons. A connectionist architecture termed thesequence encoderis used to learn nearly 75,000 wordform representations through exposure to strings of stress‐marked phonemes or letters. First, the mechanisms and efficacy of the sequence encoder are demonstrated and shown to overcome problems with traditional slot‐based (...) codes. Then, two large‐scale simulations are reported that learned to represent lexicons of either phonological or orthographic wordforms. In doing so, the models learned the statistics of their lexicons as shown by better processing of well‐formed pseudowords as opposed to ill‐formed (scrambled) pseudowords, and by accounting for variance in well‐formedness ratings. It is discussed how the sequence encoder may be integrated into broader models of lexical processing. (shrink)

Based on evidence that learning new characters through handwriting leads to better recognition than learning through typing, some authors proposed that the graphic motor plans acquired through handwriting contribute to recognition. More recently two alternative explanations have been put forward. First, the advantage of handwriting could be due to the perceptual variability that it provides during learning. Second, a recent study suggests that detailed visual analysis might be the source of the advantage of handwriting over typing. Indeed, in that study, (...) handwriting and composition –a method requiring a detailed visual analysis but no specific graphomotor activity– led to equivalent recognition accuracy, both higher than typing. The aim of the present study was to assess whether the contribution of detailed visual analysis is observed in preschool children and to test the variability hypothesis. To that purpose, three groups of preschool children learned new symbols either by handwriting, typing, or composition. After learning, children performed first a four-alternative recognition task and then a categorization task. The same pattern of results as the one observed in adults emerged in the four-alternative recognition task, confirming the importance of the detailed visual analysis in letter-like shape learning. In addition, results failed to reveal any difference across learning methods in the categorization task. The latter results provide no evidence for the variability hypothesis which would predict better categorization after handwriting than after typing or composition. (shrink)

This paper introduces a special issue of Cognitive Science initiated on the 25th anniversary of the publication of Parallel Distributed Processing (PDP), a two-volume work that introduced the use of neural network models as vehicles for understanding cognition. The collection surveys the core commitments of the PDP framework, the key issues the framework has addressed, and the debates the framework has spawned, and presents viewpoints on the current status of these issues. The articles focus on both historical roots and contemporary (...) developments in learning, optimality theory, perception, memory, language, conceptual knowledge, cognitive control, and consciousness. Here we consider the approach more generally, reviewing the original motivations, the resulting framework, and the central tenets of the underlying theory. We then evaluate the impact of PDP both on the field at large and within specific subdomains of cognitive science and consider the current role of PDP models within the broader landscape of contemporary theoretical frameworks in cognitive science. Looking to the future, we consider the implications for cognitive science of the recent success of machine learning systems called “deep networks”—systems that build on key ideas presented in the PDP volumes. (shrink)

We discuss the development of cognitive neuroscience in terms of the tension between the greater sophistication in cognitive concepts and methods of the cognitive sciences and the increasing power of more standard biological approaches to understanding brain structure and function. There have been major technological developments in brain imaging and advances in simulation, but there have also been shifts in emphasis, with topics such as thinking, consciousness, and social cognition becoming fashionable within the brain sciences. The discipline has great promise (...) in terms of applications to mental health and education, provided it does not abandon the cognitive perspective and succumb to reductionism. (shrink)

The self‐teaching hypothesis describes how children progress toward skilled sight‐word reading. It proposes that children do this via phonological recoding with assistance from contextual cues, to identify the target pronunciation for a novel letter string, and in so doing create an opportunity to self‐teach new orthographic knowledge. We present a new computational implementation of self‐teaching within the dual‐route cascaded (DRC) model of reading aloud, and we explore how decoding and contextual cues can work together to enable accurate self‐teaching under a (...) variety of circumstances. The new model (ST‐DRC) uses DRC’s sublexical route and the interactivity between the lexical and sublexical routes to simulate phonological recoding. Known spoken words are activated in response to novel printed words, triggering an opportunity for orthographic learning, which is the basis for skilled sight‐word reading. ST‐DRC also includes new computational mechanisms for simulating how contextual information aids word identification, and it demonstrates how partial decoding and ambiguous context interact to achieve irregular‐word learning. Beyond modeling orthographic learning and self‐teaching, ST‐DRC’s performance suggests new avenues for empirical research on how difficult word classes such as homographs and potentiophones are learned. (shrink)