Computational explorations of semantic cognition

Motivated by the widespread use of distributional models of semantics within the cognitive science community, we follow a computational modelling approach in order to better understand and expand the applicability of such models, as well as to test potential ways in which they can be improved and extended. We review evidence in favour of the assumption that distributional models capture important aspects of semantic cognition. We look at the models’ ability to account for behavioural data and fMRI patterns of brain activity, and investigate the structure of model-based, semantic networks. We test whether introducing affective information, obtained from a neural network model designed to predict emojis from co-occurring text, can improve the performance of linguistic and linguistic-visual models of semantics, in accounting for similarity/relatedness ratings. We find that adding visual and affective representations improves performance, especially for concrete and abstract words, respectively. We describe a processing model based on distributional semantics, in which activation spreads throughout a semantic network, as dictated by the patterns of semantic similarity between words. We show that the activation profile of the network, measured at various time points, can account for response time and accuracies in lexical and semantic decision tasks, as well as for concreteness/imageability and similarity/relatedness ratings. We evaluate the differences between concrete and abstract words, in terms of the structure of the semantic networks derived from distributional models of semantics. We examine how the structure is related to a number of factors that have been argued to differ between concrete and abstract words, namely imageability, age of acquisition, hedonic valence, contextual diversity, and semantic diversity. We use distributional models to explore factors that might be responsible for the poor linguistic performance of children suffering from Developmental Language Disorder. Based on the assumption that certain model parameters can be given a psychological interpretation, we start from “healthy” models, and generate “lesioned” models, by manipulating the parameters. This allows us to determine the importance of each factor, and their effects with respect to learning concrete vs abstract words

Combining Language Corpora With Experimental and Computational Approaches for Language Acquisition Research

Historically, first language acquisition research was a painstaking process of observation, requiring the laborious hand coding of children's linguistic productions, followed by the generation of abstract theoretical proposals for how the developmental process unfolds. Recently, the ability to collect large-scale corpora of children's language exposure has revolutionized the field. New techniques enable more precise measurements of children's actual language input, and these corpora constrain computational and cognitive theories of language development, which can then generate predictions about learning behavior. We describe several instances where corpus, computational, and experimental work have been productively combined to uncover the first language acquisition process and the richness of multimodal properties of the environment, highlighting how these methods can be extended to address related issues in second language research. Finally, we outline some of the difficulties that can be encountered when applying multimethod approaches and show how these difficulties can be obviated

Sequencing in SLA: Phonological Memory, Chunking and Points of Order.

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/139863/1/Ellis1996Chunking.pd

The role of language and sensorimotor information in memory for concepts

The linguistic-simulation approach to conceptual representations has been investigated for some time, but the role of language and sensorimotor information in memory for objects and words, both short- and long-term, has not been examined in detail. In the present thesis, I look at the interplay of sensorimotor and linguistic information in conceptual knowledge and examine which aspects of concepts are represented in memory tasks. I also aim to establish the role of consciously accessing conceptual information in word recognition and memory. The thesis includes three self-contained papers which show that the conceptual system relies on linguistic or sensorimotor information according to task demands. In the paper in Chapter 4, I examined the linguistic bootstrapping hypothesis, which postulates that linguistic labels can serve as placeholders for complex sensorimotor representations. I tested the capacity of working memory for object concepts using an articulatory suppression task to block access to language. I found that working memory capacity for contextually related object concepts when relying on sensorimotor information is higher than the traditionally reported capacity of 3-4 for simple shapes or colours. Additionally, when linguistic labels are available to deputise for complex sensorimotor information, the capacity further increases by up to two object concepts. In Chapters 5 and 6, I examined the content of conceptual information stored in long-term memory, and the role of sensorimotor simulation and consciously available information in word recognition and word memory. The studies revealed that consciously generated imagery is not reliably measured, and moreover, it does not contribute to word recognition in a consistent manner. Some of the effects of imageability found in the literature can be explained or subsumed by sensorimotor information, which is not fully available through conscious awareness. However, conscious imagery may be a useful strategy to support word memory when trying to explicitly remember words. The thesis demonstrates that both linguistic labels and sensorimotor information contribute to memory for concepts. The way a concept is represented in different tasks varies depending on task demands. Linguistic information is used to circumvent resource capacity limits, while sensorimotor information guides behaviour by providing more detailed information about the meaning of concepts, and our previous experience with them

Word Knowledge and Word Usage

Word storage and processing define a multi-factorial domain of scientific inquiry whose thorough investigation goes well beyond the boundaries of traditional disciplinary taxonomies, to require synergic integration of a wide range of methods, techniques and empirical and experimental findings. The present book intends to approach a few central issues concerning the organization, structure and functioning of the Mental Lexicon, by asking domain experts to look at common, central topics from complementary standpoints, and discuss the advantages of developing converging perspectives. The book will explore the connections between computational and algorithmic models of the mental lexicon, word frequency distributions and information theoretical measures of word families, statistical correlations across psycho-linguistic and cognitive evidence, principles of machine learning and integrative brain models of word storage and processing. Main goal of the book will be to map out the landscape of future research in this area, to foster the development of interdisciplinary curricula and help single-domain specialists understand and address issues and questions as they are raised in other disciplines

