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We present a self-organizing approach to sentence processing that sheds new light on notional plurality effects in agreement attraction, using pseudopartitive subject noun phrases (e.g., "a bottle of pills"). We first show that notional plurality ratings (numerosity judgments for subject noun phrases) predict verb agreement choices in pseudopartitives, in line with the "Marking" component of the Marking and Morphing theory of agreement processing. However, no account to date has derived notional plurality values from independently needed principles of language processing. We argue on the basis of new experimental evidence and a dynamical systems model that the theoretical black box of notional plurality can be unpacked into objectively measurable semantic features. With these semantic features driving structure formation (and hence agreement choice), our model reproduces the human verb production patterns as a byproduct of normal processing. Finally, we discuss how the self-organizing approach might be extended to other agreement attraction phenomena.

Gough and Tunmer's (1986) simple view of reading (SVR) proposed that reading comprehension (RC) is a function of language comprehension (LC) and word recognition/decoding. Braze et al. (2007) presented data suggesting an extension of the SVR in which knowledge of vocabulary (V) affected RC over and above the effects of LC. Tunmer and Chapman (2012) found a similar independent contribution of V to RC when the data were analyzed by hierarchical regression. However, additional analysis by factor analysis and structural equation modeling indicated that the effect of V on RC was, in fact, completely captured by LC itself and there was no need to posit a separate direct effect of V on RC. In the present study, we present new data from young adults with sub-optimal reading skill (N = 286). Latent variable and regression analyses support Gough and Tunmer's original proposal and the conclusions of Tunmer and Chapman that V can be considered a component of LC and not an independent contributor to RC.

Psycholinguistic research spanning a number of decades has produced diverging results with regard to the nature of constraint integration in online sentence processing. For example, evidence that language users anticipatorily fixate likely upcoming referents in advance of evidence in the speech signal supports rapid context integration. By contrast, evidence that language users activate representations that conflict with contextual constraints, or only indirectly satisfy them, supports nonintegration or late integration. Here we report on a self-organizing neural network framework that addresses 1 aspect of constraint integration: the integration of incoming lexical information (i.e., an incoming word) with sentence context information (i.e., from preceding words in an unfolding utterance). In 2 simulations, we show that the framework predicts both classic results concerned with lexical ambiguity resolution (Swinney, 1979; Tanenhaus, Leiman, & Seidenberg, 1979), which suggest late context integration, and results demonstrating anticipatory eye movements (e.g., Altmann & Kamide, 1999), which support rapid context integration. We also report 2 experiments using the visual world paradigm that confirm a new prediction of the framework. Listeners heard sentences like "The boy will eat the white …" while viewing visual displays with objects like a "white cake" (i.e., a predictable direct object of "eat"), "white car" (i.e., an object not predicted by "eat," but consistent with "white"), and distractors. In line with our simulation predictions, we found that while listeners fixated "white cake" most, they also fixated "white car" more than unrelated distractors in this highly constraining sentence (and visual) context.

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.

This study is part of a broader project aimed at developing cognitive and neurocognitive profiles of adolescent and young adult readers whose educational and occupational prospects are constrained by their limited literacy skills. We explore the relationships among reading-related abilities in participants ages 16 to 24 years spanning a wide range of reading ability. Two specific questions are addressed: (a) Does the simple view of reading capture all nonrandom variation in reading comprehension? (b) Does orally assessed vocabulary knowledge account for variance in reading comprehension, as predicted by the lexical quality hypothesis? A comprehensive battery of cognitive and educational tests was employed to assess phonological awareness, decoding, verbal working memory, listening comprehension, reading comprehension, word knowledge, and experience with print. In this heterogeneous sample, decoding ability clearly played an important role in reading comprehension. The simple view of reading gave a reasonable fit to the data, although it did not capture all of the reliable variance in reading comprehension as predicted. Orally assessed vocabulary knowledge captured unique variance in reading comprehension even after listening comprehension and decoding skill were accounted for. We explore how a specific connectionist model of lexical representation and lexical access can account for these findings.

