Shared practices,understanding, language and joint attention

In responding to the commentaries from Hobson (2007) , Moore (2007) and Rakoczy (2007) , we first discuss the commonalities our approach has with other theories and then elaborate on the nature of shared practices and their relationship with language and understanding. We then address views of representation, arguing against an empiricist approach and for a constructivist, action‐based account of knowledge and development. Finally, we briefly discuss implications following from our approach regarding how existing research would be reinterpreted and directions for new research.     

How language relates to belief,desire, and emotion understanding

Two experiments examined syntax and semantics as correlates of theory-of-mind (ToM). In Experiment 1 children’s language was examined at 3 years of age in relation to ToM at 3, 3.5, 4, and 5.5 years. Semantics predicted unique variance in later belief understanding but not desire understanding. Syntax did not explain unique variance in belief or desire. In Experiment 2 two measures of syntax and a measure of semantics were used with 65 3–5-year-olds. The syntax measures tested children’s understanding of word order and embedded clauses. They were related to false belief, but contrary to some predictions, were also related to emotion recognition. Performance on language control tasks with low syntactic demands correlated equally well with false belief. In both experiments performance on the syntax and semantics tasks was highly inter-correlated. We argue that ToM is related to general language ability rather than syntax or semantics per se.     

Tacit and accessible understanding of language

The empirical nature of our understanding of language is explored. I first show that there are several important and different distinctions between tacit and accessible awareness. I then present empirical evidence concerning our understanding of language. The data suggests that our awareness of sentence-meanings is sometimes merely tacit according to one of these distinctions, but is accessible according to another. I present and defend an interpretation of this mixed view. The present project is shown to impact on several diverse areas, including inferential role semantics and holism, the nature of learning, and the role of linguistics in the law. I am indebted to a number of people for their useful feedback, especially Peter Ludlow, Paul Pietroski, and two anonymous reviewers. Earlier versions of this paper were presented at an Eastern meeting of the APA, a meeting of the Society for Exact Philosophy at Simon Fraser University, and at a semantics workshop in Ottawa, Canada. I greatly appreciate the comments from those audiences.     

Computer understanding of natural language

The state of research in automatic natural language processing at Yale University is described. Five programs are discussed: SAM, FRUMP, PAM, TALESPIN, and POLITICS.     

Seeing, acting, understanding: motor resonance in language comprehension

Observing actions and understanding sentences about actions activates corresponding motor processes in the observer-comprehender. In 5 experiments, the authors addressed 2 novel questions regarding language-based motor resonance. The 1st question asks whether visual motion that is associated with an action produces motor resonance in sentence comprehension. The 2nd question asks whether motor resonance is modulated during sentence comprehension. The authors' experiments provide an affirmative response to both questions. A rotating visual stimulus affects both actual manual rotation and the comprehension of manual rotation sentences. Motor resonance is modulated by the linguistic input and is a rather immediate and localized phenomenon. The results are discussed in the context of theories of action observation and mental simulation.     

Applying automated deduction to natural language understanding

Very few natural language understanding applications employ methods from automated deduction. This is mainly because (i) a high level of interdisciplinary knowledge is required, (ii) there is a huge gap between formal semantic theory and practical implementation, and (iii) statistical rather than symbolic approaches dominate the current trends in natural language processing. Moreover, abduction rather than deduction is generally viewed as a promising way to apply reasoning in natural language understanding. We describe three applications where we show how first-order theorem proving and finite model construction can efficiently be employed in language understanding.The first is a text understanding system building semantic representations of texts, developed in the late 1990s. Theorem provers are here used to signal inconsistent interpretations and to check whether new contributions to the discourse are informative or not. This application shows that it is feasible to use general-purpose theorem provers for first-order logic, and that it pays off to use a battery of different inference engines as in practice they complement each other in terms of performance.The second application is a spoken-dialogue interface to a mobile robot and an automated home. We use the first-order theorem prover spass for checking inconsistencies and newness of information, but the inference tasks are complemented with the finite model builder mace used in parallel to the prover. The model builder is used to check for satisfiability of the input; in addition, the produced finite and minimal models are used to determine the actions that the robot or automated house has to execute. When the semantic representation of the dialogue as well as the number of objects in the context are kept fairly small, response times are acceptable to human users.The third demonstration of successful use of first-order inference engines comes from the task of recognising entailment between two (short) texts. We run a robust parser producing semantic representations for both texts, and use the theorem prover vampire to check whether one text entails the other. For many examples it is hard to compute the appropriate background knowledge in order to produce a proof, and the model builders mace and paradox are used to estimate the likelihood of an entailment.     

