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Word meanings extend over time due to a functional need for maintaining communicative expressivity within a compact lexicon. Previous scholars have suggested that word meanings extend via a process of chaining, whereby novel items link to existing ones close in semantic space. Recent work has formalized this idea using computational models grounded typically in the exemplar and prototype theories of categorization
that are either memory-intensive or simplistic in representation. We propose an alternative account of chaining that optimizes cognitive efficiency by trading off representational accuracy with memory complexity. We operationalize this efficient chaining as an infinite mixture model and show how it constructs the internal representations of word meaning adaptively through time while predicting the historical development of English verb meanings with precision and limited resources.

CogSci 2022

Infinite mixture chaining: Efficient temporal construction of word meaning

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EMNLP 2021

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

technical paper

Join us for CogSci2022! Our 44th Annual Conference will be centered around the theme of diversity, with an accompanying range of keynotes and invited speakers who themselves highlight the diversity of our field. Our three symposia are Cognition across Diverse Populations, Cognition within Diverse Societies, and Global Challenges for Cognitive Science. The conference is being offered in a fully hybrid environment for onsite and online attendees.
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The 44th Annual Meeting of the Cognitive Science Society highlighted research on the theme Cognitive Diversity in addition to the full breadth and diversity of research topics offered by the society’s membership. 





We examined how rhythmic activities affect children’s perspective-taking in a referential communication task with 69 Chinese 5- to 6-year-old children. The child first played an instrument with a virtual partner in one of three coordination conditions: synchrony, asynchrony, and antiphase synchrony. Eye movements were then monitored with the partner giving instructions to identify a shape referent which included a pre-nominal scalar adjective (e.g., big cubic block). Participants with awareness of their partner’s perspective could, in principle, identify the intended referent before the shape was named when the target contrast (a small cubic block) was in shared ground whereas a competitor contrast was occluded for the partner. Children in the asynchrony and antiphase synchrony conditions, but not the synchrony condition, showed anticipatory looks to the target, suggesting that playing instruments asynchronously or in alternation facilitates perspective-taking, likely by training self-other discrimination and inhibitory control.

Rhythmic Coordination Affects Children’s Perspective-Taking during Online Communication

The application of artificial intelligence (AI) for text generation in creative domains raises questions regarding the credibility of AI-generated content. In two studies, we explored if readers can differentiate between AI-based and human-written texts (generated based on the first line of texts and poems of classic authors) and how the stylistic qualities of these texts are rated. Participants read 9 AI-based continuations and either 9 human-written continuations (Study 1, N=120) or 9 original continuations (Study 2, N=302). Participants' task was to decide whether a continuation was written with an AI-tool or not, to indicate their confidence in each decision, and to assess the stylistic text quality. Results showed that participants generally had low accuracy for differentiating between text types but were overconfident in their decisions. Regarding the assessment of stylistic quality, AI-continuations were perceived as less well-written, inspiring, fascinating, interesting, and aesthetic than both human-written and original continuations.

The Pure Poet: How Good is the Subjective Credibility and Stylistic Quality of Literary Short Texts Written with an Artificial Intelligence Tool as Compared to Texts Written by Human Authors?

To enable natural and fluid human-robot interactions, robots need to not only be able to communicate with humans through natural language, but also do so in a way that complies with the norms of human interaction, such as politeness norms. Doing so is particularly challenging, however, in part due to the sensitivity of such norms to a host of different contextual and intentional factors. In this work, we explore computational models of context-sensitive human politeness norms, using explainable machine learning models to demonstrate the value of both speaker intention and task context in predicting adherence with indirect speech norms. We argue that this type of model, if integrated into a robot cognitive architecture, could be highly successful at enabling robots to predict when they themselves should similarly adhere to these norms.

Leveraging Intentional Factors and Task Context to Predict Linguistic Norm Adherence

To what extent can experience from language contribute to our conceptual knowledge? 
Computational explorations of this question have shed light on the ability of powerful neural language models (LMs)---informed solely through text input---to encode and elicit information about concepts and properties. To extend this line of research, we present a framework that uses neural-network language models (LMs) to perform property induction---a task in which humans generalize novel property knowledge (has sesamoid bones) from one or more concepts (robins) to others (sparrow, canary). Patterns of property induction observed in humans have shed considerable light on the nature and organization of human conceptual knowledge. Inspired by this insight, we use our framework to explore the property inductions of LMs, and find that they show an inductive preference to generalize novel properties on the basis of category membership, suggesting the presence of a taxonomic bias in their representations.