A central question for psycholinguistics concerns the role of grammatical constraints in online sentence processing. Many current theories maintain that the language processing mechanism constructs a parse or parses that are grammatically consistent with the whole of the perceived input each time it processes a word. Several bottom-up, dynamical models make a contrasting prediction: partial parses which are syntactically compatible with only a proper subpart of the input are sometimes constructed, at least temporarily. Three self-paced reading experiments probed for interference from such locally coherent structures. The first tested for a distracting effect of irrelevant Subject-Predicate interpretations of Noun Phrase-Verb Phrase sequences (e.g., "The coach smiled at the player tossed a frisbee") on reading times. The second addressed the question of whether the interference effects can be treated as lexical interference, instead of involving the formation of locally coherent syntactic structures. The third replicated the reading time effects of the first two experiments with grammaticality judgments. We evaluate the dynamical account, comparing it to other approaches that also predict effects of local coherence, and arguing against accounts which rule out the formation of merely locally coherent structures.

Dynamical, self-organizing models of sentence processing predict "digging-in" effects: The more committed the parser becomes to a wrong syntactic choice, the harder it is to reanalyze. Experiment 1 replicates previous grammaticality judgment studies (F. Ferreira & J. M. Henderson, 1991b, 1993), revealing a deleterious effect of lengthening the ambiguous region of a garden-path sentence. The authors interpret this result as a digging-in effect. Experiment 2 finds a corresponding effect on reading times. Experiment 3 finds that making 2 wrong attachments is worse than making 1. Non-self-organizing models require multiple stipulations to predict both kinds of effects. The authors show that, under an appropriately formulated self-organizing account, both results stem from self-reinforcement of node and link activations, a feature that is needed independently. An implemented model is given.

Proposes a dynamical systems approach to parsing in which syntactic hypotheses are associated with attractors in a metric space. The experiments discussed documented various contingent frequency effects that cut across traditional linguistic grains, each of which was predicted by the dynamical systems model. (47 references) (Author/CK)

The relationship between form and meaning is central to the theory of language. Traditionally, syntax and semantics are viewed as two different levels of representation. Based on insights from the intersection of dynamical systems theory and the theory of computation, and guided by linguistic data, I argue that there is only one space, a syntacticsemantic one. I model it here as a stable, countably infinite attractor of an iterated map dynamical system. One advantage of this approach is that it supports a unified treatment of grammatical and ungrammatical processing. [ABSTRACT FROM AUTHOR]

Training recurrent neural networks on infinite state languages has been successful with languages in which the minimal number of machine states grows linearly with sentence length, but has faired poorly with exponential state-growth languages. A new architecture learns several exponential state-growth languages nearly perfectly by hill climbing.

This exploratory study investigated relations between individual differences in cortical grey matter structure and young adult readers’ cognitive profiles. Whole-brain analyses revealed neuroanatomical correlations with word and nonword reading ability (decoding), and experience with printed matter. Decoding was positively correlated with grey matter volume (GMV) in left superior temporal sulcus, and thickness (GMT) in right superior temporal gyrus. Print exposure was negatively correlated with GMT in left inferior frontal gyrus (pars opercularis) and left fusiform gyrus (including the visual word form area). Both measures also correlated with supramarginal gyrus (SMG), but in spatially distinct subregions: decoding was positively associated with GMV in left anterior SMG, and print exposure was negatively associated with GMT in left posterior SMG. Our comprehensive approach to assessment both confirms and refines our understanding of the novel relation between the structure of pSMG and proficient reading, and unifies previous research relating cortical structure and reading skill. [ABSTRACT FROM AUTHOR]

This paper analyses a three-layer connectionist network that solves a translation-invariance problem, offering a novel explanation for transposed letter effects in word reading. Analysis of the hidden unit encodings provides insight into two central issues in cognitive science: (1) What is the novelty of claims of “modality-specific” encodings? and (2) How can a learning system establish a complex internal structure needed to solve a problem? Although these topics (embodied cognition and learnability) are often treated separately, we find a close relationship between them: modality-specific features help the network discover an abstract encoding by causing it to break the initial symmetries of the hidden units in an effective way. While this neural model is extremely simple compared to the human brain, our results suggest that neural networks need not be black boxes and that carefully examining their encoding behaviours may reveal how they differ from classical ideas about the mind-world relationship. [ABSTRACT FROM AUTHOR]

