An individual differences perspective on pragmatic abilities in the preschool years.

Pragmatic abilities are fundamental to successful language use and learning. Individual differences studies contribute to understanding the psychological processes involved in pragmatic reasoning. Small sample sizes, insufficient measurement tools, and a lack of theoretical precision have hindered progress, however. Three studies addressed these challenges in three- to 5-year-old German-speaking children (N = 228, 121 female). Studies 1 and 2 assessed the psychometric properties of six pragmatics tasks. Study 3 investigated relations among pragmatics tasks and between pragmatics and other cognitive abilities. The tasks were found to measure stable variation between individuals. Via a computational cognitive model, individual differences were traced back to a latent pragmatics construct. This presents the basis for understanding the relations between pragmatics and other cognitive abilities. RESEARCH HIGHLIGHTS: Individual differences in pragmatic abilities are important to understanding variation in language development. Research in this domain lacks a precise theoretical framework and psychometrically high-quality measures. We present six tasks capturing a wide range of pragmatic abilities with excellent re-test reliability. We use a computational cognitive model to provide a substantive theory of individual differences in pragmatic abilities.

Great ape communication as contextual social inference: a computational modelling perspective.

Human communication has been described as a contextual social inference process. Research into great ape communication has been inspired by this view to look for the evolutionary roots of the social, cognitive and interactional processes involved in human communication. This approach has been highly productive, yet it is partly compromised by the widespread focus on how great apes use and understand individual signals. This paper introduces a computational model that formalizes great ape communication as a multi-faceted social inference process that integrates (a) information contained in the signals that make up an utterance, (b) the relationship between communicative partners and (c) the social context. This model makes accurate qualitative and quantitative predictions about real-world communicative interactions between semi-wild-living chimpanzees. When enriched with a pragmatic reasoning process, the model explains repeatedly reported differences between humans and great apes in the interpretation of ambiguous signals (e.g. pointing or iconic gestures). This approach has direct implications for observational and experimental studies of great ape communication and provides a new tool for theorizing about the evolution of uniquely human communication. This article is part of the theme issue 'Revisiting the human 'interaction engine': comparative approaches to social action coordination'.

Predicting pragmatic cue integration in adults' and children's inferences about novel word meanings.

Language is learned in complex social settings where listeners must reconstruct speakers' intended meanings from context. To navigate this challenge, children can use pragmatic reasoning to learn the meaning of unfamiliar words. A critical challenge for pragmatic reasoning is that it requires integrating multiple information sources, which have typically been studied separately. Here we study this integration process. First, we experimentally isolate two sources of pragmatic information: expectations about informative communication and common ground. Next, we use a probabilistic model of conversational reasoning to formalize how these information sources should be combined and how this process might develop. We use this model to generate quantitative predictions, which we test against new experimental data from 3- to 5-year-old children (N = 243) and adults (N = 694). Results show close alignment between model predictions and data. Furthermore, the model provided a better explanation of the data compared with simpler alternative models assuming that participants selectively ignore one information source. This work integrates distinct sets of findings regarding information sources for early language learning and suggests that pragmatic reasoning models can provide a quantitative framework for understanding developmental changes in language learning. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Warm (for Winter): Inferring Comparison Classes in Communication.

The meanings of natural language utterances depend heavily on context. Yet, what counts as context is often only implicit in conversation. The utterance it's warm outside signals that the temperature outside is relatively high, but the temperature could be high relative to a number of different comparison classes: other days of the year, other weeks, other seasons, etc. Theories of context sensitivity in language agree that the comparison class is a crucial variable for understanding meaning, but little is known about how a listener decides upon the comparison class. Using the case study of gradable adjectives (e.g., warm), we extend a Bayesian model of pragmatic inference to reason flexibly about the comparison class and test its qualitative predictions in a large-scale free-production experiment. We find that human listeners infer the comparison class by reasoning about the kinds of observations that would be remarkable enough for a speaker to mention, given the speaker and listener's shared knowledge of the world. Further, we quantitatively synthesize the model and data using Bayesian data analysis, which reveals that usage frequency and a preference for basic-level categories are two main factors in comparison class inference. This work presents new data and reveals the mechanisms by which human listeners recover the relevant aspects of context when understanding language.