A Property Induction Framework for Neural Language Models

Artificial language learning research has shown that, under some conditions, adult speakers tend to probability-match to inconsistent variation in their input, while in others, they regularize by reducing that variation. We demonstrate that this framework can characterize speaker behavior in a natural-language morphological inflection task: 
the lexicon can be used to estimate variation in speaker productions. In the task of German plural inflection, we find that speakers probability-match a lexical distribution conditioned on phonology, and largely disregard an alternative possible strategy of conditional regularization based on grammatical gender.

Regularization or lexical probability-matching? How German speakers generalize plural morphology

Prior work has shown a “natural” preference in the Verb Phrase for direct object Nouns to linearly precede the Verb. There is also evidence of a “natural” preference in the Noun Phrase to order Nouns before Adjectives. Given this, we asked how domain-general biases like regularization and language-specific biases like the preference for “natural” orders could jointly contribute to the emergence of these two common word orders cross-linguistically. Using a silent gesture paradigm (in which we presented iconic gestures without speech), we exposed different participants to competing Verb Phrase (NounVerb vs. VerbNoun) and Noun Phrase (NounAdj vs. AdjNoun) word orders at varying frequencies. In Noun Phrase contrast conditions, we found that regularization was greatest when the domain-general bias towards regularization and the linguistic bias to order Nouns before Adjectives were aligned. In Verb Phrase conditions, participants regularized to the same extent regardless of input: They opted for greater regularity, even at the expense of aligning with underlying word order biases. We discuss the implications of our work for understanding the effects of domain-general biases on language.

Regularization of Word Order in the Verb Phrase differs from the Noun Phrase: Evidence from an online silent gesture perception paradigm

Languages often express grammatical information through inflectional morphology, in which grammatical features are grouped into strings of morphemes. In this work, we propose that cross-linguistic generalizations about morphological fusion, in which multiple features are expressed through one morpheme, can be explained in part by optimization of processing efficiency, as formalized using the memory--surprisal tradeoff of Hahn et al. (2021). We show in a toy setting that fusion of highly informative neighboring morphemes can lead to greater processing efficiency under our processing model. Next, based on paradigm and frequency data from four languages, we consider both total fusion and gradable fusion using empirical measures developed by Rathi et al. (2021), and find that the degree of fusion is predicted by closeness of optimal morpheme ordering as determined by optimization of processing efficiency. Finally, we show that optimization of processing efficiency can successfully predict typological patterns involving suppletion.

Explaining patterns of fusion in morphological paradigms using the memory--surprisal tradeoff

Responsive predicates are clause-embedding predicates like English 'know' and 'guess' that can take both declarative and interrogative clausal complements. The meanings of responsive predicates when they take a declarative complement and when they take an interrogative complement are hypothesized to be constrained in systematic ways across languages, suggesting that these constraints represent semantic universals. We report an artificial language learning experiment showing that one of these proposed constraints is indeed reflected in the inferences participants make while learning a novel responsive predicate. Our results add support to a growing body of evidence linking semantic universals to learning.

Learnability and constraints on the semantics of clause-embedding predicates

Rumelhart Symposium

Humans are surprisingly adept at interpretingwhat is happening around them –theyspontaneously and rapidlysegmentand organizetheirdynamic experience into coherent event construals. Such event construals may offer a starting point for assembling a linguistic description of the event during speaking (Levelt, 1989). However, the precise format of event representations and their mapping to language have remained elusive, partly because research on how people mentally segment and perceive events (see Radvansky & Zacks, 2014 for a review) has largely proceeded separately from analyses of how events are encoded in language (see Truswell, 2019 for a review).

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Rhythmic Coordination Affects Children’s Perspective-Taking during Online Communication

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Predicting emergent linguistic compositions through time: Syntactic frame extension via multimodal chaining

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