Long-distance verb-argument dependencies generally require the integration of a fronted argument when the verb is encountered for sentence interpretation. Under a parsing model that handles long-distance dependencies through a cue-based retrieval mechanism, retrieval is hampered when retrieval cues also resonate with non-target elements (retrieval interference). However, similarity-based interference may also stem from interference arising during the encoding of elements in memory (encoding interference), an effect that is not directly accountable for by a cue-based retrieval mechanism. Although encoding and retrieval interference are clearly distinct at the theoretical level, it is difficult to disentangle the two on empirical grounds, since encoding interference may also manifest at the retrieval region. We report two self-paced reading experiments aimed at teasing apart the role of each component in gender and number subject-verb agreement in Italian and English object relative clauses. In Italian, the verb does not agree in gender with the subject, thus providing no cue for retrieval. In English, although present tense verbs agree in number with the subject, past tense verbs do not, allowing us to test the role of number as a retrieval cue within the same language. Results from both experiments converge, showing similarity-based interference at encoding, and some evidence for an effect at retrieval. After having pointed out the non-negligible role of encoding in sentence comprehension, and noting that Lewis and Vasishth's (2005) ACT-R model of sentence processing, the most fully developed cue-based retrieval approach to sentence processing does not predict encoding effects, we propose an augmentation of this model that predicts these effects. We then also propose a self-organizing sentence processing model (SOSP), which has the advantage of accounting for retrieval and encoding interference with a single mechanism. [ABSTRACT FROM AUTHOR]

This paper aims to analyze the emergence of task differentiation in a model complex system, characterized by an absence of hierarchical control, yet able to exhibit coordinated behavior and collective function. The analysis focuses on linear network filters, i.e., networks of coupled linear oscillators with a differentiated steady-state response to exogenous harmonic excitation. It demonstrates how an optimal allocation of excitation sensitivities across the network nodes in a condition of resonance may be constructed either using global information about the network topology and spectral properties or through the iterated dynamics of a nonlinear, nonsmooth learning paradigm that only relies on local information within the network. Explicit conditions on the topology and desired resonant mode shape are derived to guarantee local asymptotic stability of fixed points of the learning dynamics. The analysis demonstrates the possibly semistable nature of the fixed point with all zero excitation sensitivities, a condition of system collapse that can be reached from an open set of initial conditions but that is unstable under the learning dynamics. Theoretical and numerical results also show the existence of periodic responses, as well as of connecting dynamics between fixed points, resulting in recurrent metastable behavior and noise-induced transitions along cycles of such connections. Structural additions to a core network that conserve desired spectral properties are proposed as a defensive mechanism for fault tolerance and shielding of the core against targeted harm. [ABSTRACT FROM AUTHOR]

Learning is typically understood as a process in which the behavior of an organism is progressively shaped until it closely approximates a target form. It is easy to comprehend how a motor skill or a vocabulary can be progressively learned-in each case, one can conceptualize a series of intermediate steps which lead to the formation of a proFIcient behavior. With grammar, it is more difficult to think in these terms. For example, center embedding recursive structures seem to involve a complex interplay between multiple symbolic rules which have to be in place simultaneously for the system to work at all, so it is not obvious how the mechanism could gradually come into being. Here, we offer empirical evidence from a new artificial language (or "artificial grammar") learning paradigm, Locus Prediction, that, despite the conceptual conundrum, recursion acquisition occurs gradually, at least for a simple formal language. In particular, we focus on a variant of the simplest recursive language, anbn, and find evidence that (i) participants trained on two levels of structure (essentially ab and aabb) generalize to the next higher level (aaabbb) more readily than participants trained on one level of structure (ab) combined with a filler sentence; nevertheless, they do not generalize immediately; (ii) participants trained up to three levels (ab, aabb, aaabbb) generalize more readily to four levels than participants trained on two levels generalize to three; (iii) when we present the levels in succession, starting with the lower levels and including more and more of the higher levels, participants show evidence of transitioning between the levels gradually, exhibiting intermediate patterns of behavior on which they were not trained; (iv) the intermediate patterns of behavior are associated with perturbations of an attractor in the sense of dynamical systems theory. We argue that all of these behaviors indicate a theory ofmental representation in which recursive systems lie on a continuumof grammar systems which are organized so that grammars that produce similar behaviors are near one another, and that people learning a recursive system are navigating progressively through the space of these grammars. [ABSTRACT FROM AUTHOR]

