Cognitive network science: A review of research on cognition through the lens of network representations, processes, and dynamics

10.1155/2019/2108423Complexity2019210842

Understanding Semantic Implicit Learning through distributional linguistic patterns: A computational perspective

The research presented in this PhD dissertation provides a computational perspective on Semantic Implicit Learning (SIL). It puts forward the idea that SIL does not depend on semantic knowledge as classically conceived but upon semantic-like knowledge gained through distributional analysis of massive linguistic input. Using methods borrowed from the machine learning and artificial intelligence literature, we construct computational models, which can simulate the performance observed during behavioural tasks of semantic implicit learning in a human-like way. We link this methodology to the current literature on implicit learning, arguing that this behaviour is a necessary by-product of efficient language processing. Chapter 1 introduces the computational problem posed by implicit learning in general, and semantic implicit learning, in particular, as well as the computational framework, used to tackle them. Chapter 2 introduces distributional semantics models as a way to learn semantic-like representations from exposure to linguistic input. Chapter 3 reports two studies on large datasets of semantic priming which seek to identify the computational model of semantic knowledge that best fits the data under conditions that resemble SIL tasks. We find that a model which acquires semantic-like knowledge gained through distributional analysis of massive linguistic input provides the best fit to the data. Chapter 4 generalises the results of the previous two studies by looking at the performance of the same models in languages other than English. Chapter 5 applies the results of the two previous Chapters on eight datasets of semantic implicit learning. Crucially, these datasets use various semantic manipulations and speakers of different L1s enabling us to test the predictions of different models of semantics. Chapter 6 examines more closely two assumptions which we have taken for granted throughout this thesis. Firstly, we test whether a simpler model based on phonological information can explain the generalisation patterns observed in the tasks. Secondly, we examine whether our definition of the computational problem in Chapter 5 is reasonable. Chapter 7 summarises and discusses the implications for implicit language learning and computational models of cognition. Furthermore, we offer one more study that seeks to bridge the literature on distributional models of semantics to `deeper' models of semantics by learning semantic relations. There are two main contributions of this dissertation to the general field of implicit learning research. Firstly, we highlight the superiority of distributional models of semantics in modelling unconscious semantic knowledge. Secondly, we question whether `deep' semantic knowledge is needed to achieve above chance performance in SIIL tasks. We show how a simple model that learns through distributional analysis of the patterns found in the linguistic input can match the behavioural results in different languages. Furthermore, we link these models to more general problems faced in psycholinguistics such as language processing and learning of semantic relations.Alexandros Onassis Foundatio

The interaction of central and peripheral processing in L2 handwritten production : evidence from cross-linguistic variations

The current study explores the interplay between central and peripheral processes in second language (L2) handwriting among bilinguals with diverse orthographic backgrounds. Our investigation delves into the cross-linguistic transfer effect in Spanish–English and Chinese–English bilinguals, emphasizing lexical frequency and phoneme-grapheme (P-O) consistency in spelling-to-dictation and immediate copying tasks. Results reveal that the interaction between central and peripheral processes in L2 handwritten production is shaped by the bilinguals' native language (L1) orthographic characteristics. Spanish–English bilinguals exhibited sensitivity to P-O consistency and the spread of this effect from central to peripheral processes throughout both tasks. Conversely, Chinese–English bilinguals showed heightened sensitivity to lexical frequency during orthographic planning and motor execution, particularly in the immediate copying task. In a broader context, these findings suggest that the parallel and cascading coordination of the L2 writing system is modulated by cross-linguistic variations. The implications of our findings hold relevance for handwriting production and bilingualism research

