Morten H. Christiansen

Toward a Connectionist Model of Recursion in Human Linguistic Performance

Naturally occurring speech contains only a limited amount of complex recursive structure, and this is reflected in the empirically documented difficulties that people experience when processing such structures. We present a connectionist model of human performance in processing recursive language structures. The model is trained on simple artificial languages. We find that the qualitative performance profile of the model matches human behavior, both on the relative difficulty of center-embedding and crossdependency, and between the processing of these complex recursive structures and right-branching recursive constructions. We analyze how these differences in performance are reflected in the internal representations of the model by performing discriminant analyses on these representations both before and after training. Furthermore, we show how a network trained to process recursive structures can also generate such structures in a probabilistic fashion. This work suggests a novel explanation of people’s limited recursive performance, without assuming the existence of a mentally represented competence grammar allowing unbounded recursion.

Learning to segment speech using multiple cues: A connectionist model.

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.

Modality-constrained statistical learning of tactile, visual, and auditory sequences.

The authors investigated the extent to which touch, vision, and audition mediate the processing of statistical regularities within sequential input. Few researchers have conducted rigorous comparisons across sensory modalities; in particular, the sense of touch has been virtually ignored. The current data reveal not only commonalities but also modality constraints affecting statistical learning across the senses. To be specific, the authors found that the auditory modality displayed a quantitative learning advantage compared with vision and touch. In addition, they discovered qualitative learning biases among the senses: Primarily, audition afforded better learning for the final part of input sequences. These findings are discussed in terms of whether statistical learning is likely to consist of a single, unitary mechanism or multiple, modality-constrained ones.

Language evolution: consensus and controversies

Why is language the way it is? How did language come to be this way? And why is our species alone in having complex language? These are old unsolved questions that have seen a renaissance in the dramatic recent growth in research being published on the origins and evolution of human language. This review provides a broad overview of some of the important current work in this area. We highlight new methodologies (such as computational modeling), emerging points of consensus (such as the importance of pre-adaptation), and the major remaining controversies (such as gestural origins of language). We also discuss why language evolution is such a difficult problem, and suggest probable directions research may take in the near future.

Experience and sentence processing: statistical learning and relative clause comprehension.

Many explanations of the difficulties associated with interpreting object relative clauses appeal to the demands that object relatives make on working memory. MacDonald and Christiansen [MacDonald, M. C., & Christiansen, M. H. (2002). Reassessing working memory: Comment on Just and Carpenter (1992) and Waters and Caplan (1996). Psychological Review, 109, 35-54] pointed to variations in reading experience as a source of differences, arguing that the unique word order of object relatives makes their processing more difficult and more sensitive to the effects of previous experience than the processing of subject relatives. This hypothesis was tested in a large-scale study manipulating reading experiences of adults over several weeks. The group receiving relative clause experience increased reading speeds for object relatives more than for subject relatives, whereas a control experience group did not. The reading time data were compared to performance of a computational model given different amounts of experience. The results support claims for experience-based individual differences and an important role for statistical learning in sentence comprehension processes.

Statistical Learning Within and Between Modalities Pitting Abstract Against Stimulus-Specific Representations

When learners encode sequential patterns and generalize their knowledge to novel instances, are they relying on abstract or stimulus-specific representations? Research on artificial grammar learning (AGL) has shown transfer of learning from one stimulus set to another, and such findings have encouraged the view that statistical learning is mediated by abstract representations that are independent of the sense modality or perceptual features of the stimuli. Using a novel modification of the standard AGL paradigm, we obtained data to the contrary. These experiments pitted abstract processing against stimulus-specific learning. The findings show that statistical learning results in knowledge that is stimulus-specific rather than abstract. They show furthermore that learning can proceed in parallel for multiple input streams along separate perceptual dimensions or sense modalities. We conclude that learning sequential structure and generalizing to novel stimuli inherently involve learning mechanisms that are closely tied to the perceptual characteristics of the input.

Sequential learning in non-human primates.

Sequential learning plays a role in a variety of common tasks, such as human language processing, animal communication, and the learning of action sequences. In this article, we investigate sequential learning in non-human primates from a comparative perspective, focusing on three areas: the learning of arbitrary, fixed sequences; statistical learning; and the learning of hierarchical structure. Although primates exhibit many similarities to humans in their performance on sequence learning tasks, there are also important differences. Crucially, non-human primates appear to be limited in their ability to learn and represent the hierarchical structure of sequences. We consider the evolutionary implications of these differences and suggest that limitations in sequential learning may help explain why non-human primates lack human-like language.

Phonological typicality influences on-line sentence comprehension

Since Saussure, the relationship between the sound and the meaning of words has been regarded as largely arbitrary. Here, however, we show that a probabilistic relationship exists between the sound of a word and its lexical category. Corpus analyses of nouns and verbs indicate that the phonological properties of the individual words in these two lexical categories form relatively separate and coherent clusters, with some nouns sounding more typical of the noun category than others and likewise for verbs. Additional analyses reveal that the phonological properties of nouns and verbs affect lexical access, and we also demonstrate the influence of such properties during the on-line processing of both simple unambiguous and syntactically ambiguous sentences. Thus, although the sound of a word may not provide cues to its specific meaning, phonological typicality, the degree to which the sound properties of an individual word are typical of other words in its lexical category, affects both word- and sentence-level language processing. The findings are consistent with a perspective on language comprehension in which sensitivity to multiple syntactic constraints in adulthood emerges as a product of language-development processes that are driven by the integration of multiple cues to linguistic structure, including phonological typicality.

The Phonological-Distributional Coherence Hypothesis: Cross-Linguistic Evidence in Language Acquisition.

Several phonological and prosodic properties of words have been shown to relate to differences between grammatical categories. Distributional information about grammatical categories is also a rich source in the childs language environment. In this paper we hypothesise that such cues operate in tandem for developing the childs knowledge about grammatical categories. We term this the Phonological-Distributional Coherence Hypothesis (PDCH). We tested the PDCH by analysing phonological and distributional information in distinguishing open from closed class words and nouns from verbs in four languages: English, Dutch, French, and Japanese. We found an interaction between phonological and distributional cues for all four languages indicating that when distributional cues were less reliable, phonological cues were stronger. This provides converging evidence that language is structured such that language learning benefits from the integration of information about category from contextual and sound-based sources, and that the childs language environment is less impoverished than we might suspect.

Domain generality versus modality specificity: the paradox of statistical learning

Statistical learning (SL) is typically considered to be a domain-general mechanism by which cognitive systems discover the underlying distributional properties of the input. However, recent studies examining whether there are commonalities in the learning of distributional information across different domains or modalities consistently reveal modality and stimulus specificity. Therefore, important questions are how and why a hypothesized domain-general learning mechanism systematically produces such effects. Here, we offer a theoretical framework according to which SL is not a unitary mechanism, but a set of domain-general computational principles that operate in different modalities and, therefore, are subject to the specific constraints characteristic of their respective brain regions. This framework offers testable predictions and we discuss its computational and neurobiological plausibility.