Psycholinguistics seminars

Metaphor is a pervasive phenomenon in language and cognition. To date, the vast majority of psycholinguistic research on metaphor has focused on noun-noun metaphors of the form An X is a Y (e.g., My job is a jail). Yet there is evidence that verb metaphor (e.g., I sailed through my exams) is more common. Despite this, comparatively little work has examined how verb metaphors are processed. In this talk, I will propose a novel account for verb metaphor comprehension: verb metaphors are understood in the same way that analogies are—as comparisons processed via structure-mapping. I will discuss the predictions that arise from applying the analogical framework to verb metaphor and present a series of experiments showing that verb metaphoric extension is consistent with those predictions.

Socially Assistive Robots (SARs) have shown great potential to help children in therapeutic and healthcare contexts. SARs have been used for companionship, learning enhancement, social and communication skills rehabilitation for children with special needs (e.g., autism), and mood improvement. Robots can be used as novel tools to assess and rehabilitate children’s communication skills and mental well-being by providing affordable and accessible therapeutic and mental health services. In this talk, I will present the various studies I have conducted during my PhD and at the Cambridge Affective Intelligence and Robotics Lab to explore how robots can help assess and rehabilitate children’s communication skills and mental well-being. More specifically, I will provide both quantitative and qualitative results and findings from (i) an exploratory study with children with autism and global developmental disorders to investigate the use of intelligent personal assistants in therapy; (ii) an empirical study involving children with and without language disorders interacting with a physical robot, a virtual agent, and a human counterpart to assess their linguistic skills; (iii) an 8-week longitudinal study involving children with autism and language disorders who interacted either with a physical or a virtual robot to rehabilitate their linguistic skills; and (iv) an empirical study to aid the assessment of mental well-being in children. These findings can inform and help the child-robot interaction community design and develop new adaptive robots to help assess and rehabilitate linguistic skills and mental well-being in children.

Learning verbs is difficult and critical to learning one's native language. Children appear to benefit from seeing multiple events and comparing them to each other, and structural alignment theory provides a good theoretical framework to guide research into how preschool children may be comparing events as they learn new verbs. The talk will include 6 studies of early verb learning that make use of eye-tracking procedures as well as other behavioral (pointing) procedures, and that test key predictions from SA theory including the prediction that seeing similar examples before more varied examples helps observers learn how to compare (progressive alignment) and the prediction that when events have very low alignability with other events, that is one cue that the events should be ignored. Whether or how statistical learning may also be at work will be considered.

What do people do when they comprehend language in discourse? According to many psychologists, they build and maintain cognitive representations of utterances in four complementary mental models for discourse that interact with each other: the surface text, the text base, the situation model, and the context model. When people encounter metaphors in these utterances, they need to incorporate them into each of these mental representations for the discourse. Since influential metaphor theories define metaphor as a form of (figurative) analogy, involving cross-domain mapping of a smaller or greater extent, the general expectation has been that metaphor comprehension is also based on analogizing. This expectation, however, has been partly borne out by the data, but not completely. There is no one-to-one relationship between metaphor as (conceptual) structure (analogy) and metaphor as (psychological) process (analogizing). According to Deliberate Metaphor Theory (DMT), only some metaphors are handled by analogy. Instead, most metaphors are presumably handled by lexical disambiguation. This is a hypothesis that brings together most metaphor research in a provocatively new way: it means that most metaphors are not processed metaphorically, which produces a paradox of metaphor. In this talk I will sketch out how this paradox arises and how it can be resolved by a new version of DMT, which I have described in my forthcoming book Slowing metaphor down: Updating Deliberate Metaphor Theory (currently under review). In this theory, the distinction between, but also the relation between, analogy in metaphorical structure versus analogy in metaphorical process is of central importance.

Children need inference in order to learn the meanings of words. They must infer the referent from the situation in which a target word is said. Furthermore, to be able to use the word in other situations, they also need to infer what other referents the word can be generalized to. As verbs refer to relations between arguments, verb learning requires relational analogical inference, something which is challenging to young children. To overcome this difficulty, young children recruit a diverse range of cues in their inference of verb meanings, including, but not limited to, syntactic cues and social and pragmatic cues as well as statistical cues. They also utilize perceptual similarity (object similarity) in progressive alignment to extract relational verb meanings and further to gain insights about relational verb meanings. However, just having a list of these cues is not useful: the cues must be selected, combined, and coordinated to produce the optimal interpretation in a particular context. This process involves abductive reasoning, similar to what scientists do to form hypotheses from a range of facts or evidence. In this talk, I discuss how children use a chain of inferences to learn meanings of verbs. I consider not only the process of analogical mapping and progressive alignment, but also how children use abductive inference to find the source of analogy and gain insights into the general principles underlying verb learning. I also present recent findings from my laboratory that show that prelinguistic human infants use a rudimentary form of abductive reasoning, which enables the first step of word learning.