Situated language understanding as filtering perceived affordances

We introduce a computational theory of situated language understanding in which the meaning of words and utterances depends on the physical environment and the goals and plans of communication partners. According to the theory, concepts that ground linguistic meaning are neither internal nor external to language users, but instead span the objective-subjective boundary. To model the possible interactions between subject and object, the theory relies on the notion of perceived affordances: structured units of interaction that can be used for prediction at multiple levels of abstraction. Language understanding is treated as a process of filtering perceived affordances. The theory accounts for many aspects of the situated nature of human language use and provides a unified solution to a number of demands on any theory of language understanding including conceptual combination, prototypicality effects, and the generative nature of lexical items. To support the theory, we describe an implemented system that understands verbal commands situated in a virtual gaming environment. The implementation uses probabilistic hierarchical plan recognition to generate perceived affordances. The system has been evaluated on its ability to correctly interpret free-form spontaneous verbal commands recorded from unrehearsed game play between human players. The system is able to "step into the shoes" of human players and correctly respond to a broad range of verbal commands in which linguistic meaning depends on social and physical context. We quantitatively compare the system's predictions in response to direct player commands with the actions taken by human players and show generalization to unseen data across a range of situations and verbal constructions.     

On the neurobiological investigation of language understanding in context

There are two significant problems in using functional neuroimaging methods to study language. Improving the state of functional brain imaging will depend on understanding how the dependent measure of brain imaging differs from behavioral dependent measures (the "dependent measure problem") and how the activation of the motor system may be confounded with non-motor aspects of processing in certain experimental designs (the "motor output problem"). To address these problems, it may be necessary to shift the focus of language research from the study of linguistic competence to the understanding of language use. This will require investigations of language processing in full multi-modal and environmental context, monitoring of natural behaviors, novel experimental design, and network-based analysis. Such a combined naturalistic approach could lead to tremendous new insights into language and the brain.     

Early understanding of emotion: Evidence from natural language

Abstract

Young children's early understanding of emotion was investigated by examining their use of emotion terms such as happy, sad, mud, and cry. Five children's emotion language was examined longitudinally from the age of 2 to 5 years, and as a comparison their reference to pains via such terms as burn, sting, and hurt was also examined. In Phase 1 we confirmed and extended prior findings demonstrating that by 2 years of age terms for the basic emotions of happiness, sadness, anger, and fear are commonly used by children as are terms for such related states as crying and hurting. At this early age children produce such terms to refer to self and to others, and to past and future as well as to present states. Over the years from 2 to 5 children's emotion vocabulary expands, their discussion of hypothetical emotions gets underway, and the complexity of their emotion utterances increases. In Phase 2 our analyses go beyond children's production of emotion terms to analyses of their conception of emotion. We focus especially on when children use emotion terms to refer to subjective experiential states of persons. From their earliest uses of these terms in our data children     

Children's understanding of natural language hierarchies

Three experiments examined young children's ability to evaluate the relationship between concepts as one of inclusion. In Experiment 1, the same group of 4-to 7-year-old children were given three tasks: one in which they made judgments about whether “all” or “some” members of a category were included in another category, and two tasks in which they made inferences based on knowledge of inclusion relations. The majority of children succeeded on at least one of the tasks, thereby implying that they could evaluate inclusion relations. Two further experiments provided more evidence for this hypothesis: Experiment 2 confirmed that nursery children could answer quantitative questions about conceptual interrelationts; Experiment 3 demonstrated that nursery schoolers could solve certain inference problems involving the construction and evaluation of hierarchies.     

Genes, language development, and language disorders

Genetic factors are important contributors to language and learning disorders, and discovery of the underlying genes can help delineate the basic neurological pathways that are involved. This information, in turn, can help define disorders and their perceptual and processing deficits. Initial molecular genetic studies of dyslexia, for example, appear to converge on defects in neuronal and axonal migration. Further study of individuals with abnormalities of these genes may lead to the recognition of characteristic cognitive deficits attributable to the neurological dysfunction. Such abnormalities may affect other disorders as well, and studies of co-morbidity of dyslexia with attention deficit disorder and speech sound disorder are helping to define the scope of these genes and show the etiological and cognitive commonalities between these conditions. The genetic contributions to specific language impairment (SLI) are not as well defined at this time, but similar molecular approaches are being applied to identify genes that influence SLI and comorbid disorders. While there is co-morbidity of SLI with dyslexia, it appears that most of the common genetic effects may be with the language characteristics of autism spectrum disorders rather than with dyslexia and related disorders. Identification of these genes and their neurological and cognitive effects should lay out a functional network of interacting genes and pathways that subserve language development. Understanding these processes can form the basis for refined procedures for diagnosis and treatment.     

Individual differences in children's emotion understanding: effects of age and language

Over the last two decades, it has been established that children's emotion understanding changes as they develop. Recent studies have also begun to address individual differences in children's emotion understanding. The first goal of this study was to examine the development of these individual differences across a wide age range with a test assessing nine different components of emotion understanding. The second goal was to examine the relation between language ability and individual differences in emotion understanding. Eighty children ranging in age from 4 to 11 years were tested. Children displayed a clear improvement with age in both their emotion understanding and language ability. In each age group, there were clear individual differences in emotion understanding and language ability. Age and language ability together explained 72% of emotion understanding variance; 20% of this variance was explained by age alone and 27% by language ability alone. The results are discussed in terms of their theoretical and practical implications.