Logic, Probability, and Pragmatics in Syllogistic Reasoning.

Syllogistic reasoning lies at the intriguing intersection of natural and formal reasoning of language and logic. Syllogisms comprise a formal system of reasoning yet make use of natural language quantifiers (e.g., all, some) and invite natural language conclusions. The conclusions people tend to draw from syllogisms, however, deviate substantially from the purely logical system. Are principles of natural language understanding to blame? We introduce a probabilistic pragmatic perspective on syllogistic reasoning: We decompose reasoning with natural language arguments into two subproblems: language comprehension and language production. We formalize models of these processes within the Rational Speech Act framework and explore the pressures that pragmatic reasoning places on the production of conclusions. We test our models on a recent, large data set of syllogistic reasoning and find that the selection process of conclusions from syllogisms are best modeled as a pragmatic speaker who has the goal of aligning the beliefs of a naive listener with those of their own. We compare our model to previously published models that implement two alternative theories-Mental Models and Probability Heuristics-finding that our model quantitatively predicts the full distributions of responses as well as or better than previous accounts, but with far fewer parameters. Our results suggest that human syllogistic reasoning may be best understood not as a poor approximation to ideal logical reasoning, but rather as rational probabilistic inference in support of natural communication.

Modeling Individual Differences in Children's Information Integration During Pragmatic Word Learning.

Pragmatics is foundational to language use and learning. Computational cognitive models have been successfully used to predict pragmatic phenomena in adults and children - on an aggregate level. It is unclear if they can be used to predict behavior on an individual level. We address this question in children (N = 60, 3- to 5-year-olds), taking advantage of recent work on pragmatic cue integration. In Part 1, we use data from four independent tasks to estimate child-specific sensitivity parameters to three information sources: semantic knowledge, expectations about speaker informativeness, and sensitivity to common ground. In Part 2, we use these parameters to generate participant-specific trial-by-trial predictions for a new task that jointly manipulated all three information sources. The model accurately predicted children's behavior in the majority of trials. This work advances a substantive theory of individual differences in which the primary locus of developmental variation is sensitivity to individual information sources.

How young children integrate information sources to infer the meaning of words.

Before formal education begins, children typically acquire a vocabulary of thousands of words. This learning process requires the use of many different information sources in their social environment, including their current state of knowledge and the context in which they hear words used. How is this information integrated? We specify a developmental model according to which children consider information sources in an age-specific way and integrate them via Bayesian inference. This model accurately predicted 2-5-year-old children's word learning across a range of experimental conditions in which they had to integrate three information sources. Model comparison suggests that the central locus of development is an increased sensitivity to individual information sources, rather than changes in integration ability. This work presents a developmental theory of information integration during language learning and illustrates how formal models can be used to make a quantitative test of the predictive and explanatory power of competing theories.

Polite Speech Emerges From Competing Social Goals.

Language is a remarkably efficient tool for transmitting information. Yet human speakers make statements that are inefficient, imprecise, or even contrary to their own beliefs, all in the service of being polite. What rational machinery underlies polite language use? Here, we show that polite speech emerges from the competition of three communicative goals: to convey information, to be kind, and to present oneself in a good light. We formalize this goal tradeoff using a probabilistic model of utterance production, which predicts human utterance choices in socially sensitive situations with high quantitative accuracy, and we show that our full model is superior to its variants with subsets of the three goals. This utility-theoretic approach to speech acts takes a step toward explaining the richness and subtlety of social language use.

The language of generalization.

Language provides simple ways of communicating generalizable knowledge to each other (e.g., "Birds fly," "John hikes," and "Fire makes smoke"). Though found in every language and emerging early in development, the language of generalization is philosophically puzzling and has resisted precise formalization. Here, we propose the first formal account of generalizations conveyed with language that makes quantitative predictions about human understanding. The basic idea is that the language of generalization expresses that an event or a property occurs relatively often, where what counts as relatively often depends upon one's prior expectations. We formalize this simple idea in a probabilistic model of language understanding, which we test in 3 diverse case studies: generalizations about categories (generic language), events (habitual language), and causes (causal language). We find that the model explains the gradience in human endorsements that has perplexed previous attempts to formalize this swath of linguistic expressions. This work opens the door to understanding precisely how abstract knowledge is learned from language. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

A Bayesian decision-making framework for replication.