We examine two connectionist networks-a fractal learning neural network (FLNN) and a Simple Recurrent Network (SRN)-that are trained to process center-embedded symbol sequences. Previous work provides evidence that connectionist networks trained on infinite-state languages tend to form fractal encodings. Most such work focuses on simple counting recursion cases (e.g. [ABSTRACT FROM AUTHOR]

The Visual World Paradigm (VWP) presents listeners with a challenging problem: They must integrate two disparate signals, the spoken language and the visual context, in support of action (e.g., complex movements of the eyes across a scene). We present Impulse Processing, a dynamical systems approach to incremental eye movements in the visual world that suggests a framework for integrating language, vision, and action generally. Our approach assumes that impulses driven by the language and the visual context impinge minutely on a dynamical landscape of attractors corresponding to the potential eye-movement behaviors of the system. We test three unique predictions of our approach in an empirical study in the VWP, and describe an implementation in an artificial neural network. We discuss the Impulse Processing framework in relation to other models of the VWP. [ABSTRACT FROM AUTHOR]

Functional magnetic resonance imaging (fMRI) was used to investigate the impact of literacy skills in young adults on the distribution of cerebral activity during comprehension of sentences in spoken and printed form. The aim was to discover where speech and print streams merge, and whether their convergence is affected by the level of reading skill. The results from different analyses all point to the conclusion that neural integration of sentence processing across speech and print varies positively with the reader's skill. Further, they identify the inferior frontal region as the principal site of speech-print integration and a major focus of reading comprehension differences. The findings provide new evidence of the role of the inferior frontal region in supporting supramodal systems of linguistic representation. [ABSTRACT FROM AUTHOR]

Standard generative linguistic theory, which uses discrete symbolic models of cognition, has some strengths and weaknesses. It is strong on providing a network of outposts that make scientific travel in the jungles of natural language feasible. It is weak in that it currently depends on the elaborate and unformalized use of intuition to develop critical supporting assumptions about each data point. In this regard, it is not in a position to characterize natural language systems in the lawful terms that ecological psychologists strive for. Connectionist learning models offer some help: They define lawful relations between linguistic environments and language systems. But our understanding of them is currently weak, especially when it comes to natural language syntax. Fortunately, symbolic linguistic analysis can help connectionism if the two meet via dynamical systems theory. I discuss a case in point: Insights from linguistic explorations of natural language syntax appear to have identified information structures that are particularly relevant to understanding ecologically appealing but analytically mysterious connectionist learning models. [ABSTRACT FROM AUTHOR]

Connectionist network learning of context-free languages has so far been applied only to very simple cases and has often made use of an external stack. Learning complex context-free languages with a homogeneous neural mechanism looks like a much harder problem. The current paper takes a step toward solving this problem by analyzing context-free grammar computation (without addressing learning) in a class of analog computers called dynamical automata, which are naturally implemented in connectionist networks. The result is a widely applicable method of using fractal sets to organize infinite-state computations in a bounded state space. An appealing consequence is the development of parameter-space maps, which locate various complex computers in spatial relationships to one another. An example suggests that such a global perspective on the organization of the parameter space may be helpful for solving the hard problem of getting connectionist networks to learn complex grammars from examples. [ABSTRACT FROM AUTHOR]