Statistical Knowledge and Learning in Phonology

This thesis deals with the theory of the phonetic component of grammar in a formal probabilistic inference framework: (1) it has been recognized since the beginning of generative phonology that some language-specific phonetic implementation is actually context-dependent, and thus it can be said that there are gradient "phonetic processes" in grammar in addition to categorical "phonological processes." However, no explicit theory has been developed to characterize these processes. Meanwhile, (2) it is understood that language acquisition and perception are both really informed guesswork: the result of both types of inference can be reasonably thought to be a less-than-perfect committment, with multiple candidate grammars or parses considered and each associated with some degree of credence. Previous research has used probability theory to formalize these inferences in implemented computational models, especially in phonetics and phonology. In this role, computational models serve to demonstrate the existence of working learning/per- ception/parsing systems assuming a faithful implementation of one particular theory of human language, and are not intended to adjudicate whether that theory is correct. The current thesis (1) develops a theory of the phonetic component of grammar and how it relates to the greater phonological system and (2) uses a formal Bayesian treatment of learning to evaluate this theory of the phonological architecture and for making predictions about how the resulting grammars will be organized. The coarse description of the consequence for linguistic theory is that the processes we think of as "allophonic" are actually language-specific, gradient phonetic processes, assigned to the phonetic component of grammar; strict allophones have no representation in the output of the categorical phonological grammar

Vector Semantics

This open access book introduces Vector semantics, which links the formal theory of word vectors to the cognitive theory of linguistics. The computational linguists and deep learning researchers who developed word vectors have relied primarily on the ever-increasing availability of large corpora and of computers with highly parallel GPU and TPU compute engines, and their focus is with endowing computers with natural language capabilities for practical applications such as machine translation or question answering. Cognitive linguists investigate natural language from the perspective of human cognition, the relation between language and thought, and questions about conceptual universals, relying primarily on in-depth investigation of language in use. In spite of the fact that these two schools both have ‘linguistics’ in their name, so far there has been very limited communication between them, as their historical origins, data collection methods, and conceptual apparatuses are quite different. Vector semantics bridges the gap by presenting a formal theory, cast in terms of linear polytopes, that generalizes both word vectors and conceptual structures, by treating each dictionary definition as an equation, and the entire lexicon as a set of equations mutually constraining all meanings

¿Qué es la fonología computacional?

Computational phonology is not one thing. Rather, it is an umbrella term which may refer to work on formal language theory, computer-implemented models of cognitive processes, and corpus methods derived from the literature on natural language processing (NLP). This article gives an overview of these distinct areas, identifying commonalities and differences in the goals of each area, as well as highlighting recent results of interest. The overview is necessarily brief and subjective. Broadly speaking, it is argued that learning is a pervasive theme in these areas, but the core questions and concerns vary too much to define a coherent field. Computational phonologists are more united by a shared body of formal knowledge than they are by a shared sense of what the important questions are.La fonología computacional no representa un campo unitario, sino que es un término genérico que puede hacer referencia a obras sobre teorías de lenguajes formales; a modelos de procesos cognitivos implementados por ordenador; y a métodos de trabajo con corpus, derivados de la bibliografía sobre procesamiento del lenguaje natural (PLN). Este artículo ofrece una visión de conjunto de estas distintas áreas, identifica los puntos comunes y las diferencias en los objetivos de cada una, y pone de relieve algunos de los últimos resultados más relevantes. Esta visión de conjunto es necesariamente breve y subjetiva. En términos generales, se argumenta que el aprendizaje es un tema recurrente en estos ámbitos, pero las preguntas y los problemas centrales varían demasiado como para definir un área de estudio unitaria y coherente. Los fonólogos computacionales están unidos por un cúmulo común de conocimientos formales más que por un parecer compartido acerca de cuáles son las preguntas importantes

The role of phonology in visual word recognition: evidence from Chinese

Posters - Letter/Word Processing V: abstract no. 5024The hypothesis of bidirectional coupling of orthography and phonology predicts that phonology plays a role in visual word recognition, as observed in the effects of feedforward and feedback spelling to sound consistency on lexical decision. However, because orthography and phonology are closely related in alphabetic languages (homophones in alphabetic languages are usually orthographically similar), it is difficult to exclude an influence of orthography on phonological effects in visual word recognition. Chinese languages contain many written homophones that are orthographically dissimilar, allowing a test of the claim that phonological effects can be independent of orthographic similarity. We report a study of visual word recognition in Chinese based on a mega-analysis of lexical decision performance with 500 characters. The results from multiple regression analyses, after controlling for orthographic frequency, stroke number, and radical frequency, showed main effects of feedforward and feedback consistency, as well as interactions between these variables and phonological frequency and number of homophones. Implications of these results for resonance models of visual word recognition are discussed.postprin