It is well established that the opportunity to compare learning stimuli in a novel word learning/extension task elicits a larger number of conceptually relevant generalizations than standard no-comparison conditions. I will present results suggesting that the effectiveness of comparison depends on factors such as semantic distance, number of training items, dimension distinctiveness and interactions with age. I will address these issues in the case of familiar and unfamiliar object nouns and relational nouns. The alignment strategies followed by children during learning and at test (i.e., when learning items are compared and how children reach a solution) will be described with eye-tracking data. We will also assess the extent to which children’s performance in these tasks are associated with executive functions (inhibition and flexibility) and world knowledge. Finally, we will consider these issues in children with cognitive deficits (Intellectual deficiency, DLD)

A common result is that comparison settings (i.e., several stimuli introduced simultaneously) favor conceptualization and generalization. However still little is known of the solving strategies used by children to compare and generalize novel words. Understanding the temporal dynamics of children’s solving strategies may help assess which processes underlie generalization. We tested children in noun and relational noun generalization tasks and collected eye tracking data. To analyze and interpret the data we followed predictions made by existing models of analogical reasoning and generalization. The data reveals clear patterns of exploration in which participants compare learning items before searching for a solution. Analyses of the beginning of trials show that early comparisons favor generalization and that errors may be caused by a lake of early comparison. Children then pursue their search in different ways according to the task. In this presentation I will present the generalization strategies revealed by eye tracking, compare the strategies from both tasks and confront them to existing models.

In this talk, I will present the results of two recent systematic reviews and meta-analyses related to analogical reasoning and metaphor processing in autism, together with the results of a study that investigated verbal analogical reasoning and metaphor processing in the same sample of participants. Both metaphors and analogies rely on exploiting similarities, and they necessitate contextual processing. Nevertheless, our findings relating to metaphor processing and analogical reasoning showed distinct patterns. Whereas analogical reasoning emerged as a relative strength in autism, metaphor processing was found to be a relative weakness. Additionally, both meta-analytic studies investigated the relations between the level of intelligence of participants included in the studies, and the effect size of group differences between the autistic and typically developing (TD) samples. These analyses suggested in the case of analogical reasoning that the relative advantage of ASD participants might only be present in the case of individuals with lower levels of intelligence. By contrast, impairments in metaphor processing appeared to be more pronounced in the case of individuals with relatively lower levels of (verbal) intelligence. In our experimental study, we administered both verbal analogies and metaphors to the same sample of high-functioning autistic participants and TD controls. The two groups were matched on age, verbal IQ, working memory and educational background. Our aim was to understand better the similarities and differences between processing analogies and metaphors, and to see whether the advantage in analogical reasoning and disadvantage in metaphor processing is universal in autism.

Across species, humans are uniquely able to acquire deep relational systems of the kind needed for mathematics, science, and human language. Analogical comparison processes are a major contributor to this ability. Analogical comparison engages a structure-mapping process (Gentner, 1983) that fosters learning in at least three ways: first, it highlights common relational systems and thereby promotes abstraction; second, it promotes inferences from known situations to less familiar situations; and, third, it reveals potentially important differences between examples. In short, structure-mapping is a domain-general learning process by which abstract, portable knowledge can arise from experience. It is operative from early infancy on, and is critical to the rapid learning we see in human children. Although structure-mapping processes are present pre-linguistically, their scope is greatly amplified by language. Analogical processes are instrumental in learning relational language, and the reverse is also true: relational language acts to preserve relational abstractions and render them accessible for future learning and reasoning. Although structure-mapping processes are present pre-linguistically, their scope is greatly amplified by language. Analogical processes are instrumental in learning relational language, and the reverse is also true: relational language acts to preserve relational abstractions and render them accessible for future learning and reasoning.

In this talk, I will present some data showing that people’s similarity judgments among word pairs reflect distinctions between abstract semantic relations like contrast, cause-effect, or part-whole. Further, the extent that individual participants’ similarity judgments discriminate between abstract semantic relations was linearly associated with both fluid and crystallized verbal intelligence, albeit more strongly with fluid intelligence. Finally, I will compare three models according to their ability to predict these similarity judgments. All models take as input vector representations of individual word meanings, but they differ in their representation of relations: one model does not represent relations at all, a second model represents relations implicitly, and a third model represents relations explicitly. Across the three models, the third model served as the best predictor of human similarity judgments suggesting the importance of explicit relation representation to fully account for human semantic cognition.