Word association research and the L2 lexicon

Since its modern inception in the late nineteenth century, research on word associations has developed into a large and diverse area of study, including work with both applied linguistic and psycholinguistic orientations. However, despite significant recent interest in the use of word association to investigate second language (L2) vocabulary knowledge and testing, there has until now been no systematic attempt to review the wider word association research tradition for the benefit of second language-oriented researchers and practitioners. This paper seeks to address this, drawing together linguistic research from the past 150 years, with a focus on research published since 2000. We evaluate the current state of L2 word association research, before identifying methodological and theoretical themes from a broader range of disciplinary approaches. Emerging from this, new paradigms are identified which have potential to catalyse a new phase of work for second-language word association scholars, and which indicate priority foci for future work

The Evolution of Language Universals: Optimal Design and Adaptation

Inquiry into the evolution of syntactic universals is hampered by severe limitations on the available evidence. Theories of selective function nevertheless lead to predictions of local optimaliiy that can be tested scientifically. This thesis refines a diagnostic, originally proposed by Parker and Maynard Smith (1990), for identifying selective functions on this basis and applies it to the evolution of two syntactic universals: (I) the distinction between open and closed lexical classes, and (2) nested constituent structure. In the case of the former, it is argued that the selective role of the closed class items is primarily to minimise the amount of redundancy in the lexicon. In the case of the latter, the emergence of nested phrase structure is argued to have been a by-product of selection for the ability to perform insertion operations on sequences - a function that plausibly pre-dated the emergence of modem language competence. The evidence for these claims is not just that these properties perform plausibly fitness-related functions, but that they appear to perform them in a way that is improbably optimal. A number of interesting findings follow when examining the selective role of the closed classes. In particular, case, agreement and the requirement that sentences have subjects are expected consequences of an optimised lexicon, the theory thereby relating these properties to natural selection for the first time. It also motivates the view that language variation is confined to parameters associated with closed class items, in turn explaining why parameter confiicts fail to arise in bilingualism. The simplest representation of sequences that is optimised for efficient insertions can represent both nested constituent structure and long-distance dependencies in a unified way, thus suggesting that movement is intrinsic to the representation of constituency rather than an 'imperfection'. The basic structure of phrases also follows from this representation and helps to explain the interaction between case and theta assignment. These findings bring together a surprising array of phenomena, reinforcing its correctness as the representational basis of syntactic structures. The diagnostic overcomes shortcomings in the approach of Pinker and Bloom (1990), who argued that the appearance of 'adaptive complexity' in the design of a trait could be used as evidence of its selective function, but there is no reason to expect the refinements of natural selection to increase complexity in any given case. Optimality considerations are also applied in this thesis to filter theories of the nature of unobserved linguistic representations as well as theories of their functions. In this context, it is argued that, despite Chomsky's (1995) resistance to the idea, it is possible to motivate the guiding principles of the Minimalist Program in terms of evolutionary optimisation, especially if we allow the possibility that properties of language were selected for non-communicative functions and that redundancy is sometimes costly rather than beneficial

Statistical language learning

Theoretical arguments based on the "poverty of the stimulus" have denied a priori the possibility that abstract linguistic representations can be learned inductively from exposure to the environment, given that the linguistic input available to the child is both underdetermined and degenerate. I reassess such learnability arguments by exploring a) the type and amount of statistical information implicitly available in the input in the form of distributional and phonological cues; b) psychologically plausible inductive mechanisms for constraining the search space; c) the nature of linguistic representations, algebraic or statistical. To do so I use three methodologies: experimental procedures, linguistic analyses based on large corpora of naturally occurring speech and text, and computational models implemented in computer simulations. In Chapters 1,2, and 5, I argue that long-distance structural dependencies - traditionally hard to explain with simple distributional analyses based on ngram statistics - can indeed be learned associatively provided the amount of intervening material is highly variable or invariant (the Variability effect). In Chapter 3, I show that simple associative mechanisms instantiated in Simple Recurrent Networks can replicate the experimental findings under the same conditions of variability. Chapter 4 presents successes and limits of such results across perceptual modalities (visual vs. auditory) and perceptual presentation (temporal vs. sequential), as well as the impact of long and short training procedures. In Chapter 5, I show that generalisation to abstract categories from stimuli framed in non-adjacent dependencies is also modulated by the Variability effect. In Chapter 6, I show that the putative separation of algebraic and statistical styles of computation based on successful speech segmentation versus unsuccessful generalisation experiments (as published in a recent Science paper) is premature and is the effect of a preference for phonological properties of the input. In chapter 7 computer simulations of learning irregular constructions suggest that it is possible to learn from positive evidence alone, despite Gold's celebrated arguments on the unlearnability of natural languages. Evolutionary simulations in Chapter 8 show that irregularities in natural languages can emerge from full regularity and remain stable across generations of simulated agents. In Chapter 9 I conclude that the brain may endowed with a powerful statistical device for detecting structure, generalising, segmenting speech, and recovering from overgeneralisations. The experimental and computational evidence gathered here suggests that statistical language learning is more powerful than heretofore acknowledged by the current literature