Replication is the cornerstone of science - but when and why? Not all studies need replication, especially when resources are limited. We propose that a decision-making framework based on Bayesian philosophy of science provides a basis for choosing which studies to replicate.

Privileged Functional Connectivity between the Visual Word Form Area and the Language System.

The visual word form area (VWFA) is a region in the left occipitotemporal sulcus of literate individuals that is purportedly specialized for visual word recognition. However, there is considerable controversy about its functional specificity and connectivity, with some arguing that it serves as a domain-general, rather than word-specific, visual processor. The VWFA is a critical region for testing hypotheses about the nature of cortical organization, because it is known to develop only through experience (i.e., reading acquisition), and widespread literacy is too recent to have influenced genetic determinants of brain organization. Using a combination of advanced fMRI analysis techniques, including individual functional localization, multivoxel pattern analysis, and high-resolution resting-state functional connectivity (RSFC) analyses, with data from 33 healthy adult human participants, we demonstrate that (1) the VWFA can discriminate words from nonword letter strings (pseudowords); (2) the VWFA has preferential RSFC with Wernicke's area and other core regions of the language system; and (3) the strength of the RSFC between the VWFA and Wernicke's area predicts performance on a semantic classification task with words but not other categories of visual stimuli. Our results are consistent with the hypothesis that the VWFA is specialized for lexical processing of real words because of its functional connectivity with Wernicke's area.SIGNIFICANCE STATEMENT The visual word form area (VWFA) is critical for determining the nature of category-related organization of the ventral visual system. However, its functional specificity and connectivity are fiercely debated. Recent work concluded that the VWFA is a domain-general, rather than word-specific, visual processor with no preferential functional connectivity with the language system. Using more advanced techniques, our results stand in stark contrast to these earlier findings. We demonstrate that the VWFA is highly specialized for lexical processing of real words, and that a fundamental factor driving this specialization is its preferential intrinsic functional connectivity with core regions of the language system. Our results support the hypothesis that intrinsic functional connectivity contributes to category-related specialization within the human ventral visual system.

Avoiding frostbite: It helps to learn from others.

Machines that learn and think like people must be able to learn from others. Social learning speeds up the learning process and - in combination with language - is a gateway to abstract and unobservable information. Social learning also facilitates the accumulation of knowledge across generations, helping people and artificial intelligences learn things that no individual could learn in a lifetime.

Functional connectivity constrains the category-related organization of human ventral occipitotemporal cortex.

One of the most robust and oft-replicated findings in cognitive neuroscience is that several spatially distinct, functionally dissociable ventral occipitotemporal cortex (VOTC) regions respond preferentially to different categories of concrete entities. However, the determinants of this category-related organization remain to be fully determined. One recent proposal is that privileged connectivity of these VOTC regions with other regions that store and/or process category-relevant properties may be a major contributing factor. To test this hypothesis, we used a multicategory functional magnetic resonance imaging (MRI) localizer to individually define category-related brain regions of interest (ROIs) in a large group of subjects (n = 33). We then used these ROIs in resting-state functional connectivity MRI analyses to explore spontaneous functional connectivity among these regions. We demonstrate that during rest, distinct category-preferential VOTC regions show differentially stronger functional connectivity with other regions that have congruent category-preference, as defined by the functional localizer. Importantly, a "tool"-preferential region in the left medial fusiform gyrus showed differentially stronger functional connectivity with other left lateralized cortical regions associated with perceiving and knowing about common tools-posterior middle temporal gyrus (involved in perception of nonbiological motion), lateral parietal cortex (critical for reaching, grasping, manipulating), and ventral premotor cortex (involved in storing/executing motor programs)-relative to other category-related regions in VOTC of both the right and left hemisphere. Our findings support the claim that privileged connectivity with other cortical regions that store and/or process category-relevant properties constrains the category-related organization of VOTC.